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/region_layer.c | 1191 +++++++++++++++++++++++++++++-----------------------------
1 files changed, 596 insertions(+), 595 deletions(-)
diff --git a/lib/detecter_tools/darknet/region_layer.c b/lib/detecter_tools/darknet/region_layer.c
index 59adfe5..7aa1a19 100644
--- a/lib/detecter_tools/darknet/region_layer.c
+++ b/lib/detecter_tools/darknet/region_layer.c
@@ -1,595 +1,596 @@
-#include "region_layer.h"
-#include "activations.h"
-#include "blas.h"
-#include "box.h"
-#include "dark_cuda.h"
-#include "utils.h"
-#include <stdio.h>
-#include <assert.h>
-#include <string.h>
-#include <stdlib.h>
-
-#define DOABS 1
-
-region_layer make_region_layer(int batch, int w, int h, int n, int classes, int coords, int max_boxes)
-{
- region_layer l = { (LAYER_TYPE)0 };
- l.type = REGION;
-
- l.n = n;
- l.batch = batch;
- l.h = h;
- l.w = w;
- l.classes = classes;
- l.coords = coords;
- l.cost = (float*)xcalloc(1, sizeof(float));
- l.biases = (float*)xcalloc(n * 2, sizeof(float));
- l.bias_updates = (float*)xcalloc(n * 2, sizeof(float));
- l.outputs = h*w*n*(classes + coords + 1);
- l.inputs = l.outputs;
- l.max_boxes = max_boxes;
- l.truths = max_boxes*(5);
- l.delta = (float*)xcalloc(batch * l.outputs, sizeof(float));
- l.output = (float*)xcalloc(batch * l.outputs, sizeof(float));
- int i;
- for(i = 0; i < n*2; ++i){
- l.biases[i] = .5;
- }
-
- l.forward = forward_region_layer;
- l.backward = backward_region_layer;
-#ifdef GPU
- l.forward_gpu = forward_region_layer_gpu;
- l.backward_gpu = backward_region_layer_gpu;
- l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
- l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
-#endif
-
- fprintf(stderr, "detection\n");
- srand(time(0));
-
- return l;
-}
-
-void resize_region_layer(layer *l, int w, int h)
-{
-#ifdef GPU
- int old_w = l->w;
- int old_h = l->h;
-#endif
- l->w = w;
- l->h = h;
-
- l->outputs = h*w*l->n*(l->classes + l->coords + 1);
- l->inputs = l->outputs;
-
- l->output = (float*)xrealloc(l->output, l->batch * l->outputs * sizeof(float));
- l->delta = (float*)xrealloc(l->delta, l->batch * l->outputs * sizeof(float));
-
-#ifdef GPU
- //if (old_w < w || old_h < h)
- {
- cuda_free(l->delta_gpu);
- cuda_free(l->output_gpu);
-
- l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs);
- l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs);
- }
-#endif
-}
-
-box get_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h)
-{
- box b;
- b.x = (i + logistic_activate(x[index + 0])) / w;
- b.y = (j + logistic_activate(x[index + 1])) / h;
- b.w = exp(x[index + 2]) * biases[2*n];
- b.h = exp(x[index + 3]) * biases[2*n+1];
- if(DOABS){
- b.w = exp(x[index + 2]) * biases[2*n] / w;
- b.h = exp(x[index + 3]) * biases[2*n+1] / h;
- }
- return b;
-}
-
-float delta_region_box(box truth, float *x, float *biases, int n, int index, int i, int j, int w, int h, float *delta, float scale)
-{
- box pred = get_region_box(x, biases, n, index, i, j, w, h);
- float iou = box_iou(pred, truth);
-
- float tx = (truth.x*w - i);
- float ty = (truth.y*h - j);
- float tw = log(truth.w / biases[2*n]);
- float th = log(truth.h / biases[2*n + 1]);
- if(DOABS){
- tw = log(truth.w*w / biases[2*n]);
- th = log(truth.h*h / biases[2*n + 1]);
- }
-
- delta[index + 0] = scale * (tx - logistic_activate(x[index + 0])) * logistic_gradient(logistic_activate(x[index + 0]));
- delta[index + 1] = scale * (ty - logistic_activate(x[index + 1])) * logistic_gradient(logistic_activate(x[index + 1]));
- delta[index + 2] = scale * (tw - x[index + 2]);
- delta[index + 3] = scale * (th - x[index + 3]);
- return iou;
-}
-
-void delta_region_class(float *output, float *delta, int index, int class_id, int classes, tree *hier, float scale, float *avg_cat, int focal_loss)
-{
- int i, n;
- if(hier){
- float pred = 1;
- while(class_id >= 0){
- pred *= output[index + class_id];
- int g = hier->group[class_id];
- int offset = hier->group_offset[g];
- for(i = 0; i < hier->group_size[g]; ++i){
- delta[index + offset + i] = scale * (0 - output[index + offset + i]);
- }
- delta[index + class_id] = scale * (1 - output[index + class_id]);
-
- class_id = hier->parent[class_id];
- }
- *avg_cat += pred;
- } else {
- // 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 + 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 + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]);
-
- delta[index + n] *= alpha*grad;
-
- if (n == class_id) *avg_cat += output[index + n];
- }
- }
- else {
- // default
- for (n = 0; n < classes; ++n) {
- delta[index + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]);
- if (n == class_id) *avg_cat += output[index + n];
- }
- }
- }
-}
-
-float logit(float x)
-{
- return log(x/(1.-x));
-}
-
-float tisnan(float x)
-{
- return (x != x);
-}
-
-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*(l.coords + l.classes + 1) + entry*l.w*l.h + loc;
-}
-
-void softmax_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output);
-void forward_region_layer(const region_layer l, network_state state)
-{
- int i,j,b,t,n;
- int size = l.coords + l.classes + 1;
- memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
- #ifndef GPU
- flatten(l.output, l.w*l.h, size*l.n, l.batch, 1);
- #endif
- for (b = 0; b < l.batch; ++b){
- for(i = 0; i < l.h*l.w*l.n; ++i){
- int index = size*i + b*l.outputs;
- l.output[index + 4] = logistic_activate(l.output[index + 4]);
- }
- }
-
-
-#ifndef GPU
- if (l.softmax_tree){
- for (b = 0; b < l.batch; ++b){
- for(i = 0; i < l.h*l.w*l.n; ++i){
- int index = size*i + b*l.outputs;
- softmax_tree(l.output + index + 5, 1, 0, 1, l.softmax_tree, l.output + index + 5);
- }
- }
- } else if (l.softmax){
- for (b = 0; b < l.batch; ++b){
- for(i = 0; i < l.h*l.w*l.n; ++i){
- int index = size*i + b*l.outputs;
- softmax(l.output + index + 5, l.classes, 1, l.output + index + 5, 1);
- }
- }
- }
-#endif
- if(!state.train) return;
- memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
- float avg_iou = 0;
- float recall = 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) {
- if(l.softmax_tree){
- int onlyclass_id = 0;
- for(t = 0; t < l.max_boxes; ++t){
- box truth = float_to_box(state.truth + t*5 + b*l.truths);
- if(!truth.x) break; // continue;
- int class_id = state.truth[t*5 + b*l.truths + 4];
- float maxp = 0;
- int maxi = 0;
- if(truth.x > 100000 && truth.y > 100000){
- for(n = 0; n < l.n*l.w*l.h; ++n){
- int index = size*n + b*l.outputs + 5;
- float scale = l.output[index-1];
- float p = scale*get_hierarchy_probability(l.output + index, l.softmax_tree, class_id);
- if(p > maxp){
- maxp = p;
- maxi = n;
- }
- }
- int index = size*maxi + b*l.outputs + 5;
- delta_region_class(l.output, l.delta, index, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat, l.focal_loss);
- ++class_count;
- onlyclass_id = 1;
- break;
- }
- }
- if(onlyclass_id) continue;
- }
- for (j = 0; j < l.h; ++j) {
- for (i = 0; i < l.w; ++i) {
- for (n = 0; n < l.n; ++n) {
- int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs;
- box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h);
- float best_iou = 0;
- int best_class_id = -1;
- for(t = 0; t < l.max_boxes; ++t){
- box truth = float_to_box(state.truth + t*5 + b*l.truths);
- int class_id = state.truth[t * 5 + b*l.truths + 4];
- if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file
- if(!truth.x) break; // continue;
- float iou = box_iou(pred, truth);
- if (iou > best_iou) {
- best_class_id = state.truth[t*5 + b*l.truths + 4];
- best_iou = iou;
- }
- }
- avg_anyobj += l.output[index + 4];
- l.delta[index + 4] = l.noobject_scale * ((0 - l.output[index + 4]) * logistic_gradient(l.output[index + 4]));
- if(l.classfix == -1) l.delta[index + 4] = l.noobject_scale * ((best_iou - l.output[index + 4]) * logistic_gradient(l.output[index + 4]));
- else{
- if (best_iou > l.thresh) {
- l.delta[index + 4] = 0;
- if(l.classfix > 0){
- delta_region_class(l.output, l.delta, index + 5, best_class_id, l.classes, l.softmax_tree, l.class_scale*(l.classfix == 2 ? l.output[index + 4] : 1), &avg_cat, l.focal_loss);
- ++class_count;
- }
- }
- }
-
- if(*(state.net.seen) < 12800){
- box truth = {0};
- truth.x = (i + .5)/l.w;
- truth.y = (j + .5)/l.h;
- truth.w = l.biases[2*n];
- truth.h = l.biases[2*n+1];
- if(DOABS){
- truth.w = l.biases[2*n]/l.w;
- truth.h = l.biases[2*n+1]/l.h;
- }
- delta_region_box(truth, l.output, l.biases, n, index, i, j, l.w, l.h, l.delta, .01);
- }
- }
- }
- }
- for(t = 0; t < l.max_boxes; ++t){
- box truth = float_to_box(state.truth + t*5 + b*l.truths);
- int class_id = state.truth[t * 5 + b*l.truths + 4];
- if (class_id >= l.classes) {
- printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes-1);
- getchar();
- continue; // if label contains class_id more than number of classes in the cfg-file
- }
-
- if(!truth.x) break; // continue;
- float best_iou = 0;
- int best_index = 0;
- int best_n = 0;
- i = (truth.x * l.w);
- j = (truth.y * l.h);
- //printf("%d %f %d %f\n", i, truth.x*l.w, j, truth.y*l.h);
- box truth_shift = truth;
- truth_shift.x = 0;
- truth_shift.y = 0;
- //printf("index %d %d\n",i, j);
- for(n = 0; n < l.n; ++n){
- int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs;
- box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h);
- if(l.bias_match){
- pred.w = l.biases[2*n];
- pred.h = l.biases[2*n+1];
- if(DOABS){
- pred.w = l.biases[2*n]/l.w;
- pred.h = l.biases[2*n+1]/l.h;
- }
- }
- //printf("pred: (%f, %f) %f x %f\n", pred.x, pred.y, pred.w, pred.h);
- pred.x = 0;
- pred.y = 0;
- float iou = box_iou(pred, truth_shift);
- if (iou > best_iou){
- best_index = index;
- best_iou = iou;
- best_n = n;
- }
- }
- //printf("%d %f (%f, %f) %f x %f\n", best_n, best_iou, truth.x, truth.y, truth.w, truth.h);
-
- float iou = delta_region_box(truth, l.output, l.biases, best_n, best_index, i, j, l.w, l.h, l.delta, l.coord_scale);
- if(iou > .5) recall += 1;
- avg_iou += iou;
-
- //l.delta[best_index + 4] = iou - l.output[best_index + 4];
- avg_obj += l.output[best_index + 4];
- l.delta[best_index + 4] = l.object_scale * (1 - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
- if (l.rescore) {
- l.delta[best_index + 4] = l.object_scale * (iou - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
- }
-
- if (l.map) class_id = l.map[class_id];
- delta_region_class(l.output, l.delta, best_index + 5, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat, l.focal_loss);
- ++count;
- ++class_count;
- }
- }
- //printf("\n");
- #ifndef GPU
- flatten(l.delta, l.w*l.h, size*l.n, l.batch, 0);
- #endif
- *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
- printf("Region Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, Avg Recall: %f, count: %d\n", avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, count);
-}
-
-void backward_region_layer(const region_layer l, network_state state)
-{
- axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
-}
-
-void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness, int *map)
-{
- int i;
- float *const predictions = l.output;
- #pragma omp parallel for
- for (i = 0; i < l.w*l.h; ++i){
- int j, n;
- int row = i / l.w;
- int col = i % l.w;
- for(n = 0; n < l.n; ++n){
- int index = i*l.n + n;
- int p_index = index * (l.classes + 5) + 4;
- float scale = predictions[p_index];
- if(l.classfix == -1 && scale < .5) scale = 0;
- int box_index = index * (l.classes + 5);
- boxes[index] = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);
- boxes[index].x *= w;
- boxes[index].y *= h;
- boxes[index].w *= w;
- boxes[index].h *= h;
-
- int class_index = index * (l.classes + 5) + 5;
- if(l.softmax_tree){
-
- hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);
- int found = 0;
- if(map){
- for(j = 0; j < 200; ++j){
- float prob = scale*predictions[class_index+map[j]];
- probs[index][j] = (prob > thresh) ? prob : 0;
- }
- } else {
- for(j = l.classes - 1; j >= 0; --j){
- if(!found && predictions[class_index + j] > .5){
- found = 1;
- } else {
- predictions[class_index + j] = 0;
- }
- float prob = predictions[class_index+j];
- probs[index][j] = (scale > thresh) ? prob : 0;
- }
- }
- } else {
- for(j = 0; j < l.classes; ++j){
- float prob = scale*predictions[class_index+j];
- probs[index][j] = (prob > thresh) ? prob : 0;
- }
- }
- if(only_objectness){
- probs[index][0] = scale;
- }
- }
- }
-}
-
-#ifdef GPU
-
-void forward_region_layer_gpu(const region_layer l, network_state state)
-{
- /*
- if(!state.train){
- copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1);
- return;
- }
- */
- flatten_ongpu(state.input, l.h*l.w, l.n*(l.coords + l.classes + 1), l.batch, 1, l.output_gpu);
- if(l.softmax_tree){
- int i;
- int count = 5;
- for (i = 0; i < l.softmax_tree->groups; ++i) {
- int group_size = l.softmax_tree->group_size[i];
- softmax_gpu(l.output_gpu+count, group_size, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + count);
- count += group_size;
- }
- }else if (l.softmax){
- softmax_gpu(l.output_gpu+5, l.classes, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + 5);
- }
-
- float* in_cpu = (float*)xcalloc(l.batch * l.inputs, 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);
- }
- cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs);
- //cudaStreamSynchronize(get_cuda_stream());
- network_state cpu_state = state;
- cpu_state.train = state.train;
- cpu_state.truth = truth_cpu;
- cpu_state.input = in_cpu;
- forward_region_layer(l, cpu_state);
- //cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs);
- free(cpu_state.input);
- if(!state.train) return;
- cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs);
- //cudaStreamSynchronize(get_cuda_stream());
- if(cpu_state.truth) free(cpu_state.truth);
-}
-
-void backward_region_layer_gpu(region_layer l, network_state state)
-{
- flatten_ongpu(l.delta_gpu, l.h*l.w, l.n*(l.coords + l.classes + 1), l.batch, 0, state.delta);
-}
-#endif
-
-
-void correct_region_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative)
-{
- int i;
- int new_w = 0;
- int new_h = 0;
- if (((float)netw / w) < ((float)neth / h)) {
- new_w = netw;
- new_h = (h * netw) / w;
- }
- else {
- new_h = neth;
- new_w = (w * neth) / h;
- }
- for (i = 0; i < n; ++i) {
- box b = dets[i].bbox;
- b.x = (b.x - (netw - new_w) / 2. / netw) / ((float)new_w / netw);
- b.y = (b.y - (neth - new_h) / 2. / neth) / ((float)new_h / neth);
- b.w *= (float)netw / new_w;
- b.h *= (float)neth / new_h;
- if (!relative) {
- b.x *= w;
- b.w *= w;
- b.y *= h;
- b.h *= h;
- }
- dets[i].bbox = b;
- }
-}
-
-
-void get_region_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, float tree_thresh, int relative, detection *dets)
-{
- int i, j, n, z;
- float *predictions = l.output;
- if (l.batch == 2) {
- 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 + l.coords + 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.;
- }
- }
- 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 index = n*l.w*l.h + i;
- for (j = 0; j < l.classes; ++j) {
- dets[index].prob[j] = 0;
- }
- int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords);
- int box_index = entry_index(l, 0, n*l.w*l.h + i, 0);
- int mask_index = entry_index(l, 0, n*l.w*l.h + i, 4);
- float scale = l.background ? 1 : predictions[obj_index];
- dets[index].bbox = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);// , l.w*l.h);
- dets[index].objectness = scale > thresh ? scale : 0;
- if (dets[index].mask) {
- for (j = 0; j < l.coords - 4; ++j) {
- dets[index].mask[j] = l.output[mask_index + j*l.w*l.h];
- }
- }
-
- int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + !l.background);
- if (l.softmax_tree) {
-
- hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);// , l.w*l.h);
- if (map) {
- for (j = 0; j < 200; ++j) {
- int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + map[j]);
- float prob = scale*predictions[class_index];
- dets[index].prob[j] = (prob > thresh) ? prob : 0;
- }
- }
- else {
- int j = hierarchy_top_prediction(predictions + class_index, l.softmax_tree, tree_thresh, l.w*l.h);
- dets[index].prob[j] = (scale > thresh) ? scale : 0;
- }
- }
- else {
- if (dets[index].objectness) {
- for (j = 0; j < l.classes; ++j) {
- int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + j);
- float prob = scale*predictions[class_index];
- dets[index].prob[j] = (prob > thresh) ? prob : 0;
- }
- }
- }
- }
- }
- correct_region_boxes(dets, l.w*l.h*l.n, w, h, netw, neth, relative);
-}
-
-void zero_objectness(layer l)
-{
- int i, n;
- for (i = 0; i < l.w*l.h; ++i) {
- for (n = 0; n < l.n; ++n) {
- int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords);
- l.output[obj_index] = 0;
- }
- }
-}
+#include "region_layer.h"
+#include "activations.h"
+#include "blas.h"
+#include "box.h"
+#include "dark_cuda.h"
+#include "utils.h"
+#include <stdio.h>
+#include <assert.h>
+#include <string.h>
+#include <stdlib.h>
+
+#define DOABS 1
+
+region_layer make_region_layer(int batch, int w, int h, int n, int classes, int coords, int max_boxes)
+{
+ region_layer l = { (LAYER_TYPE)0 };
+ l.type = REGION;
+
+ l.n = n;
+ l.batch = batch;
+ l.h = h;
+ l.w = w;
+ l.classes = classes;
+ l.coords = coords;
+ l.cost = (float*)xcalloc(1, sizeof(float));
+ l.biases = (float*)xcalloc(n * 2, sizeof(float));
+ l.bias_updates = (float*)xcalloc(n * 2, sizeof(float));
+ l.outputs = h*w*n*(classes + coords + 1);
+ l.inputs = l.outputs;
+ l.max_boxes = max_boxes;
+ l.truth_size = 4 + 2;
+ l.truths = max_boxes*l.truth_size;
+ l.delta = (float*)xcalloc(batch * l.outputs, sizeof(float));
+ l.output = (float*)xcalloc(batch * l.outputs, sizeof(float));
+ int i;
+ for(i = 0; i < n*2; ++i){
+ l.biases[i] = .5;
+ }
+
+ l.forward = forward_region_layer;
+ l.backward = backward_region_layer;
+#ifdef GPU
+ l.forward_gpu = forward_region_layer_gpu;
+ l.backward_gpu = backward_region_layer_gpu;
+ l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
+ l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
+#endif
+
+ fprintf(stderr, "detection\n");
+ srand(time(0));
+
+ return l;
+}
+
+void resize_region_layer(layer *l, int w, int h)
+{
+#ifdef GPU
+ int old_w = l->w;
+ int old_h = l->h;
+#endif
+ l->w = w;
+ l->h = h;
+
+ l->outputs = h*w*l->n*(l->classes + l->coords + 1);
+ l->inputs = l->outputs;
+
+ l->output = (float*)xrealloc(l->output, l->batch * l->outputs * sizeof(float));
+ l->delta = (float*)xrealloc(l->delta, l->batch * l->outputs * sizeof(float));
+
+#ifdef GPU
+ //if (old_w < w || old_h < h)
+ {
+ cuda_free(l->delta_gpu);
+ cuda_free(l->output_gpu);
+
+ l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs);
+ l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs);
+ }
+#endif
+}
+
+box get_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h)
+{
+ box b;
+ b.x = (i + logistic_activate(x[index + 0])) / w;
+ b.y = (j + logistic_activate(x[index + 1])) / h;
+ b.w = exp(x[index + 2]) * biases[2*n];
+ b.h = exp(x[index + 3]) * biases[2*n+1];
+ if(DOABS){
+ b.w = exp(x[index + 2]) * biases[2*n] / w;
+ b.h = exp(x[index + 3]) * biases[2*n+1] / h;
+ }
+ return b;
+}
+
+float delta_region_box(box truth, float *x, float *biases, int n, int index, int i, int j, int w, int h, float *delta, float scale)
+{
+ box pred = get_region_box(x, biases, n, index, i, j, w, h);
+ float iou = box_iou(pred, truth);
+
+ float tx = (truth.x*w - i);
+ float ty = (truth.y*h - j);
+ float tw = log(truth.w / biases[2*n]);
+ float th = log(truth.h / biases[2*n + 1]);
+ if(DOABS){
+ tw = log(truth.w*w / biases[2*n]);
+ th = log(truth.h*h / biases[2*n + 1]);
+ }
+
+ delta[index + 0] = scale * (tx - logistic_activate(x[index + 0])) * logistic_gradient(logistic_activate(x[index + 0]));
+ delta[index + 1] = scale * (ty - logistic_activate(x[index + 1])) * logistic_gradient(logistic_activate(x[index + 1]));
+ delta[index + 2] = scale * (tw - x[index + 2]);
+ delta[index + 3] = scale * (th - x[index + 3]);
+ return iou;
+}
+
+void delta_region_class(float *output, float *delta, int index, int class_id, int classes, tree *hier, float scale, float *avg_cat, int focal_loss)
+{
+ int i, n;
+ if(hier){
+ float pred = 1;
+ while(class_id >= 0){
+ pred *= output[index + class_id];
+ int g = hier->group[class_id];
+ int offset = hier->group_offset[g];
+ for(i = 0; i < hier->group_size[g]; ++i){
+ delta[index + offset + i] = scale * (0 - output[index + offset + i]);
+ }
+ delta[index + class_id] = scale * (1 - output[index + class_id]);
+
+ class_id = hier->parent[class_id];
+ }
+ *avg_cat += pred;
+ } else {
+ // 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 + 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 + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]);
+
+ delta[index + n] *= alpha*grad;
+
+ if (n == class_id) *avg_cat += output[index + n];
+ }
+ }
+ else {
+ // default
+ for (n = 0; n < classes; ++n) {
+ delta[index + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]);
+ if (n == class_id) *avg_cat += output[index + n];
+ }
+ }
+ }
+}
+
+float logit(float x)
+{
+ return log(x/(1.-x));
+}
+
+float tisnan(float x)
+{
+ return (x != x);
+}
+
+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*(l.coords + l.classes + 1) + entry*l.w*l.h + loc;
+}
+
+void softmax_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output);
+void forward_region_layer(const region_layer l, network_state state)
+{
+ int i,j,b,t,n;
+ int size = l.coords + l.classes + 1;
+ memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
+ #ifndef GPU
+ flatten(l.output, l.w*l.h, size*l.n, l.batch, 1);
+ #endif
+ for (b = 0; b < l.batch; ++b){
+ for(i = 0; i < l.h*l.w*l.n; ++i){
+ int index = size*i + b*l.outputs;
+ l.output[index + 4] = logistic_activate(l.output[index + 4]);
+ }
+ }
+
+
+#ifndef GPU
+ if (l.softmax_tree){
+ for (b = 0; b < l.batch; ++b){
+ for(i = 0; i < l.h*l.w*l.n; ++i){
+ int index = size*i + b*l.outputs;
+ softmax_tree(l.output + index + 5, 1, 0, 1, l.softmax_tree, l.output + index + 5);
+ }
+ }
+ } else if (l.softmax){
+ for (b = 0; b < l.batch; ++b){
+ for(i = 0; i < l.h*l.w*l.n; ++i){
+ int index = size*i + b*l.outputs;
+ softmax(l.output + index + 5, l.classes, 1, l.output + index + 5, 1);
+ }
+ }
+ }
+#endif
+ if(!state.train) return;
+ memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
+ float avg_iou = 0;
+ float recall = 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) {
+ if(l.softmax_tree){
+ int onlyclass_id = 0;
+ for(t = 0; t < l.max_boxes; ++t){
+ box truth = float_to_box(state.truth + t*l.truth_size + b*l.truths);
+ if(!truth.x) break; // continue;
+ int class_id = state.truth[t*l.truth_size + b*l.truths + 4];
+ float maxp = 0;
+ int maxi = 0;
+ if(truth.x > 100000 && truth.y > 100000){
+ for(n = 0; n < l.n*l.w*l.h; ++n){
+ int index = size*n + b*l.outputs + 5;
+ float scale = l.output[index-1];
+ float p = scale*get_hierarchy_probability(l.output + index, l.softmax_tree, class_id);
+ if(p > maxp){
+ maxp = p;
+ maxi = n;
+ }
+ }
+ int index = size*maxi + b*l.outputs + 5;
+ delta_region_class(l.output, l.delta, index, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat, l.focal_loss);
+ ++class_count;
+ onlyclass_id = 1;
+ break;
+ }
+ }
+ if(onlyclass_id) continue;
+ }
+ for (j = 0; j < l.h; ++j) {
+ for (i = 0; i < l.w; ++i) {
+ for (n = 0; n < l.n; ++n) {
+ int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs;
+ box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h);
+ float best_iou = 0;
+ int best_class_id = -1;
+ for(t = 0; t < l.max_boxes; ++t){
+ box truth = float_to_box(state.truth + t*l.truth_size + b*l.truths);
+ int class_id = state.truth[t * l.truth_size + b*l.truths + 4];
+ if (class_id >= l.classes) continue; // if label contains class_id more than number of classes in the cfg-file
+ if(!truth.x) break; // continue;
+ float iou = box_iou(pred, truth);
+ if (iou > best_iou) {
+ best_class_id = state.truth[t*l.truth_size + b*l.truths + 4];
+ best_iou = iou;
+ }
+ }
+ avg_anyobj += l.output[index + 4];
+ l.delta[index + 4] = l.noobject_scale * ((0 - l.output[index + 4]) * logistic_gradient(l.output[index + 4]));
+ if(l.classfix == -1) l.delta[index + 4] = l.noobject_scale * ((best_iou - l.output[index + 4]) * logistic_gradient(l.output[index + 4]));
+ else{
+ if (best_iou > l.thresh) {
+ l.delta[index + 4] = 0;
+ if(l.classfix > 0){
+ delta_region_class(l.output, l.delta, index + 5, best_class_id, l.classes, l.softmax_tree, l.class_scale*(l.classfix == 2 ? l.output[index + 4] : 1), &avg_cat, l.focal_loss);
+ ++class_count;
+ }
+ }
+ }
+
+ if(*(state.net.seen) < 12800){
+ box truth = {0};
+ truth.x = (i + .5)/l.w;
+ truth.y = (j + .5)/l.h;
+ truth.w = l.biases[2*n];
+ truth.h = l.biases[2*n+1];
+ if(DOABS){
+ truth.w = l.biases[2*n]/l.w;
+ truth.h = l.biases[2*n+1]/l.h;
+ }
+ delta_region_box(truth, l.output, l.biases, n, index, i, j, l.w, l.h, l.delta, .01);
+ }
+ }
+ }
+ }
+ for(t = 0; t < l.max_boxes; ++t){
+ box truth = float_to_box(state.truth + t*l.truth_size + b*l.truths);
+ int class_id = state.truth[t * l.truth_size + b*l.truths + 4];
+ if (class_id >= l.classes) {
+ printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes-1);
+ getchar();
+ continue; // if label contains class_id more than number of classes in the cfg-file
+ }
+
+ if(!truth.x) break; // continue;
+ float best_iou = 0;
+ int best_index = 0;
+ int best_n = 0;
+ i = (truth.x * l.w);
+ j = (truth.y * l.h);
+ //printf("%d %f %d %f\n", i, truth.x*l.w, j, truth.y*l.h);
+ box truth_shift = truth;
+ truth_shift.x = 0;
+ truth_shift.y = 0;
+ //printf("index %d %d\n",i, j);
+ for(n = 0; n < l.n; ++n){
+ int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs;
+ box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h);
+ if(l.bias_match){
+ pred.w = l.biases[2*n];
+ pred.h = l.biases[2*n+1];
+ if(DOABS){
+ pred.w = l.biases[2*n]/l.w;
+ pred.h = l.biases[2*n+1]/l.h;
+ }
+ }
+ //printf("pred: (%f, %f) %f x %f\n", pred.x, pred.y, pred.w, pred.h);
+ pred.x = 0;
+ pred.y = 0;
+ float iou = box_iou(pred, truth_shift);
+ if (iou > best_iou){
+ best_index = index;
+ best_iou = iou;
+ best_n = n;
+ }
+ }
+ //printf("%d %f (%f, %f) %f x %f\n", best_n, best_iou, truth.x, truth.y, truth.w, truth.h);
+
+ float iou = delta_region_box(truth, l.output, l.biases, best_n, best_index, i, j, l.w, l.h, l.delta, l.coord_scale);
+ if(iou > .5) recall += 1;
+ avg_iou += iou;
+
+ //l.delta[best_index + 4] = iou - l.output[best_index + 4];
+ avg_obj += l.output[best_index + 4];
+ l.delta[best_index + 4] = l.object_scale * (1 - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
+ if (l.rescore) {
+ l.delta[best_index + 4] = l.object_scale * (iou - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
+ }
+
+ if (l.map) class_id = l.map[class_id];
+ delta_region_class(l.output, l.delta, best_index + 5, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat, l.focal_loss);
+ ++count;
+ ++class_count;
+ }
+ }
+ //printf("\n");
+ #ifndef GPU
+ flatten(l.delta, l.w*l.h, size*l.n, l.batch, 0);
+ #endif
+ *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
+ printf("Region Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, Avg Recall: %f, count: %d\n", avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, count);
+}
+
+void backward_region_layer(const region_layer l, network_state state)
+{
+ axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
+}
+
+void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness, int *map)
+{
+ int i;
+ float *const predictions = l.output;
+ #pragma omp parallel for
+ for (i = 0; i < l.w*l.h; ++i){
+ int j, n;
+ int row = i / l.w;
+ int col = i % l.w;
+ for(n = 0; n < l.n; ++n){
+ int index = i*l.n + n;
+ int p_index = index * (l.classes + 5) + 4;
+ float scale = predictions[p_index];
+ if(l.classfix == -1 && scale < .5) scale = 0;
+ int box_index = index * (l.classes + 5);
+ boxes[index] = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);
+ boxes[index].x *= w;
+ boxes[index].y *= h;
+ boxes[index].w *= w;
+ boxes[index].h *= h;
+
+ int class_index = index * (l.classes + 5) + 5;
+ if(l.softmax_tree){
+
+ hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);
+ int found = 0;
+ if(map){
+ for(j = 0; j < 200; ++j){
+ float prob = scale*predictions[class_index+map[j]];
+ probs[index][j] = (prob > thresh) ? prob : 0;
+ }
+ } else {
+ for(j = l.classes - 1; j >= 0; --j){
+ if(!found && predictions[class_index + j] > .5){
+ found = 1;
+ } else {
+ predictions[class_index + j] = 0;
+ }
+ float prob = predictions[class_index+j];
+ probs[index][j] = (scale > thresh) ? prob : 0;
+ }
+ }
+ } else {
+ for(j = 0; j < l.classes; ++j){
+ float prob = scale*predictions[class_index+j];
+ probs[index][j] = (prob > thresh) ? prob : 0;
+ }
+ }
+ if(only_objectness){
+ probs[index][0] = scale;
+ }
+ }
+ }
+}
+
+#ifdef GPU
+
+void forward_region_layer_gpu(const region_layer l, network_state state)
+{
+ /*
+ if(!state.train){
+ copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1);
+ return;
+ }
+ */
+ flatten_ongpu(state.input, l.h*l.w, l.n*(l.coords + l.classes + 1), l.batch, 1, l.output_gpu);
+ if(l.softmax_tree){
+ int i;
+ int count = 5;
+ for (i = 0; i < l.softmax_tree->groups; ++i) {
+ int group_size = l.softmax_tree->group_size[i];
+ softmax_gpu(l.output_gpu+count, group_size, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + count);
+ count += group_size;
+ }
+ }else if (l.softmax){
+ softmax_gpu(l.output_gpu+5, l.classes, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + 5);
+ }
+
+ float* in_cpu = (float*)xcalloc(l.batch * l.inputs, 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);
+ }
+ cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs);
+ //cudaStreamSynchronize(get_cuda_stream());
+ network_state cpu_state = state;
+ cpu_state.train = state.train;
+ cpu_state.truth = truth_cpu;
+ cpu_state.input = in_cpu;
+ forward_region_layer(l, cpu_state);
+ //cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs);
+ free(cpu_state.input);
+ if(!state.train) return;
+ cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs);
+ //cudaStreamSynchronize(get_cuda_stream());
+ if(cpu_state.truth) free(cpu_state.truth);
+}
+
+void backward_region_layer_gpu(region_layer l, network_state state)
+{
+ flatten_ongpu(l.delta_gpu, l.h*l.w, l.n*(l.coords + l.classes + 1), l.batch, 0, state.delta);
+}
+#endif
+
+
+void correct_region_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative)
+{
+ int i;
+ int new_w = 0;
+ int new_h = 0;
+ if (((float)netw / w) < ((float)neth / h)) {
+ new_w = netw;
+ new_h = (h * netw) / w;
+ }
+ else {
+ new_h = neth;
+ new_w = (w * neth) / h;
+ }
+ for (i = 0; i < n; ++i) {
+ box b = dets[i].bbox;
+ b.x = (b.x - (netw - new_w) / 2. / netw) / ((float)new_w / netw);
+ b.y = (b.y - (neth - new_h) / 2. / neth) / ((float)new_h / neth);
+ b.w *= (float)netw / new_w;
+ b.h *= (float)neth / new_h;
+ if (!relative) {
+ b.x *= w;
+ b.w *= w;
+ b.y *= h;
+ b.h *= h;
+ }
+ dets[i].bbox = b;
+ }
+}
+
+
+void get_region_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, float tree_thresh, int relative, detection *dets)
+{
+ int i, j, n, z;
+ float *predictions = l.output;
+ if (l.batch == 2) {
+ 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 + l.coords + 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.;
+ }
+ }
+ 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 index = n*l.w*l.h + i;
+ for (j = 0; j < l.classes; ++j) {
+ dets[index].prob[j] = 0;
+ }
+ int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords);
+ int box_index = entry_index(l, 0, n*l.w*l.h + i, 0);
+ int mask_index = entry_index(l, 0, n*l.w*l.h + i, 4);
+ float scale = l.background ? 1 : predictions[obj_index];
+ dets[index].bbox = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);// , l.w*l.h);
+ dets[index].objectness = scale > thresh ? scale : 0;
+ if (dets[index].mask) {
+ for (j = 0; j < l.coords - 4; ++j) {
+ dets[index].mask[j] = l.output[mask_index + j*l.w*l.h];
+ }
+ }
+
+ int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + !l.background);
+ if (l.softmax_tree) {
+
+ hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);// , l.w*l.h);
+ if (map) {
+ for (j = 0; j < 200; ++j) {
+ int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + map[j]);
+ float prob = scale*predictions[class_index];
+ dets[index].prob[j] = (prob > thresh) ? prob : 0;
+ }
+ }
+ else {
+ int j = hierarchy_top_prediction(predictions + class_index, l.softmax_tree, tree_thresh, l.w*l.h);
+ dets[index].prob[j] = (scale > thresh) ? scale : 0;
+ }
+ }
+ else {
+ if (dets[index].objectness) {
+ for (j = 0; j < l.classes; ++j) {
+ int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + 1 + j);
+ float prob = scale*predictions[class_index];
+ dets[index].prob[j] = (prob > thresh) ? prob : 0;
+ }
+ }
+ }
+ }
+ }
+ correct_region_boxes(dets, l.w*l.h*l.n, w, h, netw, neth, relative);
+}
+
+void zero_objectness(layer l)
+{
+ int i, n;
+ for (i = 0; i < l.w*l.h; ++i) {
+ for (n = 0; n < l.n; ++n) {
+ int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords);
+ l.output[obj_index] = 0;
+ }
+ }
+}
--
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