mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
375 lines
12 KiB
C
375 lines
12 KiB
C
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#include "yolo_layer.h"
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#include "activations.h"
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#include "blas.h"
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#include "box.h"
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#include "cuda.h"
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#include "utils.h"
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#include <stdio.h>
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#include <assert.h>
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#include <string.h>
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#include <stdlib.h>
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layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes)
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{
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int i;
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layer l = {0};
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l.type = YOLO;
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l.n = n;
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l.total = total;
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l.batch = batch;
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l.h = h;
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l.w = w;
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l.c = n*(classes + 4 + 1);
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l.out_w = l.w;
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l.out_h = l.h;
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l.out_c = l.c;
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l.classes = classes;
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l.cost = calloc(1, sizeof(float));
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l.biases = calloc(total*2, sizeof(float));
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if(mask) l.mask = mask;
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else{
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l.mask = calloc(n, sizeof(int));
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for(i = 0; i < n; ++i){
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l.mask[i] = i;
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}
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}
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l.bias_updates = calloc(n*2, sizeof(float));
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l.outputs = h*w*n*(classes + 4 + 1);
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l.inputs = l.outputs;
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l.truths = 90*(4 + 1);
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l.delta = calloc(batch*l.outputs, sizeof(float));
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l.output = calloc(batch*l.outputs, sizeof(float));
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for(i = 0; i < total*2; ++i){
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l.biases[i] = .5;
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}
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l.forward = forward_yolo_layer;
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l.backward = backward_yolo_layer;
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#ifdef GPU
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l.forward_gpu = forward_yolo_layer_gpu;
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l.backward_gpu = backward_yolo_layer_gpu;
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l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
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l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
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#endif
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fprintf(stderr, "detection\n");
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srand(0);
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return l;
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}
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void resize_yolo_layer(layer *l, int w, int h)
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{
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l->w = w;
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l->h = h;
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l->outputs = h*w*l->n*(l->classes + 4 + 1);
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l->inputs = l->outputs;
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l->output = realloc(l->output, l->batch*l->outputs*sizeof(float));
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l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float));
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#ifdef GPU
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cuda_free(l->delta_gpu);
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cuda_free(l->output_gpu);
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l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs);
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l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs);
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#endif
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}
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box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride)
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{
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box b;
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b.x = (i + x[index + 0*stride]) / lw;
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b.y = (j + x[index + 1*stride]) / lh;
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b.w = exp(x[index + 2*stride]) * biases[2*n] / w;
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b.h = exp(x[index + 3*stride]) * biases[2*n+1] / h;
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return b;
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}
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float 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)
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{
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box pred = get_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride);
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float iou = box_iou(pred, truth);
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float tx = (truth.x*lw - i);
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float ty = (truth.y*lh - j);
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float tw = log(truth.w*w / biases[2*n]);
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float th = log(truth.h*h / biases[2*n + 1]);
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delta[index + 0*stride] = scale * (tx - x[index + 0*stride]);
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delta[index + 1*stride] = scale * (ty - x[index + 1*stride]);
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delta[index + 2*stride] = scale * (tw - x[index + 2*stride]);
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delta[index + 3*stride] = scale * (th - x[index + 3*stride]);
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return iou;
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}
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void delta_yolo_class(float *output, float *delta, int index, int class, int classes, int stride, float *avg_cat)
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{
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int n;
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if (delta[index]){
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delta[index + stride*class] = 1 - output[index + stride*class];
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if(avg_cat) *avg_cat += output[index + stride*class];
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return;
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}
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for(n = 0; n < classes; ++n){
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delta[index + stride*n] = ((n == class)?1 : 0) - output[index + stride*n];
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if(n == class && avg_cat) *avg_cat += output[index + stride*n];
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}
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}
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static int entry_index(layer l, int batch, int location, int entry)
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{
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int n = location / (l.w*l.h);
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int loc = location % (l.w*l.h);
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return batch*l.outputs + n*l.w*l.h*(4+l.classes+1) + entry*l.w*l.h + loc;
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}
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void forward_yolo_layer(const layer l, network net)
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{
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int i,j,b,t,n;
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memcpy(l.output, net.input, l.outputs*l.batch*sizeof(float));
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#ifndef GPU
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for (b = 0; b < l.batch; ++b){
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for(n = 0; n < l.n; ++n){
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int index = entry_index(l, b, n*l.w*l.h, 0);
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activate_array(l.output + index, 2*l.w*l.h, LOGISTIC);
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index = entry_index(l, b, n*l.w*l.h, 4);
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activate_array(l.output + index, (1+l.classes)*l.w*l.h, LOGISTIC);
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}
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}
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#endif
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memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
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if(!net.train) return;
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float avg_iou = 0;
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float recall = 0;
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float recall75 = 0;
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float avg_cat = 0;
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float avg_obj = 0;
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float avg_anyobj = 0;
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int count = 0;
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int class_count = 0;
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*(l.cost) = 0;
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for (b = 0; b < l.batch; ++b) {
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for (j = 0; j < l.h; ++j) {
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for (i = 0; i < l.w; ++i) {
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for (n = 0; n < l.n; ++n) {
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int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0);
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box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, net.w, net.h, l.w*l.h);
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float best_iou = 0;
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int best_t = 0;
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for(t = 0; t < l.max_boxes; ++t){
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box truth = float_to_box(net.truth + t*(4 + 1) + b*l.truths, 1);
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if(!truth.x) break;
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float iou = box_iou(pred, truth);
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if (iou > best_iou) {
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best_iou = iou;
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best_t = t;
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}
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}
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int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4);
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avg_anyobj += l.output[obj_index];
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l.delta[obj_index] = 0 - l.output[obj_index];
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if (best_iou > l.ignore_thresh) {
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l.delta[obj_index] = 0;
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}
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if (best_iou > l.truth_thresh) {
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l.delta[obj_index] = 1 - l.output[obj_index];
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int class = net.truth[best_t*(4 + 1) + b*l.truths + 4];
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if (l.map) class = l.map[class];
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int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1);
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delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, 0);
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box truth = float_to_box(net.truth + best_t*(4 + 1) + b*l.truths, 1);
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delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, net.w, net.h, l.delta, (2-truth.w*truth.h), l.w*l.h);
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}
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}
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}
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}
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for(t = 0; t < l.max_boxes; ++t){
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box truth = float_to_box(net.truth + t*(4 + 1) + b*l.truths, 1);
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if(!truth.x) break;
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float best_iou = 0;
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int best_n = 0;
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i = (truth.x * l.w);
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j = (truth.y * l.h);
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box truth_shift = truth;
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truth_shift.x = truth_shift.y = 0;
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for(n = 0; n < l.total; ++n){
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box pred = {0};
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pred.w = l.biases[2*n]/net.w;
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pred.h = l.biases[2*n+1]/net.h;
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float iou = box_iou(pred, truth_shift);
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if (iou > best_iou){
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best_iou = iou;
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best_n = n;
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}
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}
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int mask_n = int_index(l.mask, best_n, l.n);
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if(mask_n >= 0){
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int box_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0);
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float iou = delta_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, net.w, net.h, l.delta, (2-truth.w*truth.h), l.w*l.h);
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int obj_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4);
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avg_obj += l.output[obj_index];
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l.delta[obj_index] = 1 - l.output[obj_index];
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int class = net.truth[t*(4 + 1) + b*l.truths + 4];
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if (l.map) class = l.map[class];
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int class_index = entry_index(l, b, mask_n*l.w*l.h + j*l.w + i, 4 + 1);
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delta_yolo_class(l.output, l.delta, class_index, class, l.classes, l.w*l.h, &avg_cat);
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++count;
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++class_count;
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if(iou > .5) recall += 1;
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if(iou > .75) recall75 += 1;
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avg_iou += iou;
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}
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}
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}
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*(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
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printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", net.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);
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}
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void backward_yolo_layer(const layer l, network net)
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{
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axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, net.delta, 1);
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}
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void correct_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative)
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{
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int i;
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int new_w=0;
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int new_h=0;
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if (((float)netw/w) < ((float)neth/h)) {
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new_w = netw;
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new_h = (h * netw)/w;
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} else {
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new_h = neth;
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new_w = (w * neth)/h;
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}
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for (i = 0; i < n; ++i){
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box b = dets[i].bbox;
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b.x = (b.x - (netw - new_w)/2./netw) / ((float)new_w/netw);
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b.y = (b.y - (neth - new_h)/2./neth) / ((float)new_h/neth);
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b.w *= (float)netw/new_w;
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b.h *= (float)neth/new_h;
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if(!relative){
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b.x *= w;
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b.w *= w;
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b.y *= h;
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b.h *= h;
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}
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dets[i].bbox = b;
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}
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}
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int yolo_num_detections(layer l, float thresh)
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{
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int i, n;
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int count = 0;
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for (i = 0; i < l.w*l.h; ++i){
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for(n = 0; n < l.n; ++n){
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int obj_index = entry_index(l, 0, n*l.w*l.h + i, 4);
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if(l.output[obj_index] > thresh){
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++count;
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}
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}
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}
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return count;
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}
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void avg_flipped_yolo(layer l)
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{
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int i,j,n,z;
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float *flip = l.output + l.outputs;
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for (j = 0; j < l.h; ++j) {
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for (i = 0; i < l.w/2; ++i) {
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for (n = 0; n < l.n; ++n) {
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for(z = 0; z < l.classes + 4 + 1; ++z){
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int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i;
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int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1);
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float swap = flip[i1];
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flip[i1] = flip[i2];
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flip[i2] = swap;
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if(z == 0){
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flip[i1] = -flip[i1];
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flip[i2] = -flip[i2];
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}
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}
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}
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}
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}
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for(i = 0; i < l.outputs; ++i){
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l.output[i] = (l.output[i] + flip[i])/2.;
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}
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}
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int get_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets)
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{
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int i,j,n;
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float *predictions = l.output;
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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;
|
||
|
|
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);
|
||
|
|
return count;
|
||
|
|
}
|
||
|
|
|
||
|
|
#ifdef GPU
|
||
|
|
|
||
|
|
void forward_yolo_layer_gpu(const layer l, network net)
|
||
|
|
{
|
||
|
|
copy_gpu(l.batch*l.inputs, net.input_gpu, 1, l.output_gpu, 1);
|
||
|
|
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);
|
||
|
|
activate_array_gpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC);
|
||
|
|
index = entry_index(l, b, n*l.w*l.h, 4);
|
||
|
|
activate_array_gpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
if(!net.train || l.onlyforward){
|
||
|
|
cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs);
|
||
|
|
return;
|
||
|
|
}
|
||
|
|
|
||
|
|
cuda_pull_array(l.output_gpu, net.input, l.batch*l.inputs);
|
||
|
|
forward_yolo_layer(l, net);
|
||
|
|
cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs);
|
||
|
|
}
|
||
|
|
|
||
|
|
void backward_yolo_layer_gpu(const layer l, network net)
|
||
|
|
{
|
||
|
|
axpy_gpu(l.batch*l.inputs, 1, l.delta_gpu, 1, net.delta_gpu, 1);
|
||
|
|
}
|
||
|
|
#endif
|
||
|
|
|