#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 "dark_cuda.h"
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#include "utils.h"
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#include <math.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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extern int check_mistakes;
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layer make_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes, int max_boxes)
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{
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int i;
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layer l = { (LAYER_TYPE)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 = (float*)xcalloc(1, sizeof(float));
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l.biases = (float*)xcalloc(total * 2, sizeof(float));
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if(mask) l.mask = mask;
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else{
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l.mask = (int*)xcalloc(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 = (float*)xcalloc(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.max_boxes = max_boxes;
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l.truth_size = 4 + 2;
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l.truths = l.max_boxes*l.truth_size; // 90*(4 + 1);
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l.labels = (int*)xcalloc(batch * l.w*l.h*l.n, sizeof(int));
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for (i = 0; i < batch * l.w*l.h*l.n; ++i) l.labels[i] = -1;
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l.class_ids = (int*)xcalloc(batch * l.w*l.h*l.n, sizeof(int));
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for (i = 0; i < batch * l.w*l.h*l.n; ++i) l.class_ids[i] = -1;
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l.delta = (float*)xcalloc(batch * l.outputs, sizeof(float));
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l.output = (float*)xcalloc(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.output_avg_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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free(l.output);
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if (cudaSuccess == cudaHostAlloc(&l.output, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;
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else {
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cudaGetLastError(); // reset CUDA-error
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l.output = (float*)xcalloc(batch * l.outputs, sizeof(float));
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}
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free(l.delta);
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if (cudaSuccess == cudaHostAlloc(&l.delta, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;
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else {
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cudaGetLastError(); // reset CUDA-error
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l.delta = (float*)xcalloc(batch * l.outputs, sizeof(float));
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}
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#endif
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fprintf(stderr, "yolo\n");
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srand(time(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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if (l->embedding_output) l->embedding_output = (float*)xrealloc(l->output, l->batch * l->embedding_size * l->n * l->h * l->w * sizeof(float));
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if (l->labels) l->labels = (int*)xrealloc(l->labels, l->batch * l->n * l->h * l->w * sizeof(int));
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if (l->class_ids) l->class_ids = (int*)xrealloc(l->class_ids, l->batch * l->n * l->h * l->w * sizeof(int));
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if (!l->output_pinned) l->output = (float*)xrealloc(l->output, l->batch*l->outputs * sizeof(float));
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if (!l->delta_pinned) l->delta = (float*)xrealloc(l->delta, l->batch*l->outputs*sizeof(float));
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#ifdef GPU
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if (l->output_pinned) {
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CHECK_CUDA(cudaFreeHost(l->output));
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if (cudaSuccess != cudaHostAlloc(&l->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {
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cudaGetLastError(); // reset CUDA-error
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l->output = (float*)xcalloc(l->batch * l->outputs, sizeof(float));
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l->output_pinned = 0;
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}
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}
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if (l->delta_pinned) {
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CHECK_CUDA(cudaFreeHost(l->delta));
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if (cudaSuccess != cudaHostAlloc(&l->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {
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cudaGetLastError(); // reset CUDA-error
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l->delta = (float*)xcalloc(l->batch * l->outputs, sizeof(float));
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l->delta_pinned = 0;
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}
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}
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cuda_free(l->delta_gpu);
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cuda_free(l->output_gpu);
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cuda_free(l->output_avg_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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l->output_avg_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, int new_coords)
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{
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box b;
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// ln - natural logarithm (base = e)
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// x` = t.x * lw - i; // x = ln(x`/(1-x`)) // x - output of previous conv-layer
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// y` = t.y * lh - i; // y = ln(y`/(1-y`)) // y - output of previous conv-layer
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// w = ln(t.w * net.w / anchors_w); // w - output of previous conv-layer
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// h = ln(t.h * net.h / anchors_h); // h - output of previous conv-layer
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if (new_coords) {
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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 = x[index + 2 * stride] * x[index + 2 * stride] * 4 * biases[2 * n] / w;
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b.h = x[index + 3 * stride] * x[index + 3 * stride] * 4 * biases[2 * n + 1] / h;
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}
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else {
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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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}
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return b;
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}
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static inline float fix_nan_inf(float val)
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{
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if (isnan(val) || isinf(val)) val = 0;
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return val;
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}
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static inline float clip_value(float val, const float max_val)
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{
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if (val > max_val) {
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//printf("\n val = %f > max_val = %f \n", val, max_val);
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val = max_val;
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}
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else if (val < -max_val) {
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//printf("\n val = %f < -max_val = %f \n", val, -max_val);
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val = -max_val;
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}
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return val;
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}
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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)
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{
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if (delta[index + 0 * stride] || delta[index + 1 * stride] || delta[index + 2 * stride] || delta[index + 3 * stride]) {
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(*rewritten_bbox)++;
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}
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ious all_ious = { 0 };
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// i - step in layer width
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// j - step in layer height
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// Returns a box in absolute coordinates
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box pred = get_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride, new_coords);
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all_ious.iou = box_iou(pred, truth);
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all_ious.giou = box_giou(pred, truth);
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all_ious.diou = box_diou(pred, truth);
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all_ious.ciou = box_ciou(pred, truth);
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// avoid nan in dx_box_iou
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if (pred.w == 0) { pred.w = 1.0; }
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if (pred.h == 0) { pred.h = 1.0; }
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if (iou_loss == MSE) // old loss
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{
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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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if (new_coords) {
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//tx = (truth.x*lw - i + 0.5) / 2;
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//ty = (truth.y*lh - j + 0.5) / 2;
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tw = sqrt(truth.w*w / (4 * biases[2 * n]));
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th = sqrt(truth.h*h / (4 * biases[2 * n + 1]));
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}
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//printf(" tx = %f, ty = %f, tw = %f, th = %f \n", tx, ty, tw, th);
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//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]);
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// accumulate delta
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delta[index + 0 * stride] += scale * (tx - x[index + 0 * stride]) * iou_normalizer;
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delta[index + 1 * stride] += scale * (ty - x[index + 1 * stride]) * iou_normalizer;
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delta[index + 2 * stride] += scale * (tw - x[index + 2 * stride]) * iou_normalizer;
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delta[index + 3 * stride] += scale * (th - x[index + 3 * stride]) * iou_normalizer;
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}
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else {
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// https://github.com/generalized-iou/g-darknet
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// https://arxiv.org/abs/1902.09630v2
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// https://giou.stanford.edu/
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all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss);
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// jacobian^t (transpose)
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//float dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr);
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//float dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db);
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//float dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr));
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//float dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db));
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// jacobian^t (transpose)
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float dx = all_ious.dx_iou.dt;
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float dy = all_ious.dx_iou.db;
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float dw = all_ious.dx_iou.dl;
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float dh = all_ious.dx_iou.dr;
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// predict exponential, apply gradient of e^delta_t ONLY for w,h
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if (new_coords) {
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//dw *= 8 * x[index + 2 * stride];
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//dh *= 8 * x[index + 3 * stride];
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//dw *= 8 * x[index + 2 * stride] * biases[2 * n] / w;
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//dh *= 8 * x[index + 3 * stride] * biases[2 * n + 1] / h;
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//float grad_w = 8 * exp(-x[index + 2 * stride]) / pow(exp(-x[index + 2 * stride]) + 1, 3);
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//float grad_h = 8 * exp(-x[index + 3 * stride]) / pow(exp(-x[index + 3 * stride]) + 1, 3);
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//dw *= grad_w;
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//dh *= grad_h;
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}
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else {
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dw *= exp(x[index + 2 * stride]);
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dh *= exp(x[index + 3 * stride]);
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}
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//dw *= exp(x[index + 2 * stride]);
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//dh *= exp(x[index + 3 * stride]);
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// normalize iou weight
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dx *= iou_normalizer;
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dy *= iou_normalizer;
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dw *= iou_normalizer;
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dh *= iou_normalizer;
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dx = fix_nan_inf(dx);
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dy = fix_nan_inf(dy);
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dw = fix_nan_inf(dw);
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dh = fix_nan_inf(dh);
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if (max_delta != FLT_MAX) {
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dx = clip_value(dx, max_delta);
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dy = clip_value(dy, max_delta);
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dw = clip_value(dw, max_delta);
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dh = clip_value(dh, max_delta);
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}
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if (!accumulate) {
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delta[index + 0 * stride] = 0;
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delta[index + 1 * stride] = 0;
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delta[index + 2 * stride] = 0;
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delta[index + 3 * stride] = 0;
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}
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// accumulate delta
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delta[index + 0 * stride] += dx;
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delta[index + 1 * stride] += dy;
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delta[index + 2 * stride] += dw;
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delta[index + 3 * stride] += dh;
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}
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return all_ious;
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}
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void averages_yolo_deltas(int class_index, int box_index, int stride, int classes, float *delta)
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{
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int classes_in_one_box = 0;
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int c;
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for (c = 0; c < classes; ++c) {
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if (delta[class_index + stride*c] > 0) classes_in_one_box++;
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}
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if (classes_in_one_box > 0) {
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delta[box_index + 0 * stride] /= classes_in_one_box;
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delta[box_index + 1 * stride] /= classes_in_one_box;
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delta[box_index + 2 * stride] /= classes_in_one_box;
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delta[box_index + 3 * stride] /= classes_in_one_box;
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}
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}
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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)
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{
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int n;
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if (delta[index + stride*class_id]){
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float y_true = 1;
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if(label_smooth_eps) y_true = y_true * (1 - label_smooth_eps) + 0.5*label_smooth_eps;
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float result_delta = y_true - output[index + stride*class_id];
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if(!isnan(result_delta) && !isinf(result_delta)) delta[index + stride*class_id] = result_delta;
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//delta[index + stride*class_id] = 1 - output[index + stride*class_id];
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if (classes_multipliers) delta[index + stride*class_id] *= classes_multipliers[class_id];
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if(avg_cat) *avg_cat += output[index + stride*class_id];
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return;
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}
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// Focal loss
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if (focal_loss) {
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// Focal Loss
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float alpha = 0.5; // 0.25 or 0.5
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//float gamma = 2; // hardcoded in many places of the grad-formula
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int ti = index + stride*class_id;
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float pt = output[ti] + 0.000000000000001F;
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// http://fooplot.com/#W3sidHlwZSI6MCwiZXEiOiItKDEteCkqKDIqeCpsb2coeCkreC0xKSIsImNvbG9yIjoiIzAwMDAwMCJ9LHsidHlwZSI6MTAwMH1d
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float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1); // http://blog.csdn.net/linmingan/article/details/77885832
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//float grad = (1 - pt) * (2 * pt*logf(pt) + pt - 1); // https://github.com/unsky/focal-loss
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for (n = 0; n < classes; ++n) {
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delta[index + stride*n] = (((n == class_id) ? 1 : 0) - output[index + stride*n]);
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delta[index + stride*n] *= alpha*grad;
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if (n == class_id && avg_cat) *avg_cat += output[index + stride*n];
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}
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}
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else {
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// default
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for (n = 0; n < classes; ++n) {
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float y_true = ((n == class_id) ? 1 : 0);
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if (label_smooth_eps) y_true = y_true * (1 - label_smooth_eps) + 0.5*label_smooth_eps;
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float result_delta = y_true - output[index + stride*n];
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if (!isnan(result_delta) && !isinf(result_delta)) delta[index + stride*n] = result_delta;
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if (classes_multipliers && n == class_id) delta[index + stride*class_id] *= classes_multipliers[class_id] * cls_normalizer;
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if (n == class_id && avg_cat) *avg_cat += output[index + stride*n];
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}
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}
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}
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int compare_yolo_class(float *output, int classes, int class_index, int stride, float objectness, int class_id, float conf_thresh)
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{
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int j;
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for (j = 0; j < classes; ++j) {
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//float prob = objectness * output[class_index + stride*j];
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float prob = output[class_index + stride*j];
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if (prob > conf_thresh) {
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return 1;
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}
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}
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return 0;
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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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typedef struct train_yolo_args {
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layer l;
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network_state state;
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int b;
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float tot_iou;
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float tot_giou_loss;
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float tot_iou_loss;
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int count;
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int class_count;
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} train_yolo_args;
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void *process_batch(void* ptr)
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{
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{
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train_yolo_args *args = (train_yolo_args*)ptr;
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const layer l = args->l;
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network_state state = args->state;
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int b = args->b;
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int i, j, t, n;
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//printf(" b = %d \n", b, b);
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//float tot_iou = 0;
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float tot_giou = 0;
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float tot_diou = 0;
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float tot_ciou = 0;
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//float tot_iou_loss = 0;
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//float tot_giou_loss = 0;
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float tot_diou_loss = 0;
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float tot_ciou_loss = 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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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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const int class_index = entry_index(l, b, n * l.w * l.h + j * l.w + i, 4 + 1);
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const int obj_index = entry_index(l, b, n * l.w * l.h + j * l.w + i, 4);
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const int box_index = entry_index(l, b, n * l.w * l.h + j * l.w + i, 0);
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const int stride = l.w * l.h;
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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);
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float best_match_iou = 0;
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int best_match_t = 0;
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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_stride(state.truth + t * l.truth_size + b * l.truths, 1);
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if (!truth.x) break; // continue;
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int class_id = state.truth[t * l.truth_size + b * l.truths + 4];
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if (class_id >= l.classes || class_id < 0) {
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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);
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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);
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if (check_mistakes) getchar();
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continue; // if label contains class_id more than number of classes in the cfg-file and class_id check garbage value
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}
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float objectness = l.output[obj_index];
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if (isnan(objectness) || isinf(objectness)) l.output[obj_index] = 0;
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int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w * l.h, objectness, class_id, 0.25f);
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float iou = box_iou(pred, truth);
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if (iou > best_match_iou && class_id_match == 1) {
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best_match_iou = iou;
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best_match_t = t;
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}
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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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avg_anyobj += l.output[obj_index];
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l.delta[obj_index] = l.obj_normalizer * (0 - l.output[obj_index]);
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if (best_match_iou > l.ignore_thresh) {
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if (l.objectness_smooth) {
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const float delta_obj = l.obj_normalizer * (best_match_iou - l.output[obj_index]);
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if (delta_obj > l.delta[obj_index]) l.delta[obj_index] = delta_obj;
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}
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else l.delta[obj_index] = 0;
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}
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else if (state.net.adversarial) {
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int stride = l.w * l.h;
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float scale = pred.w * pred.h;
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if (scale > 0) scale = sqrt(scale);
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l.delta[obj_index] = scale * l.obj_normalizer * (0 - l.output[obj_index]);
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int cl_id;
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int found_object = 0;
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for (cl_id = 0; cl_id < l.classes; ++cl_id) {
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if (l.output[class_index + stride * cl_id] * l.output[obj_index] > 0.25) {
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l.delta[class_index + stride * cl_id] = scale * (0 - l.output[class_index + stride * cl_id]);
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found_object = 1;
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}
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}
|
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
|
