#include "region_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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#define DOABS 1
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region_layer make_region_layer(int batch, int w, int h, int n, int classes, int coords, int max_boxes)
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{
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region_layer l = {0};
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l.type = REGION;
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l.n = n;
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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.classes = classes;
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l.coords = coords;
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l.cost = calloc(1, sizeof(float));
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l.biases = calloc(n*2, sizeof(float));
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l.bias_updates = calloc(n*2, sizeof(float));
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l.outputs = h*w*n*(classes + coords + 1);
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l.inputs = l.outputs;
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l.max_boxes = max_boxes;
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l.truths = max_boxes*(5);
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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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int i;
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for(i = 0; i < n*2; ++i){
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l.biases[i] = .5;
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}
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l.forward = forward_region_layer;
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l.backward = backward_region_layer;
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#ifdef GPU
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l.forward_gpu = forward_region_layer_gpu;
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l.backward_gpu = backward_region_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_region_layer(layer *l, int w, int h)
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{
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int old_w = l->w;
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int old_h = l->h;
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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 + l->coords + 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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if (old_w < w || old_h < h) {
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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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}
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#endif
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}
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box get_region_box(float *x, float *biases, int n, int index, int i, int j, int w, int h)
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{
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box b;
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b.x = (i + logistic_activate(x[index + 0])) / w;
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b.y = (j + logistic_activate(x[index + 1])) / h;
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b.w = exp(x[index + 2]) * biases[2*n];
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b.h = exp(x[index + 3]) * biases[2*n+1];
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if(DOABS){
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b.w = exp(x[index + 2]) * biases[2*n] / w;
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b.h = exp(x[index + 3]) * biases[2*n+1] / h;
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}
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return b;
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}
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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)
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{
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box pred = get_region_box(x, biases, n, index, i, j, w, h);
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float iou = box_iou(pred, truth);
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float tx = (truth.x*w - i);
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float ty = (truth.y*h - j);
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float tw = log(truth.w / biases[2*n]);
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float th = log(truth.h / biases[2*n + 1]);
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if(DOABS){
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tw = log(truth.w*w / biases[2*n]);
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th = log(truth.h*h / biases[2*n + 1]);
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}
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delta[index + 0] = scale * (tx - logistic_activate(x[index + 0])) * logistic_gradient(logistic_activate(x[index + 0]));
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delta[index + 1] = scale * (ty - logistic_activate(x[index + 1])) * logistic_gradient(logistic_activate(x[index + 1]));
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delta[index + 2] = scale * (tw - x[index + 2]);
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delta[index + 3] = scale * (th - x[index + 3]);
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return iou;
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}
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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)
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{
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int i, n;
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if(hier){
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float pred = 1;
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while(class_id >= 0){
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pred *= output[index + class_id];
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int g = hier->group[class_id];
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int offset = hier->group_offset[g];
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for(i = 0; i < hier->group_size[g]; ++i){
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delta[index + offset + i] = scale * (0 - output[index + offset + i]);
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}
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delta[index + class_id] = scale * (1 - output[index + class_id]);
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class_id = hier->parent[class_id];
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}
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*avg_cat += pred;
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} else {
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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 + 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 + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]);
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delta[index + n] *= alpha*grad;
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if (n == class_id) *avg_cat += output[index + 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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delta[index + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]);
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if (n == class_id) *avg_cat += output[index + n];
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}
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}
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}
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}
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float logit(float x)
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{
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return log(x/(1.-x));
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}
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float tisnan(float x)
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{
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return (x != x);
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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*(l.coords + l.classes + 1) + entry*l.w*l.h + loc;
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}
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void softmax_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output);
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void forward_region_layer(const region_layer l, network_state state)
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{
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int i,j,b,t,n;
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int size = l.coords + l.classes + 1;
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memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
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#ifndef GPU
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flatten(l.output, l.w*l.h, size*l.n, l.batch, 1);
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#endif
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for (b = 0; b < l.batch; ++b){
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for(i = 0; i < l.h*l.w*l.n; ++i){
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int index = size*i + b*l.outputs;
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l.output[index + 4] = logistic_activate(l.output[index + 4]);
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}
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}
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#ifndef GPU
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if (l.softmax_tree){
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for (b = 0; b < l.batch; ++b){
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for(i = 0; i < l.h*l.w*l.n; ++i){
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int index = size*i + b*l.outputs;
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softmax_tree(l.output + index + 5, 1, 0, 1, l.softmax_tree, l.output + index + 5);
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}
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}
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} else if (l.softmax){
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for (b = 0; b < l.batch; ++b){
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for(i = 0; i < l.h*l.w*l.n; ++i){
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int index = size*i + b*l.outputs;
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softmax(l.output + index + 5, l.classes, 1, l.output + index + 5, 1);
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}
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}
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}
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#endif
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if(!state.train) return;
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memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
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float avg_iou = 0;
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float recall = 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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if(l.softmax_tree){
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int onlyclass_id = 0;
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for(t = 0; t < l.max_boxes; ++t){
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box truth = float_to_box(state.truth + t*5 + b*l.truths);
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if(!truth.x) break;
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int class_id = state.truth[t*5 + b*l.truths + 4];
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float maxp = 0;
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int maxi = 0;
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if(truth.x > 100000 && truth.y > 100000){
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for(n = 0; n < l.n*l.w*l.h; ++n){
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int index = size*n + b*l.outputs + 5;
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float scale = l.output[index-1];
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float p = scale*get_hierarchy_probability(l.output + index, l.softmax_tree, class_id);
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if(p > maxp){
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maxp = p;
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maxi = n;
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}
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}
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int index = size*maxi + b*l.outputs + 5;
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delta_region_class(l.output, l.delta, index, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat, l.focal_loss);
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++class_count;
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onlyclass_id = 1;
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break;
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}
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}
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if(onlyclass_id) continue;
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}
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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 index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs;
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box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h);
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float best_iou = 0;
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int best_class_id = -1;
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for(t = 0; t < l.max_boxes; ++t){
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box truth = float_to_box(state.truth + t*5 + b*l.truths);
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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_class_id = state.truth[t*5 + b*l.truths + 4];
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best_iou = iou;
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}
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}
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avg_anyobj += l.output[index + 4];
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l.delta[index + 4] = l.noobject_scale * ((0 - l.output[index + 4]) * logistic_gradient(l.output[index + 4]));
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if(l.classfix == -1) l.delta[index + 4] = l.noobject_scale * ((best_iou - l.output[index + 4]) * logistic_gradient(l.output[index + 4]));
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else{
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if (best_iou > l.thresh) {
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l.delta[index + 4] = 0;
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if(l.classfix > 0){
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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);
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++class_count;
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}
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}
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}
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if(*(state.net.seen) < 12800){
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box truth = {0};
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truth.x = (i + .5)/l.w;
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truth.y = (j + .5)/l.h;
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truth.w = l.biases[2*n];
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truth.h = l.biases[2*n+1];
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if(DOABS){
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truth.w = l.biases[2*n]/l.w;
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truth.h = l.biases[2*n+1]/l.h;
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}
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delta_region_box(truth, l.output, l.biases, n, index, i, j, l.w, l.h, l.delta, .01);
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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(state.truth + t*5 + b*l.truths);
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if(!truth.x) break;
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float best_iou = 0;
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int best_index = 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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//printf("%d %f %d %f\n", i, truth.x*l.w, j, truth.y*l.h);
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box truth_shift = truth;
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truth_shift.x = 0;
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truth_shift.y = 0;
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//printf("index %d %d\n",i, j);
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for(n = 0; n < l.n; ++n){
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int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs;
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box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h);
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if(l.bias_match){
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pred.w = l.biases[2*n];
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pred.h = l.biases[2*n+1];
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if(DOABS){
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pred.w = l.biases[2*n]/l.w;
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pred.h = l.biases[2*n+1]/l.h;
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}
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}
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//printf("pred: (%f, %f) %f x %f\n", pred.x, pred.y, pred.w, pred.h);
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pred.x = 0;
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pred.y = 0;
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float iou = box_iou(pred, truth_shift);
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if (iou > best_iou){
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best_index = index;
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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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//printf("%d %f (%f, %f) %f x %f\n", best_n, best_iou, truth.x, truth.y, truth.w, truth.h);
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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);
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if(iou > .5) recall += 1;
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avg_iou += iou;
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//l.delta[best_index + 4] = iou - l.output[best_index + 4];
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avg_obj += l.output[best_index + 4];
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l.delta[best_index + 4] = l.object_scale * (1 - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
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if (l.rescore) {
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l.delta[best_index + 4] = l.object_scale * (iou - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
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}
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int class_id = state.truth[t*5 + b*l.truths + 4];
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if (l.map) class_id = l.map[class_id];
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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);
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++count;
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++class_count;
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}
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}
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//printf("\n");
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#ifndef GPU
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flatten(l.delta, l.w*l.h, size*l.n, l.batch, 0);
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#endif
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*(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
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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);
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}
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void backward_region_layer(const region_layer l, network_state state)
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{
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axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
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}
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void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness, int *map)
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{
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int i,j,n;
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float *predictions = l.output;
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for (i = 0; i < l.w*l.h; ++i){
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int row = i / l.w;
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int col = i % l.w;
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for(n = 0; n < l.n; ++n){
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int index = i*l.n + n;
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int p_index = index * (l.classes + 5) + 4;
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float scale = predictions[p_index];
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if(l.classfix == -1 && scale < .5) scale = 0;
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int box_index = index * (l.classes + 5);
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boxes[index] = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);
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boxes[index].x *= w;
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boxes[index].y *= h;
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boxes[index].w *= w;
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boxes[index].h *= h;
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int class_index = index * (l.classes + 5) + 5;
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if(l.softmax_tree){
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hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);
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int found = 0;
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if(map){
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for(j = 0; j < 200; ++j){
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float prob = scale*predictions[class_index+map[j]];
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probs[index][j] = (prob > thresh) ? prob : 0;
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}
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} else {
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for(j = l.classes - 1; j >= 0; --j){
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if(!found && predictions[class_index + j] > .5){
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found = 1;
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} else {
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predictions[class_index + j] = 0;
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}
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float prob = predictions[class_index+j];
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probs[index][j] = (scale > thresh) ? prob : 0;
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}
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}
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} else {
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for(j = 0; j < l.classes; ++j){
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float prob = scale*predictions[class_index+j];
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probs[index][j] = (prob > thresh) ? prob : 0;
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}
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}
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if(only_objectness){
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probs[index][0] = scale;
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}
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}
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}
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}
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#ifdef GPU
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void forward_region_layer_gpu(const region_layer l, network_state state)
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{
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/*
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if(!state.train){
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copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1);
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return;
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}
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*/
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flatten_ongpu(state.input, l.h*l.w, l.n*(l.coords + l.classes + 1), l.batch, 1, l.output_gpu);
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if(l.softmax_tree){
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int i;
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int count = 5;
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for (i = 0; i < l.softmax_tree->groups; ++i) {
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int group_size = l.softmax_tree->group_size[i];
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softmax_gpu(l.output_gpu+count, group_size, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + count);
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count += group_size;
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}
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}else if (l.softmax){
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softmax_gpu(l.output_gpu+5, l.classes, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + 5);
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}
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float *in_cpu = calloc(l.batch*l.inputs, sizeof(float));
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float *truth_cpu = 0;
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if(state.truth){
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int num_truth = l.batch*l.truths;
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truth_cpu = calloc(num_truth, sizeof(float));
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cuda_pull_array(state.truth, truth_cpu, num_truth);
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}
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cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs);
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//cudaStreamSynchronize(get_cuda_stream());
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network_state cpu_state = state;
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cpu_state.train = state.train;
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cpu_state.truth = truth_cpu;
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cpu_state.input = in_cpu;
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forward_region_layer(l, cpu_state);
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//cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs);
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free(cpu_state.input);
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if(!state.train) return;
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cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs);
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//cudaStreamSynchronize(get_cuda_stream());
|
if(cpu_state.truth) free(cpu_state.truth);
|
}
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void backward_region_layer_gpu(region_layer l, network_state state)
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{
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flatten_ongpu(l.delta_gpu, l.h*l.w, l.n*(l.coords + l.classes + 1), l.batch, 0, state.delta);
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}
|
#endif
|
|
|
void correct_region_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative)
|
{
|
int i;
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int new_w = 0;
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int new_h = 0;
|
if (((float)netw / w) < ((float)neth / h)) {
|
new_w = netw;
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new_h = (h * netw) / w;
|
}
|
else {
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new_h = neth;
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new_w = (w * neth) / h;
|
}
|
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;
|
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) {
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for (z = 0; z < l.classes + l.coords + 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) {
|
flip[i1] = -flip[i1];
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flip[i2] = -flip[i2];
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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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for (i = 0; i < l.w*l.h; ++i) {
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int row = i / l.w;
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int col = i % l.w;
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for (n = 0; n < l.n; ++n) {
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int index = n*l.w*l.h + i;
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for (j = 0; j < l.classes; ++j) {
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dets[index].prob[j] = 0;
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}
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int obj_index = entry_index(l, 0, n*l.w*l.h + i, l.coords);
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int box_index = entry_index(l, 0, n*l.w*l.h + i, 0);
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int mask_index = entry_index(l, 0, n*l.w*l.h + i, 4);
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float scale = l.background ? 1 : predictions[obj_index];
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dets[index].bbox = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);// , l.w*l.h);
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dets[index].objectness = scale > thresh ? scale : 0;
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if (dets[index].mask) {
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for (j = 0; j < l.coords - 4; ++j) {
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dets[index].mask[j] = l.output[mask_index + j*l.w*l.h];
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}
|
}
|
|
int class_index = entry_index(l, 0, n*l.w*l.h + i, l.coords + !l.background);
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if (l.softmax_tree) {
|
|
hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);// , l.w*l.h);
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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];
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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);
|
}
|
