2016-08-06 01:27:07 +03:00
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#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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2016-11-16 09:53:58 +03:00
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#define DOABS 1
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2016-08-06 01:27:07 +03:00
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region_layer make_region_layer(int batch, int w, int h, int n, int classes, int coords)
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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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2016-09-12 23:55:20 +03:00
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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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2016-08-06 01:27:07 +03:00
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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.truths = 30*(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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2016-09-12 23:55:20 +03:00
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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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2016-09-25 09:12:54 +03:00
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l.forward = forward_region_layer;
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l.backward = backward_region_layer;
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2016-08-06 01:27:07 +03:00
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#ifdef GPU
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2016-09-25 09:12:54 +03:00
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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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2016-08-06 01:27:07 +03:00
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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, "Region Layer\n");
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srand(0);
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return l;
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}
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2016-11-16 09:53:58 +03:00
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void resize_region_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 + 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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cuda_free(l->delta_gpu);
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cuda_free(l->output_gpu);
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l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs);
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l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs);
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#endif
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}
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2016-09-12 23:55:20 +03:00
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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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2016-08-06 01:27:07 +03:00
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{
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box b;
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2016-11-11 19:48:40 +03:00
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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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2016-09-12 23:55:20 +03:00
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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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2016-11-11 19:48:40 +03:00
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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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2016-08-06 01:27:07 +03:00
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return b;
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}
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2016-09-12 23:55:20 +03:00
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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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2016-08-06 01:27:07 +03:00
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{
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2016-09-12 23:55:20 +03:00
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box pred = get_region_box(x, biases, n, index, i, j, w, h);
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2016-08-06 01:27:07 +03:00
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float iou = box_iou(pred, truth);
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2016-11-11 19:48:40 +03:00
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float tx = (truth.x*w - i);
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float ty = (truth.y*h - j);
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2016-09-12 23:55:20 +03:00
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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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2016-11-11 19:48:40 +03:00
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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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2016-11-06 00:09:21 +03:00
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}
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2016-11-11 19:48:40 +03:00
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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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2016-09-12 23:55:20 +03:00
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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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2016-08-06 01:27:07 +03:00
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return iou;
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}
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2016-11-08 10:42:19 +03:00
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void delta_region_class(float *output, float *delta, int index, int class, int classes, tree *hier, float scale, float *avg_cat)
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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 >= 0){
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pred *= output[index + class];
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int g = hier->group[class];
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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] = scale * (1 - output[index + class]);
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class = hier->parent[class];
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}
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*avg_cat += pred;
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} else {
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for(n = 0; n < classes; ++n){
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delta[index + n] = scale * (((n == class)?1 : 0) - output[index + n]);
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if(n == class) *avg_cat += output[index + n];
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}
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}
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}
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2016-08-06 01:27:07 +03:00
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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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2016-11-06 00:09:21 +03:00
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void softmax_tree(float *input, int batch, int inputs, float temp, tree *hierarchy, float *output);
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2016-08-06 01:27:07 +03:00
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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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2016-11-16 09:53:58 +03:00
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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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2016-08-06 01:27:07 +03:00
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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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2016-11-11 19:48:40 +03:00
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}
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}
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2016-11-16 09:53:58 +03:00
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#ifndef GPU
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2016-11-11 19:48:40 +03:00
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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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2016-11-06 00:09:21 +03:00
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softmax_tree(l.output + index + 5, 1, 0, 1, l.softmax_tree, l.output + index + 5);
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2016-11-11 19:48:40 +03:00
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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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2016-10-21 23:16:43 +03:00
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softmax(l.output + index + 5, l.classes, 1, l.output + index + 5);
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2016-08-06 01:27:07 +03:00
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}
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}
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}
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2016-11-16 09:53:58 +03:00
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#endif
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2016-08-06 01:27:07 +03:00
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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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2016-09-12 23:55:20 +03:00
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float recall = 0;
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2016-08-06 01:27:07 +03:00
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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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2016-11-08 10:42:19 +03:00
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int class_count = 0;
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2016-08-06 01:27:07 +03:00
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*(l.cost) = 0;
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for (b = 0; b < l.batch; ++b) {
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2016-11-16 09:53:58 +03:00
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if(l.softmax_tree){
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int onlyclass = 0;
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for(t = 0; t < 30; ++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 = 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 p = get_hierarchy_probability(l.output + index, l.softmax_tree, class);
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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, l.classes, l.softmax_tree, l.class_scale, &avg_cat);
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++class_count;
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onlyclass = 1;
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break;
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}
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}
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if(onlyclass) continue;
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}
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2016-08-06 01:27:07 +03:00
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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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2016-09-12 23:55:20 +03:00
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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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2016-08-06 01:27:07 +03:00
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float best_iou = 0;
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2016-11-08 10:42:19 +03:00
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int best_class = -1;
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2016-08-06 01:27:07 +03:00
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for(t = 0; t < 30; ++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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2016-11-08 10:42:19 +03:00
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if (iou > best_iou) {
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best_class = state.truth[t*5 + b*l.truths + 4];
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best_iou = iou;
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}
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2016-08-06 01:27:07 +03:00
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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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2016-11-08 10:42:19 +03:00
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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, l.classes, l.softmax_tree, l.class_scale*(l.classfix == 2 ? l.output[index + 4] : 1), &avg_cat);
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++class_count;
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}
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}
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}
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2016-08-06 01:27:07 +03:00
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2016-11-06 00:09:21 +03:00
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if(*(state.net.seen) < 12800){
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2016-08-06 01:27:07 +03:00
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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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2016-11-06 00:09:21 +03:00
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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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2016-11-11 19:48:40 +03:00
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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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2016-09-12 23:55:20 +03:00
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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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2016-08-06 01:27:07 +03:00
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}
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}
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}
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}
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for(t = 0; t < 30; ++t){
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box truth = float_to_box(state.truth + t*5 + b*l.truths);
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2016-11-06 00:09:21 +03:00
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2016-08-06 01:27:07 +03:00
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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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2016-11-06 03:27:31 +03:00
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//printf("index %d %d\n",i, j);
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2016-08-06 01:27:07 +03:00
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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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2016-09-12 23:55:20 +03:00
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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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2016-11-06 00:09:21 +03:00
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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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2016-11-11 19:48:40 +03:00
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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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2016-11-06 00:09:21 +03:00
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}
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2016-11-06 03:27:31 +03:00
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//printf("pred: (%f, %f) %f x %f\n", pred.x, pred.y, pred.w, pred.h);
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2016-08-06 01:27:07 +03:00
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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);
|
|
|
|
if (iou > best_iou){
|
|
|
|
best_index = index;
|
|
|
|
best_iou = iou;
|
|
|
|
best_n = n;
|
|
|
|
}
|
|
|
|
}
|
2016-11-06 03:27:31 +03:00
|
|
|
//printf("%d %f (%f, %f) %f x %f\n", best_n, best_iou, truth.x, truth.y, truth.w, truth.h);
|
2016-08-06 01:27:07 +03:00
|
|
|
|
2016-09-12 23:55:20 +03:00
|
|
|
float iou = delta_region_box(truth, l.output, l.biases, best_n, best_index, i, j, l.w, l.h, l.delta, l.coord_scale);
|
|
|
|
if(iou > .5) recall += 1;
|
2016-08-06 01:27:07 +03:00
|
|
|
avg_iou += iou;
|
|
|
|
|
|
|
|
//l.delta[best_index + 4] = iou - l.output[best_index + 4];
|
|
|
|
avg_obj += l.output[best_index + 4];
|
|
|
|
l.delta[best_index + 4] = l.object_scale * (1 - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
|
|
|
|
if (l.rescore) {
|
|
|
|
l.delta[best_index + 4] = l.object_scale * (iou - l.output[best_index + 4]) * logistic_gradient(l.output[best_index + 4]);
|
|
|
|
}
|
|
|
|
|
2016-11-06 00:09:21 +03:00
|
|
|
|
|
|
|
int class = state.truth[t*5 + b*l.truths + 4];
|
|
|
|
if (l.map) class = l.map[class];
|
2016-11-08 10:42:19 +03:00
|
|
|
delta_region_class(l.output, l.delta, best_index + 5, class, l.classes, l.softmax_tree, l.class_scale, &avg_cat);
|
2016-08-06 01:27:07 +03:00
|
|
|
++count;
|
2016-11-08 10:42:19 +03:00
|
|
|
++class_count;
|
2016-08-06 01:27:07 +03:00
|
|
|
}
|
|
|
|
}
|
2016-11-06 03:27:31 +03:00
|
|
|
//printf("\n");
|
2016-11-16 09:53:58 +03:00
|
|
|
#ifndef GPU
|
|
|
|
flatten(l.delta, l.w*l.h, size*l.n, l.batch, 0);
|
|
|
|
#endif
|
2016-08-06 01:27:07 +03:00
|
|
|
*(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
|
2016-11-08 10:42:19 +03:00
|
|
|
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);
|
2016-08-06 01:27:07 +03:00
|
|
|
}
|
|
|
|
|
|
|
|
void backward_region_layer(const region_layer l, network_state state)
|
|
|
|
{
|
|
|
|
axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
|
|
|
|
}
|
|
|
|
|
2016-09-25 09:12:54 +03:00
|
|
|
void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness)
|
|
|
|
{
|
|
|
|
int i,j,n;
|
|
|
|
float *predictions = l.output;
|
|
|
|
for (i = 0; i < l.w*l.h; ++i){
|
|
|
|
int row = i / l.w;
|
|
|
|
int col = i % l.w;
|
|
|
|
for(n = 0; n < l.n; ++n){
|
|
|
|
int index = i*l.n + n;
|
|
|
|
int p_index = index * (l.classes + 5) + 4;
|
|
|
|
float scale = predictions[p_index];
|
2016-11-08 10:42:19 +03:00
|
|
|
if(l.classfix == -1 && scale < .5) scale = 0;
|
2016-09-25 09:12:54 +03:00
|
|
|
int box_index = index * (l.classes + 5);
|
2016-11-06 00:09:21 +03:00
|
|
|
boxes[index] = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);
|
|
|
|
boxes[index].x *= w;
|
|
|
|
boxes[index].y *= h;
|
|
|
|
boxes[index].w *= w;
|
|
|
|
boxes[index].h *= h;
|
|
|
|
|
|
|
|
int class_index = index * (l.classes + 5) + 5;
|
|
|
|
if(l.softmax_tree){
|
2016-11-08 10:42:19 +03:00
|
|
|
|
2016-11-06 00:09:21 +03:00
|
|
|
hierarchy_predictions(predictions + class_index, l.classes, l.softmax_tree, 0);
|
|
|
|
int found = 0;
|
|
|
|
for(j = l.classes - 1; j >= 0; --j){
|
2016-11-16 09:53:58 +03:00
|
|
|
if(1){
|
|
|
|
if(!found && predictions[class_index + j] > .5){
|
|
|
|
found = 1;
|
|
|
|
} else {
|
|
|
|
predictions[class_index + j] = 0;
|
|
|
|
}
|
|
|
|
float prob = predictions[class_index+j];
|
|
|
|
probs[index][j] = (scale > thresh) ? prob : 0;
|
|
|
|
}else{
|
|
|
|
float prob = scale*predictions[class_index+j];
|
|
|
|
probs[index][j] = (prob > thresh) ? prob : 0;
|
2016-11-06 00:09:21 +03:00
|
|
|
}
|
|
|
|
}
|
|
|
|
}else{
|
|
|
|
for(j = 0; j < l.classes; ++j){
|
|
|
|
float prob = scale*predictions[class_index+j];
|
|
|
|
probs[index][j] = (prob > thresh) ? prob : 0;
|
|
|
|
}
|
2016-09-25 09:12:54 +03:00
|
|
|
}
|
|
|
|
if(only_objectness){
|
|
|
|
probs[index][0] = scale;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2016-08-06 01:27:07 +03:00
|
|
|
#ifdef GPU
|
|
|
|
|
|
|
|
void forward_region_layer_gpu(const region_layer l, network_state state)
|
|
|
|
{
|
|
|
|
/*
|
|
|
|
if(!state.train){
|
|
|
|
copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1);
|
|
|
|
return;
|
|
|
|
}
|
|
|
|
*/
|
2016-11-16 09:53:58 +03:00
|
|
|
flatten_ongpu(state.input, l.h*l.w, l.n*(l.coords + l.classes + 1), l.batch, 1, l.output_gpu);
|
|
|
|
if(l.softmax_tree){
|
|
|
|
int i;
|
|
|
|
int count = 5;
|
|
|
|
for (i = 0; i < l.softmax_tree->groups; ++i) {
|
|
|
|
int group_size = l.softmax_tree->group_size[i];
|
|
|
|
softmax_gpu(l.output_gpu+count, group_size, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + count);
|
|
|
|
count += group_size;
|
|
|
|
}
|
|
|
|
}else if (l.softmax){
|
|
|
|
softmax_gpu(l.output_gpu+5, l.classes, l.classes + 5, l.w*l.h*l.n*l.batch, 1, l.output_gpu + 5);
|
|
|
|
}
|
2016-08-06 01:27:07 +03:00
|
|
|
|
|
|
|
float *in_cpu = calloc(l.batch*l.inputs, sizeof(float));
|
|
|
|
float *truth_cpu = 0;
|
|
|
|
if(state.truth){
|
|
|
|
int num_truth = l.batch*l.truths;
|
|
|
|
truth_cpu = calloc(num_truth, sizeof(float));
|
|
|
|
cuda_pull_array(state.truth, truth_cpu, num_truth);
|
|
|
|
}
|
2016-11-16 09:53:58 +03:00
|
|
|
cuda_pull_array(l.output_gpu, in_cpu, l.batch*l.inputs);
|
2016-08-06 01:27:07 +03:00
|
|
|
network_state cpu_state = state;
|
|
|
|
cpu_state.train = state.train;
|
|
|
|
cpu_state.truth = truth_cpu;
|
|
|
|
cpu_state.input = in_cpu;
|
|
|
|
forward_region_layer(l, cpu_state);
|
2016-11-16 09:53:58 +03:00
|
|
|
//cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs);
|
2016-08-06 01:27:07 +03:00
|
|
|
free(cpu_state.input);
|
2016-11-16 09:53:58 +03:00
|
|
|
if(!state.train) return;
|
|
|
|
cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs);
|
2016-08-06 01:27:07 +03:00
|
|
|
if(cpu_state.truth) free(cpu_state.truth);
|
|
|
|
}
|
|
|
|
|
|
|
|
void backward_region_layer_gpu(region_layer l, network_state state)
|
|
|
|
{
|
2016-11-16 09:53:58 +03:00
|
|
|
flatten_ongpu(l.delta_gpu, l.h*l.w, l.n*(l.coords + l.classes + 1), l.batch, 0, state.delta);
|
2016-08-06 01:27:07 +03:00
|
|
|
}
|
|
|
|
#endif
|
|
|
|
|