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Big changes to detection

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This commit is contained in:
Joseph Redmon
2015-03-04 14:56:38 -08:00
parent 5f4a5f59b0
commit fb9e0fe336
17 changed files with 298 additions and 151 deletions
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147
src/detection_layer.c
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@ -1,72 +1,123 @@
int detection_out_height(detection_layer layer)
#include "detection_layer.h"
#include "activations.h"
#include "softmax_layer.h"
#include "blas.h"
#include "cuda.h"
#include <stdio.h>
#include <stdlib.h>
int get_detection_layer_locations(detection_layer layer)
{
return layer.size + layer.h*layer.stride;
return layer.inputs / (layer.classes+layer.coords+layer.rescore);
}
int detection_out_width(detection_layer layer)
int get_detection_layer_output_size(detection_layer layer)
{
return layer.size + layer.w*layer.stride;
return get_detection_layer_locations(layer)*(layer.classes+layer.coords);
}
detection_layer *make_detection_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation)
detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore)
{
int i;
size = 2*(size/2)+1; //HA! And you thought you'd use an even sized filter...
detection_layer *layer = calloc(1, sizeof(detection_layer));
layer->h = h;
layer->w = w;
layer->c = c;
layer->n = n;
layer->batch = batch;
layer->stride = stride;
layer->size = size;
assert(c%n == 0);
layer->inputs = inputs;
layer->classes = classes;
layer->coords = coords;
layer->rescore = rescore;
int outputs = get_detection_layer_output_size(*layer);
layer->output = calloc(batch*outputs, sizeof(float));
layer->delta = calloc(batch*outputs, sizeof(float));
#ifdef GPU
layer->output_gpu = cuda_make_array(0, batch*outputs);
layer->delta_gpu = cuda_make_array(0, batch*outputs);
#endif
layer->filters = calloc(c*size*size, sizeof(float));
layer->filter_updates = calloc(c*size*size, sizeof(float));
layer->filter_momentum = calloc(c*size*size, sizeof(float));
float scale = 1./(size*size*c);
for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*(rand_uniform());
int out_h = detection_out_height(*layer);
int out_w = detection_out_width(*layer);
layer->output = calloc(layer->batch * out_h * out_w * n, sizeof(float));
layer->delta = calloc(layer->batch * out_h * out_w * n, sizeof(float));
layer->activation = activation;
fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
fprintf(stderr, "Detection Layer\n");
srand(0);
return layer;
}
void forward_detection_layer(const detection_layer layer, float *in)
void forward_detection_layer(const detection_layer layer, float *in, float *truth)
{
int out_h = detection_out_height(layer);
int out_w = detection_out_width(layer);
int i,j,fh, fw,c;
memset(layer.output, 0, layer->batch*layer->n*out_h*out_w*sizeof(float));
for(c = 0; c < layer.c; ++c){
for(i = 0; i < layer.h; ++i){
for(j = 0; j < layer.w; ++j){
float val = layer->input[j+(i + c*layer.h)*layer.w];
for(fh = 0; fh < layer.size; ++fh){
for(fw = 0; fw < layer.size; ++fw){
int h = i*layer.stride + fh;
int w = j*layer.stride + fw;
layer.output[w+(h+c/n*out_h)*out_w] += val*layer->filters[fw+(fh+c*layer.size)*layer.size];
}
}
}
int in_i = 0;
int out_i = 0;
int locations = get_detection_layer_locations(layer);
int i,j;
for(i = 0; i < layer.batch*locations; ++i){
int mask = (!truth || !truth[out_i + layer.classes - 1]);
float scale = 1;
if(layer.rescore) scale = in[in_i++];
for(j = 0; j < layer.classes; ++j){
layer.output[out_i++] = scale*in[in_i++];
}
softmax_array(layer.output + out_i - layer.classes, layer.classes, layer.output + out_i - layer.classes);
activate_array(layer.output+out_i, layer.coords, SIGMOID);
for(j = 0; j < layer.coords; ++j){
layer.output[out_i++] = mask*in[in_i++];
}
//printf("%d\n", mask);
//for(j = 0; j < layer.classes+layer.coords; ++j) printf("%f ", layer.output[i*(layer.classes+layer.coords)+j]);
//printf ("\n");
}
}
void backward_detection_layer(const detection_layer layer, float *delta)
void backward_detection_layer(const detection_layer layer, float *in, float *delta)
{
int locations = get_detection_layer_locations(layer);
int i,j;
int in_i = 0;
int out_i = 0;
for(i = 0; i < layer.batch*locations; ++i){
float scale = 1;
float latent_delta = 0;
if(layer.rescore) scale = in[in_i++];
for(j = 0; j < layer.classes; ++j){
latent_delta += in[in_i]*layer.delta[out_i];
delta[in_i++] = scale*layer.delta[out_i++];
}
for(j = 0; j < layer.coords; ++j){
delta[in_i++] = layer.delta[out_i++];
}
gradient_array(in + in_i - layer.coords, layer.coords, SIGMOID, layer.delta + out_i - layer.coords);
if(layer.rescore) delta[in_i-layer.coords-layer.classes-layer.rescore] = latent_delta;
}
}
#ifdef GPU
void forward_detection_layer_gpu(const detection_layer layer, float *in, float *truth)
{
int outputs = get_detection_layer_output_size(layer);
float *in_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
float *truth_cpu = 0;
if(truth){
truth_cpu = calloc(layer.batch*outputs, sizeof(float));
cuda_pull_array(truth, truth_cpu, layer.batch*outputs);
}
cuda_pull_array(in, in_cpu, layer.batch*layer.inputs);
forward_detection_layer(layer, in_cpu, truth_cpu);
cuda_push_array(layer.output_gpu, layer.output, layer.batch*outputs);
free(in_cpu);
if(truth_cpu) free(truth_cpu);
}
void backward_detection_layer_gpu(detection_layer layer, float *in, float *delta)
{
int outputs = get_detection_layer_output_size(layer);
float *in_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
float *delta_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
cuda_pull_array(in, in_cpu, layer.batch*layer.inputs);
cuda_pull_array(layer.delta_gpu, layer.delta, layer.batch*outputs);
backward_detection_layer(layer, in_cpu, delta_cpu);
cuda_push_array(delta, delta_cpu, layer.batch*layer.inputs);
free(in_cpu);
free(delta_cpu);
}
#endif