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route handles input images well....ish

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Joseph Redmon
2015-05-11 13:46:49 -07:00
parent dc0d7bb8a8
commit 516f019ba6
31 changed files with 1250 additions and 1819 deletions
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210
src/detection_layer.c
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@ -8,47 +8,49 @@
#include <string.h>
#include <stdlib.h>
int get_detection_layer_locations(detection_layer layer)
int get_detection_layer_locations(detection_layer l)
{
return layer.inputs / (layer.classes+layer.coords+layer.rescore+layer.background);
return l.inputs / (l.classes+l.coords+l.rescore+l.background);
}
int get_detection_layer_output_size(detection_layer layer)
int get_detection_layer_output_size(detection_layer l)
{
return get_detection_layer_locations(layer)*(layer.background + layer.classes + layer.coords);
return get_detection_layer_locations(l)*(l.background + l.classes + l.coords);
}
detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore, int background, int nuisance)
detection_layer make_detection_layer(int batch, int inputs, int classes, int coords, int rescore, int background, int nuisance)
{
detection_layer *layer = calloc(1, sizeof(detection_layer));
detection_layer l = {0};
l.type = DETECTION;
layer->batch = batch;
layer->inputs = inputs;
layer->classes = classes;
layer->coords = coords;
layer->rescore = rescore;
layer->nuisance = nuisance;
layer->cost = calloc(1, sizeof(float));
layer->does_cost=1;
layer->background = background;
int outputs = get_detection_layer_output_size(*layer);
layer->output = calloc(batch*outputs, sizeof(float));
layer->delta = calloc(batch*outputs, sizeof(float));
l.batch = batch;
l.inputs = inputs;
l.classes = classes;
l.coords = coords;
l.rescore = rescore;
l.nuisance = nuisance;
l.cost = calloc(1, sizeof(float));
l.does_cost=1;
l.background = background;
int outputs = get_detection_layer_output_size(l);
l.outputs = outputs;
l.output = calloc(batch*outputs, sizeof(float));
l.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);
l.output_gpu = cuda_make_array(0, batch*outputs);
l.delta_gpu = cuda_make_array(0, batch*outputs);
#endif
fprintf(stderr, "Detection Layer\n");
srand(0);
return layer;
return l;
}
void dark_zone(detection_layer layer, int class, int start, network_state state)
void dark_zone(detection_layer l, int class, int start, network_state state)
{
int index = start+layer.background+class;
int size = layer.classes+layer.coords+layer.background;
int index = start+l.background+class;
int size = l.classes+l.coords+l.background;
int location = (index%(7*7*size)) / size ;
int r = location / 7;
int c = location % 7;
@ -60,9 +62,9 @@ void dark_zone(detection_layer layer, int class, int start, network_state state)
if((c + dc) > 6 || (c + dc) < 0) continue;
int di = (dr*7 + dc) * size;
if(state.truth[index+di]) continue;
layer.output[index + di] = 0;
l.output[index + di] = 0;
//if(!state.truth[start+di]) continue;
//layer.output[start + di] = 1;
//l.output[start + di] = 1;
}
}
}
@ -299,47 +301,47 @@ dbox diou(box a, box b)
return dd;
}
void forward_detection_layer(const detection_layer layer, network_state state)
void forward_detection_layer(const detection_layer l, network_state state)
{
int in_i = 0;
int out_i = 0;
int locations = get_detection_layer_locations(layer);
int locations = get_detection_layer_locations(l);
int i,j;
for(i = 0; i < layer.batch*locations; ++i){
int mask = (!state.truth || state.truth[out_i + layer.background + layer.classes + 2]);
for(i = 0; i < l.batch*locations; ++i){
int mask = (!state.truth || state.truth[out_i + l.background + l.classes + 2]);
float scale = 1;
if(layer.rescore) scale = state.input[in_i++];
else if(layer.nuisance){
layer.output[out_i++] = 1-state.input[in_i++];
if(l.rescore) scale = state.input[in_i++];
else if(l.nuisance){
l.output[out_i++] = 1-state.input[in_i++];
scale = mask;
}
else if(layer.background) layer.output[out_i++] = scale*state.input[in_i++];
else if(l.background) l.output[out_i++] = scale*state.input[in_i++];
for(j = 0; j < layer.classes; ++j){
layer.output[out_i++] = scale*state.input[in_i++];
for(j = 0; j < l.classes; ++j){
l.output[out_i++] = scale*state.input[in_i++];
}
if(layer.nuisance){
if(l.nuisance){
}else if(layer.background){
softmax_array(layer.output + out_i - layer.classes-layer.background, layer.classes+layer.background, layer.output + out_i - layer.classes-layer.background);
activate_array(state.input+in_i, layer.coords, LOGISTIC);
}else if(l.background){
softmax_array(l.output + out_i - l.classes-l.background, l.classes+l.background, l.output + out_i - l.classes-l.background);
activate_array(state.input+in_i, l.coords, LOGISTIC);
}
for(j = 0; j < layer.coords; ++j){
layer.output[out_i++] = mask*state.input[in_i++];
for(j = 0; j < l.coords; ++j){
l.output[out_i++] = mask*state.input[in_i++];
}
}
if(layer.does_cost && state.train && 0){
if(l.does_cost && state.train && 0){
int count = 0;
float avg = 0;
*(layer.cost) = 0;
int size = get_detection_layer_output_size(layer) * layer.batch;
memset(layer.delta, 0, size * sizeof(float));
for (i = 0; i < layer.batch*locations; ++i) {
int classes = layer.nuisance+layer.classes;
int offset = i*(classes+layer.coords);
*(l.cost) = 0;
int size = get_detection_layer_output_size(l) * l.batch;
memset(l.delta, 0, size * sizeof(float));
for (i = 0; i < l.batch*locations; ++i) {
int classes = l.nuisance+l.classes;
int offset = i*(classes+l.coords);
for (j = offset; j < offset+classes; ++j) {
*(layer.cost) += pow(state.truth[j] - layer.output[j], 2);
layer.delta[j] = state.truth[j] - layer.output[j];
*(l.cost) += pow(state.truth[j] - l.output[j], 2);
l.delta[j] = state.truth[j] - l.output[j];
}
box truth;
truth.x = state.truth[j+0];
@ -347,17 +349,17 @@ void forward_detection_layer(const detection_layer layer, network_state state)
truth.w = state.truth[j+2];
truth.h = state.truth[j+3];
box out;
out.x = layer.output[j+0];
out.y = layer.output[j+1];
out.w = layer.output[j+2];
out.h = layer.output[j+3];
out.x = l.output[j+0];
out.y = l.output[j+1];
out.w = l.output[j+2];
out.h = l.output[j+3];
if(!(truth.w*truth.h)) continue;
//printf("iou: %f\n", iou);
dbox d = diou(out, truth);
layer.delta[j+0] = d.dx;
layer.delta[j+1] = d.dy;
layer.delta[j+2] = d.dw;
layer.delta[j+3] = d.dh;
l.delta[j+0] = d.dx;
l.delta[j+1] = d.dy;
l.delta[j+2] = d.dw;
l.delta[j+3] = d.dh;
int sqr = 1;
if(sqr){
@ -367,7 +369,7 @@ void forward_detection_layer(const detection_layer layer, network_state state)
out.h *= out.h;
}
float iou = box_iou(truth, out);
*(layer.cost) += pow((1-iou), 2);
*(l.cost) += pow((1-iou), 2);
avg += iou;
++count;
}
@ -375,24 +377,24 @@ void forward_detection_layer(const detection_layer layer, network_state state)
}
/*
int count = 0;
for(i = 0; i < layer.batch*locations; ++i){
for(j = 0; j < layer.classes+layer.background; ++j){
printf("%f, ", layer.output[count++]);
for(i = 0; i < l.batch*locations; ++i){
for(j = 0; j < l.classes+l.background; ++j){
printf("%f, ", l.output[count++]);
}
printf("\n");
for(j = 0; j < layer.coords; ++j){
printf("%f, ", layer.output[count++]);
for(j = 0; j < l.coords; ++j){
printf("%f, ", l.output[count++]);
}
printf("\n");
}
*/
/*
if(layer.background || 1){
for(i = 0; i < layer.batch*locations; ++i){
int index = i*(layer.classes+layer.coords+layer.background);
for(j= 0; j < layer.classes; ++j){
if(state.truth[index+j+layer.background]){
//dark_zone(layer, j, index, state);
if(l.background || 1){
for(i = 0; i < l.batch*locations; ++i){
int index = i*(l.classes+l.coords+l.background);
for(j= 0; j < l.classes; ++j){
if(state.truth[index+j+l.background]){
//dark_zone(l, j, index, state);
}
}
}
@ -400,66 +402,66 @@ void forward_detection_layer(const detection_layer layer, network_state state)
*/
}
void backward_detection_layer(const detection_layer layer, network_state state)
void backward_detection_layer(const detection_layer l, network_state state)
{
int locations = get_detection_layer_locations(layer);
int locations = get_detection_layer_locations(l);
int i,j;
int in_i = 0;
int out_i = 0;
for(i = 0; i < layer.batch*locations; ++i){
for(i = 0; i < l.batch*locations; ++i){
float scale = 1;
float latent_delta = 0;
if(layer.rescore) scale = state.input[in_i++];
else if (layer.nuisance) state.delta[in_i++] = -layer.delta[out_i++];
else if (layer.background) state.delta[in_i++] = scale*layer.delta[out_i++];
for(j = 0; j < layer.classes; ++j){
latent_delta += state.input[in_i]*layer.delta[out_i];
state.delta[in_i++] = scale*layer.delta[out_i++];
if(l.rescore) scale = state.input[in_i++];
else if (l.nuisance) state.delta[in_i++] = -l.delta[out_i++];
else if (l.background) state.delta[in_i++] = scale*l.delta[out_i++];
for(j = 0; j < l.classes; ++j){
latent_delta += state.input[in_i]*l.delta[out_i];
state.delta[in_i++] = scale*l.delta[out_i++];
}
if (layer.nuisance) {
if (l.nuisance) {
}else if (layer.background) gradient_array(layer.output + out_i, layer.coords, LOGISTIC, layer.delta + out_i);
for(j = 0; j < layer.coords; ++j){
state.delta[in_i++] = layer.delta[out_i++];
}else if (l.background) gradient_array(l.output + out_i, l.coords, LOGISTIC, l.delta + out_i);
for(j = 0; j < l.coords; ++j){
state.delta[in_i++] = l.delta[out_i++];
}
if(layer.rescore) state.delta[in_i-layer.coords-layer.classes-layer.rescore-layer.background] = latent_delta;
if(l.rescore) state.delta[in_i-l.coords-l.classes-l.rescore-l.background] = latent_delta;
}
}
#ifdef GPU
void forward_detection_layer_gpu(const detection_layer layer, network_state state)
void forward_detection_layer_gpu(const detection_layer l, network_state state)
{
int outputs = get_detection_layer_output_size(layer);
float *in_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
int outputs = get_detection_layer_output_size(l);
float *in_cpu = calloc(l.batch*l.inputs, sizeof(float));
float *truth_cpu = 0;
if(state.truth){
truth_cpu = calloc(layer.batch*outputs, sizeof(float));
cuda_pull_array(state.truth, truth_cpu, layer.batch*outputs);
truth_cpu = calloc(l.batch*outputs, sizeof(float));
cuda_pull_array(state.truth, truth_cpu, l.batch*outputs);
}
cuda_pull_array(state.input, in_cpu, layer.batch*layer.inputs);
cuda_pull_array(state.input, in_cpu, l.batch*l.inputs);
network_state cpu_state;
cpu_state.train = state.train;
cpu_state.truth = truth_cpu;
cpu_state.input = in_cpu;
forward_detection_layer(layer, cpu_state);
cuda_push_array(layer.output_gpu, layer.output, layer.batch*outputs);
cuda_push_array(layer.delta_gpu, layer.delta, layer.batch*outputs);
forward_detection_layer(l, cpu_state);
cuda_push_array(l.output_gpu, l.output, l.batch*outputs);
cuda_push_array(l.delta_gpu, l.delta, l.batch*outputs);
free(cpu_state.input);
if(cpu_state.truth) free(cpu_state.truth);
}
void backward_detection_layer_gpu(detection_layer layer, network_state state)
void backward_detection_layer_gpu(detection_layer l, network_state state)
{
int outputs = get_detection_layer_output_size(layer);
int outputs = get_detection_layer_output_size(l);
float *in_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
float *delta_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
float *in_cpu = calloc(l.batch*l.inputs, sizeof(float));
float *delta_cpu = calloc(l.batch*l.inputs, sizeof(float));
float *truth_cpu = 0;
if(state.truth){
truth_cpu = calloc(layer.batch*outputs, sizeof(float));
cuda_pull_array(state.truth, truth_cpu, layer.batch*outputs);
truth_cpu = calloc(l.batch*outputs, sizeof(float));
cuda_pull_array(state.truth, truth_cpu, l.batch*outputs);
}
network_state cpu_state;
cpu_state.train = state.train;
@ -467,10 +469,10 @@ void backward_detection_layer_gpu(detection_layer layer, network_state state)
cpu_state.truth = truth_cpu;
cpu_state.delta = delta_cpu;
cuda_pull_array(state.input, in_cpu, layer.batch*layer.inputs);
cuda_pull_array(layer.delta_gpu, layer.delta, layer.batch*outputs);
backward_detection_layer(layer, cpu_state);
cuda_push_array(state.delta, delta_cpu, layer.batch*layer.inputs);
cuda_pull_array(state.input, in_cpu, l.batch*l.inputs);
cuda_pull_array(l.delta_gpu, l.delta, l.batch*outputs);
backward_detection_layer(l, cpu_state);
cuda_push_array(state.delta, delta_cpu, l.batch*l.inputs);
free(in_cpu);
free(delta_cpu);