mirror/darknet
1
0
Fork 0
mirror of https://github.com/pjreddie/darknet.git synced 2023-08-10 21:13:14 +03:00
Code Issues Projects Releases Wiki Activity

refactoring and added DARK ZONE

Browse Source
This commit is contained in:
Joseph Redmon
2015-03-11 22:20:15 -07:00
parent f047cfff99
commit dcb000b553
37 changed files with 640 additions and 918 deletions
Download Patch File Download Diff File Expand all files Collapse all files

95
src/detection_layer.c
View File

@ -39,28 +39,52 @@ detection_layer *make_detection_layer(int batch, int inputs, int classes, int co
return layer;
}
void forward_detection_layer(const detection_layer layer, float *in, float *truth)
void forward_detection_layer(const detection_layer layer, network_state state)
{
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]);
int mask = (!state.truth || state.truth[out_i + layer.classes + 2]);
float scale = 1;
if(layer.rescore) scale = in[in_i++];
if(layer.rescore) scale = state.input[in_i++];
for(j = 0; j < layer.classes; ++j){
layer.output[out_i++] = scale*in[in_i++];
layer.output[out_i++] = scale*state.input[in_i++];
}
if(!layer.rescore){
softmax_array(layer.output + out_i - layer.classes, layer.classes, layer.output + out_i - layer.classes);
activate_array(state.input+in_i, layer.coords, LOGISTIC);
}
softmax_array(layer.output + out_i - layer.classes, layer.classes, layer.output + out_i - layer.classes);
activate_array(in+in_i, layer.coords, LOGISTIC);
for(j = 0; j < layer.coords; ++j){
layer.output[out_i++] = mask*in[in_i++];
layer.output[out_i++] = mask*state.input[in_i++];
}
}
}
void backward_detection_layer(const detection_layer layer, float *in, float *delta)
void dark_zone(detection_layer layer, int index, network_state state)
{
int size = layer.classes+layer.rescore+layer.coords;
int location = (index%(7*7*size)) / size ;
int r = location / 7;
int c = location % 7;
int class = index%size;
if(layer.rescore) --class;
int dr, dc;
for(dr = -1; dr <= 1; ++dr){
for(dc = -1; dc <= 1; ++dc){
if(!(dr || dc)) continue;
if((r + dr) > 6 || (r + dr) < 0) continue;
if((c + dc) > 6 || (c + dc) < 0) continue;
int di = (dr*7 + dc) * size;
if(state.truth[index+di]) continue;
layer.delta[index + di] = 0;
}
}
}
void backward_detection_layer(const detection_layer layer, network_state state)
{
int locations = get_detection_layer_locations(layer);
int i,j;
@ -69,49 +93,68 @@ void backward_detection_layer(const detection_layer layer, float *in, float *del
for(i = 0; i < layer.batch*locations; ++i){
float scale = 1;
float latent_delta = 0;
if(layer.rescore) scale = in[in_i++];
if(layer.rescore) scale = state.input[in_i++];
if(!layer.rescore){
for(j = 0; j < layer.classes-1; ++j){
if(state.truth[out_i + j]) dark_zone(layer, out_i+j, state);
}
}
for(j = 0; j < layer.classes; ++j){
latent_delta += in[in_i]*layer.delta[out_i];
delta[in_i++] = scale*layer.delta[out_i++];
latent_delta += state.input[in_i]*layer.delta[out_i];
state.delta[in_i++] = scale*layer.delta[out_i++];
}
gradient_array(layer.output + out_i, layer.coords, LOGISTIC, layer.delta + out_i);
if (!layer.rescore) gradient_array(layer.output + out_i, layer.coords, LOGISTIC, layer.delta + out_i);
for(j = 0; j < layer.coords; ++j){
delta[in_i++] = layer.delta[out_i++];
state.delta[in_i++] = layer.delta[out_i++];
}
if(layer.rescore) delta[in_i-layer.coords-layer.classes-layer.rescore] = latent_delta;
if(layer.rescore) state.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)
void forward_detection_layer_gpu(const detection_layer layer, network_state state)
{
int outputs = get_detection_layer_output_size(layer);
float *in_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
float *truth_cpu = 0;
if(truth){
if(state.truth){
truth_cpu = calloc(layer.batch*outputs, sizeof(float));
cuda_pull_array(truth, truth_cpu, layer.batch*outputs);
cuda_pull_array(state.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_pull_array(state.input, in_cpu, layer.batch*layer.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);
free(in_cpu);
if(truth_cpu) free(truth_cpu);
free(cpu_state.input);
if(cpu_state.truth) free(cpu_state.truth);
}
void backward_detection_layer_gpu(detection_layer layer, float *in, float *delta)
void backward_detection_layer_gpu(detection_layer layer, network_state state)
{
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));
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);
}
network_state cpu_state;
cpu_state.train = state.train;
cpu_state.input = in_cpu;
cpu_state.truth = truth_cpu;
cpu_state.delta = delta_cpu;
cuda_pull_array(in, in_cpu, layer.batch*layer.inputs);
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, in_cpu, delta_cpu);
cuda_push_array(delta, delta_cpu, layer.batch*layer.inputs);
backward_detection_layer(layer, cpu_state);
cuda_push_array(state.delta, delta_cpu, layer.batch*layer.inputs);
free(in_cpu);
free(delta_cpu);