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network.c
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#include "darknet.h"
#include <stdio.h>
#include <time.h>
#include <assert.h>
#include "network.h"
#include "image.h"
#include "data.h"
#include "utils.h"
#include "blas.h"
#include "crop_layer.h"
#include "connected_layer.h"
#include "gru_layer.h"
#include "rnn_layer.h"
#include "crnn_layer.h"
#include "conv_lstm_layer.h"
#include "local_layer.h"
#include "convolutional_layer.h"
#include "activation_layer.h"
#include "detection_layer.h"
#include "region_layer.h"
#include "normalization_layer.h"
#include "batchnorm_layer.h"
#include "maxpool_layer.h"
#include "reorg_layer.h"
#include "reorg_old_layer.h"
#include "avgpool_layer.h"
#include "cost_layer.h"
#include "softmax_layer.h"
#include "dropout_layer.h"
#include "route_layer.h"
#include "shortcut_layer.h"
#include "scale_channels_layer.h"
#include "sam_layer.h"
#include "yolo_layer.h"
#include "gaussian_yolo_layer.h"
#include "upsample_layer.h"
#include "parser.h"
load_args get_base_args(network *net)
{
load_args args = { 0 };
args.w = net->w;
args.h = net->h;
args.size = net->w;
args.min = net->min_crop;
args.max = net->max_crop;
args.angle = net->angle;
args.aspect = net->aspect;
args.exposure = net->exposure;
args.center = net->center;
args.saturation = net->saturation;
args.hue = net->hue;
return args;
}
int64_t get_current_iteration(network net)
{
return *net.cur_iteration;
}
int get_current_batch(network net)
{
int batch_num = (*net.seen)/(net.batch*net.subdivisions);
return batch_num;
}
/*
void reset_momentum(network net)
{
if (net.momentum == 0) return;
net.learning_rate = 0;
net.momentum = 0;
net.decay = 0;
#ifdef GPU
//if(net.gpu_index >= 0) update_network_gpu(net);
#endif
}
*/
void reset_network_state(network *net, int b)
{
int i;
for (i = 0; i < net->n; ++i) {
#ifdef GPU
layer l = net->layers[i];
if (l.state_gpu) {
fill_ongpu(l.outputs, 0, l.state_gpu + l.outputs*b, 1);
}
if (l.h_gpu) {
fill_ongpu(l.outputs, 0, l.h_gpu + l.outputs*b, 1);
}
#endif
}
}
void reset_rnn(network *net)
{
reset_network_state(net, 0);
}
float get_current_seq_subdivisions(network net)
{
int sequence_subdivisions = net.init_sequential_subdivisions;
if (net.num_steps > 0)
{
int batch_num = get_current_batch(net);
int i;
for (i = 0; i < net.num_steps; ++i) {
if (net.steps[i] > batch_num) break;
sequence_subdivisions *= net.seq_scales[i];
}
}
if (sequence_subdivisions < 1) sequence_subdivisions = 1;
if (sequence_subdivisions > net.subdivisions) sequence_subdivisions = net.subdivisions;
return sequence_subdivisions;
}
int get_sequence_value(network net)
{
int sequence = 1;
if (net.sequential_subdivisions != 0) sequence = net.subdivisions / net.sequential_subdivisions;
if (sequence < 1) sequence = 1;
return sequence;
}
float get_current_rate(network net)
{
int batch_num = get_current_batch(net);
int i;
float rate;
if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power);
switch (net.policy) {
case CONSTANT:
return net.learning_rate;
case STEP:
return net.learning_rate * pow(net.scale, batch_num/net.step);
case STEPS:
rate = net.learning_rate;
for(i = 0; i < net.num_steps; ++i){
if(net.steps[i] > batch_num) return rate;
rate *= net.scales[i];
//if(net.steps[i] > batch_num - 1 && net.scales[i] > 1) reset_momentum(net);
}
return rate;
case EXP:
return net.learning_rate * pow(net.gamma, batch_num);
case POLY:
return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
//if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power);
//return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
case RANDOM:
return net.learning_rate * pow(rand_uniform(0,1), net.power);
case SIG:
return net.learning_rate * (1./(1.+exp(net.gamma*(batch_num - net.step))));
case SGDR:
{
int last_iteration_start = 0;
int cycle_size = net.batches_per_cycle;
while ((last_iteration_start + cycle_size) < batch_num)
{
last_iteration_start += cycle_size;
cycle_size *= net.batches_cycle_mult;
}
rate = net.learning_rate_min +
0.5*(net.learning_rate - net.learning_rate_min)
* (1. + cos((float)(batch_num - last_iteration_start)*3.14159265 / cycle_size));
return rate;
}
default:
fprintf(stderr, "Policy is weird!\n");
return net.learning_rate;
}
}
char *get_layer_string(LAYER_TYPE a)
{
switch(a){
case CONVOLUTIONAL:
return "convolutional";
case ACTIVE:
return "activation";
case LOCAL:
return "local";
case DECONVOLUTIONAL:
return "deconvolutional";
case CONNECTED:
return "connected";
case RNN:
return "rnn";
case GRU:
return "gru";
case LSTM:
return "lstm";
case CRNN:
return "crnn";
case MAXPOOL:
return "maxpool";
case REORG:
return "reorg";
case AVGPOOL:
return "avgpool";
case SOFTMAX:
return "softmax";
case DETECTION:
return "detection";
case REGION:
return "region";
case YOLO:
return "yolo";
case GAUSSIAN_YOLO:
return "Gaussian_yolo";
case DROPOUT:
return "dropout";
case CROP:
return "crop";
case COST:
return "cost";
case ROUTE:
return "route";
case SHORTCUT:
return "shortcut";
case SCALE_CHANNELS:
return "scale_channels";
case SAM:
return "sam";
case NORMALIZATION:
return "normalization";
case BATCHNORM:
return "batchnorm";
default:
break;
}
return "none";
}
network make_network(int n)
{
network net = {0};
net.n = n;
net.layers = (layer*)xcalloc(net.n, sizeof(layer));
net.seen = (uint64_t*)xcalloc(1, sizeof(uint64_t));
net.cur_iteration = (int*)xcalloc(1, sizeof(int));
#ifdef GPU
net.input_gpu = (float**)xcalloc(1, sizeof(float*));
net.truth_gpu = (float**)xcalloc(1, sizeof(float*));
net.input16_gpu = (float**)xcalloc(1, sizeof(float*));
net.output16_gpu = (float**)xcalloc(1, sizeof(float*));
net.max_input16_size = (size_t*)xcalloc(1, sizeof(size_t));
net.max_output16_size = (size_t*)xcalloc(1, sizeof(size_t));
#endif
return net;
}
void forward_network(network net, network_state state)
{
state.workspace = net.workspace;
int i;
for(i = 0; i < net.n; ++i){
state.index = i;
layer l = net.layers[i];
if(l.delta && state.train){
scal_cpu(l.outputs * l.batch, 0, l.delta, 1);
}
//double time = get_time_point();
l.forward(l, state);
//printf("%d - Predicted in %lf milli-seconds.\n", i, ((double)get_time_point() - time) / 1000);
state.input = l.output;
/*
float avg_val = 0;
int k;
for (k = 0; k < l.outputs; ++k) avg_val += l.output[k];
printf(" i: %d - avg_val = %f \n", i, avg_val / l.outputs);
*/
}
}
void update_network(network net)
{
int i;
int update_batch = net.batch*net.subdivisions;
float rate = get_current_rate(net);
for(i = 0; i < net.n; ++i){
layer l = net.layers[i];
if(l.update){
l.update(l, update_batch, rate, net.momentum, net.decay);
}
}
}
float *get_network_output(network net)
{
#ifdef GPU
if (gpu_index >= 0) return get_network_output_gpu(net);
#endif
int i;
for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
return net.layers[i].output;
}
float get_network_cost(network net)
{
int i;
float sum = 0;
int count = 0;
for(i = 0; i < net.n; ++i){
if(net.layers[i].cost){
sum += net.layers[i].cost[0];
++count;
}
}
return sum/count;
}
int get_predicted_class_network(network net)
{
float *out = get_network_output(net);
int k = get_network_output_size(net);
return max_index(out, k);
}
void backward_network(network net, network_state state)
{
int i;
float *original_input = state.input;
float *original_delta = state.delta;
state.workspace = net.workspace;
for(i = net.n-1; i >= 0; --i){
state.index = i;
if(i == 0){
state.input = original_input;
state.delta = original_delta;
}else{
layer prev = net.layers[i-1];
state.input = prev.output;
state.delta = prev.delta;
}
layer l = net.layers[i];
if (l.stopbackward) break;
if (l.onlyforward) continue;
l.backward(l, state);
}
}
float train_network_datum(network net, float *x, float *y)
{
#ifdef GPU
if(gpu_index >= 0) return train_network_datum_gpu(net, x, y);
#endif
network_state state={0};
*net.seen += net.batch;
state.index = 0;
state.net = net;
state.input = x;
state.delta = 0;
state.truth = y;
state.train = 1;
forward_network(net, state);
backward_network(net, state);
float error = get_network_cost(net);
//if(((*net.seen)/net.batch)%net.subdivisions == 0) update_network(net);
return error;
}
float train_network_sgd(network net, data d, int n)
{
int batch = net.batch;
float* X = (float*)xcalloc(batch * d.X.cols, sizeof(float));
float* y = (float*)xcalloc(batch * d.y.cols, sizeof(float));
int i;
float sum = 0;
for(i = 0; i < n; ++i){
get_random_batch(d, batch, X, y);
net.current_subdivision = i;
float err = train_network_datum(net, X, y);
sum += err;
}
free(X);
free(y);
return (float)sum/(n*batch);
}
float train_network(network net, data d)
{
return train_network_waitkey(net, d, 0);
}
float train_network_waitkey(network net, data d, int wait_key)
{
assert(d.X.rows % net.batch == 0);
int batch = net.batch;
int n = d.X.rows / batch;
float* X = (float*)xcalloc(batch * d.X.cols, sizeof(float));
float* y = (float*)xcalloc(batch * d.y.cols, sizeof(float));
int i;
float sum = 0;
for(i = 0; i < n; ++i){
get_next_batch(d, batch, i*batch, X, y);
net.current_subdivision = i;
float err = train_network_datum(net, X, y);
sum += err;
if(wait_key) wait_key_cv(5);
}
(*net.cur_iteration) += 1;
#ifdef GPU
update_network_gpu(net);
#else // GPU
update_network(net);
#endif // GPU
free(X);
free(y);
return (float)sum/(n*batch);
}
float train_network_batch(network net, data d, int n)
{
int i,j;
network_state state={0};
state.index = 0;
state.net = net;
state.train = 1;
state.delta = 0;
float sum = 0;
int batch = 2;
for(i = 0; i < n; ++i){
for(j = 0; j < batch; ++j){
int index = random_gen()%d.X.rows;
state.input = d.X.vals[index];
state.truth = d.y.vals[index];
forward_network(net, state);
backward_network(net, state);
sum += get_network_cost(net);
}
update_network(net);
}
return (float)sum/(n*batch);
}
int recalculate_workspace_size(network *net)
{
#ifdef GPU
cuda_set_device(net->gpu_index);
if (gpu_index >= 0) cuda_free(net->workspace);
#endif
int i;
size_t workspace_size = 0;
for (i = 0; i < net->n; ++i) {
layer l = net->layers[i];
//printf(" %d: layer = %d,", i, l.type);
if (l.type == CONVOLUTIONAL) {
l.workspace_size = get_convolutional_workspace_size(l);
}
else if (l.type == CONNECTED) {
l.workspace_size = get_connected_workspace_size(l);
}
if (l.workspace_size > workspace_size) workspace_size = l.workspace_size;
net->layers[i] = l;
}
#ifdef GPU
if (gpu_index >= 0) {
printf("\n try to allocate additional workspace_size = %1.2f MB \n", (float)workspace_size / 1000000);
net->workspace = cuda_make_array(0, workspace_size / sizeof(float) + 1);
printf(" CUDA allocate done! \n");
}
else {
free(net->workspace);
net->workspace = (float*)xcalloc(1, workspace_size);
}
#else
free(net->workspace);
net->workspace = (float*)xcalloc(1, workspace_size);
#endif
//fprintf(stderr, " Done!\n");
return 0;
}
void set_batch_network(network *net, int b)
{
net->batch = b;
int i;
for(i = 0; i < net->n; ++i){
net->layers[i].batch = b;
#ifdef CUDNN
if(net->layers[i].type == CONVOLUTIONAL){
cudnn_convolutional_setup(net->layers + i, cudnn_fastest, 0);
}
else if (net->layers[i].type == MAXPOOL) {
cudnn_maxpool_setup(net->layers + i);
}
#endif
}
recalculate_workspace_size(net); // recalculate workspace size
}
int resize_network(network *net, int w, int h)
{
#ifdef GPU
cuda_set_device(net->gpu_index);
if(gpu_index >= 0){
cuda_free(net->workspace);
if (net->input_gpu) {
cuda_free(*net->input_gpu);
*net->input_gpu = 0;
cuda_free(*net->truth_gpu);
*net->truth_gpu = 0;
}
if (net->input_state_gpu) cuda_free(net->input_state_gpu);
if (net->input_pinned_cpu) {
if (net->input_pinned_cpu_flag) cudaFreeHost(net->input_pinned_cpu);
else free(net->input_pinned_cpu);
}
}
#endif
int i;
//if(w == net->w && h == net->h) return 0;
net->w = w;
net->h = h;
int inputs = 0;
size_t workspace_size = 0;
//fprintf(stderr, "Resizing to %d x %d...\n", w, h);
//fflush(stderr);
for (i = 0; i < net->n; ++i){
layer l = net->layers[i];
//printf(" (resize %d: layer = %d) , ", i, l.type);
if(l.type == CONVOLUTIONAL){
resize_convolutional_layer(&l, w, h);
}
else if (l.type == CRNN) {
resize_crnn_layer(&l, w, h);
}else if (l.type == CONV_LSTM) {
resize_conv_lstm_layer(&l, w, h);
}else if(l.type == CROP){
resize_crop_layer(&l, w, h);
}else if(l.type == MAXPOOL){
resize_maxpool_layer(&l, w, h);
}else if (l.type == LOCAL_AVGPOOL) {
resize_maxpool_layer(&l, w, h);
}else if (l.type == BATCHNORM) {
resize_batchnorm_layer(&l, w, h);
}else if(l.type == REGION){
resize_region_layer(&l, w, h);
}else if (l.type == YOLO) {
resize_yolo_layer(&l, w, h);
}else if (l.type == GAUSSIAN_YOLO) {
resize_gaussian_yolo_layer(&l, w, h);
}else if(l.type == ROUTE){
resize_route_layer(&l, net);
}else if (l.type == SHORTCUT) {
resize_shortcut_layer(&l, w, h, net);
}else if (l.type == SCALE_CHANNELS) {
resize_scale_channels_layer(&l, net);
}else if (l.type == SAM) {
resize_sam_layer(&l, w, h);
}else if (l.type == DROPOUT) {
resize_dropout_layer(&l, inputs);
l.out_w = l.w = w;
l.out_h = l.h = h;
l.output = net->layers[i - 1].output;
l.delta = net->layers[i - 1].delta;
#ifdef GPU
l.output_gpu = net->layers[i-1].output_gpu;
l.delta_gpu = net->layers[i-1].delta_gpu;
#endif
}else if (l.type == UPSAMPLE) {
resize_upsample_layer(&l, w, h);
}else if(l.type == REORG){
resize_reorg_layer(&l, w, h);
} else if (l.type == REORG_OLD) {
resize_reorg_old_layer(&l, w, h);
}else if(l.type == AVGPOOL){
resize_avgpool_layer(&l, w, h);
}else if(l.type == NORMALIZATION){
resize_normalization_layer(&l, w, h);
}else if(l.type == COST){
resize_cost_layer(&l, inputs);
}else{
fprintf(stderr, "Resizing type %d \n", (int)l.type);
error("Cannot resize this type of layer");
}
if(l.workspace_size > workspace_size) workspace_size = l.workspace_size;
inputs = l.outputs;
net->layers[i] = l;
//if(l.type != DROPOUT)
{
w = l.out_w;
h = l.out_h;
}
//if(l.type == AVGPOOL) break;
}
#ifdef GPU
const int size = get_network_input_size(*net) * net->batch;
if(gpu_index >= 0){
printf(" try to allocate additional workspace_size = %1.2f MB \n", (float)workspace_size / 1000000);
net->workspace = cuda_make_array(0, workspace_size/sizeof(float) + 1);
net->input_state_gpu = cuda_make_array(0, size);
if (cudaSuccess == cudaHostAlloc(&net->input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped))
net->input_pinned_cpu_flag = 1;
else {
cudaGetLastError(); // reset CUDA-error
net->input_pinned_cpu = (float*)xcalloc(size, sizeof(float));
net->input_pinned_cpu_flag = 0;
}
printf(" CUDA allocate done! \n");
}else {
free(net->workspace);
net->workspace = (float*)xcalloc(1, workspace_size);
if(!net->input_pinned_cpu_flag)
net->input_pinned_cpu = (float*)xrealloc(net->input_pinned_cpu, size * sizeof(float));
}
#else
free(net->workspace);
net->workspace = (float*)xcalloc(1, workspace_size);
#endif
//fprintf(stderr, " Done!\n");
return 0;
}
int get_network_output_size(network net)
{
int i;
for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
return net.layers[i].outputs;
}
int get_network_input_size(network net)
{
return net.layers[0].inputs;
}
detection_layer get_network_detection_layer(network net)
{
int i;
for(i = 0; i < net.n; ++i){
if(net.layers[i].type == DETECTION){
return net.layers[i];
}
}
fprintf(stderr, "Detection layer not found!!\n");
detection_layer l = { (LAYER_TYPE)0 };
return l;
}
image get_network_image_layer(network net, int i)
{
layer l = net.layers[i];
if (l.out_w && l.out_h && l.out_c){
return float_to_image(l.out_w, l.out_h, l.out_c, l.output);
}
image def = {0};
return def;
}
layer* get_network_layer(network* net, int i)
{
return net->layers + i;
}
image get_network_image(network net)
{
int i;
for(i = net.n-1; i >= 0; --i){
image m = get_network_image_layer(net, i);
if(m.h != 0) return m;
}
image def = {0};
return def;
}
void visualize_network(network net)
{
image *prev = 0;
int i;
char buff[256];
for(i = 0; i < net.n; ++i){
sprintf(buff, "Layer %d", i);
layer l = net.layers[i];
if(l.type == CONVOLUTIONAL){
prev = visualize_convolutional_layer(l, buff, prev);
}
}
}
void top_predictions(network net, int k, int *index)
{
int size = get_network_output_size(net);
float *out = get_network_output(net);
top_k(out, size, k, index);
}
// A version of network_predict that uses a pointer for the network
// struct to make the python binding work properly.
float *network_predict_ptr(network *net, float *input)
{
return network_predict(*net, input);
}
float *network_predict(network net, float *input)
{
#ifdef GPU
if(gpu_index >= 0) return network_predict_gpu(net, input);
#endif
network_state state = {0};
state.net = net;
state.index = 0;
state.input = input;
state.truth = 0;
state.train = 0;
state.delta = 0;
forward_network(net, state);
float *out = get_network_output(net);
return out;
}
int num_detections(network *net, float thresh)
{
int i;
int s = 0;
for (i = 0; i < net->n; ++i) {
layer l = net->layers[i];
if (l.type == YOLO) {
s += yolo_num_detections(l, thresh);
}
if (l.type == GAUSSIAN_YOLO) {
s += gaussian_yolo_num_detections(l, thresh);
}
if (l.type == DETECTION || l.type == REGION) {
s += l.w*l.h*l.n;
}
}
return s;
}
int num_detections_batch(network *net, float thresh, int batch)
{
int i;
int s = 0;
for (i = 0; i < net->n; ++i) {
layer l = net->layers[i];
if (l.type == YOLO) {
s += yolo_num_detections_batch(l, thresh, batch);
}
if (l.type == DETECTION || l.type == REGION) {
s += l.w*l.h*l.n;
}
}
return s;
}
detection *make_network_boxes(network *net, float thresh, int *num)
{
layer l = net->layers[net->n - 1];
int i;
int nboxes = num_detections(net, thresh);
if (num) *num = nboxes;
detection* dets = (detection*)xcalloc(nboxes, sizeof(detection));
for (i = 0; i < nboxes; ++i) {
dets[i].prob = (float*)xcalloc(l.classes, sizeof(float));
// tx,ty,tw,th uncertainty
dets[i].uc = (float*)xcalloc(4, sizeof(float)); // Gaussian_YOLOv3
if (l.coords > 4) {
dets[i].mask = (float*)xcalloc(l.coords - 4, sizeof(float));
}
}
return dets;
}
detection *make_network_boxes_batch(network *net, float thresh, int *num, int batch)
{
int i;
layer l = net->layers[net->n - 1];
int nboxes = num_detections_batch(net, thresh, batch);
assert(num != NULL);
*num = nboxes;
detection* dets = (detection*)calloc(nboxes, sizeof(detection));
for (i = 0; i < nboxes; ++i) {
dets[i].prob = (float*)calloc(l.classes, sizeof(float));
if (l.coords > 4) {
dets[i].mask = (float*)calloc(l.coords - 4, sizeof(float));
}
}
return dets;
}
void custom_get_region_detections(layer l, int w, int h, int net_w, int net_h, float thresh, int *map, float hier, int relative, detection *dets, int letter)
{
box* boxes = (box*)xcalloc(l.w * l.h * l.n, sizeof(box));
float** probs = (float**)xcalloc(l.w * l.h * l.n, sizeof(float*));
int i, j;
for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float*)xcalloc(l.classes, sizeof(float));
get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, map);
for (j = 0; j < l.w*l.h*l.n; ++j) {
dets[j].classes = l.classes;
dets[j].bbox = boxes[j];
dets[j].objectness = 1;
for (i = 0; i < l.classes; ++i) {
dets[j].prob[i] = probs[j][i];
}
}
free(boxes);
free_ptrs((void **)probs, l.w*l.h*l.n);
//correct_region_boxes(dets, l.w*l.h*l.n, w, h, net_w, net_h, relative);
correct_yolo_boxes(dets, l.w*l.h*l.n, w, h, net_w, net_h, relative, letter);
}
void fill_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, detection *dets, int letter)
{
int prev_classes = -1;
int j;
for (j = 0; j < net->n; ++j) {
layer l = net->layers[j];
if (l.type == YOLO) {
int count = get_yolo_detections(l, w, h, net->w, net->h, thresh, map, relative, dets, letter);
dets += count;
if (prev_classes < 0) prev_classes = l.classes;
else if (prev_classes != l.classes) {
printf(" Error: Different [yolo] layers have different number of classes = %d and %d - check your cfg-file! \n",
prev_classes, l.classes);
}
}
if (l.type == GAUSSIAN_YOLO) {
int count = get_gaussian_yolo_detections(l, w, h, net->w, net->h, thresh, map, relative, dets, letter);
dets += count;
}
if (l.type == REGION) {
custom_get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets, letter);
//get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets);
dets += l.w*l.h*l.n;
}
if (l.type == DETECTION) {
get_detection_detections(l, w, h, thresh, dets);
dets += l.w*l.h*l.n;
}
}
}
void fill_network_boxes_batch(network *net, int w, int h, float thresh, float hier, int *map, int relative, detection *dets, int letter, int batch)
{
int prev_classes = -1;
int j;
for (j = 0; j < net->n; ++j) {
layer l = net->layers[j];
if (l.type == YOLO) {
int count = get_yolo_detections_batch(l, w, h, net->w, net->h, thresh, map, relative, dets, letter, batch);
dets += count;
if (prev_classes < 0) prev_classes = l.classes;
else if (prev_classes != l.classes) {
printf(" Error: Different [yolo] layers have different number of classes = %d and %d - check your cfg-file! \n",
prev_classes, l.classes);
}
}
if (l.type == REGION) {
custom_get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets, letter);
//get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets);
dets += l.w*l.h*l.n;
}
if (l.type == DETECTION) {
get_detection_detections(l, w, h, thresh, dets);
dets += l.w*l.h*l.n;
}
}
}
detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter)
{
detection *dets = make_network_boxes(net, thresh, num);
fill_network_boxes(net, w, h, thresh, hier, map, relative, dets, letter);
return dets;
}
void free_detections(detection *dets, int n)
{
int i;
for (i = 0; i < n; ++i) {
free(dets[i].prob);
if (dets[i].uc) free(dets[i].uc);
if (dets[i].mask) free(dets[i].mask);
}
free(dets);
}
void free_batch_detections(det_num_pair *det_num_pairs, int n)
{
int i;
for(i=0; i<n; ++i)
free_detections(det_num_pairs[i].dets, det_num_pairs[i].num);
free(det_num_pairs);
}
// JSON format:
//{
// "frame_id":8990,
// "objects":[
// {"class_id":4, "name":"aeroplane", "relative coordinates":{"center_x":0.398831, "center_y":0.630203, "width":0.057455, "height":0.020396}, "confidence":0.793070},
// {"class_id":14, "name":"bird", "relative coordinates":{"center_x":0.398831, "center_y":0.630203, "width":0.057455, "height":0.020396}, "confidence":0.265497}
// ]
//},
char *detection_to_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, char *filename)
{
const float thresh = 0.005; // function get_network_boxes() has already filtred dets by actual threshold
char *send_buf = (char *)calloc(1024, sizeof(char));
if (!send_buf) return 0;
if (filename) {
sprintf(send_buf, "{\n \"frame_id\":%lld, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename);
}
else {
sprintf(send_buf, "{\n \"frame_id\":%lld, \n \"objects\": [ \n", frame_id);
}
int i, j;
int class_id = -1;
for (i = 0; i < nboxes; ++i) {
for (j = 0; j < classes; ++j) {
int show = strncmp(names[j], "dont_show", 9);
if (dets[i].prob[j] > thresh && show)
{
if (class_id != -1) strcat(send_buf, ", \n");
class_id = j;
char *buf = (char *)calloc(2048, sizeof(char));
if (!buf) return 0;
//sprintf(buf, "{\"image_id\":%d, \"category_id\":%d, \"bbox\":[%f, %f, %f, %f], \"score\":%f}",
// image_id, j, dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h, dets[i].prob[j]);
sprintf(buf, " {\"class_id\":%d, \"name\":\"%s\", \"relative_coordinates\":{\"center_x\":%f, \"center_y\":%f, \"width\":%f, \"height\":%f}, \"confidence\":%f}",
j, names[j], dets[i].bbox.x, dets[i].bbox.y, dets[i].bbox.w, dets[i].bbox.h, dets[i].prob[j]);
int send_buf_len = strlen(send_buf);
int buf_len = strlen(buf);
int total_len = send_buf_len + buf_len + 100;
send_buf = (char *)realloc(send_buf, total_len * sizeof(char));
if (!send_buf) {
if (buf) free(buf);
return 0;// exit(-1);
}
strcat(send_buf, buf);
free(buf);
}
}
}
strcat(send_buf, "\n ] \n}");
return send_buf;
}
float *network_predict_image(network *net, image im)
{
//image imr = letterbox_image(im, net->w, net->h);
float *p;
if(net->batch != 1) set_batch_network(net, 1);
if (im.w == net->w && im.h == net->h) {
// Input image is the same size as our net, predict on that image
p = network_predict(*net, im.data);
}
else {
// Need to resize image to the desired size for the net
image imr = resize_image(im, net->w, net->h);
p = network_predict(*net, imr.data);
free_image(imr);
}
return p;
}
det_num_pair* network_predict_batch(network *net, image im, int batch_size, int w, int h, float thresh, float hier, int *map, int relative, int letter)
{
network_predict(*net, im.data);
det_num_pair *pdets = (struct det_num_pair *)calloc(batch_size, sizeof(det_num_pair));
int num;
for(int batch=0; batch<batch_size; batch++){
detection *dets = make_network_boxes_batch(net, thresh, &num, batch);
fill_network_boxes_batch(net, w, h, thresh, hier, map, relative, dets, letter, batch);
pdets[batch].num = num;
pdets[batch].dets = dets;
}
return pdets;
}
float *network_predict_image_letterbox(network *net, image im)
{
//image imr = letterbox_image(im, net->w, net->h);
float *p;
if (net->batch != 1) set_batch_network(net, 1);
if (im.w == net->w && im.h == net->h) {