cost_layer.c
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#include "cost_layer.h"
#include "utils.h"
#include "cuda.h"
#include "blas.h"
#include <math.h>
#include <string.h>
#include <stdlib.h>
#include <stdio.h>
COST_TYPE get_cost_type(char *s)
{
if (strcmp(s, "seg")==0) return SEG;
if (strcmp(s, "sse")==0) return SSE;
if (strcmp(s, "masked")==0) return MASKED;
if (strcmp(s, "smooth")==0) return SMOOTH;
if (strcmp(s, "L1")==0) return L1;
if (strcmp(s, "wgan")==0) return WGAN;
fprintf(stderr, "Couldn't find cost type %s, going with SSE\n", s);
return SSE;
}
char *get_cost_string(COST_TYPE a)
{
switch(a){
case SEG:
return "seg";
case SSE:
return "sse";
case MASKED:
return "masked";
case SMOOTH:
return "smooth";
case L1:
return "L1";
case WGAN:
return "wgan";
}
return "sse";
}
cost_layer make_cost_layer(int batch, int inputs, COST_TYPE cost_type, float scale)
{
fprintf(stderr, "cost %4d\n", inputs);
cost_layer l = {0};
l.type = COST;
l.scale = scale;
l.batch = batch;
l.inputs = inputs;
l.outputs = inputs;
l.cost_type = cost_type;
l.delta = calloc(inputs*batch, sizeof(float));
l.output = calloc(inputs*batch, sizeof(float));
l.cost = calloc(1, sizeof(float));
l.forward = forward_cost_layer;
l.backward = backward_cost_layer;
#ifdef GPU
l.forward_gpu = forward_cost_layer_gpu;
l.backward_gpu = backward_cost_layer_gpu;
l.delta_gpu = cuda_make_array(l.output, inputs*batch);
l.output_gpu = cuda_make_array(l.delta, inputs*batch);
#endif
return l;
}
void resize_cost_layer(cost_layer *l, int inputs)
{
l->inputs = inputs;
l->outputs = inputs;
l->delta = realloc(l->delta, inputs*l->batch*sizeof(float));
l->output = realloc(l->output, inputs*l->batch*sizeof(float));
#ifdef GPU
cuda_free(l->delta_gpu);
cuda_free(l->output_gpu);
l->delta_gpu = cuda_make_array(l->delta, inputs*l->batch);
l->output_gpu = cuda_make_array(l->output, inputs*l->batch);
#endif
}
void forward_cost_layer(cost_layer l, network net)
{
if (!net.truth) return;
if(l.cost_type == MASKED){
int i;
for(i = 0; i < l.batch*l.inputs; ++i){
if(net.truth[i] == SECRET_NUM) net.input[i] = SECRET_NUM;
}
}
if(l.cost_type == SMOOTH){
smooth_l1_cpu(l.batch*l.inputs, net.input, net.truth, l.delta, l.output);
}else if(l.cost_type == L1){
l1_cpu(l.batch*l.inputs, net.input, net.truth, l.delta, l.output);
} else {
l2_cpu(l.batch*l.inputs, net.input, net.truth, l.delta, l.output);
}
l.cost[0] = sum_array(l.output, l.batch*l.inputs);
}
void backward_cost_layer(const cost_layer l, network net)
{
axpy_cpu(l.batch*l.inputs, l.scale, l.delta, 1, net.delta, 1);
}
#ifdef GPU
void pull_cost_layer(cost_layer l)
{
cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs);
}
void push_cost_layer(cost_layer l)
{
cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs);
}
int float_abs_compare (const void * a, const void * b)
{
float fa = *(const float*) a;
if(fa < 0) fa = -fa;
float fb = *(const float*) b;
if(fb < 0) fb = -fb;
return (fa > fb) - (fa < fb);
}
void forward_cost_layer_gpu(cost_layer l, network net)
{
if (!net.truth) return;
if(l.smooth){
scal_gpu(l.batch*l.inputs, (1-l.smooth), net.truth_gpu, 1);
add_gpu(l.batch*l.inputs, l.smooth * 1./l.inputs, net.truth_gpu, 1);
}
if(l.cost_type == SMOOTH){
smooth_l1_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu);
} else if (l.cost_type == L1){
l1_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu);
} else if (l.cost_type == WGAN){
wgan_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu);
} else {
l2_gpu(l.batch*l.inputs, net.input_gpu, net.truth_gpu, l.delta_gpu, l.output_gpu);
}
if (l.cost_type == SEG && l.noobject_scale != 1) {
scale_mask_gpu(l.batch*l.inputs, l.delta_gpu, 0, net.truth_gpu, l.noobject_scale);
scale_mask_gpu(l.batch*l.inputs, l.output_gpu, 0, net.truth_gpu, l.noobject_scale);
}
if (l.cost_type == MASKED) {
mask_gpu(l.batch*l.inputs, net.delta_gpu, SECRET_NUM, net.truth_gpu, 0);
}
if(l.ratio){
cuda_pull_array(l.delta_gpu, l.delta, l.batch*l.inputs);
qsort(l.delta, l.batch*l.inputs, sizeof(float), float_abs_compare);
int n = (1-l.ratio) * l.batch*l.inputs;
float thresh = l.delta[n];
thresh = 0;
printf("%f\n", thresh);
supp_gpu(l.batch*l.inputs, thresh, l.delta_gpu, 1);
}
if(l.thresh){
supp_gpu(l.batch*l.inputs, l.thresh*1./l.inputs, l.delta_gpu, 1);
}
cuda_pull_array(l.output_gpu, l.output, l.batch*l.inputs);
l.cost[0] = sum_array(l.output, l.batch*l.inputs);
}
void backward_cost_layer_gpu(const cost_layer l, network net)
{
axpy_gpu(l.batch*l.inputs, l.scale, l.delta_gpu, 1, net.delta_gpu, 1);
}
#endif