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it finally works!
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@liuliu
liuliu committed Jun 12, 2012
1 parent 3c8775e commit 9945338
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Showing 4 changed files with 61 additions and 31 deletions.
11 changes: 6 additions & 5 deletions bin/dpmcreate.c
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Original file line number Diff line number Diff line change
Expand Up @@ -80,20 +80,21 @@ int main(int argc, char** argv)
int components = atoi(argv[4]);
int parts = atoi(argv[5]);
free(file);
ccv_dpm_param_t detector = { .interval = 8, .min_neighbors = 1, .flags = 0, .threshold = 0.0 };
ccv_dpm_param_t detector = { .interval = 8, .min_neighbors = 0, .flags = 0, .threshold = 0.0 };
ccv_dpm_new_param_t params = { .components = components,
.detector = detector,
.parts = parts,
.min_area = 3000,
.max_area = 5000,
.symmetric = 1,
.alpha = 0.1,
.alpha_ratio = 0.85,
.iterations = 10,
.relabels = 10,
.balance = 1.75,
.alpha_ratio = 0.95,
.iterations = 20,
.relabels = 5,
.C = 0.002,
.percentile_breakdown = 0.05,
.overlap = 0.8,
.overlap = 0.75,
.grayscale = 0 };
ccv_dpm_mixture_model_new(posfiles, bboxes, posnum, bgfiles, bgnum, negnum, argv[6], params);
free(posfiles);
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2 changes: 1 addition & 1 deletion bin/dpmdetect.c
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@ int main(int argc, char** argv)
ccv_dense_matrix_t* image = 0;
ccv_read(argv[1], &image, CCV_IO_ANY_FILE);
ccv_dpm_mixture_model_t* model = ccv_load_dpm_mixture_model(argv[2]);
ccv_dpm_param_t params = { .interval = 8, .min_neighbors = 1, .flags = 0, .threshold = 0 };
ccv_dpm_param_t params = { .interval = 8, .min_neighbors = 1, .flags = 0, .threshold = 0.3 };
if (image != 0)
{
unsigned int elapsed_time = get_current_time();
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1 change: 1 addition & 0 deletions lib/ccv.h
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Original file line number Diff line number Diff line change
Expand Up @@ -620,6 +620,7 @@ typedef struct {
double overlap; // 0.7
double alpha;
double alpha_ratio; // 0.85
double balance; // 1.0
double C;
double percentile_breakdown; // 0.95
ccv_dpm_param_t detector;
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78 changes: 53 additions & 25 deletions lib/ccv_dpm.c
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Original file line number Diff line number Diff line change
Expand Up @@ -887,18 +887,19 @@ static ccv_array_t* _ccv_dpm_collect_all(gsl_rng* rng, ccv_dense_matrix_t* image
return av;
}

#define NEGATIVE_CACHE_SIZE (500)
#define NEGATIVE_CACHE_SIZE (100)

static ccv_array_t* _ccv_dpm_collect_from_background(gsl_rng* rng, char** bgfiles, int bgnum, ccv_dpm_mixture_model_t* model, ccv_dpm_new_param_t params, float threshold)
static void _ccv_dpm_collect_from_background(ccv_array_t* av, gsl_rng* rng, char** bgfiles, int bgnum, ccv_dpm_mixture_model_t* model, ccv_dpm_new_param_t params, float threshold)
{
int i, j;
ccv_array_t* av = ccv_array_new(64, sizeof(ccv_dpm_feature_vector_t*));
int* order = (int*)ccmalloc(sizeof(int) * bgnum);
for (i = 0; i < bgnum; i++)
order[i] = i;
gsl_ran_shuffle(rng, order, bgnum, sizeof(int));
for (i = 0; i < bgnum; i++)
{
printf("\b\b\b\b\b\b%3d%%) ", av->rnum * 100 / NEGATIVE_CACHE_SIZE);
fflush(stdout);
ccv_dense_matrix_t* image = 0;
ccv_read(bgfiles[order[i]], &image, (params.grayscale ? CCV_IO_GRAY : 0) | CCV_IO_ANY_FILE);
ccv_array_t* at = _ccv_dpm_collect_all(rng, image, model, params.detector, threshold);
Expand All @@ -913,7 +914,6 @@ static ccv_array_t* _ccv_dpm_collect_from_background(gsl_rng* rng, char** bgfile
break;
}
ccfree(order);
return av;
}

static void _ccv_dpm_regularize_mixture_model(ccv_dpm_mixture_model_t* model, double regz)
Expand Down Expand Up @@ -1174,6 +1174,7 @@ static void _ccv_dpm_check_params(ccv_dpm_new_param_t params)
assert(params.alpha > 0 && params.alpha < 1);
assert(params.alpha_ratio > 0 && params.alpha_ratio < 1);
assert(params.C > 0);
assert(params.balance > 0);
assert(params.percentile_breakdown > 0 && params.percentile_breakdown <= 1);
assert(params.detector.interval > 0);
}
Expand Down Expand Up @@ -1324,7 +1325,9 @@ void ccv_dpm_mixture_model_new(char** posfiles, ccv_rect_t* bboxes, int posnum,
int* order = (int*)ccmalloc(sizeof(int) * (posnum + negnum));
int pos_prog = 0, neg_prog = 0;
double previous_loss = 0, positive_loss = 0, negative_loss = 0, loss = 0;
int* bgmarked = (int*)ccmalloc(sizeof(int) * bgnum);
// need to re-weight for each examples
double pos_weight = sqrt((double)negnum / posnum * params.balance); // positive weight
double neg_weight = sqrt((double)posnum / negnum / params.balance); // negative weight
c = t = r = 0;
_ccv_dpm_read_gradient_descent_progress(&c, &t, &r, &previous_loss, &positive_loss, &negative_loss, &loss, &pos_prog, &neg_prog, order, posnum + negnum, gradient_progress_checkpoint);
ccv_array_t* negv = 0;
Expand Down Expand Up @@ -1354,7 +1357,6 @@ void ccv_dpm_mixture_model_new(char** posfiles, ccv_rect_t* bboxes, int posnum,
params.detector.threshold = 0;
for (; t < params.iterations; t++)
{
memset(bgmarked, 0, sizeof(int) * bgnum); // it needn't to be persistent
unsigned int elapsed_time = _ccv_dpm_time_measure();
if (r == 0)
{
Expand All @@ -1381,14 +1383,14 @@ void ccv_dpm_mixture_model_new(char** posfiles, ccv_rect_t* bboxes, int posnum,
{
double score = _ccv_dpm_vector_score(model, posv[k]); // the loss for mini-batch method (computed on model)
if (score <= 1)
_ccv_dpm_stochastic_gradient_descent(_model, posv[k], 1, alpha, regz_rate, params.symmetric);
_ccv_dpm_stochastic_gradient_descent(_model, posv[k], 1, alpha * pos_weight, regz_rate, params.symmetric);
double hinge_loss = ccv_max(0, 1.0 - score);
positive_loss += hinge_loss;
loss += hinge_loss;
loss += pos_weight * hinge_loss;
++pos_prog;
for (j = 0; j < last_update; j++)
printf("\b");
last_update = printf(" - with loss %.5lf (positive %.5lf, negative %.5f) at rate %.5lf -+- %2d%% ", loss / (pos_prog + neg_prog), positive_loss / pos_prog, negative_loss / neg_prog, alpha, (i + 1) * 100 / (posnum + negnum));
last_update = printf(" - with loss %.5lf (positive %.5lf, negative %.5f) at rate %.5lf -+- %2d%% ", loss / (pos_prog + neg_prog), positive_loss / pos_prog, negative_loss / neg_prog, alpha, (i + 1) * 100 / (posnum + negnum));
fflush(stdout);
}
} else {
Expand All @@ -1397,40 +1399,49 @@ void ccv_dpm_mixture_model_new(char** posfiles, ccv_rect_t* bboxes, int posnum,
{
if (negv)
{
ccv_array_t* av = ccv_array_new(64, sizeof(ccv_dpm_feature_vector_t*));
for (j = 0; j < negv->rnum; j++)
{
ccv_dpm_feature_vector_t* v = *(ccv_dpm_feature_vector_t**)ccv_array_get(negv, j);
_ccv_dpm_feature_vector_free(v);
double score = _ccv_dpm_vector_score(model, v);
if (score >= -1)
ccv_array_push(av, &v);
else
_ccv_dpm_feature_vector_free(v);
}
ccv_array_free(negv);
negv = av;
} else {
negv = ccv_array_new(64, sizeof(ccv_dpm_feature_vector_t*));
}
for (j = 0; j < last_update; j++)
printf("\b");
last_update = printf(" - pause, collecting negative examples, current loss %.5lf (positive %.5lf, negative %.5f) --- %2d%% ", loss / ccv_max(1, pos_prog + neg_prog), positive_loss / ccv_max(1, pos_prog), negative_loss / ccv_max(1, neg_prog), (i + 1) * 100 / (posnum + negnum));
last_update = printf(" - pause, collecting negative examples, current loss %.5lf (positive %.5lf, negative %.5f) --- %2d%% ( 0%%) ", loss / ccv_max(1, pos_prog + neg_prog), positive_loss / ccv_max(1, pos_prog), negative_loss / ccv_max(1, neg_prog), (i + 1) * 100 / (posnum + negnum));
fflush(stdout);
negv = _ccv_dpm_collect_from_background(rng, bgfiles, bgnum, model, params, 0);
if (negv->rnum < NEGATIVE_CACHE_SIZE)
_ccv_dpm_collect_from_background(negv, rng, bgfiles, bgnum, model, params, 0);
}
if (negv && negv->rnum > 0)
{
ccv_dpm_feature_vector_t* v = *(ccv_dpm_feature_vector_t**)ccv_array_get(negv, (k - posnum) % negv->rnum);
double score = _ccv_dpm_vector_score(model, v);
if (score >= -1)
_ccv_dpm_stochastic_gradient_descent(_model, v, -1, alpha, regz_rate, params.symmetric);
_ccv_dpm_stochastic_gradient_descent(_model, v, -1, alpha * neg_weight, regz_rate, params.symmetric);
double hinge_loss = ccv_max(0, 1.0 + score);
negative_loss += hinge_loss;
loss += hinge_loss;
loss += neg_weight * hinge_loss;
++neg_prog;
for (j = 0; j < last_update; j++)
printf("\b");
last_update = printf(" - with loss %.5lf (positive %.5lf, negative %.5f) at rate %.5lf --- %2d%% ", loss / (pos_prog + neg_prog), positive_loss / pos_prog, negative_loss / neg_prog, alpha, (i + 1) * 100 / (posnum + negnum));
last_update = printf(" - with loss %.5lf (positive %.5lf, negative %.5f) at rate %.5lf --- %2d%% ", loss / (pos_prog + neg_prog), positive_loss / pos_prog, negative_loss / neg_prog, alpha, (i + 1) * 100 / (posnum + negnum));
fflush(stdout);
}
}
if (i % REGQ == REGQ - 1)
{
for (j = 0; j < last_update; j++)
printf("\b");
last_update = printf(" - with loss %.5lf (positive %.5lf, negative %.5f) at rate %.5lf -o- %2d%% ", loss / (pos_prog + neg_prog), positive_loss / pos_prog, negative_loss / neg_prog, alpha, (i + 1) * 100 / (posnum + negnum));
last_update = printf(" - with loss %.5lf (positive %.5lf, negative %.5f) at rate %.5lf -o- %2d%% ", loss / (pos_prog + neg_prog), positive_loss / pos_prog, negative_loss / neg_prog, alpha, (i + 1) * 100 / (posnum + negnum));
fflush(stdout);
_ccv_dpm_regularize_mixture_model(_model, 1.0 - pow(1.0 - alpha / (double)((posnum + negnum) * (!!params.symmetric + 1)), REGQ));
_ccv_dpm_write_gradient_descent_progress(c, t, i, previous_loss, positive_loss, negative_loss, loss, pos_prog, neg_prog, order, posnum + negnum, gradient_progress_checkpoint);
Expand All @@ -1449,15 +1460,24 @@ void ccv_dpm_mixture_model_new(char** posfiles, ccv_rect_t* bboxes, int posnum,
printf("\n");
if (negv)
{
ccv_array_t* av = ccv_array_new(64, sizeof(ccv_dpm_feature_vector_t*));
for (j = 0; j < negv->rnum; j++)
{
ccv_dpm_feature_vector_t* v = *(ccv_dpm_feature_vector_t**)ccv_array_get(negv, i);
_ccv_dpm_feature_vector_free(v);
ccv_dpm_feature_vector_t* v = *(ccv_dpm_feature_vector_t**)ccv_array_get(negv, j);
double score = _ccv_dpm_vector_score(model, v);
if (score >= -1)
ccv_array_push(av, &v);
else
_ccv_dpm_feature_vector_free(v);
}
ccv_array_free(negv);
negv = 0;
negv = av;
}
_ccv_dpm_regularize_mixture_model(_model, 1.0 - pow(1.0 - alpha * (((posnum + negnum) % REGQ) + 1) / (double)((posnum + negnum) * (!!params.symmetric + 1)), ((posnum + negnum) % REGQ) + 1));
_ccv_dpm_mixture_model_cleanup(model);
ccfree(model);
model = _model;
_ccv_dpm_write_checkpoint(model, checkpoint);
// check symmetric property of generated root feature
if (params.symmetric)
for (i = 0; i < params.components; i++)
Expand All @@ -1466,10 +1486,6 @@ void ccv_dpm_mixture_model_new(char** posfiles, ccv_rect_t* bboxes, int posnum,
_ccv_dpm_check_root_classifier_symmetry(root_classifier->root.w);
}
printf(" - %d iteration takes %.2lf seconds, with %d positive reactions and %d negative reactions at loss %.5lf, %d more to go (%d of %d)\n", t + 1, (double)(_ccv_dpm_time_measure() - elapsed_time) / 1000.0, pos_prog, neg_prog, loss / (pos_prog + neg_prog), params.iterations - t - 1, c + 1, params.relabels);
_ccv_dpm_mixture_model_cleanup(model);
ccfree(model);
model = _model;
_ccv_dpm_write_checkpoint(model, checkpoint);
j = 0;
double* scores = (double*)ccmalloc(posnum * sizeof(double));
for (i = 0; i < posnum; i++)
Expand All @@ -1487,14 +1503,27 @@ void ccv_dpm_mixture_model_new(char** posfiles, ccv_rect_t* bboxes, int posnum,
printf("\n");
alpha *= params.alpha_ratio; // it will decrease with each iteration
pos_prog = neg_prog = r = 0;
if (fabs(previous_loss - loss) < 1e-5)
char persist[512];
sprintf(persist, "%s/model.%d.%d", dir, c, t);
_ccv_dpm_write_checkpoint(model, persist);
if (fabs(previous_loss - loss) < 1e-4)
{
printf(" - aborting iteration at %d because we didn't gain much\n", t + 1);
break;
}
previous_loss = loss;
positive_loss = negative_loss = loss = 0;
}
if (negv)
{
for (j = 0; j < negv->rnum; j++)
{
ccv_dpm_feature_vector_t* v = *(ccv_dpm_feature_vector_t**)ccv_array_get(negv, j);
_ccv_dpm_feature_vector_free(v);
}
ccv_array_free(negv);
negv = 0;
}
t = 0;
previous_loss = positive_loss = negative_loss = loss = 0;
for (i = 0; i < posnum; i++)
Expand All @@ -1505,7 +1534,6 @@ void ccv_dpm_mixture_model_new(char** posfiles, ccv_rect_t* bboxes, int posnum,
remove(gradient_progress_checkpoint);
ccfree(order);
ccfree(posv);
ccfree(bgmarked);
_ccv_dpm_mixture_model_cleanup(model);
ccfree(model);
gsl_rng_free(rng);
Expand Down

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