[d865bb]: vq / vqsplit.c  Maximize  Restore  History

Download this file

613 lines (512 with data), 18.0 kB

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
/********************************************************************
* *
* THIS FILE IS PART OF THE OggVorbis SOFTWARE CODEC SOURCE CODE. *
* USE, DISTRIBUTION AND REPRODUCTION OF THIS LIBRARY SOURCE IS *
* GOVERNED BY A BSD-STYLE SOURCE LICENSE INCLUDED WITH THIS SOURCE *
* IN 'COPYING'. PLEASE READ THESE TERMS BEFORE DISTRIBUTING. *
* *
* THE OggVorbis SOURCE CODE IS (C) COPYRIGHT 1994-2001 *
* by the Xiph.Org Foundation http://www.xiph.org/ *
* *
********************************************************************
function: build a VQ codebook and the encoding decision 'tree'
last mod: $Id$
********************************************************************/
/* This code is *not* part of libvorbis. It is used to generate
trained codebooks offline and then spit the results into a
pregenerated codebook that is compiled into libvorbis. It is an
expensive (but good) algorithm. Run it on big iron. */
/* There are so many optimizations to explore in *both* stages that
considering the undertaking is almost withering. For now, we brute
force it all */
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include <string.h>
#include <sys/time.h>
#include "vqgen.h"
#include "vqsplit.h"
#include "bookutil.h"
/* Codebook generation happens in two steps:
1) Train the codebook with data collected from the encoder: We use
one of a few error metrics (which represent the distance between a
given data point and a candidate point in the training set) to
divide the training set up into cells representing roughly equal
probability of occurring.
2) Generate the codebook and auxiliary data from the trained data set
*/
/* Building a codebook from trained set **********************************
The codebook in raw form is technically finished once it's trained.
However, we want to finalize the representative codebook values for
each entry and generate auxiliary information to optimize encoding.
We generate the auxiliary coding tree using collected data,
probably the same data as in the original training */
/* At each recursion, the data set is split in half. Cells with data
points on side A go into set A, same with set B. The sets may
overlap. If the cell overlaps the deviding line only very slightly
(provided parameter), we may choose to ignore the overlap in order
to pare the tree down */
long *isortvals;
int iascsort(const void *a,const void *b){
long av=isortvals[*((long *)a)];
long bv=isortvals[*((long *)b)];
return(av-bv);
}
static float _Ndist(int el,float *a, float *b){
int i;
float acc=0.f;
for(i=0;i<el;i++){
float val=(a[i]-b[i]);
acc+=val*val;
}
return sqrt(acc);
}
#define _Npoint(i) (pointlist+dim*(i))
#define _Nnow(i) (entrylist+dim*(i))
/* goes through the split, but just counts it and returns a metric*/
int vqsp_count(float *entrylist,float *pointlist,int dim,
long *membership,long *reventry,
long *entryindex,long entries,
long *pointindex,long points,int splitp,
long *entryA,long *entryB,
long besti,long bestj,
long *entriesA,long *entriesB,long *entriesC){
long i,j;
long A=0,B=0,C=0;
long pointsA=0;
long pointsB=0;
long *temppointsA=NULL;
long *temppointsB=NULL;
if(splitp){
temppointsA=_ogg_malloc(points*sizeof(long));
temppointsB=_ogg_malloc(points*sizeof(long));
}
memset(entryA,0,sizeof(long)*entries);
memset(entryB,0,sizeof(long)*entries);
/* Do the points belonging to this cell occur on sideA, sideB or
both? */
for(i=0;i<points;i++){
float *ppt=_Npoint(pointindex[i]);
long firstentry=membership[pointindex[i]];
if(firstentry==besti){
entryA[reventry[firstentry]]=1;
if(splitp)temppointsA[pointsA++]=pointindex[i];
continue;
}
if(firstentry==bestj){
entryB[reventry[firstentry]]=1;
if(splitp)temppointsB[pointsB++]=pointindex[i];
continue;
}
{
float distA=_Ndist(dim,ppt,_Nnow(besti));
float distB=_Ndist(dim,ppt,_Nnow(bestj));
if(distA<distB){
entryA[reventry[firstentry]]=1;
if(splitp)temppointsA[pointsA++]=pointindex[i];
}else{
entryB[reventry[firstentry]]=1;
if(splitp)temppointsB[pointsB++]=pointindex[i];
}
}
}
/* The entry splitting isn't total, so that storage has to be
allocated for recursion. Reuse the entryA/entryB vectors */
/* keep the entries in ascending order (relative to the original
list); we rely on that stability when ordering p/q choice */
for(j=0;j<entries;j++){
if(entryA[j] && entryB[j])C++;
if(entryA[j])entryA[A++]=entryindex[j];
if(entryB[j])entryB[B++]=entryindex[j];
}
*entriesA=A;
*entriesB=B;
*entriesC=C;
if(splitp){
memcpy(pointindex,temppointsA,sizeof(long)*pointsA);
memcpy(pointindex+pointsA,temppointsB,sizeof(long)*pointsB);
free(temppointsA);
free(temppointsB);
}
return(pointsA);
}
int lp_split(float *pointlist,long totalpoints,
codebook *b,
long *entryindex,long entries,
long *pointindex,long points,
long *membership,long *reventry,
long depth, long *pointsofar){
encode_aux_nearestmatch *t=b->c->nearest_tree;
/* The encoder, regardless of book, will be using a straight
euclidian distance-to-point metric to determine closest point.
Thus we split the cells using the same (we've already trained the
codebook set spacing and distribution using special metrics and
even a midpoint division won't disturb the basic properties) */
int dim=b->dim;
float *entrylist=b->valuelist;
long ret;
long *entryA=_ogg_calloc(entries,sizeof(long));
long *entryB=_ogg_calloc(entries,sizeof(long));
long entriesA=0;
long entriesB=0;
long entriesC=0;
long pointsA=0;
long i,j,k;
long besti=-1;
long bestj=-1;
char spinbuf[80];
sprintf(spinbuf,"splitting [%ld left]... ",totalpoints-*pointsofar);
/* one reverse index needed */
for(i=0;i<b->entries;i++)reventry[i]=-1;
for(i=0;i<entries;i++)reventry[entryindex[i]]=i;
/* We need to find the dividing hyperplane. find the median of each
axis as the centerpoint and the normal facing farthest point */
/* more than one way to do this part. For small sets, we can brute
force it. */
if(entries<8 || (float)points*entries*entries<16.f*1024*1024){
/* try every pair possibility */
float best=0;
float this;
for(i=0;i<entries-1;i++){
for(j=i+1;j<entries;j++){
spinnit(spinbuf,entries-i);
vqsp_count(b->valuelist,pointlist,dim,
membership,reventry,
entryindex,entries,
pointindex,points,0,
entryA,entryB,
entryindex[i],entryindex[j],
&entriesA,&entriesB,&entriesC);
this=(entriesA-entriesC)*(entriesB-entriesC);
/* when choosing best, we also want some form of stability to
make sure more branches are pared later; secondary
weighting isn;t needed as the entry lists are in ascending
order, and we always try p/q in the same sequence */
if( (besti==-1) ||
(this>best) ){
best=this;
besti=entryindex[i];
bestj=entryindex[j];
}
}
}
}else{
float *p=alloca(dim*sizeof(float));
float *q=alloca(dim*sizeof(float));
float best=0.f;
/* try COG/normal and furthest pairs */
/* meanpoint */
/* eventually, we want to select the closest entry and figure n/c
from p/q (because storing n/c is too large */
for(k=0;k<dim;k++){
spinnit(spinbuf,entries);
p[k]=0.f;
for(j=0;j<entries;j++)
p[k]+=b->valuelist[entryindex[j]*dim+k];
p[k]/=entries;
}
/* we go through the entries one by one, looking for the entry on
the other side closest to the point of reflection through the
center */
for(i=0;i<entries;i++){
float *ppi=_Nnow(entryindex[i]);
float ref_best=0.f;
float ref_j=-1;
float this;
spinnit(spinbuf,entries-i);
for(k=0;k<dim;k++)
q[k]=2*p[k]-ppi[k];
for(j=0;j<entries;j++){
if(j!=i){
float this=_Ndist(dim,q,_Nnow(entryindex[j]));
if(ref_j==-1 || this<=ref_best){ /* <=, not <; very important */
ref_best=this;
ref_j=entryindex[j];
}
}
}
vqsp_count(b->valuelist,pointlist,dim,
membership,reventry,
entryindex,entries,
pointindex,points,0,
entryA,entryB,
entryindex[i],ref_j,
&entriesA,&entriesB,&entriesC);
this=(entriesA-entriesC)*(entriesB-entriesC);
/* when choosing best, we also want some form of stability to
make sure more branches are pared later; secondary
weighting isn;t needed as the entry lists are in ascending
order, and we always try p/q in the same sequence */
if( (besti==-1) ||
(this>best) ){
best=this;
besti=entryindex[i];
bestj=ref_j;
}
}
if(besti>bestj){
long temp=besti;
besti=bestj;
bestj=temp;
}
}
/* find cells enclosing points */
/* count A/B points */
pointsA=vqsp_count(b->valuelist,pointlist,dim,
membership,reventry,
entryindex,entries,
pointindex,points,1,
entryA,entryB,
besti,bestj,
&entriesA,&entriesB,&entriesC);
/* fprintf(stderr,"split: total=%ld depth=%ld set A=%ld:%ld:%ld=B\n",
entries,depth,entriesA-entriesC,entriesC,entriesB-entriesC);*/
{
long thisaux=t->aux++;
if(t->aux>=t->alloc){
t->alloc*=2;
t->ptr0=_ogg_realloc(t->ptr0,sizeof(long)*t->alloc);
t->ptr1=_ogg_realloc(t->ptr1,sizeof(long)*t->alloc);
t->p=_ogg_realloc(t->p,sizeof(long)*t->alloc);
t->q=_ogg_realloc(t->q,sizeof(long)*t->alloc);
}
t->p[thisaux]=besti;
t->q[thisaux]=bestj;
if(entriesA==1){
ret=1;
t->ptr0[thisaux]=entryA[0];
*pointsofar+=pointsA;
}else{
t->ptr0[thisaux]= -t->aux;
ret=lp_split(pointlist,totalpoints,b,entryA,entriesA,pointindex,pointsA,
membership,reventry,depth+1,pointsofar);
}
if(entriesB==1){
ret++;
t->ptr1[thisaux]=entryB[0];
*pointsofar+=points-pointsA;
}else{
t->ptr1[thisaux]= -t->aux;
ret+=lp_split(pointlist,totalpoints,b,entryB,entriesB,pointindex+pointsA,
points-pointsA,membership,reventry,
depth+1,pointsofar);
}
}
free(entryA);
free(entryB);
return(ret);
}
static int _node_eq(encode_aux_nearestmatch *v, long a, long b){
long Aptr0=v->ptr0[a];
long Aptr1=v->ptr1[a];
long Bptr0=v->ptr0[b];
long Bptr1=v->ptr1[b];
/* the possibility of choosing the same p and q, but switched, can;t
happen because we always look for the best p/q in the same search
order and the search is stable */
if(Aptr0==Bptr0 && Aptr1==Bptr1)
return(1);
return(0);
}
void vqsp_book(vqgen *v, codebook *b, long *quantlist){
long i,j;
static_codebook *c=(static_codebook *)b->c;
encode_aux_nearestmatch *t;
memset(b,0,sizeof(codebook));
memset(c,0,sizeof(static_codebook));
b->c=c;
t=c->nearest_tree=_ogg_calloc(1,sizeof(encode_aux_nearestmatch));
c->maptype=2;
/* make sure there are no duplicate entries and that every
entry has points */
for(i=0;i<v->entries;){
/* duplicate? if so, eliminate */
for(j=0;j<i;j++){
if(_Ndist(v->elements,_now(v,i),_now(v,j))==0.f){
fprintf(stderr,"found a duplicate entry! removing...\n");
v->entries--;
memcpy(_now(v,i),_now(v,v->entries),sizeof(float)*v->elements);
memcpy(quantlist+i*v->elements,quantlist+v->entries*v->elements,
sizeof(long)*v->elements);
break;
}
}
if(j==i)i++;
}
{
v->assigned=_ogg_calloc(v->entries,sizeof(long));
for(i=0;i<v->points;i++){
float *ppt=_point(v,i);
float firstmetric=_Ndist(v->elements,_now(v,0),ppt);
long firstentry=0;
if(!(i&0xff))spinnit("checking... ",v->points-i);
for(j=0;j<v->entries;j++){
float thismetric=_Ndist(v->elements,_now(v,j),ppt);
if(thismetric<firstmetric){
firstmetric=thismetric;
firstentry=j;
}
}
v->assigned[firstentry]++;
}
for(j=0;j<v->entries;){
if(v->assigned[j]==0){
fprintf(stderr,"found an unused entry! removing...\n");
v->entries--;
memcpy(_now(v,j),_now(v,v->entries),sizeof(float)*v->elements);
v->assigned[j]=v->assigned[v->elements];
memcpy(quantlist+j*v->elements,quantlist+v->entries*v->elements,
sizeof(long)*v->elements);
continue;
}
j++;
}
}
fprintf(stderr,"Building a book with %ld unique entries...\n",v->entries);
{
long *entryindex=_ogg_malloc(v->entries*sizeof(long *));
long *pointindex=_ogg_malloc(v->points*sizeof(long));
long *membership=_ogg_malloc(v->points*sizeof(long));
long *reventry=_ogg_malloc(v->entries*sizeof(long));
long pointssofar=0;
for(i=0;i<v->entries;i++)entryindex[i]=i;
for(i=0;i<v->points;i++)pointindex[i]=i;
t->alloc=4096;
t->ptr0=_ogg_malloc(sizeof(long)*t->alloc);
t->ptr1=_ogg_malloc(sizeof(long)*t->alloc);
t->p=_ogg_malloc(sizeof(long)*t->alloc);
t->q=_ogg_malloc(sizeof(long)*t->alloc);
t->aux=0;
c->dim=v->elements;
c->entries=v->entries;
c->lengthlist=_ogg_calloc(c->entries,sizeof(long));
b->valuelist=v->entrylist; /* temporary; replaced later */
b->dim=c->dim;
b->entries=c->entries;
for(i=0;i<v->points;i++)membership[i]=-1;
for(i=0;i<v->points;i++){
float *ppt=_point(v,i);
long firstentry=0;
float firstmetric=_Ndist(v->elements,_now(v,0),ppt);
if(!(i&0xff))spinnit("assigning... ",v->points-i);
for(j=1;j<v->entries;j++){
if(v->assigned[j]!=-1){
float thismetric=_Ndist(v->elements,_now(v,j),ppt);
if(thismetric<=firstmetric){
firstmetric=thismetric;
firstentry=j;
}
}
}
membership[i]=firstentry;
}
fprintf(stderr,"Leaves added: %d \n",
lp_split(v->pointlist,v->points,
b,entryindex,v->entries,
pointindex,v->points,
membership,reventry,
0,&pointssofar));
free(pointindex);
free(membership);
free(reventry);
fprintf(stderr,"Paring/rerouting redundant branches... ");
/* The tree is likely big and redundant. Pare and reroute branches */
{
int changedflag=1;
while(changedflag){
changedflag=0;
/* span the tree node by node; list unique decision nodes and
short circuit redundant branches */
for(i=0;i<t->aux;){
int k;
/* check list of unique decisions */
for(j=0;j<i;j++)
if(_node_eq(t,i,j))break;
if(j<i){
/* a redundant entry; find all higher nodes referencing it and
short circuit them to the previously noted unique entry */
changedflag=1;
for(k=0;k<t->aux;k++){
if(t->ptr0[k]==-i)t->ptr0[k]=-j;
if(t->ptr1[k]==-i)t->ptr1[k]=-j;
}
/* Now, we need to fill in the hole from this redundant
entry in the listing. Insert the last entry in the list.
Fix the forward pointers to that last entry */
t->aux--;
t->ptr0[i]=t->ptr0[t->aux];
t->ptr1[i]=t->ptr1[t->aux];
t->p[i]=t->p[t->aux];
t->q[i]=t->q[t->aux];
for(k=0;k<t->aux;k++){
if(t->ptr0[k]==-t->aux)t->ptr0[k]=-i;
if(t->ptr1[k]==-t->aux)t->ptr1[k]=-i;
}
/* hole plugged */
}else
i++;
}
fprintf(stderr,"\rParing/rerouting redundant branches... "
"%ld remaining ",t->aux);
}
fprintf(stderr,"\n");
}
}
/* run all training points through the decision tree to get a final
probability count */
{
long *probability=_ogg_malloc(c->entries*sizeof(long));
for(i=0;i<c->entries;i++)probability[i]=1; /* trivial guard */
b->dim=c->dim;
/* sigh. A necessary hack */
for(i=0;i<t->aux;i++)t->p[i]*=c->dim;
for(i=0;i<t->aux;i++)t->q[i]*=c->dim;
for(i=0;i<v->points;i++){
/* we use the linear matcher regardless becuase the trainer
doesn't convert log to linear */
int ret=_best(b,v->pointlist+i*v->elements,1);
probability[ret]++;
if(!(i&0xff))spinnit("counting hits... ",v->points-i);
}
for(i=0;i<t->aux;i++)t->p[i]/=c->dim;
for(i=0;i<t->aux;i++)t->q[i]/=c->dim;
build_tree_from_lengths(c->entries,probability,c->lengthlist);
free(probability);
}
/* Sort the entries by codeword length, short to long (eases
assignment and packing to do it now) */
{
long *wordlen=c->lengthlist;
long *index=_ogg_malloc(c->entries*sizeof(long));
long *revindex=_ogg_malloc(c->entries*sizeof(long));
int k;
for(i=0;i<c->entries;i++)index[i]=i;
isortvals=c->lengthlist;
qsort(index,c->entries,sizeof(long),iascsort);
/* rearrange storage; ptr0/1 first as it needs a reverse index */
/* n and c stay unchanged */
for(i=0;i<c->entries;i++)revindex[index[i]]=i;
for(i=0;i<t->aux;i++){
if(!(i&0x3f))spinnit("sorting... ",t->aux-i);
if(t->ptr0[i]>=0)t->ptr0[i]=revindex[t->ptr0[i]];
if(t->ptr1[i]>=0)t->ptr1[i]=revindex[t->ptr1[i]];
t->p[i]=revindex[t->p[i]];
t->q[i]=revindex[t->q[i]];
}
free(revindex);
/* map lengthlist and vallist with index */
c->lengthlist=_ogg_calloc(c->entries,sizeof(long));
b->valuelist=_ogg_malloc(sizeof(float)*c->entries*c->dim);
c->quantlist=_ogg_malloc(sizeof(long)*c->entries*c->dim);
for(i=0;i<c->entries;i++){
long e=index[i];
for(k=0;k<c->dim;k++){
b->valuelist[i*c->dim+k]=v->entrylist[e*c->dim+k];
c->quantlist[i*c->dim+k]=quantlist[e*c->dim+k];
}
c->lengthlist[i]=wordlen[e];
}
free(wordlen);
}
fprintf(stderr,"Done. \n\n");
}

Get latest updates about Open Source Projects, Conferences and News.

Sign up for the SourceForge newsletter:

JavaScript is required for this form.





No, thanks