godotengine
/
godot
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
godot/thirdparty/astcenc/astcenc_find_best_partition...

781 lines
26 KiB
C++
Raw Blame History

// SPDX-License-Identifier: Apache-2.0
// ----------------------------------------------------------------------------
// Copyright 2011-2023 Arm Limited
//
// Licensed under the Apache License, Version 2.0 (the "License"); you may not
// use this file except in compliance with the License. You may obtain a copy
// of the License at:
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
// WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
// License for the specific language governing permissions and limitations
// under the License.
// ----------------------------------------------------------------------------
#if !defined(ASTCENC_DECOMPRESS_ONLY)
/**
* @brief Functions for finding best partition for a block.
*
* The partition search operates in two stages. The first pass uses kmeans clustering to group
* texels into an ideal partitioning for the requested partition count, and then compares that
* against the 1024 partitionings generated by the ASTC partition hash function. The generated
* partitions are then ranked by the number of texels in the wrong partition, compared to the ideal
* clustering. All 1024 partitions are tested for similarity and ranked, apart from duplicates and
* partitionings that actually generate fewer than the requested partition count, but only the top
* N candidates are actually put through a more detailed search. N is determined by the compressor
* quality preset.
*
* For the detailed search, each candidate is checked against two possible encoding methods:
*
* - The best partitioning assuming different chroma colors (RGB + RGB or RGB + delta endpoints).
* - The best partitioning assuming same chroma colors (RGB + scale endpoints).
*
* This is implemented by computing the compute mean color and dominant direction for each
* partition. This defines two lines, both of which go through the mean color value.
*
* - One line has a direction defined by the dominant direction; this is used to assess the error
* from using an uncorrelated color representation.
* - The other line goes through (0,0,0,1) and is used to assess the error from using a same chroma
* (RGB + scale) color representation.
*
* The best candidate is selected by computing the squared-errors that result from using these
* lines for endpoint selection.
*/
#include <limits>
#include "astcenc_internal.h"
/**
* @brief Pick some initial kmeans cluster centers.
*
* @param blk The image block color data to compress.
* @param texel_count The number of texels in the block.
* @param partition_count The number of partitions in the block.
* @param[out] cluster_centers The initial partition cluster center colors.
*/
static void kmeans_init(
const image_block& blk,
unsigned int texel_count,
unsigned int partition_count,
vfloat4 cluster_centers[BLOCK_MAX_PARTITIONS]
) {
promise(texel_count > 0);
promise(partition_count > 0);
unsigned int clusters_selected = 0;
float distances[BLOCK_MAX_TEXELS];
// Pick a random sample as first cluster center; 145897 from random.org
unsigned int sample = 145897 % texel_count;
vfloat4 center_color = blk.texel(sample);
cluster_centers[clusters_selected] = center_color;
clusters_selected++;
// Compute the distance to the first cluster center
float distance_sum = 0.0f;
for (unsigned int i = 0; i < texel_count; i++)
{
vfloat4 color = blk.texel(i);
vfloat4 diff = color - center_color;
float distance = dot_s(diff * diff, blk.channel_weight);
distance_sum += distance;
distances[i] = distance;
}
// More numbers from random.org for weighted-random center selection
const float cluster_cutoffs[9] {
0.626220f, 0.932770f, 0.275454f,
0.318558f, 0.240113f, 0.009190f,
0.347661f, 0.731960f, 0.156391f
};
unsigned int cutoff = (clusters_selected - 1) + 3 * (partition_count - 2);
// Pick the remaining samples as needed
while (true)
{
// Pick the next center in a weighted-random fashion.
float summa = 0.0f;
float distance_cutoff = distance_sum * cluster_cutoffs[cutoff++];
for (sample = 0; sample < texel_count; sample++)
{
summa += distances[sample];
if (summa >= distance_cutoff)
{
break;
}
}
// Clamp to a valid range and store the selected cluster center
sample = astc::min(sample, texel_count - 1);
center_color = blk.texel(sample);
cluster_centers[clusters_selected++] = center_color;
if (clusters_selected >= partition_count)
{
break;
}
// Compute the distance to the new cluster center, keep the min dist
distance_sum = 0.0f;
for (unsigned int i = 0; i < texel_count; i++)
{
vfloat4 color = blk.texel(i);
vfloat4 diff = color - center_color;
float distance = dot_s(diff * diff, blk.channel_weight);
distance = astc::min(distance, distances[i]);
distance_sum += distance;
distances[i] = distance;
}
}
}
/**
* @brief Assign texels to clusters, based on a set of chosen center points.
*
* @param blk The image block color data to compress.
* @param texel_count The number of texels in the block.
* @param partition_count The number of partitions in the block.
* @param cluster_centers The partition cluster center colors.
* @param[out] partition_of_texel The partition assigned for each texel.
*/
static void kmeans_assign(
const image_block& blk,
unsigned int texel_count,
unsigned int partition_count,
const vfloat4 cluster_centers[BLOCK_MAX_PARTITIONS],
uint8_t partition_of_texel[BLOCK_MAX_TEXELS]
) {
promise(texel_count > 0);
promise(partition_count > 0);
uint8_t partition_texel_count[BLOCK_MAX_PARTITIONS] { 0 };
// Find the best partition for every texel
for (unsigned int i = 0; i < texel_count; i++)
{
float best_distance = std::numeric_limits<float>::max();
unsigned int best_partition = 0;
vfloat4 color = blk.texel(i);
for (unsigned int j = 0; j < partition_count; j++)
{
vfloat4 diff = color - cluster_centers[j];
float distance = dot_s(diff * diff, blk.channel_weight);
if (distance < best_distance)
{
best_distance = distance;
best_partition = j;
}
}
partition_of_texel[i] = static_cast<uint8_t>(best_partition);
partition_texel_count[best_partition]++;
}
// It is possible to get a situation where a partition ends up without any texels. In this case,
// assign texel N to partition N. This is silly, but ensures that every partition retains at
// least one texel. Reassigning a texel in this manner may cause another partition to go empty,
// so if we actually did a reassignment, run the whole loop over again.
bool problem_case;
do
{
problem_case = false;
for (unsigned int i = 0; i < partition_count; i++)
{
if (partition_texel_count[i] == 0)
{
partition_texel_count[partition_of_texel[i]]--;
partition_texel_count[i]++;
partition_of_texel[i] = static_cast<uint8_t>(i);
problem_case = true;
}
}
} while (problem_case);
}
/**
* @brief Compute new cluster centers based on their center of gravity.
*
* @param blk The image block color data to compress.
* @param texel_count The number of texels in the block.
* @param partition_count The number of partitions in the block.
* @param[out] cluster_centers The new cluster center colors.
* @param partition_of_texel The partition assigned for each texel.
*/
static void kmeans_update(
const image_block& blk,
unsigned int texel_count,
unsigned int partition_count,
vfloat4 cluster_centers[BLOCK_MAX_PARTITIONS],
const uint8_t partition_of_texel[BLOCK_MAX_TEXELS]
) {
promise(texel_count > 0);
promise(partition_count > 0);
vfloat4 color_sum[BLOCK_MAX_PARTITIONS] {
vfloat4::zero(),
vfloat4::zero(),
vfloat4::zero(),
vfloat4::zero()
};
uint8_t partition_texel_count[BLOCK_MAX_PARTITIONS] { 0 };
// Find the center-of-gravity in each cluster
for (unsigned int i = 0; i < texel_count; i++)
{
uint8_t partition = partition_of_texel[i];
color_sum[partition] += blk.texel(i);
partition_texel_count[partition]++;
}
// Set the center of gravity to be the new cluster center
for (unsigned int i = 0; i < partition_count; i++)
{
float scale = 1.0f / static_cast<float>(partition_texel_count[i]);
cluster_centers[i] = color_sum[i] * scale;
}
}
/**
* @brief Compute bit-mismatch for partitioning in 2-partition mode.
*
* @param a The texel assignment bitvector for the block.
* @param b The texel assignment bitvector for the partition table.
*
* @return The number of bit mismatches.
*/
static inline unsigned int partition_mismatch2(
const uint64_t a[2],
const uint64_t b[2]
) {
int v1 = popcount(a[0] ^ b[0]) + popcount(a[1] ^ b[1]);
int v2 = popcount(a[0] ^ b[1]) + popcount(a[1] ^ b[0]);
return astc::min(v1, v2);
}
/**
* @brief Compute bit-mismatch for partitioning in 3-partition mode.
*
* @param a The texel assignment bitvector for the block.
* @param b The texel assignment bitvector for the partition table.
*
* @return The number of bit mismatches.
*/
static inline unsigned int partition_mismatch3(
const uint64_t a[3],
const uint64_t b[3]
) {
int p00 = popcount(a[0] ^ b[0]);
int p01 = popcount(a[0] ^ b[1]);
int p02 = popcount(a[0] ^ b[2]);
int p10 = popcount(a[1] ^ b[0]);
int p11 = popcount(a[1] ^ b[1]);
int p12 = popcount(a[1] ^ b[2]);
int p20 = popcount(a[2] ^ b[0]);
int p21 = popcount(a[2] ^ b[1]);
int p22 = popcount(a[2] ^ b[2]);
int s0 = p11 + p22;
int s1 = p12 + p21;
int v0 = astc::min(s0, s1) + p00;
int s2 = p10 + p22;
int s3 = p12 + p20;
int v1 = astc::min(s2, s3) + p01;
int s4 = p10 + p21;
int s5 = p11 + p20;
int v2 = astc::min(s4, s5) + p02;
return astc::min(v0, v1, v2);
}
/**
* @brief Compute bit-mismatch for partitioning in 4-partition mode.
*
* @param a The texel assignment bitvector for the block.
* @param b The texel assignment bitvector for the partition table.
*
* @return The number of bit mismatches.
*/
static inline unsigned int partition_mismatch4(
const uint64_t a[4],
const uint64_t b[4]
) {
int p00 = popcount(a[0] ^ b[0]);
int p01 = popcount(a[0] ^ b[1]);
int p02 = popcount(a[0] ^ b[2]);
int p03 = popcount(a[0] ^ b[3]);
int p10 = popcount(a[1] ^ b[0]);
int p11 = popcount(a[1] ^ b[1]);
int p12 = popcount(a[1] ^ b[2]);
int p13 = popcount(a[1] ^ b[3]);
int p20 = popcount(a[2] ^ b[0]);
int p21 = popcount(a[2] ^ b[1]);
int p22 = popcount(a[2] ^ b[2]);
int p23 = popcount(a[2] ^ b[3]);
int p30 = popcount(a[3] ^ b[0]);
int p31 = popcount(a[3] ^ b[1]);
int p32 = popcount(a[3] ^ b[2]);
int p33 = popcount(a[3] ^ b[3]);
int mx23 = astc::min(p22 + p33, p23 + p32);
int mx13 = astc::min(p21 + p33, p23 + p31);
int mx12 = astc::min(p21 + p32, p22 + p31);
int mx03 = astc::min(p20 + p33, p23 + p30);
int mx02 = astc::min(p20 + p32, p22 + p30);
int mx01 = astc::min(p21 + p30, p20 + p31);
int v0 = p00 + astc::min(p11 + mx23, p12 + mx13, p13 + mx12);
int v1 = p01 + astc::min(p10 + mx23, p12 + mx03, p13 + mx02);
int v2 = p02 + astc::min(p11 + mx03, p10 + mx13, p13 + mx01);
int v3 = p03 + astc::min(p11 + mx02, p12 + mx01, p10 + mx12);
return astc::min(v0, v1, v2, v3);
}
using mismatch_dispatch = unsigned int (*)(const uint64_t*, const uint64_t*);
/**
* @brief Count the partition table mismatches vs the data clustering.
*
* @param bsd The block size information.
* @param partition_count The number of partitions in the block.
* @param bitmaps The block texel partition assignment patterns.
* @param[out] mismatch_counts The array storing per partitioning mismatch counts.
*/
static void count_partition_mismatch_bits(
const block_size_descriptor& bsd,
unsigned int partition_count,
const uint64_t bitmaps[BLOCK_MAX_PARTITIONS],
unsigned int mismatch_counts[BLOCK_MAX_PARTITIONINGS]
) {
unsigned int active_count = bsd.partitioning_count_selected[partition_count - 1];
promise(active_count > 0);
if (partition_count == 2)
{
for (unsigned int i = 0; i < active_count; i++)
{
mismatch_counts[i] = partition_mismatch2(bitmaps, bsd.coverage_bitmaps_2[i]);
}
}
else if (partition_count == 3)
{
for (unsigned int i = 0; i < active_count; i++)
{
mismatch_counts[i] = partition_mismatch3(bitmaps, bsd.coverage_bitmaps_3[i]);
}
}
else
{
for (unsigned int i = 0; i < active_count; i++)
{
mismatch_counts[i] = partition_mismatch4(bitmaps, bsd.coverage_bitmaps_4[i]);
}
}
}
/**
* @brief Use counting sort on the mismatch array to sort partition candidates.
*
* @param partitioning_count The number of packed partitionings.
* @param mismatch_count Partitioning mismatch counts, in index order.
* @param[out] partition_ordering Partition index values, in mismatch order.
*
* @return The number of active partitions in this selection.
*/
static unsigned int get_partition_ordering_by_mismatch_bits(
unsigned int partitioning_count,
const unsigned int mismatch_count[BLOCK_MAX_PARTITIONINGS],
unsigned int partition_ordering[BLOCK_MAX_PARTITIONINGS]
) {
promise(partitioning_count > 0);
unsigned int mscount[256] { 0 };
// Create the histogram of mismatch counts
for (unsigned int i = 0; i < partitioning_count; i++)
{
mscount[mismatch_count[i]]++;
}
unsigned int active_count = partitioning_count - mscount[255];
// Create a running sum from the histogram array
// Cells store previous values only; i.e. exclude self after sum
unsigned int summa = 0;
for (unsigned int i = 0; i < 256; i++)
{
unsigned int cnt = mscount[i];
mscount[i] = summa;
summa += cnt;
}
// Use the running sum as the index, incrementing after read to allow
// sequential entries with the same count
for (unsigned int i = 0; i < partitioning_count; i++)
{
unsigned int idx = mscount[mismatch_count[i]]++;
partition_ordering[idx] = i;
}
return active_count;
}
/**
* @brief Use k-means clustering to compute a partition ordering for a block..
*
* @param bsd The block size information.
* @param blk The image block color data to compress.
* @param partition_count The desired number of partitions in the block.
* @param[out] partition_ordering The list of recommended partition indices, in priority order.
*
* @return The number of active partitionings in this selection.
*/
static unsigned int compute_kmeans_partition_ordering(
const block_size_descriptor& bsd,
const image_block& blk,
unsigned int partition_count,
unsigned int partition_ordering[BLOCK_MAX_PARTITIONINGS]
) {
vfloat4 cluster_centers[BLOCK_MAX_PARTITIONS];
uint8_t texel_partitions[BLOCK_MAX_TEXELS];
// Use three passes of k-means clustering to partition the block data
for (unsigned int i = 0; i < 3; i++)
{
if (i == 0)
{
kmeans_init(blk, bsd.texel_count, partition_count, cluster_centers);
}
else
{
kmeans_update(blk, bsd.texel_count, partition_count, cluster_centers, texel_partitions);
}
kmeans_assign(blk, bsd.texel_count, partition_count, cluster_centers, texel_partitions);
}
// Construct the block bitmaps of texel assignments to each partition
uint64_t bitmaps[BLOCK_MAX_PARTITIONS] { 0 };
unsigned int texels_to_process = astc::min(bsd.texel_count, BLOCK_MAX_KMEANS_TEXELS);
promise(texels_to_process > 0);
for (unsigned int i = 0; i < texels_to_process; i++)
{
unsigned int idx = bsd.kmeans_texels[i];
bitmaps[texel_partitions[idx]] |= 1ULL << i;
}
// Count the mismatch between the block and the format's partition tables
unsigned int mismatch_counts[BLOCK_MAX_PARTITIONINGS];
count_partition_mismatch_bits(bsd, partition_count, bitmaps, mismatch_counts);
// Sort the partitions based on the number of mismatched bits
return get_partition_ordering_by_mismatch_bits(
bsd.partitioning_count_selected[partition_count - 1],
mismatch_counts, partition_ordering);
}
/**
* @brief Insert a partitioning into an order list of results, sorted by error.
*
* @param max_values The max number of entries in the best result arrays.
* @param this_error The error of the new entry.
* @param this_partition The partition ID of the new entry.
* @param[out] best_errors The array of best error values.
* @param[out] best_partitions The array of best partition values.
*/
static void insert_result(
unsigned int max_values,
float this_error,
unsigned int this_partition,
float* best_errors,
unsigned int* best_partitions)
{
promise(max_values > 0);
// Don't bother searching if the current worst error beats the new error
if (this_error >= best_errors[max_values - 1])
{
return;
}
// Else insert into the list in error-order
for (unsigned int i = 0; i < max_values; i++)
{
// Existing result is better - move on ...
if (this_error > best_errors[i])
{
continue;
}
// Move existing results down one
for (unsigned int j = max_values - 1; j > i; j--)
{
best_errors[j] = best_errors[j - 1];
best_partitions[j] = best_partitions[j - 1];
}
// Insert new result
best_errors[i] = this_error;
best_partitions[i] = this_partition;
break;
}
}
/* See header for documentation. */
unsigned int find_best_partition_candidates(
const block_size_descriptor& bsd,
const image_block& blk,
unsigned int partition_count,
unsigned int partition_search_limit,
unsigned int best_partitions[TUNE_MAX_PARTITIONING_CANDIDATES],
unsigned int requested_candidates
) {
// Constant used to estimate quantization error for a given partitioning; the optimal value for
// this depends on bitrate. These values have been determined empirically.
unsigned int texels_per_block = bsd.texel_count;
float weight_imprecision_estim = 0.055f;
if (texels_per_block <= 20)
{
weight_imprecision_estim = 0.03f;
}
else if (texels_per_block <= 31)
{
weight_imprecision_estim = 0.04f;
}
else if (texels_per_block <= 41)
{
weight_imprecision_estim = 0.05f;
}
promise(partition_count > 0);
promise(partition_search_limit > 0);
weight_imprecision_estim = weight_imprecision_estim * weight_imprecision_estim;
unsigned int partition_sequence[BLOCK_MAX_PARTITIONINGS];
unsigned int sequence_len = compute_kmeans_partition_ordering(bsd, blk, partition_count, partition_sequence);
partition_search_limit = astc::min(partition_search_limit, sequence_len);
requested_candidates = astc::min(partition_search_limit, requested_candidates);
bool uses_alpha = !blk.is_constant_channel(3);
// Partitioning errors assuming uncorrelated-chrominance endpoints
float uncor_best_errors[TUNE_MAX_PARTITIONING_CANDIDATES];
unsigned int uncor_best_partitions[TUNE_MAX_PARTITIONING_CANDIDATES];
// Partitioning errors assuming same-chrominance endpoints
float samec_best_errors[TUNE_MAX_PARTITIONING_CANDIDATES];
unsigned int samec_best_partitions[TUNE_MAX_PARTITIONING_CANDIDATES];
for (unsigned int i = 0; i < requested_candidates; i++)
{
uncor_best_errors[i] = ERROR_CALC_DEFAULT;
samec_best_errors[i] = ERROR_CALC_DEFAULT;
}
if (uses_alpha)
{
for (unsigned int i = 0; i < partition_search_limit; i++)
{
unsigned int partition = partition_sequence[i];
const auto& pi = bsd.get_raw_partition_info(partition_count, partition);
// Compute weighting to give to each component in each partition
partition_metrics pms[BLOCK_MAX_PARTITIONS];
compute_avgs_and_dirs_4_comp(pi, blk, pms);
line4 uncor_lines[BLOCK_MAX_PARTITIONS];
line4 samec_lines[BLOCK_MAX_PARTITIONS];
processed_line4 uncor_plines[BLOCK_MAX_PARTITIONS];
processed_line4 samec_plines[BLOCK_MAX_PARTITIONS];
float uncor_line_lens[BLOCK_MAX_PARTITIONS];
float samec_line_lens[BLOCK_MAX_PARTITIONS];
for (unsigned int j = 0; j < partition_count; j++)
{
partition_metrics& pm = pms[j];
uncor_lines[j].a = pm.avg;
uncor_lines[j].b = normalize_safe(pm.dir, unit4());
uncor_plines[j].amod = uncor_lines[j].a - uncor_lines[j].b * dot(uncor_lines[j].a, uncor_lines[j].b);
uncor_plines[j].bs = uncor_lines[j].b;
samec_lines[j].a = vfloat4::zero();
samec_lines[j].b = normalize_safe(pm.avg, unit4());
samec_plines[j].amod = vfloat4::zero();
samec_plines[j].bs = samec_lines[j].b;
}
float uncor_error = 0.0f;
float samec_error = 0.0f;
compute_error_squared_rgba(pi,
blk,
uncor_plines,
samec_plines,
uncor_line_lens,
samec_line_lens,
uncor_error,
samec_error);
// Compute an estimate of error introduced by weight quantization imprecision.
// This error is computed as follows, for each partition
// 1: compute the principal-axis vector (full length) in error-space
// 2: convert the principal-axis vector to regular RGB-space
// 3: scale the vector by a constant that estimates average quantization error
// 4: for each texel, square the vector, then do a dot-product with the texel's
// error weight; sum up the results across all texels.
// 4(optimized): square the vector once, then do a dot-product with the average
// texel error, then multiply by the number of texels.
for (unsigned int j = 0; j < partition_count; j++)
{
float tpp = static_cast<float>(pi.partition_texel_count[j]);
vfloat4 error_weights(tpp * weight_imprecision_estim);
vfloat4 uncor_vector = uncor_lines[j].b * uncor_line_lens[j];
vfloat4 samec_vector = samec_lines[j].b * samec_line_lens[j];
uncor_error += dot_s(uncor_vector * uncor_vector, error_weights);
samec_error += dot_s(samec_vector * samec_vector, error_weights);
}
insert_result(requested_candidates, uncor_error, partition, uncor_best_errors, uncor_best_partitions);
insert_result(requested_candidates, samec_error, partition, samec_best_errors, samec_best_partitions);
}
}
else
{
for (unsigned int i = 0; i < partition_search_limit; i++)
{
unsigned int partition = partition_sequence[i];
const auto& pi = bsd.get_raw_partition_info(partition_count, partition);
// Compute weighting to give to each component in each partition
partition_metrics pms[BLOCK_MAX_PARTITIONS];
compute_avgs_and_dirs_3_comp_rgb(pi, blk, pms);
partition_lines3 plines[BLOCK_MAX_PARTITIONS];
for (unsigned int j = 0; j < partition_count; j++)
{
partition_metrics& pm = pms[j];
partition_lines3& pl = plines[j];
pl.uncor_line.a = pm.avg;
pl.uncor_line.b = normalize_safe(pm.dir, unit3());
pl.samec_line.a = vfloat4::zero();
pl.samec_line.b = normalize_safe(pm.avg, unit3());
pl.uncor_pline.amod = pl.uncor_line.a - pl.uncor_line.b * dot3(pl.uncor_line.a, pl.uncor_line.b);
pl.uncor_pline.bs = pl.uncor_line.b;
pl.samec_pline.amod = vfloat4::zero();
pl.samec_pline.bs = pl.samec_line.b;
}
float uncor_error = 0.0f;
float samec_error = 0.0f;
compute_error_squared_rgb(pi,
blk,
plines,
uncor_error,
samec_error);
// Compute an estimate of error introduced by weight quantization imprecision.
// This error is computed as follows, for each partition
// 1: compute the principal-axis vector (full length) in error-space
// 2: convert the principal-axis vector to regular RGB-space
// 3: scale the vector by a constant that estimates average quantization error
// 4: for each texel, square the vector, then do a dot-product with the texel's
// error weight; sum up the results across all texels.
// 4(optimized): square the vector once, then do a dot-product with the average
// texel error, then multiply by the number of texels.
for (unsigned int j = 0; j < partition_count; j++)
{
partition_lines3& pl = plines[j];
float tpp = static_cast<float>(pi.partition_texel_count[j]);
vfloat4 error_weights(tpp * weight_imprecision_estim);
vfloat4 uncor_vector = pl.uncor_line.b * pl.uncor_line_len;
vfloat4 samec_vector = pl.samec_line.b * pl.samec_line_len;
uncor_error += dot3_s(uncor_vector * uncor_vector, error_weights);
samec_error += dot3_s(samec_vector * samec_vector, error_weights);
}
insert_result(requested_candidates, uncor_error, partition, uncor_best_errors, uncor_best_partitions);
insert_result(requested_candidates, samec_error, partition, samec_best_errors, samec_best_partitions);
}
}
bool best_is_uncor = uncor_best_partitions[0] > samec_best_partitions[0];
unsigned int interleave[2 * TUNE_MAX_PARTITIONING_CANDIDATES];
for (unsigned int i = 0; i < requested_candidates; i++)
{
if (best_is_uncor)
{
interleave[2 * i] = bsd.get_raw_partition_info(partition_count, uncor_best_partitions[i]).partition_index;
interleave[2 * i + 1] = bsd.get_raw_partition_info(partition_count, samec_best_partitions[i]).partition_index;
}
else
{
interleave[2 * i] = bsd.get_raw_partition_info(partition_count, samec_best_partitions[i]).partition_index;
interleave[2 * i + 1] = bsd.get_raw_partition_info(partition_count, uncor_best_partitions[i]).partition_index;
}
}
uint64_t bitmasks[1024/64] { 0 };
unsigned int emitted = 0;
// Deduplicate the first "requested" entries
for (unsigned int i = 0; i < requested_candidates * 2; i++)
{
unsigned int partition = interleave[i];
unsigned int word = partition / 64;
unsigned int bit = partition % 64;
bool written = bitmasks[word] & (1ull << bit);
if (!written)
{
best_partitions[emitted] = partition;
bitmasks[word] |= 1ull << bit;
emitted++;
if (emitted == requested_candidates)
{
break;
}
}
}
return emitted;
}
#endif
Reference in New Issue View Git Blame Copy Permalink