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
|
// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/block_random_access_sparse_matrix.h"
#include <algorithm>
#include <memory>
#include <set>
#include <utility>
#include <vector>
#include "ceres/internal/port.h"
#include "ceres/triplet_sparse_matrix.h"
#include "ceres/types.h"
#include "glog/logging.h"
namespace ceres {
namespace internal {
using std::make_pair;
using std::pair;
using std::set;
using std::vector;
BlockRandomAccessSparseMatrix::BlockRandomAccessSparseMatrix(
const vector<int>& blocks,
const set<pair<int, int>>& block_pairs)
: kMaxRowBlocks(10 * 1000 * 1000),
blocks_(blocks) {
CHECK_LT(blocks.size(), kMaxRowBlocks);
// Build the row/column layout vector and count the number of scalar
// rows/columns.
int num_cols = 0;
block_positions_.reserve(blocks_.size());
for (int i = 0; i < blocks_.size(); ++i) {
block_positions_.push_back(num_cols);
num_cols += blocks_[i];
}
// Count the number of scalar non-zero entries and build the layout
// object for looking into the values array of the
// TripletSparseMatrix.
int num_nonzeros = 0;
for (const auto& block_pair : block_pairs) {
const int row_block_size = blocks_[block_pair.first];
const int col_block_size = blocks_[block_pair.second];
num_nonzeros += row_block_size * col_block_size;
}
VLOG(1) << "Matrix Size [" << num_cols
<< "," << num_cols
<< "] " << num_nonzeros;
tsm_.reset(new TripletSparseMatrix(num_cols, num_cols, num_nonzeros));
tsm_->set_num_nonzeros(num_nonzeros);
int* rows = tsm_->mutable_rows();
int* cols = tsm_->mutable_cols();
double* values = tsm_->mutable_values();
int pos = 0;
for (const auto& block_pair : block_pairs) {
const int row_block_size = blocks_[block_pair.first];
const int col_block_size = blocks_[block_pair.second];
cell_values_.push_back(make_pair(block_pair, values + pos));
layout_[IntPairToLong(block_pair.first, block_pair.second)] =
new CellInfo(values + pos);
pos += row_block_size * col_block_size;
}
// Fill the sparsity pattern of the underlying matrix.
for (const auto& block_pair : block_pairs) {
const int row_block_id = block_pair.first;
const int col_block_id = block_pair.second;
const int row_block_size = blocks_[row_block_id];
const int col_block_size = blocks_[col_block_id];
int pos =
layout_[IntPairToLong(row_block_id, col_block_id)]->values - values;
for (int r = 0; r < row_block_size; ++r) {
for (int c = 0; c < col_block_size; ++c, ++pos) {
rows[pos] = block_positions_[row_block_id] + r;
cols[pos] = block_positions_[col_block_id] + c;
values[pos] = 1.0;
DCHECK_LT(rows[pos], tsm_->num_rows());
DCHECK_LT(cols[pos], tsm_->num_rows());
}
}
}
}
// Assume that the user does not hold any locks on any cell blocks
// when they are calling SetZero.
BlockRandomAccessSparseMatrix::~BlockRandomAccessSparseMatrix() {
for (const auto& entry : layout_) {
delete entry.second;
}
}
CellInfo* BlockRandomAccessSparseMatrix::GetCell(int row_block_id,
int col_block_id,
int* row,
int* col,
int* row_stride,
int* col_stride) {
const LayoutType::iterator it =
layout_.find(IntPairToLong(row_block_id, col_block_id));
if (it == layout_.end()) {
return NULL;
}
// Each cell is stored contiguously as its own little dense matrix.
*row = 0;
*col = 0;
*row_stride = blocks_[row_block_id];
*col_stride = blocks_[col_block_id];
return it->second;
}
// Assume that the user does not hold any locks on any cell blocks
// when they are calling SetZero.
void BlockRandomAccessSparseMatrix::SetZero() {
if (tsm_->num_nonzeros()) {
VectorRef(tsm_->mutable_values(),
tsm_->num_nonzeros()).setZero();
}
}
void BlockRandomAccessSparseMatrix::SymmetricRightMultiply(const double* x,
double* y) const {
for (const auto& cell_position_and_data : cell_values_) {
const int row = cell_position_and_data.first.first;
const int row_block_size = blocks_[row];
const int row_block_pos = block_positions_[row];
const int col = cell_position_and_data.first.second;
const int col_block_size = blocks_[col];
const int col_block_pos = block_positions_[col];
MatrixVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
cell_position_and_data.second, row_block_size, col_block_size,
x + col_block_pos,
y + row_block_pos);
// Since the matrix is symmetric, but only the upper triangular
// part is stored, if the block being accessed is not a diagonal
// block, then use the same block to do the corresponding lower
// triangular multiply also.
if (row != col) {
MatrixTransposeVectorMultiply<Eigen::Dynamic, Eigen::Dynamic, 1>(
cell_position_and_data.second, row_block_size, col_block_size,
x + row_block_pos,
y + col_block_pos);
}
}
}
} // namespace internal
} // namespace ceres
|
