/////////////////////////////////////////////////////////////////////////
// Vertex Loader (used to read images and load the vertex data of the graph)
//bool vertex_loader(graph_type& graph, const std::string& fname,
// const std::string& line)
bool vertex_loader(graphlab::distributed_control& dc, graph_type& graph, string img_path)
{
// force a "/" at the end of the path
// make sure to check that the path is non-empty. (you do not
// want to make the empty path "" the root path "/" )
string path = img_path;
if (path.length() > 0 && path[path.length() - 1] != '/') path = path + "/";
vector<string> graph_files;
string search_prefix;
graphlab::fs_util::list_files_with_prefix(path, search_prefix, graph_files);
if (graph_files.size() == 0)
logstream(LOG_WARNING) << "No files found in " << path << std::endl;
// vertex data & id
graphlab::vertex_id_type vid(-1);
///////////////////////////////////////////////////////
// Loop over files
for(size_t i = 0; i < graph_files.size(); ++i)
{
// Each machine loads corresponding file
if (i % dc.numprocs() == dc.procid())
{
if (opts.verbose > 0)
logstream(LOG_EMPH)
<< "Process: " << dc.procid() << "/" << dc.numprocs() << " "
<< "picked image: " << graph_files[i] << "\n";
vid = i;
vertex_data vdata;
vdata.empty = false;
vdata.img_path = graph_files[i];
vdata.features.img_idx = i;
graph.add_vertex(vid, vdata);
}
}
return true;
}
WorkGraphPolicy( const graph_type & arg_graph )
: m_graph(arg_graph)
, m_queue( view_alloc( "queue" , WithoutInitializing )
, arg_graph.numRows() * 2 + 2 )
{
{ // Initialize
using policy_type = RangePolicy<std::int32_t, execution_space, TagInit>;
using closure_type = Kokkos::Impl::ParallelFor<self_type, policy_type>;
const closure_type closure(*this, policy_type(0, m_queue.size()));
closure.execute();
execution_space::fence();
}
{ // execute-after counts
using policy_type = RangePolicy<std::int32_t, execution_space, TagCount>;
using closure_type = Kokkos::Impl::ParallelFor<self_type, policy_type>;
const closure_type closure(*this,policy_type(0,m_graph.entries.size()));
closure.execute();
execution_space::fence();
}
{ // Scheduling ready tasks
using policy_type = RangePolicy<std::int32_t, execution_space, TagReady>;
using closure_type = Kokkos::Impl::ParallelFor<self_type, policy_type>;
const closure_type closure(*this,policy_type(0,m_graph.numRows()));
closure.execute();
execution_space::fence();
}
}
void merge_values(vid_vdata_vector_type& element, graph_type& graph) {
for (auto vertex : element) {
if (graph.contains_vertex(vertex.first)) {
const graphlab::lvid_type lvid = graph.local_vid(vertex.first);
graph.l_vertex(lvid).data() = vertex.second;
}
}
}
SynchronousEngine<algorithm_t>::SynchronousEngine(graph_type& graph):
iteration_counter_(0), max_iterations_(5), graph_(graph) {
vertex_programs_.resize(graph.num_local_vertices());
gather_accum_.resize(graph.num_local_vertices());
has_msg_.resize(graph.num_local_vertices(), 0);
has_msg_.resize(graph.num_local_vertices(), message_type());
active_superstep_.resize(graph.num_local_vertices(), 0);
active_minorstep_.resize(graph.num_local_vertices(), 0);
}
graph_gather_apply<Graph,GatherType>::graph_gather_apply(
graph_type& graph,
gather_fun_type gather_fun,
apply_fun_type apply_fun,
const graphlab_options& opts) :
gather_fun(gather_fun), apply_fun(apply_fun), rmi(graph.dc(), this), graph(graph),
threads(opts.get_ncpus()),
thread_barrier(opts.get_ncpus()),
gather_exchange(graph.dc(), opts.get_ncpus(), 64 * 1024) { }
SynchronousEngine<algorithm_t>::SynchronousEngine(graph_type& graph):
iteration_counter_(0), max_iterations_(5), graph_(graph) {
vertex_programs_.resize(graph.num_local_vertices());
gather_accum_.resize(graph.num_local_vertices());
has_msg_.resize(graph.num_local_vertices(), 0);
messages_.resize(graph.num_local_vertices(), message_type());
active_superstep_.resize(graph.num_local_vertices(), 0);
active_minorstep_.resize(graph.num_local_vertices(), 0);
context = new context_type(*this, graph);
aggregator = new aggregator_type(graph, context);
cout<<"sync engine init"<<endl;
}
static bool dfs(
const graph_type& graph,
const int vertex_id,
pk::vector<int, graph_type::max_num_of_vertices>& colors,
pk::stack<int, graph_type::max_num_of_vertices>& s)
{
if(colors[vertex_id] == 1)
return false;
if(colors[vertex_id] == 2)
return true;
colors[vertex_id] = 1;
const typename graph_type::adjacency_list& adjacent_edges = graph.get_adjacency_list(vertex_id);
for(int i = 0; i < adjacent_edges.size(); ++i)
{
if(!dfs(graph, adjacent_edges[i].to, colors, s))
return false;
}
colors[vertex_id] = 2;
s.push(vertex_id);
return true;
}
void write_results(char* ofile, graph_type& graph) {
std::cout << "PageRank: Writing results to: " << ofile << std::endl;
std::ofstream fout(ofile);
assert(fout.is_open());
for(graphlab::lvid_type lvid = 0; lvid < graph.get_local_graph().num_vertices(); ++lvid) {
if (!graph.l_is_master(lvid)) {
continue;
}
size_t id = graph.global_vid(lvid);
double val = graph.get_local_graph().vertex(lvid).data();
fout << id << "\t" << val << std::endl;
}
fout.close();
std::cout << "PageRank: Results written." << std::endl;
}
void visit_next_component(const graph_type& g, const int starting_vertex_id, Sequence& visited)
{
pk::queue<int, graph_type::max_num_of_vertices> q;
q.push(starting_vertex_id);
visited[starting_vertex_id] = true;
while(!q.empty())
{
const int v = q.front();
q.pop();
component_ids[v] = number_of_components;
const typename graph_type::adjacency_list& adj_v = g.get_adjacency_list(v);
for(int i = 0; i < adj_v.size(); ++i)
{
const int u = adj_v[i].to;
if(visited[u])
continue;
q.push(u);
visited[u] = true;
}
}
}
/**\brief Attempt to pop the work item at the head of the queue.
*
* Find entry 'i' such that
* ( m_queue[i] != BEGIN_TOKEN ) AND
* ( i == 0 OR m_queue[i-1] == BEGIN_TOKEN )
* if found then
* increment begin hint
* return atomic_exchange( m_queue[i] , BEGIN_TOKEN )
* else if i < total work
* return END_TOKEN
* else
* return COMPLETED_TOKEN
*
*/
KOKKOS_INLINE_FUNCTION
std::int32_t pop_work() const noexcept
{
const std::int32_t N = m_graph.numRows();
std::int32_t volatile * const ready_queue = & m_queue[0] ;
std::int32_t volatile * const begin_hint = & m_queue[2*N] ;
// begin hint is guaranteed to be less than or equal to
// actual begin location in the queue.
for ( std::int32_t i = *begin_hint ; i < N ; ++i ) {
const std::int32_t w = ready_queue[i] ;
if ( w == END_TOKEN ) { return END_TOKEN ; }
if ( ( w != BEGIN_TOKEN ) &&
( w == atomic_compare_exchange(ready_queue+i,w,(std::int32_t)BEGIN_TOKEN) ) ) {
// Attempt to claim ready work index succeeded,
// update the hint and return work index
atomic_increment( begin_hint );
return w ;
}
// arrive here when ready_queue[i] == BEGIN_TOKEN
}
return COMPLETED_TOKEN ;
}
// Second loader that only a single machine calls and pre-loads cameras.
bool vertex_loader(graph_type& graph, string img_path, vector<CameraParams>& cameras)
{
// force a "/" at the end of the path
// make sure to check that the path is non-empty. (you do not
// want to make the empty path "" the root path "/" )
string path = img_path;
if (path.length() > 0 && path[path.length() - 1] != '/') path = path + "/";
vector<string> graph_files;
string search_prefix;
graphlab::fs_util::list_files_with_prefix(path, search_prefix, graph_files);
if (graph_files.size() == 0)
logstream(LOG_WARNING) << "No files found in " << path << std::endl;
// vertex data & id
graphlab::vertex_id_type vid(-1);
///////////////////////////////////////////////////////
// Loop over files
for(size_t i = 0; i < graph_files.size(); ++i)
{
vid = i;
vertex_data vdata;
vdata.empty = false;
vdata.img_path = graph_files[i];
vdata.features.img_idx = i;
vdata.camera = cameras[i]; // addition to above function.
graph.add_vertex(vid, vdata);
}
return true;
}
/////////////////////////////////////////////////////////////////////////
// Load the distributed UAI file
bool line_parser(graph_type& graph, const std::string& filename, const std::string& textline) {
std::stringstream strm(textline);
graphlab::vertex_id_type vid;
vertex_data vdata;
vdata.dual_contrib = 0.0;
string type;
strm >> type;
if(type == "v") {
vdata.factor_type = VAR;
vdata.nvars = 1;
vdata.cards.resize(1);
strm>>vid;
strm >> vdata.cards[0];
vdata.potentials.resize(vdata.cards[0]);
//vdata.beliefs.setOnes(vdata.cards[0]);
//vdata.beliefs /= vdata.cards[0];
vdata.beliefs.setConstant(vdata.cards[0], 0.5);
vdata.unary_degree.resize(vdata.cards[0], 0);
//for(int i=0; i< vdata.cards[0]; i++){
// strm>>vdata.potentials[i];
// vdata.potentials[i] = log10(vdata.potentials[i]);
// }
// vdata.potentials.maxCoeff(&vdata.best_configuration);
graph.add_vertex(vid,vdata);
}
KOKKOS_INLINE_FUNCTION
void operator()( const TagCount , int i ) const noexcept
{
std::int32_t volatile * const count_queue =
& m_queue[ m_graph.numRows() ] ;
atomic_increment( count_queue + m_graph.entries[i] );
}
KOKKOS_INLINE_FUNCTION
void operator()( const TagReady , int w ) const noexcept
{
std::int32_t const * const count_queue =
& m_queue[ m_graph.numRows() ] ;
if ( 0 == count_queue[w] ) push_work(w);
}
void test_contains_edge(graph_type g,
vector<typename graph_type::location_type> present_locs,
vector<typename graph_type::location_type> absent_locs)
{
for (auto loc : present_locs) {
if (!g.contains_edge(loc)) {
cout << "Test contains_egde failed on " << loc.first << ',' << loc.second << '\n';
exit(1);
}
}
for (auto loc : absent_locs) {
if (g.contains_edge(loc)) {
cout << "Test does not contains_egde failed on " << loc.first << ',' << loc.second << '\n';
exit(1);
}
}
cout << "Contains edge passed\n";
}
bool line_parser(graph_type& graph, const std::string& filename,
const std::string& textline)
{
std::istringstream ssin(textline);
graphlab::vertex_id_type vid;
ssin >> vid;
int out_nb;
ssin >> out_nb;
if (out_nb == 0)
graph.add_vertex(vid);
while (out_nb--) {
graphlab::vertex_id_type other_vid;
ssin >> other_vid;
graph.add_edge(vid, other_vid);
}
return true;
}
void test_delete_edges(graph_type g,
vector<typename graph_type::location_type> kill_edges,
vector<weight_type> expected_weights)
{
for (auto k : kill_edges) {
g.delete_edge(k);
}
test_graph_iterator(g, expected_weights);
cout << "Delete edges passed\n";
}
/////////////////////////////////////////////////////////////////////////
// Edge Loader (used to read the adjacency list and add edges to the graph)
bool edge_loader(graph_type& graph, const std::string& fname,
const std::string& textline)
{
if ( textline.length() == 0 || textline[0] == '#' )
return true; // empty or comment line, return
std::stringstream strm(textline);
graphlab::vertex_id_type vid;
// first entry in the line is a vertex ID
strm >> vid;
if (opts.verbose > 0)
logstream(LOG_EMPH) << "Here's the input: "
<< textline << "\n"
<< vid << "\n";
// Line should contain at least 1 more number (degree of node)
if (!strm.good())
{
logstream(LOG_ERROR) << "The following ajacency list line is incomplete(check adj_list standard):\n"
<< textline << std::endl;
return EXIT_FAILURE;
}
// second entry is the out-degree
int outdeg;
strm >> outdeg;
graphlab::vertex_id_type other_vid;
for (int i=0; i!=outdeg; ++i)
{
// Line should contain more numbers (id of neighbours)
if (!strm.good())
{
logstream(LOG_ERROR) << "The following ajacency list line is incomplete(check adj_list standard):\n"
<< textline << std::endl;
return EXIT_FAILURE;
}
strm >> other_vid;
// only add edges in one direction
if (other_vid < vid)
continue;
if (opts.verbose > 0)
logstream(LOG_EMPH) << "Adding edge: (" << vid << "," << other_vid << ")\n";
edge_data edata; edata.empty = false;
graph.add_edge(vid,other_vid,edata);
}
return true;
}
void test_edges_at(graph_type g,
vector<typename graph_type::location_type> modify_edges,
weight_type new_value,
vector<weight_type> expected_weights)
{
for (auto m : modify_edges) {
g.edge_at(m).weight() = new_value;
}
test_graph_iterator(g, expected_weights);
cout << "Edges at passed\n";
}
KOKKOS_INLINE_FUNCTION
void completed_work( std::int32_t w ) const noexcept
{
Kokkos::memory_fence();
// Make sure the completed work function's memory accesses are flushed.
const std::int32_t N = m_graph.numRows();
std::int32_t volatile * const count_queue = & m_queue[N] ;
const std::int32_t B = m_graph.row_map(w);
const std::int32_t E = m_graph.row_map(w+1);
for ( std::int32_t i = B ; i < E ; ++i ) {
const std::int32_t j = m_graph.entries(i);
if ( 1 == atomic_fetch_add(count_queue+j,-1) ) {
push_work(j);
}
}
}
bool parse_vertex_line(graph_type &graph, const std::string &file, const std::string &line) {
if (line.empty() || line[0] == '#') {
return true;
}
char *dst;
size_t id = strtoul(line.c_str(), &dst, 10);
if (dst == line.c_str()) return false;
graph.add_vertex(id);
return true;
}
static bool run(const graph_type& graph, output_iterator output)
{
pk::vector<int, graph_type::max_num_of_vertices> colors(0, graph.get_num_of_vertices());
pk::stack<int, graph_type::max_num_of_vertices> s;
for(int v = 0; v < graph.get_num_of_vertices(); ++v)
{
if(colors[v] != 0)
continue;
if(!dfs(graph, v, colors, s))
return false;
}
while(!s.empty())
{
*output++ = s.top();
s.pop();
}
return true;
}
void find_components(const graph_type& g)
{
number_of_components = 0;
pk::vector<bool, graph_type::max_num_of_vertices> visited(false, g.get_num_of_vertices());
for(int u = 0; u < visited.size(); ++u)
{
if(!visited[u])
{
visit_next_component(g, u, visited);
++number_of_components;
}
}
}
bool parse_edge_line(graph_type &graph, const std::string &file, const std::string &line) {
if (line.empty() || line[0] == '#') {
return true;
}
char *dst;
size_t source = strtoul(line.c_str(), &dst, 10);
if (dst == line.c_str()) return false;
char *end;
size_t target = strtoul(dst, &end, 10);
if (dst == end) return false;
if (source != target) graph.add_edge(source, target);
return true;
}
KOKKOS_INLINE_FUNCTION
void push_work( const std::int32_t w ) const noexcept
{
const std::int32_t N = m_graph.numRows();
std::int32_t volatile * const ready_queue = & m_queue[0] ;
std::int32_t volatile * const end_hint = & m_queue[2*N+1] ;
// Push work to end of queue
const std::int32_t j = atomic_fetch_add( end_hint , 1 );
if ( ( N <= j ) ||
( END_TOKEN != atomic_exchange(ready_queue+j,w) ) ) {
// ERROR: past the end of queue or did not replace END_TOKEN
Kokkos::abort("WorkGraphPolicy push_work error");
}
memory_fence();
}
/////////////////////////////////////////////////////////////////////////
// Load the UAI file. Each factor as a different vertex
void loadUAIfile(graphlab::distributed_control& dc, graph_type& graph, string graph_file)
{
// Not sure why this is needed
dc.barrier();
// Open file
ifstream in(graph_file.c_str());
//CHECK(in.good(),"Could not open file: "+graph_file);
CHECK(in.good());
// Read type of network
string name;
in >> name;
//CHECK(name.compare("MARKOV")==0, "Only Markov networks are supported. Are you sure this is a typeUAI energy file?");
CHECK(name.compare("MARKOV")==0);
// Read size of graph
int nnodes, nfactors;
in >> nnodes;
//CHECK(nnodes>0, "No. of nodes can't be negative. Are you sure this is a typeUAI energy file?");
CHECK(nnodes>0);
// Read node cardinalities
vector<int> cardinalities(nnodes,0);
int cardinality_i, sum_of_cardinalities = 0;
for (int i = 0; i != nnodes; ++i)
{
in >> cardinality_i;
cardinalities[i] = cardinality_i;
sum_of_cardinalities += cardinality_i;
//cout << cardinalities[i] << " ";
//CHECK(in.good(), "Could not finish reading cardinalities. Are you sure this is a typeUAI energy file?");
CHECK(in.good());
}
// Read no. of factors
in >> nfactors;
//factor_size.resize(nfactors); factor_id.resize(nfactors);
vector<int> factor_size(nfactors,0); //vector<int> factor_id(nfactors,0);
vector< vector<int> > factor_memb; factor_memb.resize(nfactors);
int temp1, temp2;
// Loop and read factor members
for (int i=0; i!=nfactors; ++i)
{
in >> temp1;
factor_size[i] = temp1;
factor_memb[i].resize(temp1);
for (int j=0; j!=temp1; ++j)
{
in >> temp2;
factor_memb[i][j] = temp2;
}
//CHECK(in.good(), "Could not finish reading cardinalities. Are you sure this is a typeUAI energy file?");
CHECK(in.good());
}
if (opts.verbose > 0)
cout
<< "Finished Reading UAI-Preamble:"
<< " #Nodes = " << nnodes
<< ", #Factors = "<< nfactors
<< ", Average Cardinality = " << double(sum_of_cardinalities)/nfactors
<< "\n";
// Now read factor potentials
for (int i=0; i!=nfactors; ++i)
{
int cardprod; double potential_value; //, energy;
in >> cardprod;
vertex_data vdata;
vdata.nvars = factor_size[i];
if (vdata.nvars > 1) {
vdata.degree = vdata.nvars; // Factor degree.
}
vdata.cards.resize(factor_size[i]);
vdata.neighbors.resize(factor_size[i]);
vector<edge_data> edata(factor_size[i]);
int cardprod2 = 1;
for (int j=0; j!=factor_size[i]; ++j)
{
vdata.cards[j] = cardinalities[factor_memb[i][j]];
vdata.neighbors[j] = factor_memb[i][j]; // afm (check if this was intended!)
cardprod2 *= vdata.cards[j];
// Also create edge structs here
if (factor_size[i]>1)
{
edata[j].varid = factor_memb[i][j];
edata[j].card = cardinalities[edata[j].varid];
edata[j].multiplier_messages.setZero(edata[j].card);
edata[j].local_messages.setZero(edata[j].card);
}
}
//CHECK_EQ(cardprod, cardprod2, "Incorrectly sized factor");
CHECK_EQ(cardprod, cardprod2);
// Read factor potentials
vdata.potentials.resize(cardprod);
for (int k = 0; k != cardprod; ++k)
{
in >> potential_value;
//energy = Potential2Energy(potential_value);
vdata.potentials[k] = log10(potential_value);
}
//CHECK(in.good(), "Could not finish reading factor tables. Are you sure this is a typeUAI energy file?");
CHECK(in.good());
// allocate factors evenly to different machines.
if (i%dc.numprocs() != dc.procid())
continue;
// If all is well, add vertex and edges
graph.add_vertex(i,vdata);
if (factor_size[i] > 1) // if not a unary, add edges to unaries
for (int j=0; j!=factor_size[i]; ++j)
graph.add_edge(i,edata[j].varid,edata[j]);
if (opts.verbose > 1)
{
cout << "Machine #" << dc.procid() << ", Vertex Id = " << i
<< " with " << vdata.nvars << " variables.";
if (factor_size[i] > 1)
{
cout << ", Edges = ";
for (int j=0; j!=factor_size[i]; ++j)
cout << ", (" << i << "," << edata[j].varid << ")";
}
cout << "\n";
cout << "potential: " << vdata.potentials << "\n";
}
} // End of reading factors
dc.barrier();
} // end of loading UAI file
void test_aggregator(graphlab::distributed_control& dc,
graphlab::command_line_options& clopts,
graph_type& graph) {
std::cout << "Constructing an engine for all neighbors" << std::endl;
agg_engine_type engine(dc, graph, clopts);
engine.add_vertex_aggregator<size_t>("num_vertices_counter", agg_map, agg_finalize);
engine.add_edge_aggregator<size_t>("num_edges_counter", agg_edge_map, agg_edge_finalize);
// reset all
graph.transform_vertices(set_vertex_to_one);
graph.transform_edges(set_edge_to_one);
ASSERT_EQ(graph.map_reduce_vertices<size_t>(identity_vertex_map), graph.num_vertices());
graph.transform_vertices(vertex_plus_one);
ASSERT_EQ(graph.map_reduce_vertices<size_t>(identity_vertex_map), 2 * graph.num_vertices());
engine.transform_vertices(vertex_minus_one_context);
ASSERT_EQ(graph.map_reduce_vertices<size_t>(identity_vertex_map), graph.num_vertices());
ASSERT_EQ(engine.map_reduce_vertices<size_t>(identity_vertex_map_context), graph.num_vertices());
ASSERT_EQ(graph.map_reduce_edges<size_t>(identity_edge_map), graph.num_edges());
graph.transform_edges(edge_plus_one);
ASSERT_EQ(graph.map_reduce_edges<size_t>(identity_edge_map), 2 * graph.num_edges());
engine.transform_edges(edge_minus_one_context);
ASSERT_EQ(graph.map_reduce_edges<size_t>(identity_edge_map), graph.num_edges());
ASSERT_EQ(engine.map_reduce_edges<size_t>(identity_edge_map_context), graph.num_edges());
ASSERT_TRUE(engine.aggregate_now("num_vertices_counter"));
ASSERT_TRUE(engine.aggregate_now("num_edges_counter"));
ASSERT_TRUE(engine.aggregate_periodic("num_vertices_counter", 0.2));
ASSERT_TRUE(engine.aggregate_periodic("num_edges_counter", 0.2));
std::cout << "Scheduling all vertices to count their neighbors" << std::endl;
engine.signal_all(100);
std::cout << "Running!" << std::endl;
engine.start();
std::cout << "Finished" << std::endl;
}
rw_lock_manager(const graph_type& graph) :
graph(graph), locks(graph.num_vertices()) { }
bool graph_loader(graphlab::distributed_control& dc, graph_type& graph, string img_dir)
{
// force a "/" at the end of the path
// make sure to check that the path is non-empty. (you do not
// want to make the empty path "" the root path "/" )
string path = img_dir;
if (path.length() > 0 && path[path.length() - 1] != '/') path = path + "/";
vector<string> graph_files;
string search_prefix;
graphlab::fs_util::list_files_with_prefix(path, search_prefix, graph_files);
if (graph_files.size() == 0)
logstream(LOG_WARNING) << "No files found in " << path << std::endl;
if (opts.verbose > 2)
logstream(LOG_EMPH)
<< "Total number of images: " << graph_files.size() << "\n";
// vertex data & id
graphlab::vertex_id_type vid(-1);
graphlab::vertex_id_type other_vid;
///////////////////////////////////////////////////////
// Loop over files
for(size_t i = 0; i < graph_files.size(); ++i)
{
// Each machine loads corresponding file
if (i % dc.numprocs() == dc.procid())
{
if (opts.verbose > 0)
logstream(LOG_EMPH)
<< "Process: " << dc.procid() << "/" << dc.numprocs() << " "
<< "picked image: " << graph_files[i] << "\n";
vid = i;
vertex_data vdata;
vdata.empty = false;
vdata.img_path = graph_files[i];
vdata.features.img_idx = i;
graph.add_vertex(vid, vdata);
if (opts.verbose > 2)
logstream(LOG_EMPH)
<< "Vertex " << i << " Image: " << vdata.img_path << "\n";
}
}
// Adding edges between every pair of vertices to create a fully connected graph
// no duplicate edges are added
for(size_t i = 0; i < graph_files.size()-1; ++i)
{
vid = i;
for(size_t j = i+1; j < graph_files.size(); ++j)
{
other_vid = j;
if (opts.verbose > 0)
logstream(LOG_EMPH) << "Adding edge: (" << vid << "," << other_vid << ")\n";
edge_data edata; edata.empty = false;
graph.add_edge(vid,other_vid,edata);
}
}
return true;
}
// Second Graph loader that only a single machine calls and pre-loads cameras.
// It adds only the selected edges to the subgraph of the fully connected graph.
// (Used in stitch_full_main)
bool graph_loader(graph_type& graph, string img_dir, vector<CameraParams>& cameras,
vector<string>& img_path, vector<int> indices, vector<MatchesInfo>& pairwise_matches)
{
// force a "/" at the end of the path
// make sure to check that the path is non-empty. (you do not
// want to make the empty path "" the root path "/" )
string path = img_dir;
if (path.length() > 0 && path[path.length() - 1] != '/') path = path + "/";
// vertex data & id
graphlab::vertex_id_type vid(-1);
graphlab::vertex_id_type other_vid;
if (opts.verbose > 2)
logstream(LOG_EMPH)
<< "Total number of vertices in the second graph: " << indices.size() << "\n";
///////////////////////////////////////////////////////
// Loop over files
for(size_t i = 0; i < indices.size(); ++i)
{
vid = i;
vertex_data vdata;
vdata.empty = false;
vdata.img_path = img_path[i];
vdata.features.img_idx = i;
vdata.camera = cameras[i]; // addition to above function.
graph.add_vertex(vid, vdata);
}
// Adding edges between selected pair of vertices to create a subgraph of the fully connected graph
// no duplicate edges are added
if (opts.verbose > 2)
logstream(LOG_EMPH)
<< "Pairwise_matches size : " << pairwise_matches.size() << "\n";
for(size_t i = 0; i < pairwise_matches.size(); ++i)
{
if (opts.verbose > 2)
logstream(LOG_EMPH)
<< "Pairwise_matches : (" << pairwise_matches[i].src_img_idx << "," << pairwise_matches[i].dst_img_idx << ")\n";
if (pairwise_matches[i].src_img_idx >= 0 && pairwise_matches[i].dst_img_idx >= 0)
{
if (pairwise_matches[i].src_img_idx < pairwise_matches[i].dst_img_idx) // no duplicate edges are allowed
{
vid = pairwise_matches[i].src_img_idx;
other_vid = pairwise_matches[i].dst_img_idx;
if (opts.verbose > 0)
logstream(LOG_EMPH) << "Adding edge: (" << vid << "," << other_vid << ")\n";
edge_data edata; edata.empty = false;
graph.add_edge(vid,other_vid,edata);
}
}
}
return true;
}
