Performance improvements and cleanup
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tamasmeszaros
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0bfbe18a3c
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2f65bcc83f
@ -479,13 +479,18 @@ class _NofitPolyPlacer: public PlacerBoilerplate<_NofitPolyPlacer<RawShape, TBin
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using MaxNfpLevel = nfp::MaxNfpLevel<RawShape>;
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// Norming factor for the optimization function
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const double norm_;
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public:
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using Pile = nfp::Shapes<RawShape>;
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private:
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// Norming factor for the optimization function
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const double norm_;
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Pile merged_pile_;
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public:
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inline explicit _NofitPolyPlacer(const BinType& bin):
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Base(bin),
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norm_(std::sqrt(sl::area(bin)))
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@ -576,6 +581,20 @@ private:
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using Shapes = TMultiShape<RawShape>;
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template<nfp::NfpLevel lvl>
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static Shapes calcnfp(const Shapes &pile, const RawShape &orb)
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{
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Shapes ret; ret.reserve(2 * pile.size());
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for (auto &stat : pile) {
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Shapes subnfp = nfp::noFitPolygon<lvl>(stat, orb);
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for (auto &nfp : subnfp)
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ret.emplace_back(subnfp);
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}
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return nfp::merge(ret);
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}
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Shapes calcnfp(const Item &trsh, Lvl<nfp::NfpLevel::CONVEX_ONLY>)
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{
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using namespace nfp;
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@ -616,135 +635,9 @@ private:
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template<class Level>
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Shapes calcnfp(const Item &trsh, Level)
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{ // Function for arbitrary level of nfp implementation
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using namespace nfp;
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Shapes nfps;
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auto& orb = trsh.transformedShape();
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bool orbconvex = trsh.isContourConvex();
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for(Item& sh : items_) {
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nfp::NfpResult<RawShape> subnfp;
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auto& stat = sh.transformedShape();
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if(sh.isContourConvex() && orbconvex)
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subnfp = nfp::noFitPolygon<NfpLevel::CONVEX_ONLY>(stat, orb);
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else if(orbconvex)
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subnfp = nfp::noFitPolygon<NfpLevel::ONE_CONVEX>(stat, orb);
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else
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subnfp = nfp::noFitPolygon<Level::value>(stat, orb);
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correctNfpPosition(subnfp, sh, trsh);
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nfps = nfp::merge(nfps, subnfp.first);
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}
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return nfps;
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}
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// Very much experimental
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void repack(Item& item, PackResult& result) {
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if((sl::area(bin_) - this->filledArea()) >= item.area()) {
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auto prev_func = config_.object_function;
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unsigned iter = 0;
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ItemGroup backup_rf = items_;
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std::vector<Item> backup_cpy;
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for(Item& itm : items_) backup_cpy.emplace_back(itm);
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auto ofn = [this, &item, &result, &iter, &backup_cpy, &backup_rf]
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(double ratio)
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{
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auto& bin = bin_;
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iter++;
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config_.object_function = [bin, ratio](
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nfp::Shapes<RawShape>& pile,
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const Item& item,
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const ItemGroup& /*remaining*/)
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{
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pile.emplace_back(item.transformedShape());
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auto ch = sl::convexHull(pile);
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auto pbb = sl::boundingBox(pile);
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pile.pop_back();
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double parea = 0.5*(sl::area(ch) + sl::area(pbb));
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double pile_area = std::accumulate(
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pile.begin(), pile.end(), item.area(),
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[](double sum, const RawShape& sh){
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return sum + sl::area(sh);
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});
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// The pack ratio -- how much is the convex hull occupied
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double pack_rate = (pile_area)/parea;
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// ratio of waste
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double waste = 1.0 - pack_rate;
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// Score is the square root of waste. This will extend the
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// range of good (lower) values and shrink the range of bad
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// (larger) values.
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auto wscore = std::sqrt(waste);
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auto ibb = item.boundingBox();
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auto bbb = sl::boundingBox(bin);
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auto c = ibb.center();
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double norm = 0.5*pl::distance(bbb.minCorner(),
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bbb.maxCorner());
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double dscore = pl::distance(c, pbb.center()) / norm;
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return ratio*wscore + (1.0 - ratio) * dscore;
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};
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auto bb = sl::boundingBox(bin);
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double norm = bb.width() + bb.height();
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auto items = items_;
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clearItems();
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auto it = items.begin();
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while(auto pr = _trypack(*it++)) {
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this->accept(pr); if(it == items.end()) break;
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}
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auto count_diff = items.size() - items_.size();
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double score = count_diff;
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if(count_diff == 0) {
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result = _trypack(item);
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if(result) {
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std::cout << "Success" << std::endl;
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score = 0.0;
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} else {
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score += result.overfit() / norm;
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}
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} else {
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result = PackResult();
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items_ = backup_rf;
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for(unsigned i = 0; i < items_.size(); i++) {
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items_[i].get() = backup_cpy[i];
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}
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}
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std::cout << iter << " repack result: " << score << " "
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<< ratio << " " << count_diff << std::endl;
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return score;
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};
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opt::StopCriteria stopcr;
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stopcr.max_iterations = 30;
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stopcr.stop_score = 1e-20;
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opt::TOptimizer<opt::Method::L_SUBPLEX> solver(stopcr);
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solver.optimize_min(ofn, opt::initvals(0.5),
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opt::bound(0.0, 1.0));
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// optimize
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config_.object_function = prev_func;
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}
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// TODO: implement
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return {};
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}
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struct Optimum {
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@ -798,6 +691,50 @@ private:
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Radians final_rot = initial_rot;
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Shapes nfps;
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auto& bin = bin_;
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double norm = norm_;
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auto pbb = sl::boundingBox(merged_pile_);
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auto binbb = sl::boundingBox(bin);
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// This is the kernel part of the object function that is
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// customizable by the library client
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std::function<double(const Item&)> _objfunc;
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if(config_.object_function) _objfunc = config_.object_function;
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else {
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// Inside check has to be strict if no alignment was enabled
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std::function<double(const Box&)> ins_check;
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if(config_.alignment == Config::Alignment::DONT_ALIGN)
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ins_check = [&binbb, norm](const Box& fullbb) {
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double ret = 0;
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if(!sl::isInside(fullbb, binbb))
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ret += norm;
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return ret;
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};
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else
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ins_check = [&bin](const Box& fullbb) {
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double miss = overfit(fullbb, bin);
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miss = miss > 0? miss : 0;
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return std::pow(miss, 2);
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};
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_objfunc = [norm, binbb, pbb, ins_check](const Item& item)
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{
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auto ibb = item.boundingBox();
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auto fullbb = sl::boundingBox(pbb, ibb);
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double score = pl::distance(ibb.center(),
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binbb.center());
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score /= norm;
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score += ins_check(fullbb);
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return score;
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};
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}
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Pile merged_pile = merged_pile_;
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for(auto rot : config_.rotations) {
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item.translation(initial_tr);
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@ -822,57 +759,6 @@ private:
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ecache.back().accuracy(config_.accuracy);
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}
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Shapes pile;
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pile.reserve(items_.size()+1);
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// double pile_area = 0;
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for(Item& mitem : items_) {
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pile.emplace_back(mitem.transformedShape());
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// pile_area += mitem.area();
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}
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auto merged_pile = nfp::merge(pile);
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auto& bin = bin_;
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double norm = norm_;
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auto pbb = sl::boundingBox(merged_pile);
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auto binbb = sl::boundingBox(bin);
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// This is the kernel part of the object function that is
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// customizable by the library client
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std::function<double(const Item&)> _objfunc;
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if(config_.object_function) _objfunc = config_.object_function;
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else {
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// Inside check has to be strict if no alignment was enabled
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std::function<double(const Box&)> ins_check;
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if(config_.alignment == Config::Alignment::DONT_ALIGN)
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ins_check = [&binbb, norm](const Box& fullbb) {
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double ret = 0;
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if(!sl::isInside(fullbb, binbb))
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ret += norm;
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return ret;
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};
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else
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ins_check = [&bin](const Box& fullbb) {
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double miss = overfit(fullbb, bin);
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miss = miss > 0? miss : 0;
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return std::pow(miss, 2);
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};
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_objfunc = [norm, binbb, pbb, ins_check](const Item& item)
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{
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auto ibb = item.boundingBox();
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auto fullbb = sl::boundingBox(pbb, ibb);
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double score = pl::distance(ibb.center(),
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binbb.center());
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score /= norm;
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score += ins_check(fullbb);
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return score;
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};
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}
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// Our object function for placement
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auto rawobjfunc = [_objfunc, iv, startpos]
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(Vertex v, Item& itm)
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@ -1041,6 +927,7 @@ private:
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item.translation(final_tr);
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item.rotation(final_rot);
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merged_pile_ = nfp::merge(merged_pile, item.transformedShape());
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}
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if(can_pack) {
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