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1007 lines
34 KiB
C++
1007 lines
34 KiB
C++
//*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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#include <opencv2/core/wimage.hpp>
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#include <vector>
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#include <iostream>
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#include <cmath>
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#include <cassert>
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#include <fstream>
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#include <cstring>
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using namespace cv;
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#if defined _MSC_VER && _MSC_VER >= 1400
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#pragma warning(disable: 4244 4267)
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#endif
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/****************************************************************************************\
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The code below is implementation of Calonder Descriptor and RTree Classifier
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originally introduced by Michael Calonder.
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The code was integrated into OpenCV by Alexey Latyshev
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\****************************************************************************************/
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namespace cv {
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//----------------------------
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//randomized_tree.cpp
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inline uchar* getData(IplImage* image)
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{
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return reinterpret_cast<uchar*>(image->imageData);
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}
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inline float* RandomizedTree::getPosteriorByIndex(int index)
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{
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return const_cast<float*>(const_cast<const RandomizedTree*>(this)->getPosteriorByIndex(index));
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}
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inline const float* RandomizedTree::getPosteriorByIndex(int index) const
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{
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return posteriors_[index].p();
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}
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inline uchar* RandomizedTree::getPosteriorByIndex2(int index)
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{
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return posteriors2_[index].p();
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}
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template < typename PointT >
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cv::WImageView1_b extractPatch(cv::WImageView1_b const& image, PointT pt, int patch_sz = PATCH_SIZE)
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{
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const int offset = patch_sz / 2;
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// TODO: WImage{C}.View really should have const version
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cv::WImageView1_b &img_ref = const_cast< cv::WImageView1_b& >(image);
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return img_ref.View(pt.x - offset, pt.y - offset, patch_sz, patch_sz);
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}
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template < typename PointT >
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cv::WImageView3_b extractPatch3(cv::WImageView3_b const& image, PointT pt)
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{
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static const int offset = PATCH_SIZE / 2;
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// TODO: WImage{C}.View really should have const version
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cv::WImageView3_b &img_ref = const_cast< cv::WImageView3_b& >(image);
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return img_ref.View(pt.x - offset, pt.y - offset,
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PATCH_SIZE, PATCH_SIZE);
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}
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float *CSMatrixGenerator::cs_phi_ = NULL;
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int CSMatrixGenerator::cs_phi_m_ = 0;
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int CSMatrixGenerator::cs_phi_n_ = 0;
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RandomizedTree::RandomizedTree()
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: posteriors_(NULL), posteriors2_(NULL)
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{
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}
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RandomizedTree::~RandomizedTree()
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{
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freePosteriors(3);
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}
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void RandomizedTree::createNodes(int num_nodes, cv::RNG &rng)
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{
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nodes_.reserve(num_nodes);
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for (int i = 0; i < num_nodes; ++i) {
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nodes_.push_back( RTreeNode(rng(PATCH_SIZE),
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rng(PATCH_SIZE),
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rng(PATCH_SIZE),
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rng(PATCH_SIZE)) );
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}
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}
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int RandomizedTree::getIndex(uchar* patch_data) const
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{
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int index = 0;
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for (int d = 0; d < depth_; ++d) {
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int child_offset = nodes_[index](patch_data);
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index = 2*index + 1 + child_offset;
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}
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return index - nodes_.size();
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}
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void RandomizedTree::train(std::vector<BaseKeypoint> const& base_set,
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cv::RNG &rng, int depth, int views, size_t reduced_num_dim,
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int num_quant_bits)
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{
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//CalonderPatchGenerator make_patch(NULL, rng);
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PatchGenerator make_patch = PatchGenerator();
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train(base_set, rng, make_patch, depth, views, reduced_num_dim, num_quant_bits);
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}
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void RandomizedTree::train(std::vector<BaseKeypoint> const& base_set,
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cv::RNG &rng, PatchGenerator &make_patch,
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int depth, int views, size_t reduced_num_dim,
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int num_quant_bits)
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{
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init(base_set.size(), depth, rng);
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Mat patch;
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// Estimate posterior probabilities using random affine views
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std::vector<BaseKeypoint>::const_iterator keypt_it;
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int class_id = 0;
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for (keypt_it = base_set.begin(); keypt_it != base_set.end(); ++keypt_it, ++class_id) {
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for (int i = 0; i < views; ++i) {
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make_patch(keypt_it->image, Point2f(keypt_it->x,keypt_it->y) ,patch, Size(PATCH_SIZE,PATCH_SIZE),rng);
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IplImage _patch = patch;
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addExample(class_id, getData(&_patch));
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}
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}
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finalize(reduced_num_dim, num_quant_bits);
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}
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void RandomizedTree::allocPosteriorsAligned(int num_leaves, int num_classes)
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{
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printf("alloc posteriors aligned\n");
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freePosteriors(3);
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posteriors_ = new FloatSignature[num_leaves];
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for (int i=0; i<num_leaves; ++i)
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posteriors_[i].alloc(num_classes, 16);
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//(float**) malloc(num_leaves*sizeof(float*));
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//for (int i=0; i<num_leaves; ++i) {
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// //added
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// /* err_cnt += posix_memalign((void**)&posteriors_[i], 16, num_classes*sizeof(float));*/
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// posteriors_[i] = (float*)malloc(num_classes*sizeof(float));
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// memset(posteriors_[i], 0, num_classes*sizeof(float));
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//}
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posteriors2_ = new Signature[num_leaves];
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for (int i=0; i<num_leaves; ++i)
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posteriors2_[i].alloc(num_classes, 16);
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//for (int i=0; i<num_leaves; ++i) {
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// //added
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// /* err_cnt += posix_memalign((void**)&posteriors2_[i], 16, num_classes*sizeof(uchar)); */
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// posteriors2_[i] = (uchar*)malloc(num_classes*sizeof(uchar));
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// memset(posteriors2_[i], 0, num_classes*sizeof(uchar));
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//}
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//if (err_cnt) {
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// printf("Something went wrong in posix_memalign()! err_cnt=%i\n", err_cnt);
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// exit(0);
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//}
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classes_ = num_classes;
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}
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void RandomizedTree::freePosteriors(int which)
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{
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if (posteriors_ && (which&1)) {
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//for (int i=0; i<num_leaves_; ++i) {
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// if (posteriors_[i]) {
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// free(posteriors_[i]); //delete [] posteriors_[i];
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// posteriors_[i] = NULL;
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// }
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//}
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delete [] posteriors_;
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posteriors_ = NULL;
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}
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if (posteriors2_ && (which&2)) {
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//for (int i=0; i<num_leaves_; ++i)
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// free(posteriors2_[i]);
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delete [] posteriors2_;
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posteriors2_ = NULL;
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}
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classes_ = -1;
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}
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void RandomizedTree::init(int num_classes, int depth, cv::RNG &rng)
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{
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depth_ = depth;
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num_leaves_ = 1 << depth; // 2**d
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int num_nodes = num_leaves_ - 1; // 2**d - 1
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// Initialize probabilities and counts to 0
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allocPosteriorsAligned(num_leaves_, num_classes); // will set classes_ correctly
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for (int i = 0; i < num_leaves_; ++i)
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memset((void*)posteriors_[i].p(), 0, num_classes*sizeof(float));
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leaf_counts_.resize(num_leaves_);
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for (int i = 0; i < num_leaves_; ++i)
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memset((void*)posteriors2_[i].p(), 0, num_classes*sizeof(uchar));
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createNodes(num_nodes, rng);
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}
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void RandomizedTree::addExample(int class_id, uchar* patch_data)
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{
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int index = getIndex(patch_data);
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float* posterior = getPosteriorByIndex(index);
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++leaf_counts_[index];
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++posterior[class_id];
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}
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void RandomizedTree::finalize(size_t reduced_num_dim, int num_quant_bits)
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{
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// Normalize by number of patches to reach each leaf
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for (int index = 0; index < num_leaves_; ++index) {
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float* posterior = posteriors_[index].p();
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assert(posterior != NULL);
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int count = leaf_counts_[index];
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if (count != 0) {
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float normalizer = 1.0f / count;
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for (int c = 0; c < classes_; ++c) {
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*posterior *= normalizer;
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++posterior;
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}
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}
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}
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leaf_counts_.clear();
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// apply compressive sensing
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if ((int)reduced_num_dim != classes_)
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compressLeaves(reduced_num_dim);
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else {
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static bool notified = false;
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//if (!notified)
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// printf("\n[OK] NO compression to leaves applied, dim=%i\n", reduced_num_dim);
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notified = true;
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}
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// convert float-posteriors to char-posteriors (quantization step)
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makePosteriors2(num_quant_bits);
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}
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void RandomizedTree::compressLeaves(size_t reduced_num_dim)
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{
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static bool warned = false;
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if (!warned) {
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printf("\n[OK] compressing leaves with phi %i x %i\n", (int)reduced_num_dim, classes_);
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warned = true;
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}
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static bool warned2 = false;
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if ((int)reduced_num_dim == classes_) {
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if (!warned2)
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printf("[WARNING] RandomizedTree::compressLeaves: not compressing because reduced_dim == classes()\n");
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warned2 = true;
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return;
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}
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// DO NOT FREE RETURNED POINTER
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float *cs_phi = CSMatrixGenerator::getCSMatrix(reduced_num_dim, classes_, CSMatrixGenerator::PDT_BERNOULLI);
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float *cs_posteriors = new float[num_leaves_ * reduced_num_dim]; // temp, num_leaves_ x reduced_num_dim
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for (int i=0; i<num_leaves_; ++i)
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{
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//added (inside cycle)
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//float *post = getPosteriorByIndex(i);
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// float *prod = &cs_posteriors[i*reduced_num_dim];
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// cblas_sgemv(CblasRowMajor, CblasNoTrans, reduced_num_dim, classes_, 1.f, cs_phi,
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// classes_, post, 1, 0.f, prod, 1);
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float *post = getPosteriorByIndex(i);
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//Matrix multiplication
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for (int idx = 0; idx < (int)reduced_num_dim; idx++)
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{
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cs_posteriors[i*reduced_num_dim+idx] = 0.0f;
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for (int col = 0; col < classes_; col++)
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{
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cs_posteriors[i*reduced_num_dim+idx] += cs_phi[idx*reduced_num_dim + col] * post[col];
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}
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}
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}
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// copy new posteriors
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freePosteriors(3);
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allocPosteriorsAligned(num_leaves_, reduced_num_dim);
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for (int i=0; i<num_leaves_; ++i)
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memcpy(posteriors_[i].p(), &cs_posteriors[i*reduced_num_dim], reduced_num_dim*sizeof(float));
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classes_ = reduced_num_dim;
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delete [] cs_posteriors;
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}
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void RandomizedTree::makePosteriors2(int num_quant_bits)
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{
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int N = (1<<num_quant_bits) - 1;
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float perc[2];
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estimateQuantPercForPosteriors(perc);
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assert(posteriors_ != NULL);
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for (int i=0; i<num_leaves_; ++i)
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quantizeVector(posteriors_[i].p(), classes_, N, perc, posteriors2_[i].p());
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// printf("makePosteriors2 quantization bounds: %.3e, %.3e (num_leaves=%i, N=%i)\n",
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// perc[0], perc[1], num_leaves_, N);
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}
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float* RandomizedTree::getPosterior(uchar* patch_data)
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{
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return const_cast<float*>(const_cast<const RandomizedTree*>(this)->getPosterior(patch_data));
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}
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const float* RandomizedTree::getPosterior(uchar* patch_data) const
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{
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return getPosteriorByIndex( getIndex(patch_data) );
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}
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uchar* RandomizedTree::getPosterior2(uchar* patch_data)
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{
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return getPosteriorByIndex2( getIndex(patch_data) );
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}
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void RandomizedTree::quantizeVector(float *vec, int dim, int N, float bnds[2], int clamp_mode)
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{
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float map_bnd[2] = {0.f,(float)N}; // bounds of quantized target interval we're mapping to
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for (int k=0; k<dim; ++k, ++vec) {
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*vec = float(int((*vec - bnds[0])/(bnds[1] - bnds[0])*(map_bnd[1] - map_bnd[0]) + map_bnd[0]));
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// 0: clamp both, lower and upper values
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if (clamp_mode == 0) *vec = (*vec<map_bnd[0]) ? map_bnd[0] : ((*vec>map_bnd[1]) ? map_bnd[1] : *vec);
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// 1: clamp lower values only
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else if (clamp_mode == 1) *vec = (*vec<map_bnd[0]) ? map_bnd[0] : *vec;
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// 2: clamp upper values only
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else if (clamp_mode == 2) *vec = (*vec>map_bnd[1]) ? map_bnd[1] : *vec;
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// 4: no clamping
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else if (clamp_mode == 4) ; // yep, nothing
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else {
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printf("clamp_mode == %i is not valid (%s:%i).\n", clamp_mode, __FILE__, __LINE__);
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exit(1);
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}
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}
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}
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void RandomizedTree::quantizeVector(float *vec, int dim, int N, float bnds[2], uchar *dst)
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{
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int map_bnd[2] = {0, N}; // bounds of quantized target interval we're mapping to
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int tmp;
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for (int k=0; k<dim; ++k) {
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tmp = int((*vec - bnds[0])/(bnds[1] - bnds[0])*(map_bnd[1] - map_bnd[0]) + map_bnd[0]);
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*dst = (uchar)((tmp<0) ? 0 : ((tmp>N) ? N : tmp));
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++vec;
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++dst;
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}
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}
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void RandomizedTree::read(const char* file_name, int num_quant_bits)
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{
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std::ifstream file(file_name, std::ifstream::binary);
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read(file, num_quant_bits);
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file.close();
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}
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void RandomizedTree::read(std::istream &is, int num_quant_bits)
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{
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is.read((char*)(&classes_), sizeof(classes_));
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is.read((char*)(&depth_), sizeof(depth_));
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num_leaves_ = 1 << depth_;
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int num_nodes = num_leaves_ - 1;
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nodes_.resize(num_nodes);
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is.read((char*)(&nodes_[0]), num_nodes * sizeof(nodes_[0]));
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//posteriors_.resize(classes_ * num_leaves_);
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//freePosteriors(3);
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//printf("[DEBUG] reading: %i leaves, %i classes\n", num_leaves_, classes_);
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allocPosteriorsAligned(num_leaves_, classes_);
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for (int i=0; i<num_leaves_; i++)
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is.read((char*)posteriors_[i].p(), classes_ * sizeof(*posteriors_[0].p()));
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// make char-posteriors from float-posteriors
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makePosteriors2(num_quant_bits);
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}
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void RandomizedTree::write(const char* file_name) const
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{
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std::ofstream file(file_name, std::ofstream::binary);
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write(file);
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file.close();
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}
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void RandomizedTree::write(std::ostream &os) const
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{
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if (!posteriors_) {
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printf("WARNING: Cannot write float posteriors cause posteriors_ == NULL\n");
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return;
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}
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os.write((char*)(&classes_), sizeof(classes_));
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os.write((char*)(&depth_), sizeof(depth_));
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os.write((char*)(&nodes_[0]), nodes_.size() * sizeof(nodes_[0]));
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for (int i=0; i<num_leaves_; i++) {
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os.write((char*)posteriors_[i].p(), classes_ * sizeof(*posteriors_[0].p()));
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}
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}
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void RandomizedTree::savePosteriors(std::string url, bool append)
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{
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std::ofstream file(url.c_str(), (append?std::ios::app:std::ios::out));
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for (int i=0; i<num_leaves_; i++) {
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float *post = posteriors_[i].p();
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char buf[20];
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for (int i=0; i<classes_; i++) {
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sprintf(buf, "%.10e", *post++);
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file << buf << ((i<classes_-1) ? " " : "");
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}
|
|
file << std::endl;
|
|
}
|
|
file.close();
|
|
}
|
|
|
|
void RandomizedTree::savePosteriors2(std::string url, bool append)
|
|
{
|
|
std::ofstream file(url.c_str(), (append?std::ios::app:std::ios::out));
|
|
for (int i=0; i<num_leaves_; i++) {
|
|
uchar *post = posteriors2_[i].p();
|
|
for (int i=0; i<classes_; i++)
|
|
file << int(*post++) << (i<classes_-1?" ":"");
|
|
file << std::endl;
|
|
}
|
|
file.close();
|
|
}
|
|
|
|
// returns the p% percentile of data (length n vector)
|
|
static float percentile(float *data, int n, float p)
|
|
{
|
|
assert(n>0);
|
|
assert(p>=0 && p<=1);
|
|
std::vector<float> vec(data, data+n);
|
|
sort(vec.begin(), vec.end());
|
|
int ix = (int)(p*(n-1));
|
|
return vec[ix];
|
|
}
|
|
|
|
void RandomizedTree::estimateQuantPercForPosteriors(float perc[2])
|
|
{
|
|
// _estimate_ percentiles for this tree
|
|
// TODO: do this more accurately
|
|
assert(posteriors_ != NULL);
|
|
perc[0] = perc[1] = .0f;
|
|
for (int i=0; i<num_leaves_; i++) {
|
|
perc[0] += percentile(posteriors_[i].p(), classes_, LOWER_QUANT_PERC);
|
|
perc[1] += percentile(posteriors_[i].p(), classes_, UPPER_QUANT_PERC);
|
|
}
|
|
perc[0] /= num_leaves_;
|
|
perc[1] /= num_leaves_;
|
|
}
|
|
|
|
float* CSMatrixGenerator::getCSMatrix(int m, int n, PHI_DISTR_TYPE dt)
|
|
{
|
|
assert(m <= n);
|
|
|
|
if (cs_phi_m_!=m || cs_phi_n_!=n || cs_phi_==NULL) {
|
|
if (cs_phi_) delete [] cs_phi_;
|
|
cs_phi_ = new float[m*n];
|
|
}
|
|
|
|
#if 0 // debug - load the random matrix from a file (for reproducability of results)
|
|
//assert(m == 176);
|
|
//assert(n == 500);
|
|
//const char *phi = "/u/calonder/temp/dim_red/kpca_phi.txt";
|
|
const char *phi = "/u/calonder/temp/dim_red/debug_phi.txt";
|
|
std::ifstream ifs(phi);
|
|
for (size_t i=0; i<m*n; ++i) {
|
|
if (!ifs.good()) {
|
|
printf("[ERROR] RandomizedTree::makeRandomMeasMatrix: problem reading '%s'\n", phi);
|
|
exit(0);
|
|
}
|
|
ifs >> cs_phi[i];
|
|
}
|
|
ifs.close();
|
|
|
|
static bool warned=false;
|
|
if (!warned) {
|
|
printf("[NOTE] RT: reading %ix%i PHI matrix from '%s'...\n", m, n, phi);
|
|
warned=true;
|
|
}
|
|
|
|
return;
|
|
#endif
|
|
|
|
float *cs_phi = cs_phi_;
|
|
|
|
if (m == n) {
|
|
// special case - set to 0 for safety
|
|
memset(cs_phi, 0, m*n*sizeof(float));
|
|
printf("[WARNING] %s:%i: square CS matrix (-> no reduction)\n", __FILE__, __LINE__);
|
|
}
|
|
else {
|
|
cv::RNG rng(23);
|
|
|
|
// par is distr param, cf 'Favorable JL Distributions' (Baraniuk et al, 2006)
|
|
if (dt == PDT_GAUSS) {
|
|
float par = (float)(1./m);
|
|
//modified
|
|
cv::RNG _rng;
|
|
for (int i=0; i<m*n; ++i)
|
|
{
|
|
*cs_phi++ = (float)_rng.gaussian((double)par);//sample_normal<float>(0., par);
|
|
}
|
|
}
|
|
else if (dt == PDT_BERNOULLI) {
|
|
float par = (float)(1./sqrt((float)m));
|
|
for (int i=0; i<m*n; ++i)
|
|
*cs_phi++ = (rng(2)==0 ? par : -par);
|
|
}
|
|
else if (dt == PDT_DBFRIENDLY) {
|
|
float par = (float)sqrt(3./m);
|
|
for (int i=0; i<m*n; ++i) {
|
|
//added
|
|
int _i = rng(6);
|
|
*cs_phi++ = (_i==0 ? par : (_i==1 ? -par : 0.f));
|
|
}
|
|
}
|
|
else
|
|
throw("PHI_DISTR_TYPE not implemented.");
|
|
}
|
|
|
|
return cs_phi_;
|
|
}
|
|
|
|
CSMatrixGenerator::~CSMatrixGenerator()
|
|
{
|
|
if (cs_phi_) delete [] cs_phi_;
|
|
cs_phi_ = NULL;
|
|
}
|
|
|
|
|
|
//} // namespace features
|
|
|
|
//----------------------------
|
|
//rtree_classifier.cpp
|
|
//namespace features {
|
|
|
|
// Returns 16-byte aligned signatures that can be passed to getSignature().
|
|
// Release by calling free() - NOT delete!
|
|
//
|
|
// note: 1) for num_sig>1 all signatures will still be 16-byte aligned, as
|
|
// long as sig_len%16 == 0 holds.
|
|
// 2) casting necessary, otherwise it breaks gcc's strict aliasing rules
|
|
inline void RTreeClassifier::safeSignatureAlloc(uchar **sig, int num_sig, int sig_len)
|
|
{
|
|
assert(sig_len == 176);
|
|
void *p_sig;
|
|
//added
|
|
// posix_memalign(&p_sig, 16, num_sig*sig_len*sizeof(uchar));
|
|
p_sig = malloc(num_sig*sig_len*sizeof(uchar));
|
|
*sig = reinterpret_cast<uchar*>(p_sig);
|
|
}
|
|
|
|
inline uchar* RTreeClassifier::safeSignatureAlloc(int num_sig, int sig_len)
|
|
{
|
|
uchar *sig;
|
|
safeSignatureAlloc(&sig, num_sig, sig_len);
|
|
return sig;
|
|
}
|
|
|
|
inline void add(int size, const float* src1, const float* src2, float* dst)
|
|
{
|
|
while(--size >= 0) {
|
|
*dst = *src1 + *src2;
|
|
++dst; ++src1; ++src2;
|
|
}
|
|
}
|
|
|
|
inline void add(int size, const ushort* src1, const uchar* src2, ushort* dst)
|
|
{
|
|
while(--size >= 0) {
|
|
*dst = *src1 + *src2;
|
|
++dst; ++src1; ++src2;
|
|
}
|
|
}
|
|
|
|
RTreeClassifier::RTreeClassifier()
|
|
: classes_(0)
|
|
{
|
|
posteriors_ = NULL;
|
|
}
|
|
|
|
void RTreeClassifier::train(std::vector<BaseKeypoint> const& base_set,
|
|
cv::RNG &rng, int num_trees, int depth,
|
|
int views, size_t reduced_num_dim,
|
|
int num_quant_bits, bool print_status)
|
|
{
|
|
PatchGenerator make_patch = PatchGenerator();
|
|
train(base_set, rng, make_patch, num_trees, depth, views, reduced_num_dim, num_quant_bits, print_status);
|
|
}
|
|
|
|
// Single-threaded version of train(), with progress output
|
|
void RTreeClassifier::train(std::vector<BaseKeypoint> const& base_set,
|
|
cv::RNG &rng, PatchGenerator &make_patch, int num_trees,
|
|
int depth, int views, size_t reduced_num_dim,
|
|
int num_quant_bits, bool print_status)
|
|
{
|
|
if (reduced_num_dim > base_set.size()) {
|
|
if (print_status)
|
|
{
|
|
printf("INVALID PARAMS in RTreeClassifier::train: reduced_num_dim{%i} > base_set.size(){%i}\n",
|
|
(int)reduced_num_dim, (int)base_set.size());
|
|
}
|
|
return;
|
|
}
|
|
|
|
num_quant_bits_ = num_quant_bits;
|
|
classes_ = reduced_num_dim; // base_set.size();
|
|
original_num_classes_ = base_set.size();
|
|
trees_.resize(num_trees);
|
|
if (print_status)
|
|
{
|
|
printf("[OK] Training trees: base size=%i, reduced size=%i\n", (int)base_set.size(), (int)reduced_num_dim);
|
|
}
|
|
|
|
int count = 1;
|
|
if (print_status)
|
|
{
|
|
printf("[OK] Trained 0 / %i trees", num_trees); fflush(stdout);
|
|
}
|
|
//added
|
|
//BOOST_FOREACH( RandomizedTree &tree, trees_ ) {
|
|
//tree.train(base_set, rng, make_patch, depth, views, reduced_num_dim, num_quant_bits_);
|
|
//printf("\r[OK] Trained %i / %i trees", count++, num_trees);
|
|
//fflush(stdout);
|
|
for (int i=0; i<(int)trees_.size(); i++)
|
|
{
|
|
trees_[i].train(base_set, rng, make_patch, depth, views, reduced_num_dim, num_quant_bits_);
|
|
if (print_status)
|
|
{
|
|
printf("\r[OK] Trained %i / %i trees", count++, num_trees);
|
|
fflush(stdout);
|
|
}
|
|
}
|
|
|
|
if (print_status)
|
|
{
|
|
printf("\n");
|
|
countZeroElements();
|
|
printf("\n\n");
|
|
}
|
|
}
|
|
|
|
void RTreeClassifier::getSignature(IplImage* patch, float *sig)
|
|
{
|
|
// Need pointer to 32x32 patch data
|
|
uchar buffer[PATCH_SIZE * PATCH_SIZE];
|
|
uchar* patch_data;
|
|
if (patch->widthStep != PATCH_SIZE) {
|
|
//printf("[INFO] patch is padded, data will be copied (%i/%i).\n",
|
|
// patch->widthStep, PATCH_SIZE);
|
|
uchar* data = getData(patch);
|
|
patch_data = buffer;
|
|
for (int i = 0; i < PATCH_SIZE; ++i) {
|
|
memcpy((void*)patch_data, (void*)data, PATCH_SIZE);
|
|
data += patch->widthStep;
|
|
patch_data += PATCH_SIZE;
|
|
}
|
|
patch_data = buffer;
|
|
}
|
|
else {
|
|
patch_data = getData(patch);
|
|
}
|
|
|
|
memset((void*)sig, 0, classes_ * sizeof(float));
|
|
std::vector<RandomizedTree>::iterator tree_it;
|
|
|
|
// get posteriors
|
|
float **posteriors = new float*[trees_.size()]; // TODO: move alloc outside this func
|
|
float **pp = posteriors;
|
|
for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it, pp++) {
|
|
*pp = tree_it->getPosterior(patch_data);
|
|
assert(*pp != NULL);
|
|
}
|
|
|
|
// sum them up
|
|
pp = posteriors;
|
|
for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it, pp++)
|
|
add(classes_, sig, *pp, sig);
|
|
|
|
delete [] posteriors;
|
|
posteriors = NULL;
|
|
|
|
// full quantization (experimental)
|
|
#if 0
|
|
int n_max = 1<<8 - 1;
|
|
int sum_max = (1<<4 - 1)*trees_.size();
|
|
int shift = 0;
|
|
while ((sum_max>>shift) > n_max) shift++;
|
|
|
|
for (int i = 0; i < classes_; ++i) {
|
|
sig[i] = int(sig[i] + .5) >> shift;
|
|
if (sig[i]>n_max) sig[i] = n_max;
|
|
}
|
|
|
|
static bool warned = false;
|
|
if (!warned) {
|
|
printf("[WARNING] Using full quantization (RTreeClassifier::getSignature)! shift=%i\n", shift);
|
|
warned = true;
|
|
}
|
|
#else
|
|
// TODO: get rid of this multiply (-> number of trees is known at train
|
|
// time, exploit it in RandomizedTree::finalize())
|
|
float normalizer = 1.0f / trees_.size();
|
|
for (int i = 0; i < classes_; ++i)
|
|
sig[i] *= normalizer;
|
|
#endif
|
|
}
|
|
|
|
|
|
// sum up 50 byte vectors of length 176
|
|
// assume 5 bits max for input vector values
|
|
// final shift is 3 bits right
|
|
//void sum_50c_176c(uchar **pp, uchar *sig)
|
|
//{
|
|
|
|
//}
|
|
|
|
void RTreeClassifier::getSignature(IplImage* patch, uchar *sig)
|
|
{
|
|
// Need pointer to 32x32 patch data
|
|
uchar buffer[PATCH_SIZE * PATCH_SIZE];
|
|
uchar* patch_data;
|
|
if (patch->widthStep != PATCH_SIZE) {
|
|
//printf("[INFO] patch is padded, data will be copied (%i/%i).\n",
|
|
// patch->widthStep, PATCH_SIZE);
|
|
uchar* data = getData(patch);
|
|
patch_data = buffer;
|
|
for (int i = 0; i < PATCH_SIZE; ++i) {
|
|
memcpy((void*)patch_data, (void*)data, PATCH_SIZE);
|
|
data += patch->widthStep;
|
|
patch_data += PATCH_SIZE;
|
|
}
|
|
patch_data = buffer;
|
|
} else {
|
|
patch_data = getData(patch);
|
|
}
|
|
|
|
std::vector<RandomizedTree>::iterator tree_it;
|
|
|
|
// get posteriors
|
|
if (posteriors_ == NULL)
|
|
{
|
|
posteriors_ = new uchar*[trees_.size()];
|
|
//aadded
|
|
// posix_memalign((void **)&ptemp_, 16, classes_*sizeof(ushort));
|
|
ptemp_ = (ushort*)malloc(classes_*sizeof(ushort));
|
|
}
|
|
uchar **pp = posteriors_;
|
|
for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it, pp++)
|
|
*pp = tree_it->getPosterior2(patch_data);
|
|
pp = posteriors_;
|
|
|
|
#if 0 // SSE2 optimized code
|
|
sum_50t_176c(pp, sig, ptemp_); // sum them up
|
|
#else
|
|
static bool warned = false;
|
|
|
|
memset((void*)sig, 0, classes_ * sizeof(sig[0]));
|
|
ushort *sig16 = new ushort[classes_]; // TODO: make member, no alloc here
|
|
memset((void*)sig16, 0, classes_ * sizeof(sig16[0]));
|
|
for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it, pp++)
|
|
add(classes_, sig16, *pp, sig16);
|
|
|
|
// squeeze signatures into an uchar
|
|
const bool full_shifting = true;
|
|
int shift;
|
|
if (full_shifting) {
|
|
float num_add_bits_f = log((float)trees_.size())/log(2.f); // # additional bits required due to summation
|
|
int num_add_bits = int(num_add_bits_f);
|
|
if (num_add_bits_f != float(num_add_bits)) ++num_add_bits;
|
|
shift = num_quant_bits_ + num_add_bits - 8*sizeof(uchar);
|
|
//shift = num_quant_bits_ + num_add_bits - 2;
|
|
//shift = 6;
|
|
if (shift>0)
|
|
for (int i = 0; i < classes_; ++i)
|
|
sig[i] = (sig16[i] >> shift); // &3 cut off all but lowest 2 bits, 3(dec) = 11(bin)
|
|
|
|
if (!warned)
|
|
printf("[OK] RTC: quantizing by FULL RIGHT SHIFT, shift = %i\n", shift);
|
|
}
|
|
else {
|
|
printf("[ERROR] RTC: not implemented!\n");
|
|
exit(0);
|
|
}
|
|
|
|
if (!warned)
|
|
printf("[WARNING] RTC: unoptimized signature computation\n");
|
|
warned = true;
|
|
#endif
|
|
}
|
|
|
|
|
|
void RTreeClassifier::getSparseSignature(IplImage *patch, float *sig, float thresh)
|
|
{
|
|
getFloatSignature(patch, sig);
|
|
for (int i=0; i<classes_; ++i, sig++)
|
|
if (*sig < thresh) *sig = 0.f;
|
|
}
|
|
|
|
int RTreeClassifier::countNonZeroElements(float *vec, int n, double tol)
|
|
{
|
|
int res = 0;
|
|
while (n-- > 0)
|
|
res += (fabs(*vec++) > tol);
|
|
return res;
|
|
}
|
|
|
|
void RTreeClassifier::read(const char* file_name)
|
|
{
|
|
std::ifstream file(file_name, std::ifstream::binary);
|
|
read(file);
|
|
file.close();
|
|
}
|
|
|
|
void RTreeClassifier::read(std::istream &is)
|
|
{
|
|
int num_trees = 0;
|
|
is.read((char*)(&num_trees), sizeof(num_trees));
|
|
is.read((char*)(&classes_), sizeof(classes_));
|
|
is.read((char*)(&original_num_classes_), sizeof(original_num_classes_));
|
|
is.read((char*)(&num_quant_bits_), sizeof(num_quant_bits_));
|
|
|
|
if (num_quant_bits_<1 || num_quant_bits_>8) {
|
|
printf("[WARNING] RTC: suspicious value num_quant_bits_=%i found; setting to %i.\n",
|
|
num_quant_bits_, (int)DEFAULT_NUM_QUANT_BITS);
|
|
num_quant_bits_ = DEFAULT_NUM_QUANT_BITS;
|
|
}
|
|
|
|
trees_.resize(num_trees);
|
|
std::vector<RandomizedTree>::iterator tree_it;
|
|
|
|
for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it) {
|
|
tree_it->read(is, num_quant_bits_);
|
|
}
|
|
|
|
printf("[OK] Loaded RTC, quantization=%i bits\n", num_quant_bits_);
|
|
|
|
countZeroElements();
|
|
}
|
|
|
|
void RTreeClassifier::write(const char* file_name) const
|
|
{
|
|
std::ofstream file(file_name, std::ofstream::binary);
|
|
write(file);
|
|
file.close();
|
|
}
|
|
|
|
void RTreeClassifier::write(std::ostream &os) const
|
|
{
|
|
int num_trees = trees_.size();
|
|
os.write((char*)(&num_trees), sizeof(num_trees));
|
|
os.write((char*)(&classes_), sizeof(classes_));
|
|
os.write((char*)(&original_num_classes_), sizeof(original_num_classes_));
|
|
os.write((char*)(&num_quant_bits_), sizeof(num_quant_bits_));
|
|
printf("RTreeClassifier::write: num_quant_bits_=%i\n", num_quant_bits_);
|
|
|
|
std::vector<RandomizedTree>::const_iterator tree_it;
|
|
for (tree_it = trees_.begin(); tree_it != trees_.end(); ++tree_it)
|
|
tree_it->write(os);
|
|
}
|
|
|
|
void RTreeClassifier::saveAllFloatPosteriors(std::string url)
|
|
{
|
|
printf("[DEBUG] writing all float posteriors to %s...\n", url.c_str());
|
|
for (int i=0; i<(int)trees_.size(); ++i)
|
|
trees_[i].savePosteriors(url, (i==0 ? false : true));
|
|
printf("[DEBUG] done\n");
|
|
}
|
|
|
|
void RTreeClassifier::saveAllBytePosteriors(std::string url)
|
|
{
|
|
printf("[DEBUG] writing all byte posteriors to %s...\n", url.c_str());
|
|
for (int i=0; i<(int)trees_.size(); ++i)
|
|
trees_[i].savePosteriors2(url, (i==0 ? false : true));
|
|
printf("[DEBUG] done\n");
|
|
}
|
|
|
|
|
|
void RTreeClassifier::setFloatPosteriorsFromTextfile_176(std::string url)
|
|
{
|
|
std::ifstream ifs(url.c_str());
|
|
|
|
for (int i=0; i<(int)trees_.size(); ++i) {
|
|
int num_classes = trees_[i].classes_;
|
|
assert(num_classes == 176); // TODO: remove this limitation (arose due to SSE2 optimizations)
|
|
for (int k=0; k<trees_[i].num_leaves_; ++k) {
|
|
float *post = trees_[i].getPosteriorByIndex(k);
|
|
for (int j=0; j<num_classes; ++j, ++post)
|
|
ifs >> *post;
|
|
assert(ifs.good());
|
|
}
|
|
}
|
|
classes_ = 176;
|
|
|
|
//setQuantization(num_quant_bits_);
|
|
|
|
ifs.close();
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printf("[EXPERIMENTAL] read entire tree from '%s'\n", url.c_str());
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}
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float RTreeClassifier::countZeroElements()
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{
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int flt_zeros = 0;
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int ui8_zeros = 0;
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int num_elem = trees_[0].classes();
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for (int i=0; i<(int)trees_.size(); ++i)
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for (int k=0; k<(int)trees_[i].num_leaves_; ++k) {
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float *p = trees_[i].getPosteriorByIndex(k);
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uchar *p2 = trees_[i].getPosteriorByIndex2(k);
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assert(p); assert(p2);
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for (int j=0; j<num_elem; ++j, ++p, ++p2) {
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if (*p == 0.f) flt_zeros++;
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if (*p2 == 0) ui8_zeros++;
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}
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}
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num_elem = trees_.size()*trees_[0].num_leaves_*num_elem;
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float flt_perc = 100.*flt_zeros/num_elem;
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float ui8_perc = 100.*ui8_zeros/num_elem;
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printf("[OK] RTC: overall %i/%i (%.3f%%) zeros in float leaves\n", flt_zeros, num_elem, flt_perc);
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printf(" overall %i/%i (%.3f%%) zeros in uint8 leaves\n", ui8_zeros, num_elem, ui8_perc);
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|
|
|
return flt_perc;
|
|
}
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|
|
|
void RTreeClassifier::setQuantization(int num_quant_bits)
|
|
{
|
|
for (int i=0; i<(int)trees_.size(); ++i)
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|
trees_[i].applyQuantization(num_quant_bits);
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|
|
|
printf("[OK] signature quantization is now %i bits (before: %i)\n", num_quant_bits, num_quant_bits_);
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|
num_quant_bits_ = num_quant_bits;
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|
}
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//} // namespace features
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}
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