diff --git a/.DS_Store b/.DS_Store index a1a5a0d..dbfc581 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/README.md b/README.md index 7665e4f..5b72e52 100644 --- a/README.md +++ b/README.md @@ -20,19 +20,23 @@ cd pdbbind/java javac -cp cdk-2.3-SNAPSHOT.jar Surface_for_single.java cd ../.. -# compile cpp -cd octree/external && git clone --recursive https://github.com/wang-ps/octree-ext.git -cd .. && mkdir build && cd build -cmake .. && cmake --build . --config Release -export PATH=`pwd`:$PATH - -# set-up TF enviroment +# set-up TF enviroment and compile cpp conda create -n OctSurf_env tensorflow-gpu==1.14.0 conda activate OctSurf_env conda install -c conda-forge yacs tqdm +pip install sklearn +pip install matplotlib +pip install seaborn pip install -U openbabel -cmake .. -DUSE_CUDA=ON && make -cd ../../tensorflow/libs + +cd octree/external && git clone --recursive https://github.com/wang-ps/octree-ext.git +cd .. && mkdir build && cd build +cmake .. -DUSE_CUDA=ON && cmake --build . --config Release +export PATH=`pwd`:$PATH +cd python +export PATH=`pwd`:$PATH + +cd ../../../tensorflow/libs python build.py cd ../../ ``` @@ -41,11 +45,12 @@ cd ../../ ```angular2 cd pdbbind/data_folder/refined-set/1a1e bash ../octree_for_single.sh -cd ../../../.. +cd octree_folder +ls -lh +cd ../../../../ -# visualization -pip install vtk -cd visualize +# parse by python +cd python python octree_parse.py cd ../ ``` diff --git a/octree/.DS_Store b/octree/.DS_Store index 9d96ca8..018aff8 100644 Binary files a/octree/.DS_Store and b/octree/.DS_Store differ diff --git a/octree/CMakeLists.txt b/octree/CMakeLists.txt index ba6d98e..ca498e9 100644 --- a/octree/CMakeLists.txt +++ b/octree/CMakeLists.txt @@ -8,11 +8,14 @@ option(USE_PYTHON "Build the python interface with pybind11" ON) option(USE_GLOG "Use glog" OFF) option(USE_OPENMP "Use OpenMP for speed up" OFF) option(USE_RPLY "Use the library rply in the project octree" OFF) +option(KEY64 "Use 64-bit key for deeper octrees" OFF) +option(ABI "Open CXX11_ABI compatibility" OFF) set(VCPKG "" CACHE PATH "The VCPKG path, containing glog and gtest") # set languanges if(USE_CUDA) + message(STATUS "octree: USE_CUDA") project(Octree LANGUAGES CUDA CXX C) add_definitions(-DUSE_CUDA) set(CMAKE_CUDA_STANDARD 11) @@ -25,30 +28,48 @@ endif() set_property(GLOBAL PROPERTY USE_FOLDERS ON) # # c++11 Support -set(CMAKE_CXX_STANDARD 11) -set(CMAKE_POSITION_INDEPENDENT_CODE ON) +set (CMAKE_CXX_STANDARD 11) +set (CMAKE_POSITION_INDEPENDENT_CODE ON) +if(ABI) + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D_GLIBCXX_USE_CXX11_ABI=0") +endif() -# add target_link_libraries -if(USE_MINIBALL) +# add libraries +if(USE_MINIBALL) + message(STATUS "octree: USE_MINIBALL") add_definitions(-DUSE_MINIBALL) set(miniball_path "${PROJECT_SOURCE_DIR}/external/octree-ext/miniball") include_directories(${miniball_path}) endif() if(USE_RPLY) + message(STATUS "octree: USE_RPLY") add_definitions(-DUSE_RPLY) - file(GLOB src_rply - "${PROJECT_SOURCE_DIR}/external/octree-ext/rply-1.1.4/*.h" - "${PROJECT_SOURCE_DIR}/external/octree-ext/rply-1.1.4/*.c") - include_directories("${PROJECT_SOURCE_DIR}/external/octree-ext/rply-1.1.4") - add_library(rply ${src_rply}) +endif() +file(GLOB src_rply + "${PROJECT_SOURCE_DIR}/external/octree-ext/rply-1.1.4/*.h" + "${PROJECT_SOURCE_DIR}/external/octree-ext/rply-1.1.4/*.c") +include_directories("${PROJECT_SOURCE_DIR}/external/octree-ext/rply-1.1.4") +add_library(rply ${src_rply}) +set_target_properties(rply PROPERTIES FOLDER "ext") + +if(KEY64) + message(STATUS "octree: KEY64") + add_definitions(-DKEY64) endif() if(USE_GLOG) + message(STATUS "octree: USE_GLOG") add_definitions(-DUSE_GLOG) + set(glog_path "${PROJECT_SOURCE_DIR}/external/octree-ext/glog") + set(WITH_GFLAGS OFF CACHE BOOL "Disable WITH_GFLAGS") + set(BUILD_TESTING OFF CACHE BOOL "Disable BUILD_TESTING") + add_subdirectory(${glog_path}) + set_target_properties(glog PROPERTIES FOLDER "ext") endif() if(USE_WINDOWS_IO) + message(STATUS "octree: USE_WINDOWS_IO") add_definitions(-DUSE_WINDOWS_IO) endif() @@ -100,6 +121,9 @@ target_compile_features(octree_lib PUBLIC cxx_std_11) if(USE_RPLY) target_link_libraries(octree_lib rply) endif(USE_RPLY) +if(USE_GLOG) + target_link_libraries(octree_lib glog::glog) +endif(USE_GLOG) # add tools file(GLOB_RECURSE srcs "${PROJECT_SOURCE_DIR}/tools/*.cpp") @@ -116,5 +140,16 @@ endforeach(source) # add the python interface if(USE_PYTHON) + message(STATUS "octree: USE_PYTHON") add_subdirectory(python) -endif() \ No newline at end of file +endif() + +# add gtest +set(gtest_path "${PROJECT_SOURCE_DIR}/external/octree-ext/googletest") +set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) +add_subdirectory(${gtest_path} EXCLUDE_FROM_ALL) +set_target_properties(gtest gtest_main PROPERTIES FOLDER "ext") + +# add the test +add_executable(octree_test ${src_test}) +target_link_libraries(octree_test octree_lib gtest gtest_main) \ No newline at end of file diff --git a/octree/octree/contour.cpp b/octree/octree/contour.cpp index c277c72..ffa449f 100644 --- a/octree/octree/contour.cpp +++ b/octree/octree/contour.cpp @@ -1,19 +1,19 @@ #include "contour.h" -#include "marching_cube.h" +#include "marching_cube.h" void Contour::marching_cube(vector& V, vector& F) { // subdivide const int depth = octree_->info().depth(); - vector nodes_subdivided; + vector nodes_subdivided; for (int d = octree_->info().full_layer(); d < depth; ++d) { // Check subdividion of octree nodes int nnum = octree_->info().node_num(d); - vector nodes_need_subdivide; + vector nodes_need_subdivide; for (int i = 0; i < nnum; ++i) { // Only check the leaf nodes if (octree_->children_cpu(d)[i] < 0) { - uint32 keyi = octree_->key_cpu(d)[i]; + uintk keyi = octree_->key_cpu(d)[i]; if (check_subdividion(keyi, d)) { nodes_need_subdivide.push_back(keyi); } @@ -22,7 +22,7 @@ void Contour::marching_cube(vector& V, vector& F) { // check the subdivided nodes in the last iteration for (int i = 0; i < nodes_subdivided.size(); ++i) { - uint32 keyi = nodes_subdivided[i]; + uintk keyi = nodes_subdivided[i]; if (check_subdividion(keyi, d)) { nodes_need_subdivide.push_back(keyi); } @@ -37,11 +37,13 @@ void Contour::marching_cube(vector& V, vector& F) { } // marching cube - V.clear(); F.clear(); - nodes_subdivided.insert(nodes_subdivided.end(), - octree_->key_cpu(depth), octree_->key_cpu(depth) + octree_->info().node_num(depth)); + V.clear(); + F.clear(); + nodes_subdivided.insert( + nodes_subdivided.end(), octree_->key_cpu(depth), + octree_->key_cpu(depth) + octree_->info().node_num(depth)); for (int i = 0; i < nodes_subdivided.size(); ++i) { - uint32 weight_case = 0; + uintk weight_case = 0; float corner_val[8], pt[3]; octree_->key2xyz(pt, nodes_subdivided[i], depth); for (int j = 0; j < 8; ++j) { @@ -60,39 +62,43 @@ void Contour::marching_cube(vector& V, vector& F) { } // translate and scale points - const float* bbmin = octree_->info().bbmin(); - const float scale = octree_->info().bbox_max_width() / float(1 << depth); - for (int i = 0; i < V.size() / 3; ++i) { - for (int c = 0; c < 3; ++c) { - V[i * 3 + c] = V[i * 3 + c] * scale + bbmin[c]; + if (rescale_) { + const float* bbmin = octree_->info().bbmin(); + const float scale = octree_->info().bbox_max_width() / float(1 << depth); + for (int i = 0; i < V.size() / 3; ++i) { + for (int c = 0; c < 3; ++c) { + V[i * 3 + c] = V[i * 3 + c] * scale + bbmin[c]; + } } } } pair Contour::fval(int x, int y, int z) { const int depth = octree_->info().depth(); - int key = (x << 2 * depth) | (y << depth) | z; + int key = (x << 2 * depth) | (y << depth) | z; // qq: TODO: uintk auto it = fval_map_.find(key); if (it == fval_map_.end()) { auto v = value_.fval(x, y, z); - fval_map_[key] = v; // insert new element + fval_map_[key] = v; // insert new element return v; } else { return it->second; } } -bool Contour::check_subdividion(const uint32 node_key, const int depth) { +bool Contour::check_subdividion(const uintk node_key, const int depth) { // get cooridinates - uint32 xyz[3] = { 0 }; + uintk xyz[3] = {0}; octree_->key2xyz(xyz, node_key, depth); int depth_ = octree_->info().depth(); const int scale = 1 << (depth_ - depth); - for (int c = 0; c < 3; ++c) { xyz[c] *= scale; } + for (int c = 0; c < 3; ++c) { + xyz[c] *= scale; + } // check 8 cornors - const uint32 mask[8] = { 1, 2, 4, 8, 16, 32, 64, 128 }; - uint32 cube_case = 0, weight_case = 0; + const uintk mask[8] = {1, 2, 4, 8, 16, 32, 64, 128}; + uintk cube_case = 0, weight_case = 0; for (int i = 0; i < 8; ++i) { int x = xyz[0] + MarchingCube::corner_[i][0] * scale; int y = xyz[1] + MarchingCube::corner_[i][2] * scale; @@ -106,20 +112,17 @@ bool Contour::check_subdividion(const uint32 node_key, const int depth) { // check 6 faces const int coord[6][3] = { - {xyz[0], xyz[1], xyz[2]}, {xyz[0] + scale, xyz[1], xyz[2]}, - {xyz[0], xyz[1], xyz[2]}, {xyz[0], xyz[1] + scale, xyz[2]}, - {xyz[0], xyz[1], xyz[2]}, {xyz[0], xyz[1], xyz[2] + scale} - }; - const int axis1[6][3] = { - {0, 1, 0}, {0, 1, 0}, {1, 0, 0}, {1, 0, 0}, {1, 0, 0}, {1, 0, 0} - }; - const int axis2[6][3] = { - {0, 0, 1}, {0, 0, 1}, {0, 0, 1}, {0, 0, 1}, {0, 1, 0}, {0, 1, 0} - }; + {xyz[0], xyz[1], xyz[2]}, {xyz[0] + scale, xyz[1], xyz[2]}, + {xyz[0], xyz[1], xyz[2]}, {xyz[0], xyz[1] + scale, xyz[2]}, + {xyz[0], xyz[1], xyz[2]}, {xyz[0], xyz[1], xyz[2] + scale}}; + const int axis1[6][3] = {{0, 1, 0}, {0, 1, 0}, {1, 0, 0}, + {1, 0, 0}, {1, 0, 0}, {1, 0, 0}}; + const int axis2[6][3] = {{0, 0, 1}, {0, 0, 1}, {0, 0, 1}, + {0, 0, 1}, {0, 1, 0}, {0, 1, 0}}; for (int i = 0; i < 6; ++i) { for (int m = 0; m < scale; ++m) { for (int n = 0; n < scale; ++n) { - uint32 face_case = 0, wt_case = 0; + uintk face_case = 0, wt_case = 0; for (int k = 0; k < 4; ++k) { int m1 = (k & 1) ? m + 1 : m; int n1 = (k & 2) ? n + 1 : n; @@ -130,7 +133,6 @@ bool Contour::check_subdividion(const uint32 node_key, const int depth) { auto fvalue = fval(x, y, z); // pair if (fvalue.first < 0) face_case |= mask[k]; if (fvalue.second != 0) wt_case |= mask[k]; - } if (face_case != 0 && face_case != 15 && wt_case == 15) return true; } @@ -140,22 +142,22 @@ bool Contour::check_subdividion(const uint32 node_key, const int depth) { return false; } -void Contour::subdivide(uint32* key_output, const uint32 key_input) const { - // !!! caveat: depth should be smaller than 8 - typedef unsigned char ubyte; +void Contour::subdivide(uintk* key_output, const uintk key_input) const { + typedef typename KeyTrait::uints uints; + if (octree_->info().is_key2xyz()) { - const ubyte* pt = reinterpret_cast(&key_input); - ubyte x = pt[0] << 1; - ubyte y = pt[1] << 1; - ubyte z = pt[2] << 1; + const uints* pt = reinterpret_cast(&key_input); + uints x = pt[0] << 1; + uints y = pt[1] << 1; + uints z = pt[2] << 1; for (int i = 0; i < 8; ++i) { - ubyte* xyz = reinterpret_cast(key_output + i); + uints* xyz = reinterpret_cast(key_output + i); xyz[0] = (i & 1) ? x + 1 : x; xyz[1] = (i & 2) ? y + 1 : y; xyz[2] = (i & 4) ? z + 1 : z; } } else { - uint32 key_in = key_input << 3; + uintk key_in = key_input << 3; for (int i = 0; i < 8; ++i) { key_output[i] = key_in | i; } diff --git a/octree/octree/contour.h b/octree/octree/contour.h index 3723cbf..bfe954d 100644 --- a/octree/octree/contour.h +++ b/octree/octree/contour.h @@ -1,23 +1,25 @@ #ifndef _OCTREE_CONTOUR_ #define _OCTREE_CONTOUR_ -#include -#include #include +#include +#include #include "octree_parser.h" #include "octree_value.h" +#include "types.h" using std::pair; -using std::vector; using std::unordered_map; - +using std::vector; class Contour { public: - Contour(const OctreeParser* oct = nullptr): value_(oct) { + Contour(const OctreeParser* oct = nullptr, bool rescale = true) + : value_(oct), rescale_(rescale) { set_octree(oct); } + void set_octree(const OctreeParser* oct) { octree_ = oct; fval_map_.clear(); @@ -26,16 +28,15 @@ class Contour { void marching_cube(vector& V, vector& F); protected: - typedef unsigned int uint32; pair fval(int x, int y, int z); - bool check_subdividion(const uint32 node_key, const int depth); - void subdivide(uint32* key_output, const uint32 key_input) const; + bool check_subdividion(const uintk node_key, const int depth); + void subdivide(uintk* key_output, const uintk key_input) const; protected: OctreeValue value_; const OctreeParser* octree_; unordered_map > fval_map_; - + bool rescale_; }; #endif \ No newline at end of file diff --git a/octree/octree/marching_cube.cpp b/octree/octree/marching_cube.cpp index 9992f95..80ee935 100644 --- a/octree/octree/marching_cube.cpp +++ b/octree/octree/marching_cube.cpp @@ -38,7 +38,7 @@ unsigned int MarchingCube::compute_cube_case() const { return cube_case; } -void MarchingCube::contouring(vector& vtx, vector& face) const { +void MarchingCube::contouring(vector& vtx, vector& face) { // compute cube cases unsigned int cube_case = compute_cube_case(); @@ -72,6 +72,13 @@ void MarchingCube::contouring(vector& vtx, vector& face) const { } } +void MarchingCube::compute(const vector& fval, float iso_val, + const vector& left_btm, int vid) { + vtx_.clear(); + face_.clear(); + set(fval.data(), iso_val, left_btm.data(), vid); + contouring(vtx_, face_); +} void intersect_cube(vector& V, const float* pt, const float* pt_base, const float* normal) { diff --git a/octree/octree/marching_cube.h b/octree/octree/marching_cube.h index 4a09807..7062691 100644 --- a/octree/octree/marching_cube.h +++ b/octree/octree/marching_cube.h @@ -10,13 +10,19 @@ class MarchingCube { MarchingCube() : fval_(nullptr), iso_value_(0), left_btm_(nullptr), vtx_id_(0) {} MarchingCube(const float* fval, float iso_val, const float* left_btm, int vid); void set(const float* fval, float iso_val, const float* left_btm, int vid); - void contouring(vector& vtx, vector& face) const; + void contouring(vector& vtx, vector& face); + void compute(const vector& fval, float iso_val, + const vector& left_btm, int vid); unsigned int compute_cube_case() const; private: inline int btwhere(int x) const; inline void interpolation(float* pt, const float* pt1, const float* pt2, - const float f1, const float f2) const; + const float f1, const float f2) const; + + public: + vector vtx_; + vector face_; protected: const float* fval_; diff --git a/octree/octree/math_functions.cpp b/octree/octree/math_functions.cpp index 7a4f45c..0cf4823 100644 --- a/octree/octree/math_functions.cpp +++ b/octree/octree/math_functions.cpp @@ -31,7 +31,7 @@ void bounding_sphere(float& radius, float* center, const float* pt, const int np // if so, just calculate the bounding box float bbmin[3] = { 0.0f, 0.0f, 0.0f }; float bbmax[3] = { 0.0f, 0.0f, 0.0f }; - bouding_box(bbmin, bbmax, pt, npt); + bounding_box(bbmin, bbmax, pt, npt); for (int j = 0; j < dim; ++j) { center[j] = (bbmax[j] + bbmin[j]) / 2.0f; @@ -110,7 +110,7 @@ void bounding_sphere(float& radius, float* center, const float* pt, const int np #endif -void bouding_box(float* bbmin, float* bbmax, const float* pt, const int npt) { +void bounding_box(float* bbmin, float* bbmax, const float* pt, const int npt) { const int dim = 3; if (npt < 1) return; for (int i = 0; i < 3; ++i) { diff --git a/octree/octree/math_functions.h b/octree/octree/math_functions.h index 743760a..2ba4336 100644 --- a/octree/octree/math_functions.h +++ b/octree/octree/math_functions.h @@ -8,7 +8,7 @@ using std::vector; const float ESP = 1.0e-30f; void bounding_sphere(float& radius, float* center, const float* pt, const int npt); -void bouding_box(float* bbmin, float* bbmax, const float* pt, const int npt); +void bounding_box(float* bbmin, float* bbmax, const float* pt, const int npt); // !!! The matrix in this header is in column-major storage order !!! diff --git a/octree/octree/merge_octrees.cpp b/octree/octree/merge_octrees.cpp index 01c50f6..a2b9636 100644 --- a/octree/octree/merge_octrees.cpp +++ b/octree/octree/merge_octrees.cpp @@ -1,8 +1,10 @@ #include "merge_octrees.h" -#include "octree_nn.h" + #include "logs.h" +#include "octree_nn.h" +#include "types.h" -void merge_octrees(vector& octree_out, const vector octrees_in) { +void merge_octrees(vector& octree_out, const vector octrees_in) { MergeOctrees mo; mo.init(octrees_in); mo.check_input(); @@ -12,7 +14,6 @@ void merge_octrees(vector& octree_out, const vector octrees_ mo.merge_octree(); } - void MergeOctrees::init(const vector& octrees) { batch_size_ = octrees.size(); octree_parsers_.resize(batch_size_); @@ -23,7 +24,6 @@ void MergeOctrees::init(const vector& octrees) { full_layer_ = octree_parsers_[0].info().full_layer(); } - void MergeOctrees::check_input() { string err_msg; bool valid = octree_parsers_[0].info().check_format(err_msg); @@ -32,7 +32,7 @@ void MergeOctrees::check_input() { valid = octree_parsers_[i].info().check_format(err_msg); CHECK(valid) << err_msg; CHECK(octree_parsers_[0].info().is_consistent(octree_parsers_[i].info())) - << "The formats of input octrees are not consistent, check the database"; + << "The formats of input octrees are not consistent"; } } @@ -95,37 +95,38 @@ void MergeOctrees::set_batch_info() { info_batch_.set_batch_size(batch_size_); // add the neighbor property const int kNeighChannel = 8; - info_batch_.set_property(OctreeInfo::kNeigh, kNeighChannel, -1); + info_batch_.set_property(OctreeInfo::kNeigh, kNeighChannel, -1); // qq: kNeighChannel is channel, -1 is location, means the for every octants // update nodenumber - info_batch_.set_nnum(nnum_batch_.data()); + info_batch_.set_nnum(nnum_batch_.data()); // qq: cumulative node number of the batch, info_batch_.set_nempty(nnum_nempty_batch_.data()); - info_batch_.set_nnum_cum(); - info_batch_.set_ptr_dis(); - //bool valid = info_batch.check_format(err_msg); - //CHECK(valid) << err_msg; + info_batch_.set_nnum_cum(); // qq: default capacity is 0. set nnum_cum_ from d=0 to depth +2 + info_batch_.set_ptr_dis(); // qq: check + // bool valid = info_batch.check_format(err_msg); + // CHECK(valid) << err_msg; } void MergeOctrees::set_batch_parser(vector& octree_out) { - int sz = info_batch_.sizeof_octree(); + int64_t sz = info_batch_.sizeof_octree(); // qq: TODO: long sz octree_out.resize(sz); octbatch_parser_.set_cpu(octree_out.data(), &info_batch_); } void MergeOctrees::merge_octree() { - //omp_set_num_threads(8); + typedef typename KeyTrait::uints uints; + + // omp_set_num_threads(8); //#pragma omp parallel for for (int i = 0; i < batch_size_; ++i) { // copy key - // the channel and location of key is 1 and -1 (todo: !!! channel 2 for deeper key) + // the channel and location of key is 1 and -1 for (int d = 0; d < depth_ + 1; ++d) { if (!info_batch_.has_property(OctreeInfo::kKey)) break; int p = i * (depth_ + 1) + d; - uint32* des = octbatch_parser_.mutable_key_cpu(d) + nnum_cum_layer_[p]; - const uint32* src = octree_parsers_[i].key_cpu(d); + uintk* des = octbatch_parser_.mutable_key_cpu(d) + nnum_cum_layer_[p]; + const uintk* src = octree_parsers_[i].key_cpu(d); for (int j = 0; j < nnum_[p]; ++j) { des[j] = src[j]; - // !!! todo: deal with octree depth > 8 - unsigned char* ptr = reinterpret_cast(des + j); + uints* ptr = reinterpret_cast(des + j); ptr[3] = i; } } @@ -150,7 +151,8 @@ void MergeOctrees::merge_octree() { if (!info_batch_.has_property(OctreeInfo::kFeature)) break; int p = i * (depth_ + 1) + d; for (int c = 0; c < feature_channel; c++) { - float* des = octbatch_parser_.mutable_feature_cpu(d) + c * nnum_batch_[d] + nnum_cum_layer_[p]; + float* des = octbatch_parser_.mutable_feature_cpu(d) + // qq: here ~240 million with bs=1. + c * nnum_batch_[d] + nnum_cum_layer_[p]; const float* src = octree_parsers_[i].feature_cpu(d) + c * nnum_[p]; for (int j = 0; j < nnum_[p]; ++j) { des[j] = src[j]; } } @@ -188,9 +190,9 @@ void MergeOctrees::merge_octree() { calc_neigh_cpu(octbatch_parser_.mutable_neighbor_cpu(d), d, batch_size_); } else { calc_neigh_cpu(octbatch_parser_.mutable_neighbor_cpu(d), - octbatch_parser_.neighbor_cpu(d - 1), - octbatch_parser_.children_cpu(d - 1), - octbatch_parser_.info().node_num(d - 1)); + octbatch_parser_.neighbor_cpu(d - 1), + octbatch_parser_.children_cpu(d - 1), + octbatch_parser_.info().node_num(d - 1)); } } } diff --git a/octree/octree/merge_octrees.h b/octree/octree/merge_octrees.h index b5a1367..9b899f5 100644 --- a/octree/octree/merge_octrees.h +++ b/octree/octree/merge_octrees.h @@ -1,18 +1,14 @@ #ifndef _OCTREE_MERGE_OCTREES_ #define _OCTREE_MERGE_OCTREES_ -#include #include #include "octree_parser.h" using std::vector; -using std::string; -typedef uint32_t uint32; void merge_octrees(vector& octree_out, const vector octrees_in); - // A simple implementation of index matrix class Index { public: @@ -24,20 +20,15 @@ class Index { data_.assign(row_ * col_, 0); } - int operator()(int c, int r) const { - return data_[row_ * c + r]; - } + int operator()(int c, int r) const { return data_[row_ * c + r]; } - int& operator()(int c, int r) { - return data_[row_ * c + r]; - } + int& operator()(int c, int r) { return data_[row_ * c + r]; } protected: int row_, col_; vector data_; }; - class MergeOctrees { public: void init(const vector& octrees); @@ -66,5 +57,4 @@ class MergeOctrees { OctreeParser octbatch_parser_; }; - -#endif // _OCTREE_MERGE_OCTREES_ +#endif // _OCTREE_MERGE_OCTREES_ diff --git a/octree/octree/octree.cpp b/octree/octree/octree.cpp index c516c28..33a8a26 100644 --- a/octree/octree/octree.cpp +++ b/octree/octree/octree.cpp @@ -10,6 +10,7 @@ #include #include #include +#include #include "math_functions.h" #include "octree_nn.h" @@ -26,17 +27,28 @@ void Octree::set_octree(vector& data) { this->set_cpu(buffer_.data()); } -void Octree::set_octree(const char* data, const int sz) { +void Octree::set_octree(const char* data, const int64_t sz) { // qq: TODO: with batches, sz can exceed int range + resize_octree(sz); + memcpy(buffer_.data(), data, sz); + this->set_cpu(buffer_.data()); +} +void Octree::set_octree(const char* data, const int sz) { // qq: TODO: with batches, sz can exceed int range resize_octree(sz); memcpy(buffer_.data(), data, sz); this->set_cpu(buffer_.data()); } -void Octree::resize_octree(const int sz) { + +void Octree::resize_octree(const int64_t sz) { // qq: TODO: with batches, sz can exceed int range + buffer_.resize(sz); + this->set_cpu(buffer_.data()); +} +void Octree::resize_octree(const int sz) { // qq: TODO: with batches, sz can exceed int range buffer_.resize(sz); this->set_cpu(buffer_.data()); } + bool Octree::read_octree(const string& filename) { std::ifstream infile(filename, std::ios::binary); if (!infile) return false; @@ -80,9 +92,9 @@ void Octree::build(const OctreeInfo& octree_info, const Points& point_cloud) { // preprocess, get key and sort vector pts_scaled; normalize_pts(pts_scaled, point_cloud); - vector node_keys, sorted_idx; + vector node_keys, sorted_idx; sort_keys(node_keys, sorted_idx, pts_scaled); - vector unique_idx; + vector unique_idx; unique_key(node_keys, unique_idx); // build octree structure @@ -160,7 +172,7 @@ void Octree::normalize_pts(vector& pts_scaled, const Points& point_cloud) pts_scaled.resize(3 * npt); // normalize the points into the range [0, 1 << depth_) using bbox_width - #pragma omp parallel for + //#pragma omp parallel for for (int i = 0; i < npt; i++) { int i3 = i * 3; for (int j = 0; j < 3; j++) { @@ -169,26 +181,24 @@ void Octree::normalize_pts(vector& pts_scaled, const Points& point_cloud) } } -void Octree::sort_keys(vector& sorted_keys, vector& sorted_idx, +void Octree::sort_keys(vector& sorted_keys, vector& sorted_idx, const vector& pts_scaled) { - - // compute the code int depth_ = oct_info_.depth(); int npt = pts_scaled.size() / 3; - vector code(npt); - #pragma omp parallel for + vector> code; + code.reserve(npt); + + //#pragma omp parallel for for (int i = 0; i < npt; i++) { // compute key - uint32 pt[3], key; + uintk pt[3], key; for (int j = 0; j < 3; ++j) { - pt[j] = static_cast(pts_scaled[3 * i + j]); + pt[j] = static_cast(pts_scaled[3 * i + j]); } compute_key(key, pt, depth_); // generate code - uint32* ptr = reinterpret_cast(&code[i]); - ptr[0] = i; - ptr[1] = key; + code.push_back(std::make_pair(key, i)); } // sort all the code @@ -197,15 +207,14 @@ void Octree::sort_keys(vector& sorted_keys, vector& sorted_idx, // unpack the code sorted_keys.resize(npt); sorted_idx.resize(npt); - #pragma omp parallel for + //#pragma omp parallel for for (int i = 0; i < npt; i++) { - uint32* ptr = reinterpret_cast(&code[i]); - sorted_idx[i] = ptr[0]; - sorted_keys[i] = ptr[1]; + sorted_keys[i] = code[i].first; + sorted_idx[i] = code[i].second; } } -void Octree::build_structure(vector& node_keys) { +void Octree::build_structure(vector& node_keys) { const int depth_ = oct_info_.depth(); const int full_layer_ = oct_info_.full_layer(); children_.resize(depth_ + 1); @@ -214,7 +223,7 @@ void Octree::build_structure(vector& node_keys) { // layer 0 to full_layer_: the octree is full in these layers for (int curr_depth = 0; curr_depth <= full_layer_; curr_depth++) { vector& children = children_[curr_depth]; - vector& keys = keys_[curr_depth]; + vector& keys = keys_[curr_depth]; int n = 1 << 3 * curr_depth; keys.resize(n, -1); children.resize(n, -1); @@ -230,21 +239,21 @@ void Octree::build_structure(vector& node_keys) { for (int curr_depth = depth_; curr_depth > full_layer_; --curr_depth) { // compute parent key, i.e. keys of layer (curr_depth -1) int n = node_keys.size(); - vector parent_keys(n); + vector parent_keys(n); #pragma omp parallel for for (int i = 0; i < n; i++) { parent_keys[i] = node_keys[i] >> 3; } // compute unique parent key - vector parent_pidx; + vector parent_pidx; unique_key(parent_keys, parent_pidx); // augment children keys and create nodes int np = parent_keys.size(); int nch = np << 3; vector& children = children_[curr_depth]; - vector& keys = keys_[curr_depth]; + vector& keys = keys_[curr_depth]; children.resize(nch, -1); keys.resize(nch, 0); @@ -254,18 +263,18 @@ void Octree::build_structure(vector& node_keys) { } // compute base address for each node - vector addr(nch); + vector addr(nch); for (int i = 0; i < np; i++) { - for (uint32 j = parent_pidx[i]; j < parent_pidx[i + 1]; j++) { + for (uintk j = parent_pidx[i]; j < parent_pidx[i + 1]; j++) { addr[j] = i << 3; } } // set children pointer and parent pointer - #pragma omp parallel for + //#pragma omp parallel for for (int i = 0; i < n; i++) { // address - uint32 k = (node_keys[i] & 7u) | addr[i]; + uintk k = (node_keys[i] & 7u) | addr[i]; // set children pointer for layer curr_depth children[k] = i; @@ -279,7 +288,7 @@ void Octree::build_structure(vector& node_keys) { // Now the node_keys are the key for full_layer if (depth_ > full_layer_) { for (int i = 0; i < node_keys.size(); i++) { - uint32 j = node_keys[i]; + uintk j = node_keys[i]; children_[full_layer_][j] = i; } } @@ -313,7 +322,7 @@ void Octree::calc_node_num() { // compute the average signal for the last octree layer void Octree::calc_signal(const Points& point_cloud, const vector& pts_scaled, - const vector& sorted_idx, const vector& unique_idx) { + const vector& sorted_idx, const vector& unique_idx) { int depth = oct_info_.depth(); const float* normals = point_cloud.ptr(PointsInfo::kNormal); const float* features = point_cloud.ptr(PointsInfo::kFeature); @@ -331,7 +340,7 @@ void Octree::calc_signal(const Points& point_cloud, const vector& pts_sca if (node_type(t) == kLeaf) continue; vector avg_normal(channel, 0.0f); - for (uint32 j = unique_idx[t]; j < unique_idx[t + 1]; j++) { + for (uintk j = unique_idx[t]; j < unique_idx[t + 1]; j++) { int h = sorted_idx[j]; for (int c = 0; c < channel; ++c) { avg_normal[c] += normals[channel * h + c]; @@ -362,7 +371,7 @@ void Octree::calc_signal(const Points& point_cloud, const vector& pts_sca if (node_type(t) == kLeaf) continue; vector avg_feature(channel, 0.0f); - for (uint32 j = unique_idx[t]; j < unique_idx[t + 1]; j++) { + for (uintk j = unique_idx[t]; j < unique_idx[t + 1]; j++) { int h = sorted_idx[j]; for (int c = 0; c < channel; ++c) { avg_feature[c] += features[channel * h + c]; @@ -389,7 +398,7 @@ void Octree::calc_signal(const Points& point_cloud, const vector& pts_sca int valid_num = 0; vector avg_label(max_label_, 0); - for (uint32 j = unique_idx[t]; j < unique_idx[t + 1]; j++) { + for (uintk j = unique_idx[t]; j < unique_idx[t + 1]; j++) { int h = sorted_idx[j]; int l = static_cast(labels[h]); if (l < 0) { continue; } // invalid labels @@ -417,7 +426,7 @@ void Octree::calc_signal(const Points& point_cloud, const vector& pts_sca if (node_type(t) == kLeaf) continue; float avg_pt[3] = { 0.0f, 0.0f, 0.0f }; - for (uint32 j = unique_idx[t]; j < unique_idx[t + 1]; j++) { + for (uintk j = unique_idx[t]; j < unique_idx[t + 1]; j++) { int h = sorted_idx[j]; for (int c = 0; c < 3; ++c) { avg_pt[c] += pts_scaled[3 * h + c]; @@ -483,7 +492,7 @@ void Octree::calc_signal(const bool calc_normal_err, const bool calc_dist_err) { const vector& dnum_d = dnum_[d]; const vector& didx_d = didx_[d]; const vector& children_d = children_[d]; - const vector& key_d = keys_[d]; + const vector& key_d = keys_[d]; const float scale = static_cast(1 << (depth - d)); vector& normal_d = avg_normals_[d]; @@ -583,7 +592,7 @@ void Octree::calc_signal(const bool calc_normal_err, const bool calc_dist_err) { } } - uint32 ptu_base[3]; + uintk ptu_base[3]; compute_pt(ptu_base, key_d[i], d); float pt_base[3] = { ptu_base[0], ptu_base[1], ptu_base[2] }; if (has_dis) { @@ -632,7 +641,7 @@ void Octree::calc_signal(const bool calc_normal_err, const bool calc_dist_err) { for (int c = 0; c < 3; ++c) { dis += (pt_depth[c * nnum_depth + j] - pt_avg1[c]) * n_avg[c]; } - dis = abs(dis); + dis = fabsf(dis); if (dis > distance_max1) distance_max1 = dis; } @@ -761,10 +770,10 @@ void Octree::extrapolate_signal() { dis /= count; if (dis > 3.0f) dis = 3.0f; if (dis < -3.0f) dis = -3.0f; - if (abs(dis) < 1.0f) { + if (fabsf(dis) < 1.0f) { //bool has_intersection = false; // make the voxel has no intersection with the current voxel - unsigned int cube_cases = 0; + uint32 cube_cases = 0; for (int k = 0; k < 8; ++k) { float fval = dis; for (int j = 0; j < 3; ++j) { @@ -788,7 +797,7 @@ void Octree::extrapolate_signal() { for (int j = 0; j < nnum_d; ++j) { if (node_type(children_d[j]) == kLeaf) continue; key2xyz(ptj, keys_[d][j], d); - float dd = abs(pti[0] - ptj[0]) + abs(pti[1] - ptj[1]) + abs(pti[2] - ptj[2]); + float dd = fabsf(pti[0] - ptj[0]) + fabsf(pti[1] - ptj[1]) + fabsf(pti[2] - ptj[2]); if (dd < dis_min) { dis_min = dd; j_min = j; @@ -809,6 +818,7 @@ void Octree::extrapolate_signal() { } bool Octree::save_legacy(std::string& filename) { + typedef typename KeyTrait::uints uints; int depth_ = oct_info_.depth(); int full_layer_ = oct_info_.full_layer(); @@ -831,18 +841,18 @@ bool Octree::save_legacy(std::string& filename) { std::vector key(total_node_num), children(total_node_num); int idx = 0; for (int d = 0; d <= depth_; ++d) { - vector& keys = keys_[d]; + vector& keys = keys_[d]; for (int i = 0; i < keys.size(); ++i) { // calc point - uint32 k = keys[i], pt[3]; + uintk k = keys[i], pt[3]; compute_pt(pt, k, d); // compress - unsigned char* ptr = reinterpret_cast(&key[idx]); - ptr[0] = static_cast(pt[0]); - ptr[1] = static_cast(pt[1]); - ptr[2] = static_cast(pt[2]); - ptr[3] = static_cast(d); + uints* ptr = reinterpret_cast(&key[idx]); + ptr[0] = static_cast(pt[0]); + ptr[1] = static_cast(pt[1]); + ptr[2] = static_cast(pt[2]); + ptr[3] = static_cast(d); // children children[idx] = children_[d][i]; @@ -889,7 +899,7 @@ bool Octree::save_legacy(std::string& filename) { // } //} -void Octree::unique_key(vector& keys, vector& idx) { +void Octree::unique_key(vector& keys, vector& idx) { idx.clear(); idx.push_back(0); @@ -907,7 +917,7 @@ void Octree::unique_key(vector& keys, vector& idx) { void Octree::serialize() { - const int sz = oct_info_.sizeof_octree(); + const int64_t sz = oct_info_.sizeof_octree(); // qq: TODO: need update to long or uintk buffer_.resize(sz, 0); this->set_cpu(buffer_.data(), &oct_info_); //info_ = reinterpret_cast(buffer_.data()); @@ -934,11 +944,11 @@ void Octree::serialize() { } while(0) \ if (oct_info_.is_key2xyz()) { - vector > xyz; + vector > xyz; key_to_xyz(xyz); - SERIALIZE_PROPERTY(uint32, OctreeInfo::kKey, xyz); + SERIALIZE_PROPERTY(uintk, OctreeInfo::kKey, xyz); } else { - SERIALIZE_PROPERTY(uint32, OctreeInfo::kKey, keys_); + SERIALIZE_PROPERTY(uintk, OctreeInfo::kKey, keys_); } SERIALIZE_PROPERTY(int, OctreeInfo::kChild, children_); SERIALIZE_PROPERTY(float, OctreeInfo::kFeature, features); @@ -947,7 +957,7 @@ void Octree::serialize() { } template -void Octree::serialize(Dtype* des, const vector >& src, const int location) { +void Octree::serialize(Dtype* des, const vector >& src, const int location) { // qq: location to long ? if (location == -1) { for (int d = 0; d <= oct_info_.depth(); ++d) { des = std::copy(src[d].begin(), src[d].end(), des); @@ -1082,7 +1092,7 @@ void Octree::trim_octree() { int nnum_d = oct_info_.node_num(d); const vector& drop_d = drop[d]; - vector key; + vector key; for (int i = 0; i < nnum_d; ++i) { if (drop_d[i] == kDrop) continue; key.push_back(keys_[d][i]); @@ -1136,28 +1146,22 @@ void Octree::trim_octree() { serialize(); } -void Octree::key_to_xyz(vector >& xyz) { +void Octree::key_to_xyz(vector >& xyz) { + typedef typename KeyTrait::uints uints; const int depth = oct_info_.depth(); const int channel = oct_info_.channel(OctreeInfo::kKey); + //assert(channel == 1); xyz.resize(depth + 1); for (int d = 0; d <= depth; ++d) { int nnum = oct_info_.node_num(d); xyz[d].resize(nnum * channel, 0); - uint32* xyz_d = xyz[d].data(); + uintk* xyz_d = xyz[d].data(); for (int i = 0; i < nnum; ++i) { - uint32 pt[3] = { 0, 0, 0 }; + uintk pt[3] = { 0, 0, 0 }; compute_pt(pt, keys_[d][i], d); - - if (channel == 1) { - unsigned char* ptr = reinterpret_cast(xyz_d + i); - for (int c = 0; c < 3; ++c) { - ptr[c] = static_cast(pt[c]); - } - } else { - unsigned short* ptr = reinterpret_cast(xyz_d + 2 * i); - for (int c = 0; c < 3; ++c) { - ptr[c] = static_cast(pt[c]); - } + uints* ptr = reinterpret_cast(xyz_d + i); + for (int c = 0; c < 3; ++c) { + ptr[c] = static_cast(pt[c]); } } } @@ -1175,8 +1179,8 @@ void Octree::calc_split_label() { if (node_type(children_[d][i]) == kLeaf) { split_labels_[d][i] = 0; // empty node if (adaptive) { - float t = abs(avg_normals_[d][i]) + abs(avg_normals_[d][nnum_d + i]) + - abs(avg_normals_[d][2 * nnum_d + i]); + float t = fabsf(avg_normals_[d][i]) + fabsf(avg_normals_[d][nnum_d + i]) + + fabsf(avg_normals_[d][2 * nnum_d + i]); // todo: t != 0 && has_intersection if (t != 0) split_labels_[d][i] = 2; // surface-well-approximated } @@ -1203,7 +1207,7 @@ void Octree::octree2pts(Points& point_cloud, int depth_start, int depth_end, bool rescale) const { const int depth = info_->depth(); const float* bbmin = info_->bbmin(); - const float kMul = rescale ? info_->bbox_max_width() / float(1 << depth) : 1.0f; + const float kMul = info_->bbox_max_width() / float(1 << depth); valid_depth_range(depth_start, depth_end); vector pts, normals, labels; @@ -1219,13 +1223,16 @@ void Octree::octree2pts(Points& point_cloud, int depth_start, int depth_end, float n[3], pt[3]; node_normal(n, i, d); - float len = abs(n[0]) + abs(n[1]) + abs(n[2]); + float len = fabsf(n[0]) + fabsf(n[1]) + fabsf(n[2]); if (len == 0 && d != depth) continue; // for adaptive octree node_pos(pt, i, d); for (int c = 0; c < 3; ++c) { + if (rescale) { // !!! note the scale and bbmin + pt[c] = pt[c] * scale + bbmin[c]; + } normals.push_back(n[c]); - pts.push_back(pt[c] * scale + bbmin[c]); // !!! note the scale and bbmin + pts.push_back(pt[c]); } if (label_d != nullptr) labels.push_back(label_d[i]); } @@ -1235,7 +1242,7 @@ void Octree::octree2pts(Points& point_cloud, int depth_start, int depth_end, } void Octree::octree2mesh(vector& V, vector& F, int depth_start, - int depth_end) const { + int depth_end, bool rescale) const { const int depth = info_->depth(); const float* bbmin = info_->bbmin(); const float kMul = info_->bbox_max_width() / float(1 << depth); @@ -1253,7 +1260,7 @@ void Octree::octree2mesh(vector& V, vector& F, int depth_start, float n[3], pt[3], pt_ref[3]; node_normal(n, i, d); - float len = abs(n[0]) + abs(n[1]) + abs(n[2]); + float len = fabsf(n[0]) + fabsf(n[1]) + fabsf(n[2]); if (len == 0) continue; node_pos(pt, i, d, pt_ref); @@ -1279,7 +1286,10 @@ void Octree::octree2mesh(vector& V, vector& F, int depth_start, nv = vtx.size() / 3; for (int i = 0; i < nv; ++i) { for (int c = 0; c < 3; ++c) { - float vl = vtx[i * 3 + c] * scale + bbmin[c]; + float vl = vtx[i * 3 + c]; + if (rescale) { // !!! note the scale and bbmin + vl = vl * scale + bbmin[c]; + } V.push_back(vl); } } @@ -1287,122 +1297,5 @@ void Octree::octree2mesh(vector& V, vector& F, int depth_start, } -//// only work for adaptive octree -//void Octree::extrapolate_signal() { -// const int depth = info().depth(); -// const int full_layer = info().full_layer(); -// const float imul = 2.0f / sqrtf(3.0f); -// const int channel_label = info().channel(OctreeInfo::kLabel); -// const int channel_normal = 3; -// const bool has_dis = info().has_displace(); -// const bool has_label = channel_label; -// -// for (int d = depth; d >= full_layer; --d) { -// const int nnum_d = info().node_num(d); -// const int* child_d = children_cpu(d); -// const uint32* keys_d = key_cpu(d); -// float* normal_d = mutable_feature_cpu(d); -// //float* label_d = mutable_label(d); // !!!todo: label -// float* displacement_d = normal_d + 3 * nnum_d; -// -// // For adaptive octree, there is kNonEmptyLeaf, -// // but the original child_d contains only kLeaf and kInternelNode, -// // So we use children_d to mark the kNonEmptyLeaf -// vector children_d(child_d, child_d + nnum_d); -// for (int i = 0; i < nnum_d; ++i) { -// if (d == depth) break; -// if (node_type(children_d[i]) == kLeaf) { -// float n[3] = { 0 }; -// node_normal(n, i, d); -// float len = abs(n[0]) + abs(n[1]) + abs(n[2]); -// if (len != 0) { children_d[i] = -2; } -// } -// } -// -// for (int i = 0; i < nnum_d; ++i) { -// if (node_type(children_d[i]) != kLeaf) continue; -// int id = i % 8; -// int i_base = i - id; -// -// float count = ESP; // the non-empty node number -// for (int j = i_base; j < i_base + 8; ++j) { -// if (node_type(children_d[j]) != kLeaf) count += 1.0f; -// } -// -// vector n_avg(channel_normal, 0.0f); -// for (int j = i_base; j < i_base + 8; ++j) { -// if (node_type(children_d[j]) == kLeaf) continue; -// for (int c = 0; c < channel_normal; ++c) { -// n_avg[c] += normal_d[c * nnum_d + j]; -// } -// } -// -// float ilen = 1.0f / (norm2(n_avg) + ESP); -// for (int c = 0; c < channel_normal; ++c) { -// n_avg[c] *= ilen; -// normal_d[c * nnum_d + i] = n_avg[c]; // output -// } -// -// if (has_dis && count > 0.5f) { -// float xyzs[8][3] = { -// {0, 0, 0}, {0, 0, 1.0f}, {0, 1.0f, 0}, {0, 1.0f, 1.0f}, -// {1.0f, 0, 0}, {1.0f, 0, 1.0f}, {1.0f, 1.0f, 0}, {1.0f, 1.0f, 1.0f}, -// }; -// float dis = 0; -// for (int j = i_base; j < i_base + 8; ++j) { -// if (node_type(children_d[j]) == kLeaf) continue; -// dis += displacement_d[j]; -// for (int c = 0; c < channel_normal; ++c) { -// dis += normal_d[c * nnum_d + j] * (xyzs[j % 8][c] - xyzs[id][c]) * imul; -// } -// } -// dis /= count; -// if (dis > 3.0f) dis = 3.0f; -// if (dis < -3.0f) dis = -3.0f; -// if (abs(dis) < 1.0f) { -// // make the voxel has no intersection with the current voxel -// uint32 cube_cases = 0; -// for (int k = 0; k < 8; ++k) { -// float fval = dis; -// for (int j = 0; j < 3; ++j) { -// fval += (0.5 - MarchingCube::corner_[k][j]) * n_avg[j] * imul; -// } -// if (fval < 0) cube_cases |= (1 << k); -// } -// if (cube_cases != 255 && cube_cases != 0) { -// dis = dis < 0 ? -1.0f : 1.0f; -// } -// } -// -// displacement_d[i] = dis; -// } -// -// if (has_dis && count < 0.5f) { -// // find the closest point -// int j_min = -1; -// float pti[3], ptj[3], dis_min = 1.0e30f; -// key2xyz(pti, keys_d[i], d); -// for (int j = 0; j < nnum_d; ++j) { -// if (node_type(children_d[j]) == kLeaf) continue; -// key2xyz(ptj, keys_d[j], d); -// float dis = abs(pti[0] - ptj[0]) + abs(pti[1] - ptj[1]) + abs(pti[2] - ptj[2]); -// if (dis < dis_min) { -// dis_min = dis; -// j_min = j; -// } -// } -// // calc the displacement -// float dis = displacement_d[j_min]; -// key2xyz(ptj, keys_d[j_min], d); -// for (int c = 0; c < channel_normal; ++c) { -// dis += normal_d[c * nnum_d + j_min] * (ptj[c] - pti[c]) * imul; -// } -// if (dis > 0.0f) dis = 2.0f; -// if (dis < 0.0f) dis = -2.0f; -// displacement_d[i] = dis; -// } -// } -// } -//} diff --git a/octree/octree/octree.h b/octree/octree/octree.h index bbc1808..cafd664 100644 --- a/octree/octree/octree.h +++ b/octree/octree/octree.h @@ -16,8 +16,10 @@ class Octree : public OctreeParser { Octree() {} void set_octree(const Octree& octree_in); // copy from octree_in void set_octree(vector& data); // swap data and buffer_ - void set_octree(const char* data, const int sz); - void resize_octree(const int sz); + void set_octree(const char* data, const int sz); // qq: TODO: int to long + void set_octree(const char* data, const int64_t sz); + void resize_octree(const int sz); // qq: TODO: int to long + void resize_octree(const int64_t sz); const vector& buffer() const { return buffer_; } bool read_octree(const string& filename); @@ -31,29 +33,31 @@ class Octree : public OctreeParser { void serialize(); bool save_legacy(string& filename); - void octree2pts(Points& point_cloud, int depth_start, int depth_end, bool rescale = true) const; - void octree2mesh(vector& V, vector& F, int depth_start, int depth_end) const; + void octree2pts(Points& point_cloud, int depth_start, int depth_end, + bool rescale = true) const; + void octree2mesh(vector& V, vector& F, int depth_start, + int depth_end, bool rescale = true) const; protected: void clear(int depth = 0); void normalize_pts(vector& pts_scaled, const Points& pts); - void sort_keys(vector& sorted_keys, vector& sorted_idx, + void sort_keys(vector& sorted_keys, vector& sorted_idx, const vector& pts_scaled); - void unique_key(vector& node_key, vector& pidx); + void unique_key(vector& node_key, vector& pidx); - void build_structure(vector& node_keys); + void build_structure(vector& node_keys); void calc_node_num(); // called after the function build_structure() void calc_signal(const Points& point_cloud, const vector& pts_scaled, - const vector& sorted_idx, const vector& unique_idx); + const vector& sorted_idx, const vector& unique_idx); void calc_signal(const bool calc_normal_err, const bool calc_dist_err); void extrapolate_signal(); - void key_to_xyz(vector >& xyz); + void key_to_xyz(vector>& xyz); void calc_split_label(); template - void serialize(Dtype* des, const vector >& src, const int location); + void serialize(Dtype* des, const vector >& src, const int location); // qq: TODO: int to long void covered_depth_nodes(); void valid_depth_range(int& depth_start, int& depth_end) const; @@ -61,12 +65,7 @@ class Octree : public OctreeParser { protected: // the octree is serialized into buffer_ vector buffer_; - - // Note: structure of arrays(SoA), instead of array of structures(AoS), is - // adopted. The reason is that the SoA is more friendly to GPU-based implementation. - // In the future, probably I will try to implement this class with CUDA accroding - // to this CPU-based code. - vector > keys_; + vector > keys_; vector > children_; vector > displacement_; diff --git a/octree/octree/octree_conv.cpp b/octree/octree/octree_conv.cpp index af48673..4df41d2 100644 --- a/octree/octree/octree_conv.cpp +++ b/octree/octree/octree_conv.cpp @@ -63,17 +63,17 @@ void OctreeBaseConv::reshape() { // reshape workspce workspace_depth_ = curr_depth_; // the depth value used for octree2col if (is_deconvolution_layer() && stride_ == 2) workspace_depth_++; - workspace_h_ = btm_h; + workspace_h_ = btm_h; // qq: node num if (stride_ == 2) { if (is_deconvolution_layer()) { workspace_h_ = top_h >> 3; } else { workspace_h_ = btm_h >> 3; } } workspace_n_ = 1; - workspace_ha_ = workspace_h_; - int ideal_size = workspace_h_ * kernel_dim_; + workspace_ha_ = workspace_h_; // qq: node num + uint64 ideal_size = (uint64) workspace_h_ * (uint64) kernel_dim_; // qq: workspace_h_ here is node num at curr_depth. idea size 10 million * 24*27 if (ideal_size > MAX_SIZE && !is_1x1_) { - workspace_n_ = (ideal_size + MAX_SIZE - 1) / MAX_SIZE; - workspace_ha_ = (workspace_h_ + workspace_n_ - 1) / workspace_n_; + workspace_n_ = (ideal_size + MAX_SIZE - 1) / MAX_SIZE; // qq: thousands. + workspace_ha_ = (workspace_h_ + workspace_n_ - 1) / workspace_n_; // qq: million/thousand } workspace_shape_ = vector { kernel_dim_, workspace_ha_}; diff --git a/octree/octree/octree_conv.h b/octree/octree/octree_conv.h index de4b20f..5ab884e 100644 --- a/octree/octree/octree_conv.h +++ b/octree/octree/octree_conv.h @@ -76,10 +76,10 @@ class OctreeBaseConv { const int* ni_cpu_ptr_; // hold cpu data from NeighHelper::get_ni(kernel_size_) const int* ni_gpu_ptr_; // hold gpu data from NeighHelper::get_ni(kernel_size_) + uint64 MAX_SIZE; + GEMMEngine* engine_cpu_; GEMMEngine* engine_gpu_; - - int MAX_SIZE; }; } // namespace octree diff --git a/octree/octree/octree_info.cpp b/octree/octree/octree_info.cpp index e53462f..66f8db3 100644 --- a/octree/octree/octree_info.cpp +++ b/octree/octree/octree_info.cpp @@ -1,9 +1,14 @@ #include "octree_info.h" #include "points.h" +#include "types.h" #include +#ifdef KEY64 +const char OctreeInfo::kMagicStr[16] = "_OCTREE_2.0_"; +#else const char OctreeInfo::kMagicStr[16] = "_OCTREE_1.0_"; +#endif void OctreeInfo::initialize(int depth, int full_depth, bool node_displacement, bool node_feature, bool split_label, bool adaptive, int adaptive_depth, @@ -59,7 +64,7 @@ void OctreeInfo::initialize(int depth, int full_depth, bool node_displacement, set_location(OctreeInfo::kLabel, location); } - // init bounding box + // init bounding box bbmin_[0] = bbmin_[1] = bbmin_[2] = -1.0f; bbmax_[0] = bbmax_[1] = bbmax_[2] = 1.0f; @@ -74,14 +79,16 @@ void OctreeInfo::reset() { bool OctreeInfo::check_format(string& msg) const { msg.clear(); + const int max_depth = strcmp(kMagicStr, "_OCTREE_2.0_") ? 8 : 16; if (strcmp(kMagicStr, magic_str_) != 0) { - msg += "The version of octree format is not " + string(kMagicStr) + ".\n"; + msg += "The version of the provided octree format is " + + string(magic_str_) + ", not " + string(kMagicStr) + ".\n"; } if (batch_size_ < 0) { msg += "The batch_size_ should be larger than 0.\n"; } - if (depth_ < 1 || depth_ > 10) { - msg += "The depth_ should be in range [1, 8].\n"; + if (depth_ < 1 || depth_ > max_depth) { + msg += "The depth_ should be in [1, " + std::to_string(max_depth) + "].\n"; } if (full_layer_ < 0 || full_layer_ > depth_) { msg += "The full_layer_ should be in range [1, depth_].\n"; @@ -89,7 +96,7 @@ bool OctreeInfo::check_format(string& msg) const { if (is_adaptive_ && (adp_layer_ < full_layer_ || adp_layer_ > depth_)) { msg += "The adp_layer_ should be in range [full_layer_, depth_].\n"; } - const int channel_max[] = { 2, 1, 8, 1 << 30, 1, 1 }; + const int channel_max[] = { 2, 1, 8, 1 << 30, 1, 1 }; // qq: question, what does this do? kKey 2, kChild 1, kNeigh 8, kFeature 1<<30, kLabel 1, kSplit 1. Why child 1 and kneigh 8? for (int i = 0; i < kPTypeNum; ++i) { string str = std::to_string(i); if (channels_[i] < 0 && channels_[i] > channel_max[i]) { @@ -127,19 +134,33 @@ int OctreeInfo::channel(PropType ptype) const { return channels_[i]; } +int OctreeInfo::size_of(PropType ptype) const { + int sz = 0; + if (ptype == kChild || ptype == kNeigh) { // qq: question what is kNeigh exactly? + sz = sizeof(int); + } else if (ptype == kFeature || ptype == kLabel || ptype == kSplit) { + sz = sizeof(float); + } else if (ptype == kKey) { + sz = sizeof(uintk); + } else { + // pass + } + return sz; +} + int OctreeInfo::locations(PropType ptype) const { if (!has_property(ptype)) return 0; int i = property_index(ptype); return locations_[i]; } -int OctreeInfo::ptr_dis(PropType ptype, const int depth) const { +int64_t OctreeInfo::ptr_dis(PropType ptype, const int depth) const { if (!has_property(ptype)) return -1; int i = property_index(ptype); - int dis = ptr_dis_[i]; + int64_t dis = ptr_dis_[i]; //int dis = ptr_dis_[i]; if (locations(ptype) == -1) { - // !!! Note: sizeof(int) - dis += nnum_cum_[depth] * channel(ptype) * sizeof(int); + dis += (int64_t) nnum_cum_[depth] * channel(ptype) * size_of(ptype); // qq: TODO: dis should be long + if (dis==0){std::cout << "Warning in OctreeInfo: ptr_dis\n";} } else { // ignore the input parameter depth } @@ -233,7 +254,9 @@ void OctreeInfo::set_location(PropType ptype, int lc) { void OctreeInfo::set_ptr_dis() { // the accumulated pointer displacement - ptr_dis_[0] = sizeof(OctreeInfo); + // !!! ALERT !!! Make ptr_dis_[0] be equal to 8*n, otherwise there will be + // the memory alignment issue for CUDA when the we use 64 bit key. + ptr_dis_[0] = (sizeof(OctreeInfo) + 7) / 8 * 8; for (int i = 1; i <= kPTypeNum; ++i) { // note the " <= " is used here PropType ptype = static_cast(1 << (i - 1)); int lc = locations(ptype); @@ -241,8 +264,7 @@ void OctreeInfo::set_ptr_dis() { // If the property do not exist, lc is equal to 0, then num = 8, both of them // are meaningless. Their values are wiped out by channels_[i - 1] (= 0). // So the value of ptr_dis_[i] is still correct. - // !!! Note: sizeof(int) - ptr_dis_[i] = ptr_dis_[i - 1] + sizeof(int) * num * channels_[i - 1]; + ptr_dis_[i] = ptr_dis_[i - 1] + (int64_t) size_of(ptype) * num * channels_[i - 1]; // qq: TODO: here the size_of could be 4, num could be ~10 million, channels_ could be 24. int up to 2 billion. } } diff --git a/octree/octree/octree_info.h b/octree/octree/octree_info.h index de91d56..b3e7895 100644 --- a/octree/octree/octree_info.h +++ b/octree/octree/octree_info.h @@ -1,7 +1,9 @@ #ifndef _OCTREE_OCTREE_INFO_ #define _OCTREE_OCTREE_INFO_ +#include #include +#include class Points; using std::string; @@ -44,8 +46,9 @@ class OctreeInfo { int total_nnum_capacity() const { return nnum_cum_[depth_ + 2]; } int content_flags() const { return content_flags_; } int channel(PropType ptype) const; - int locations(PropType ptype) const; - int ptr_dis(PropType ptype, const int depth) const; + int size_of(PropType ptype) const; + int locations(PropType ptype) const; // qq: -1 or d, or 0 + int64_t ptr_dis(PropType ptype, const int depth) const; //int ptr_dis(PropType ptype, const int depth) const; // qq: TODO: long float bbox_max_width() const; bool is_key2xyz() const { return key2xyz_; } bool extrapolate() const { return extrapolate_; } @@ -54,7 +57,8 @@ class OctreeInfo { const float* bbmax() const { return bbmax_; } // todo: modify sizeof_octinfo() according to magic_str_ for back-compatibility int sizeof_octinfo() const { return sizeof(OctreeInfo); } - int sizeof_octree() const { return ptr_dis_[kPTypeNum]; } + int64_t sizeof_octree() const { return ptr_dis_[kPTypeNum]; } // qq: TODO: long + void set_magic_str(const char* str) { strcpy(magic_str_, str); } void set_content_flags(int cf); void set_batch_size(int b); void set_depth(int d); @@ -107,11 +111,7 @@ class OctreeInfo { char reserved_[254]; // reserved for future usage: 2018/12/12 private: - int ptr_dis_[16]; + int64_t ptr_dis_[16]; //int ptr_dis_[16]; // qq: what is this used for? // qq: TODO: long }; #endif // _OCTREE_OCTREE_INFO_t - -// bytes need for info: 16*1 + 4*4 + 1(4) + 4*2 + 1*2(2) + 16*4*3 + 4 + 16*4*2 + 3*4*2 + 2 + 254*1 + 16*4 + public: 1 + 4 -// static are omitted -// memory allignment of class in cpp \ No newline at end of file diff --git a/octree/octree/octree_nn.cpp b/octree/octree/octree_nn.cpp index 1235636..d6bf3ec 100644 --- a/octree/octree/octree_nn.cpp +++ b/octree/octree/octree_nn.cpp @@ -119,18 +119,6 @@ vector& NeighHelper::get_ni(const vector& kernel_size) { } -template -void memset_cpu(const int N, const Dtype alpha, Dtype* Y) { - if (alpha == 0) { - memset(Y, 0, sizeof(Dtype) * N); - return; - } - for (int i = 0; i < N; ++i) { - Y[i] = alpha; - } -} - - int num_elements(const vector& vec) { int count = vec.empty() ? 0 : 1; for (auto v : vec) { count *= v; } @@ -152,9 +140,21 @@ void resize_with_last_val(vector& vec, const int size) { } +template +void memset_cpu(const int N, const Dtype alpha, Dtype* Y) { + if (alpha == 0) { + memset(Y, 0, sizeof(Dtype) * N); // qq: does memset work with larger value? + return; + } + for (int i = 0; i < N; ++i) { + Y[i] = alpha; + } +} + + template void memcpy_cpu(const int N, const Dtype* X, Dtype* Y) { - if (X != Y) { + if (X != Y && N > 0) { memcpy(Y, X, sizeof(Dtype) * N); } } @@ -245,7 +245,7 @@ void octree_max_pool_cpu(Dtype* top_data, int top_h, int* mask, const Dtype* btm_data, int btm_h, int channel) { for (int c = 0; c < channel; ++c) { for (int h = 0; h < top_h; ++h) { - int hb = 8 * h; + int hb = 8 * h; // qq: TODO: long: do not have to, just involve num_nodes top_data[h] = btm_data[hb]; mask[h] = hb; for (int idx = hb + 1; idx < hb + 8; ++idx) { @@ -266,7 +266,7 @@ void octree_max_pool_cpu(Dtype* top_data, int top_h, int* mask, template void octree_max_unpool_cpu(const Dtype* top_data, int top_h, const int* mask, Dtype* btm_data, int btm_h, int channel) { - memset_cpu(btm_h * channel, Dtype(0), btm_data); + memset_cpu(btm_h * channel, Dtype(0), btm_data); // qq: TODO: long for (int c = 0; c < channel; ++c) { for (int h = 0; h < top_h; ++h) { @@ -296,25 +296,25 @@ void octree_mask_pool_cpu(Dtype* top_data, int top_h, const int* mask, } -void calc_neigh_cpu(int* neigh_split, const int* neigh, +void calc_neigh_cpu(int* neigh_split, const int* neigh, // qq: here neigh_split is for current level d, neigh, children, node_num are for d-1. const int* children, const int node_num) { const int* parent = NeighHelper::Get().get_parent_array().data(); const int* dis = NeighHelper::Get().get_dis_array().data(); //#pragma omp parallel for - for (int i = 0; i < node_num; ++i) { + for (int i = 0; i < node_num; ++i) { // qq: for every node in d-1, int l0 = children[i]; - if (l0 == -1) continue; - const int* ngh0 = neigh + (i >> 3 << 6); + if (l0 == -1) continue; // qq: if -1, means no child, empty + const int* ngh0 = neigh + (i >> 3 << 6); // qq: the children's index in level d const int* pi0 = parent + (i % 8) * 64; - int* ngh1 = neigh_split + (l0 << 6); + int* ngh1 = neigh_split + (l0 << 6); // qq: l0 could be 10 million, << 6 is times 64. might be too large when we have large batch size. for (int j = 0; j < 64; ++j) { ngh1[j] = -1; int k = ngh0[pi0[j]]; if (k != -1) { int l1 = children[k]; if (l1 != -1) { - ngh1[j] = (l1 << 3) + dis[j]; + ngh1[j] = (l1 << 3) + dis[j]; // qq: l1<<3 could 80 million with bs=1. dis is small. } } } @@ -322,32 +322,32 @@ void calc_neigh_cpu(int* neigh_split, const int* neigh, } void calc_neigh_cpu(int* neigh, const int depth, const int batch_size) { - uint32 node_num = 1 << 3 * depth; - const uint32 bound = 1 << depth; - for (uint32 n = 0; n < batch_size; ++n) { - for (uint32 i = 0; i < node_num; i += 8) { + uintk node_num = 1 << 3 * depth; + const uintk bound = 1 << depth; + for (uintk n = 0; n < batch_size; ++n) { + for (uintk i = 0; i < node_num; i += 8) { // key to xyz - uint32 x0 = 0, y0 = 0, z0 = 0; - for (uint32 d = 0; d < depth; d++) { + uintk x0 = 0, y0 = 0, z0 = 0; + for (uintk d = 0; d < depth; d++) { x0 |= (i & (1 << (3 * d + 2))) >> (2 * d + 2); y0 |= (i & (1 << (3 * d + 1))) >> (2 * d + 1); z0 |= (i & (1 << (3 * d + 0))) >> (2 * d + 0); } - for (uint32 x = 0; x < 4; ++x) { - for (uint32 y = 0; y < 4; ++y) { - for (uint32 z = 0; z < 4; ++z) { - uint32 x1 = x0 + x - 1; - uint32 y1 = y0 + y - 1; - uint32 z1 = z0 + z - 1; + for (uintk x = 0; x < 4; ++x) { + for (uintk y = 0; y < 4; ++y) { + for (uintk z = 0; z < 4; ++z) { + uintk x1 = x0 + x - 1; + uintk y1 = y0 + y - 1; + uintk z1 = z0 + z - 1; int v = -1; if ((x1 & bound) == 0 && (y1 & bound) == 0 && (z1 & bound) == 0) { - uint32 key1 = 0; + uintk key1 = 0; for (int d = 0; d < depth; d++) { - uint32 mask = 1u << d; + uintk mask = 1u << d; key1 |= ((x1 & mask) << (2 * d + 2)) | ((y1 & mask) << (2 * d + 1)) | ((z1 & mask) << (2 * d)); @@ -355,7 +355,7 @@ void calc_neigh_cpu(int* neigh, const int depth, const int batch_size) { v = key1 + n * node_num; } - uint32 xyz = (x << 4) | (y << 2) | z; + uintk xyz = (x << 4) | (y << 2) | z; neigh[xyz + i * 8 + n * node_num * 8] = v; } } @@ -365,15 +365,16 @@ void calc_neigh_cpu(int* neigh, const int depth, const int batch_size) { } -void generate_key_cpu(uint32* key_child, const uint32* key, const int* child, +template +void generate_key_cpu(Dtype* key_child, const Dtype* key, const int* child, const int node_num) { - typedef unsigned char ubyte; + typedef typename KeyTrait::uints T; for (int i = 0; i < node_num; ++i) { int label = child[i]; - if (label == -1) continue; - const ubyte* k0 = (const ubyte*)(key + i); - for (ubyte j = 0; j < 8; ++j) { - ubyte* k1 = (ubyte*)(key_child + 8 * label + j); + if (label < 0) continue; // empty + const T* k0 = reinterpret_cast(key + i); + for (T j = 0; j < 8; ++j) { + T* k1 = reinterpret_cast(key_child + 8 * label + j); k1[0] = (k0[0] << 1) | ((j & 4) >> 2); k1[1] = (k0[1] << 1) | ((j & 2) >> 1); k1[2] = (k0[2] << 1) | (j & 1); @@ -382,12 +383,14 @@ void generate_key_cpu(uint32* key_child, const uint32* key, const int* child, } } -void generate_key_cpu(uint32* key, const int depth, const int batch_size) { +template +void generate_key_cpu(Dtype* key, const int depth, const int batch_size) { + typedef typename KeyTrait::uints T; int node_num = 1 << 3 * depth; for (int n = 0; n < batch_size; ++n) { for (int k = 0; k < node_num; ++k) { - unsigned xyz = 0; - unsigned char* ptr = (unsigned char*)(&xyz); + Dtype xyz = 0; + T* ptr = reinterpret_cast(&xyz); for (int d = 0; d < depth; d++) { ptr[0] |= (k & (1 << (3 * d + 2))) >> (2 * d + 2); ptr[1] |= (k & (1 << (3 * d + 1))) >> (2 * d + 1); @@ -428,8 +431,9 @@ void bilinear_neigh_cpu(int* bidx, const int* neigh, const int* child, } } -void bilinear_xyz_cpu(uint32* xyz0, float* fracs, const int d0, const uint32* xyz1, +void bilinear_xyz_cpu(uintk* xyz0, float* fracs, const int d0, const uintk* xyz1, const int d1, const int num) { + typedef typename KeyTrait::uints uints; const float scale = static_cast(1 << (d1 - d0)); const int mask[8][3] = { // bilinear mask: {0, 0, 0}, {0, 0, 1}, {0, 1, 0}, {1, 0, 0}, // 27, 9, 9, 9 @@ -440,7 +444,7 @@ void bilinear_xyz_cpu(uint32* xyz0, float* fracs, const int d0, const uint32* xy float pt[3] = { 0.0f }; float* frac = fracs + 3 * i; int bnd[2][3] = { 0 }; - const unsigned char* ptr1 = (const unsigned char*)(xyz1 + i); + const uints* ptr1 = (const uints*)(xyz1 + i); for (int c = 0; c < 3; ++c) { pt[c] = (static_cast(ptr1[c]) + 0.5f) / scale - 0.5f; @@ -457,9 +461,9 @@ void bilinear_xyz_cpu(uint32* xyz0, float* fracs, const int d0, const uint32* xy } for (int j = 0; j < 8; ++j) { - unsigned char* ptr0 = (unsigned char*)(xyz0 + i * 8 + j); + uints* ptr0 = (uints*)(xyz0 + i * 8 + j); for (int c = 0; c < 3; ++c) { - ptr0[c] = static_cast(bnd[mask[j][c]][c]); + ptr0[c] = static_cast(bnd[mask[j][c]][c]); } ptr0[3] = ptr1[3]; } @@ -467,8 +471,16 @@ void bilinear_xyz_cpu(uint32* xyz0, float* fracs, const int d0, const uint32* xy } -void search_key_cpu(int* idx, const uint32* key, const int n_key, - const uint32* query, const int n_query) { +template +void sequence_cpu(Dtype* ptr, const int num) { + for (int i = 0; i < num; ++i) { + ptr[i] = static_cast(i); + } +} + +template +void search_key_cpu(int* idx, const Dtype* key, const int n_key, + const Dtype* query, const int n_query) { for (int i = 0; i < n_query; ++i) { int j = std::lower_bound(key, key + n_key, query[i]) - key; idx[i] = (j >= n_key || key[j] != query[i]) ? -1 : j; @@ -476,23 +488,26 @@ void search_key_cpu(int* idx, const uint32* key, const int n_key, } -void compute_key(uint32& key, const uint32* pt, const int depth) { +template +void compute_key(Dtype& key, const Dtype* pt, const int depth) { key = 0; for (int i = 0; i < depth; i++) { - uint32 mask = 1u << i; + Dtype mask = 1u << i; for (int j = 0; j < 3; j++) { key |= (pt[j] & mask) << (2 * i + 2 - j); } } } -void compute_pt(uint32* pt, const uint32& key, const int depth) { + +template +void compute_pt(Dtype* pt, const Dtype& key, const int depth) { for (int i = 0; i < 3; pt[i++] = 0u); for (int i = 0; i < depth; i++) { for (int j = 0; j < 3; j++) { // bit mask - uint32 mask = 1u << (3 * i + 2 - j); + Dtype mask = 1u << (3 * i + 2 - j); // put the bit to position i pt[j] |= (key & mask) >> (2 * i + 2 - j); } @@ -500,14 +515,16 @@ void compute_pt(uint32* pt, const uint32& key, const int depth) { } -// NOTE: !!! currently the depth should be less than 8 -void xyz2key_cpu(uint32* key, const uint32* xyz, const int num, const int depth) { +template +void xyz2key_cpu(Dtype* key, const Dtype* xyz, const int num, const int depth) { + typedef typename KeyTrait::uints T; + for (int i = 0; i < num; ++i) { - uint32 pt[3] = { 0, 0, 0 }, key_out = 0; - const unsigned char* ptr = reinterpret_cast(xyz + i); - unsigned char* ptr_out = (unsigned char*)(&key_out); + Dtype pt[3] = { 0, 0, 0 }, key_out = 0; + const T* ptr = reinterpret_cast(xyz + i); + T* ptr_out = reinterpret_cast(&key_out); for (int j = 0; j < 3; ++j) { - pt[j] = static_cast(ptr[j]); + pt[j] = static_cast(ptr[j]); } compute_key(key_out, pt, depth); ptr_out[3] = ptr[3]; @@ -515,32 +532,38 @@ void xyz2key_cpu(uint32* key, const uint32* xyz, const int num, const int depth) } } +template +void key2xyz_cpu(Dtype* xyz, const Dtype* key, const int num, const int depth) { + typedef typename KeyTrait::uints T; -void key2xyz_cpu(uint32* xyz, const uint32* key, const int num, const int depth) { for (int i = 0; i < num; ++i) { - uint32 pt[3] = { 0 }; + Dtype pt[3] = { 0 }; compute_pt(pt, key[i], depth); xyz[i] = key[i]; - unsigned char* ptr = reinterpret_cast(xyz + i); + T* ptr = reinterpret_cast(xyz + i); for (int j = 0; j < 3; ++j) { - ptr[j] = static_cast(pt[j]); + ptr[j] = static_cast(pt[j]); } } } -void key2idx_cpu(int* idx, const uint32* key, const int num) { +template +void key2idx_cpu(int* idx, const Dtype* key, const int num) { + typedef typename KeyTrait::uints T; for (int i = 0; i < num; ++i) { - const unsigned char* ptr = reinterpret_cast(key + i); + const T* ptr = reinterpret_cast(key + i); idx[i] = static_cast(ptr[3]); } } -void xyz2coord_cpu(float* pt, const uint32* xyz, const int num, const int channel) { +template +void xyz2coord_cpu(float* pt, const Dtype* xyz, const int num, const int channel) { + typedef typename KeyTrait::uints T; for (int i = 0; i < num; ++i) { - const unsigned char* ptr = reinterpret_cast(xyz + i); + const T* ptr = reinterpret_cast(xyz + i); for (int c = 0; c < channel; ++c) { pt[c * num + i] = static_cast(ptr[c]); } @@ -548,22 +571,24 @@ void xyz2coord_cpu(float* pt, const uint32* xyz, const int num, const int channe } -void coord2xyz_cpu(uint32* xyz, const float* pt, const int num, const int channel) { +template +void coord2xyz_cpu(Dtype* xyz, const float* pt, const int num, const int channel) { + typedef typename KeyTrait::uints T; for (int i = 0; i < num; ++i) { - unsigned char* ptr = reinterpret_cast(xyz + i); + T* ptr = reinterpret_cast(xyz + i); for (int c = 0; c < channel; ++c) { - ptr[c] = static_cast(pt[c * num + i]); + ptr[c] = static_cast(pt[c * num + i]); } } } -template -void key2xyz(Dtype* xyz, const uint32 key, const int depth) { - uint32 pt[3]; +template +void key2xyz(Dtype1* xyz, const Dtype2 key, const int depth) { + Dtype2 pt[3]; compute_pt(pt, key, depth); for (int c = 0; c < 3; ++c) { - xyz[c] = static_cast(pt[c]); + xyz[c] = static_cast(pt[c]); } } @@ -578,6 +603,8 @@ template void memcpy_cpu(const int N, const int* X, int* Y); template void memcpy_cpu(const int N, const unsigned* X, unsigned* Y); template void memcpy_cpu(const int N, const float* X, float* Y); template void memcpy_cpu(const int N, const double* X, double* Y); +template void sequence_cpu(int* ptr, const int num); +template void sequence_cpu(uintk* ptr, const int num); template void pad_forward_cpu(float* Y, const int Hy, const int Cy, const float* X, const int Hx, const int* label, const float dval); template void pad_forward_cpu(double* Y, const int Hy, const int Cy, @@ -616,6 +643,22 @@ template void octree_mask_pool_cpu(float* top_data, int top_h, const int* mask, const float* btm_data, int bottom_h, int channel); template void octree_mask_pool_cpu(double* top_data, int top_h, const int* mask, const double* btm_data, int bottom_h, int channel); -template void key2xyz(float* xyz, const uint32 key, const int d); -template void key2xyz(unsigned* xyz, const uint32 key, const int d); -template void key2xyz(int* xyz, const uint32 key, const int d); \ No newline at end of file +template void key2xyz(float* xyz, const uintk key, const int d); +template void key2xyz(uintk* xyz, const uintk key, const int d); +template void key2xyz(int* xyz, const uintk key, const int d); +template void generate_key_cpu(uintk* key, const int depth, const int batch_size); +template void generate_key_cpu(uintk* key_child, const uintk* key, + const int* child, const int node_num); +template void compute_key(uintk& key, const uintk* pt, const int depth); +template void compute_pt(uintk* pt, const uintk& key, const int depth); +template void search_key_cpu(int* idx, const uintk* key, const int n_key, + const uintk* query, const int n_query); +template void xyz2key_cpu(uintk* key, const uintk* xyz, const int num, + const int depth); +template void key2xyz_cpu(uintk* xyz, const uintk* key, const int num, + const int depth); +template void key2idx_cpu(int* idx, const uintk* key, const int num); +template void xyz2coord_cpu(float* pt, const uintk* xyz, const int num, + const int channel); +template void coord2xyz_cpu(uintk* xyz, const float* pt, const int num, + const int channel); \ No newline at end of file diff --git a/octree/octree/octree_nn.cu b/octree/octree/octree_nn.cu index cbd72ea..943014d 100644 --- a/octree/octree/octree_nn.cu +++ b/octree/octree/octree_nn.cu @@ -272,7 +272,7 @@ __global__ void calc_neigh_kernel(int* neigh_split, const int* neigh, void calc_neigh_gpu(int* neigh_split, const int* neigh, const int* children, const int node_num, const int* parent, const int* dis) { - int n = node_num << 6; // node_num: the non_empty node number of parent layer + int n = node_num << 6; // node_num: the non_empty node number of parent layer // qq: 2 million * 2^6 < 2billion. calc_neigh_kernel <<< CudaGetBlocks(n), kCudaThreadsNum >>> ( neigh_split, neigh, children, parent, dis, n); } @@ -323,7 +323,7 @@ __global__ void calc_full_neigh_kernel(int* neigh, const int depth, } void calc_neigh_gpu(int* neigh, const int depth, const int batch_size) { - int thread_num = batch_size * (1 << 3 * depth + 3); + int thread_num = batch_size * (1 << 3 * depth + 3); // qq: TODO: long? calc_full_neigh_kernel <<< CudaGetBlocks(thread_num), kCudaThreadsNum >>> ( neigh, depth, batch_size, thread_num); CUDA_POST_KERNEL_CHECK; @@ -382,7 +382,7 @@ __global__ void gen_full_key_kernel(Dtype* key, const int depth, template void generate_key_gpu(Dtype* key, const int depth, const int batch_size) { - int thread_num = batch_size * (1 << 3 * depth); + int thread_num = batch_size * (1 << 3 * depth); // qq: TODO: 1 << 3 * depth is much more than int gen_full_key_kernel <<< CudaGetBlocks(thread_num), kCudaThreadsNum >>> ( key, depth, batch_size, thread_num); CUDA_POST_KERNEL_CHECK; diff --git a/octree/octree/octree_nn.h b/octree/octree/octree_nn.h index 545edec..9ac22de 100644 --- a/octree/octree/octree_nn.h +++ b/octree/octree/octree_nn.h @@ -1,15 +1,15 @@ #ifndef _OCTREE_OCTREE_NN_ #define _OCTREE_OCTREE_NN_ -#include #include #include #include +#include "types.h" + using std::string; using std::vector; using std::unordered_map; -typedef uint32_t uint32; // A singleton class to hold global & common stuff for octree neighbor @@ -49,8 +49,8 @@ class NeighHelper { // !!! TODO: support gpu stream for gpu functions !!! -int num_elements(const vector& vec); -void resize_with_last_val(vector& vec, const int size); +int num_elements(const vector& shape); +void resize_with_last_val(vector& shape, const int size); template void memset_cpu(const int N, const Dtype alpha, Dtype *X); @@ -125,13 +125,17 @@ void calc_neigh_gpu(int* neigh_child, const int* neigh, const int* children, void calc_neigh_cpu(int* neigh, const int depth, const int batch_size); void calc_neigh_gpu(int* neigh, const int depth, const int batch_size); - -void generate_key_gpu(uint32* key_child, const uint32* key, const int* child, +// !!! The generated key is in the format of zipped (x,y,z,id) +template +void generate_key_gpu(Dtype* key_child, const Dtype* key, const int* child, const int node_num); -void generate_key_cpu(uint32* key_child, const uint32* key, const int* child, +template +void generate_key_cpu(Dtype* key_child, const Dtype* key, const int* child, const int node_num); -void generate_key_gpu(uint32* key, const int depth, const int batch_size); -void generate_key_cpu(uint32* key, const int depth, const int batch_size); +template +void generate_key_gpu(Dtype* key, const int depth, const int batch_size); +template +void generate_key_cpu(Dtype* key, const int depth, const int batch_size); template @@ -146,21 +150,25 @@ void bilinear_neigh_cpu(int* bidx, const int* neigh, const int* child, const int node_num, const int* table); void bilinear_neigh_gpu(int* bidx, const int* neigh, const int* child, const int node_num, const int* table); -void bilinear_xyz_cpu(uint32* xyz0, float* dis, const int d0, const uint32* xyz1, +void bilinear_xyz_cpu(uintk* xyz0, float* dis, const int d0, const uintk* xyz1, const int d1, const int num); -void bilinear_xyz_gpu(uint32* xyz0, float* dis, const int d0, const uint32* xyz1, +void bilinear_xyz_gpu(uintk* xyz0, float* dis, const int d0, const uintk* xyz1, const int d1, const int num); +template +void sequence_cpu(Dtype* ptr, const int num); template void sequence_gpu(Dtype* ptr, const int num); // TODO: The performance can be improved via descent along the octree -void search_key_cpu(int* idx, const uint32* key, const int n_key, - const uint32* query, const int n_query); -void search_key_gpu(int* idx, const uint32* key, const int n_key, - const uint32* query, const int n_query); +template +void search_key_cpu(int* idx, const Dtype* key, const int n_key, + const Dtype* query, const int n_query); +template +void search_key_gpu(int* idx, const Dtype* key, const int n_key, + const Dtype* query, const int n_query); template @@ -191,24 +199,35 @@ void octree_mask_gpu(float* out_data, const float* in_data, const int* label, // !!! Caveat: for the following two functions, pt and depth // must be consistent, i.e pt must be in the range [0, 2^depth]^3 -void compute_key(uint32& key, const uint32* pt, const int depth); -void compute_pt(uint32* pt, const uint32& key, const int depth); +template +void compute_key(Dtype& key, const Dtype* pt, const int depth); +template +void compute_pt(Dtype* pt, const Dtype& key, const int depth); -void xyz2key_cpu(uint32* key, const uint32* xyz, const int num, const int depth); -void xyz2key_gpu(uint32* key, const uint32* xyz, const int num, const int depth); -void key2xyz_cpu(uint32* xyz, const uint32* key, const int num, const int depth); -void key2xyz_gpu(uint32* xyz, const uint32* key, const int num, const int depth); +template +void xyz2key_cpu(Dtype* key, const Dtype* xyz, const int num, const int depth); +template +void xyz2key_gpu(Dtype* key, const Dtype* xyz, const int num, const int depth); +template +void key2xyz_cpu(Dtype* xyz, const Dtype* key, const int num, const int depth); +template +void key2xyz_gpu(Dtype* xyz, const Dtype* key, const int num, const int depth); -void key2idx_cpu(int* idx, const uint32* key, const int num); -void key2idx_gpu(int* idx, const uint32* key, const int num); +template +void key2idx_cpu(int* idx, const Dtype* key, const int num); +template +void key2idx_gpu(int* idx, const Dtype* key, const int num); -void xyz2coord_cpu(float* pt, const uint32* xyz, const int num, const int channel); -void xyz2coord_gpu(float* pt, const uint32* xyz, const int num, const int channel); -void coord2xyz_cpu(uint32* xyz, const float* pt, const int num, const int channel); -void coord2xyz_gpu(uint32* xyz, const float* pt, const int num, const int channel); +template +void xyz2coord_cpu(float* pt, const Dtype* xyz, const int num, const int channel); +template +void xyz2coord_gpu(float* pt, const Dtype* xyz, const int num, const int channel); +template +void coord2xyz_cpu(Dtype* xyz, const float* pt, const int num, const int channel); +template +void coord2xyz_gpu(Dtype* xyz, const float* pt, const int num, const int channel); -//int content_flag(string str); -template -void key2xyz(Dtype* xyz, const uint32 key, const int depth); +template +void key2xyz(Dtype1* xyz, const Dtype2 key, const int depth); #endif // _OCTREE_OCTREE_NN_ diff --git a/octree/octree/octree_parser.cpp b/octree/octree/octree_parser.cpp index 58b8782..8189151 100644 --- a/octree/octree/octree_parser.cpp +++ b/octree/octree/octree_parser.cpp @@ -36,8 +36,8 @@ const char* OctreeParser::ptr_cpu(const PropType ptype, const int depth) const { return p; } -const uint32* OctreeParser::key_cpu(const int depth) const { - return reinterpret_cast(ptr_cpu(OctreeInfo::kKey, depth)); +const uintk* OctreeParser::key_cpu(const int depth) const { + return reinterpret_cast(ptr_cpu(OctreeInfo::kKey, depth)); } const int* OctreeParser::children_cpu(const int depth) const { @@ -65,8 +65,8 @@ char* OctreeParser::mutable_ptr_cpu(const OctreeInfo::PropType ptype, const int return const_cast(ptr_cpu(ptype, depth)); } -uint32* OctreeParser::mutable_key_cpu(const int depth) { - return reinterpret_cast(mutable_ptr_cpu(OctreeInfo::kKey, depth)); +uintk* OctreeParser::mutable_key_cpu(const int depth) { + return reinterpret_cast(mutable_ptr_cpu(OctreeInfo::kKey, depth)); } int* OctreeParser::mutable_children_cpu(const int depth) { @@ -92,7 +92,7 @@ float* OctreeParser::mutable_split_cpu(const int depth) { ////////////////////////////////////// void OctreeParser::node_pos(float* xyz, int id, int depth, float* xyz_base) const { - const uint32* keyi = key_cpu(depth) + id; + const uintk* keyi = key_cpu(depth) + id; key2xyz(xyz, *keyi, depth); if (xyz_base != nullptr) { @@ -141,22 +141,21 @@ float OctreeParser::node_dis(int id, int depth) const { } template -void OctreeParser::key2xyz(Dtype* xyz, const uint32& key, const int depth) const { +void OctreeParser::key2xyz(Dtype* xyz, const uintk& key, const int depth) const { if (info_->is_key2xyz()) { - //!!! Caveat: the octree depth should be less than 8 - const unsigned char* pt = reinterpret_cast(&key); + typedef typename KeyTrait::uints uints; + const uints* pt = reinterpret_cast(&key); for (int c = 0; c < 3; ++c) { xyz[c] = static_cast(pt[c]); } - } - else { - uint32 pt[3]; + } else { + uintk pt[3]; compute_pt(pt, key, depth); for (int c = 0; c < 3; ++c) { xyz[c] = static_cast(pt[c]); } } } -template void OctreeParser::key2xyz(float* xyz, const unsigned& k, const int d) const; -template void OctreeParser::key2xyz(unsigned* xyz, const unsigned& k, const int d) const; -template void OctreeParser::key2xyz(int* xyz, const unsigned& k, const int d) const; +template void OctreeParser::key2xyz(float* xyz, const uintk& k, const int d) const; +template void OctreeParser::key2xyz(uintk* xyz, const uintk& k, const int d) const; +template void OctreeParser::key2xyz(int* xyz, const uintk& k, const int d) const; diff --git a/octree/octree/octree_parser.cu b/octree/octree/octree_parser.cu index c8e4572..e5809d6 100644 --- a/octree/octree/octree_parser.cu +++ b/octree/octree/octree_parser.cu @@ -29,7 +29,7 @@ void OctreeParser::set_gpu(void* ptr, OctreeInfo* octinfo) { const char* OctreeParser::ptr_gpu(const PropType ptype, const int depth) const { CHECK(d_metadata_ != nullptr); const char* p = nullptr; - int dis = info_->ptr_dis(ptype, depth); + int dis = info_->ptr_dis(ptype, depth); // qq: TODO: long if (-1 != dis) { p = d_metadata_ + dis; } diff --git a/octree/octree/octree_parser.h b/octree/octree/octree_parser.h index 54c9ed1..017a980 100644 --- a/octree/octree/octree_parser.h +++ b/octree/octree/octree_parser.h @@ -3,7 +3,8 @@ #include #include -#include + +#include "types.h" #include "octree_info.h" using std::vector; @@ -11,8 +12,6 @@ using std::string; class OctreeParser { public: - typedef uint32_t uint32; - typedef uint64_t uint64; typedef OctreeInfo::PropType PropType; enum NodeType {kNonEmptyLeaf = -2, kLeaf = -1, kInternelNode = 0 }; @@ -32,7 +31,7 @@ class OctreeParser { const char* ptr_raw_cpu() const { return h_metadata_; } const char* ptr_cpu(const PropType ptype, const int depth) const; - const uint32* key_cpu(const int depth) const; + const uintk* key_cpu(const int depth) const; const int* children_cpu(const int depth) const; const int* neighbor_cpu(const int depth) const; const float* feature_cpu(const int depth) const; @@ -41,7 +40,7 @@ class OctreeParser { const char* ptr_raw_gpu() const { return d_metadata_; } const char* ptr_gpu(const PropType ptype, const int depth) const; - const uint32* key_gpu(const int depth) const; + const uintk* key_gpu(const int depth) const; const int* children_gpu(const int depth) const; const int* neighbor_gpu(const int depth) const; const float* feature_gpu(const int depth) const; @@ -49,7 +48,7 @@ class OctreeParser { const float* split_gpu(const int depth) const; char* mutable_ptr_cpu(const PropType ptype, const int depth); - uint32* mutable_key_cpu(const int depth); + uintk* mutable_key_cpu(const int depth); int* mutable_children_cpu(const int depth); int* mutable_neighbor_cpu(const int depth); float* mutable_feature_cpu(const int depth); @@ -57,7 +56,7 @@ class OctreeParser { float* mutable_split_cpu(const int depth); char* mutable_ptr_gpu(const PropType ptype, const int depth); - uint32* mutable_key_gpu(const int depth); + uintk* mutable_key_gpu(const int depth); int* mutable_children_gpu(const int depth); int* mutable_neighbor_gpu(const int depth); float* mutable_feature_gpu(const int depth); @@ -69,7 +68,7 @@ class OctreeParser { void node_normal(float* n, int id, int depth) const; float node_dis(int id, int depth) const; template - void key2xyz(Dtype* xyz, const uint32& key, const int depth) const; + void key2xyz(Dtype* xyz, const uintk& key, const int depth) const; protected: // original pointer diff --git a/octree/octree/octree_samples.cpp b/octree/octree/octree_samples.cpp index 4e74506..a92dd04 100644 --- a/octree/octree/octree_samples.cpp +++ b/octree/octree/octree_samples.cpp @@ -1,4 +1,5 @@ #include "octree_samples.h" +#include "transform_octree.h" namespace octree { @@ -2138,18 +2139,31 @@ static const unsigned char octree_6[] = { 0000, 0000, 0000, 0000, 0000, 0000, 0x80, 0x3f, 0000, 0000, 0000, 0000, 0x00 }; -const struct embedded_file { +class embedded_file { + public: + embedded_file(const char* nm, const unsigned char* d, size_t sz) + : name(nm), data(d), size(sz) { +#ifdef KEY64 + vector octree_in(d, d + sz); + upgrade_key64(octree_64, octree_in); + data = (unsigned char *)octree_64.data(); + size = octree_64.size(); +#endif // KEY64 + } const char *name; const unsigned char *data; size_t size; -} embedded_octrees[] = { + vector octree_64; +}; + +embedded_file embedded_octrees[] = { { "octree_1", octree_1, sizeof(octree_1) - 1 }, { "octree_2", octree_2, sizeof(octree_2) - 1 }, { "octree_3", octree_3, sizeof(octree_3) - 1 }, { "octree_4", octree_4, sizeof(octree_4) - 1 }, { "octree_5", octree_5, sizeof(octree_5) - 1 }, - { "octree_6", octree_6, sizeof(octree_6) - 1 }, - { nullptr, nullptr, 0 } + { "octree_6", octree_6, sizeof(octree_6) - 1 } + //{ nullptr, nullptr, 0 } }; const unsigned char* get_one_octree(const char *name, size_t *size) { diff --git a/octree/octree/octree_value.cpp b/octree/octree/octree_value.cpp index 17012a1..7318226 100644 --- a/octree/octree/octree_value.cpp +++ b/octree/octree/octree_value.cpp @@ -2,6 +2,7 @@ #include "math_functions.h" #include +#include pair OctreeValue::fval(const float x, const float y, const float z) const { int oct_depth = octree_->info().depth(); @@ -36,7 +37,7 @@ pair OctreeValue::fval(const float x, const float y, const float z // This node has no children if (depth_child >= adp_depth) { octree_->node_normal(nm, i, depth_child); - float len = abs(nm[0]) + abs(nm[1]) + abs(nm[2]); + float len = fabsf(nm[0]) + fabsf(nm[1]) + fabsf(nm[2]); if (len != 0) { // This node has plane information phi += w * basis(pos_in, pos_child, nm); diff --git a/octree/octree/points.cpp b/octree/octree/points.cpp index f027ae2..ae87959 100644 --- a/octree/octree/points.cpp +++ b/octree/octree/points.cpp @@ -36,25 +36,6 @@ bool Points::write_points(const string& filename) const { return true; } -//bool Points::write_ply(const string & filename) const { -// std::ofstream outfile(filename, std::ios::binary); -// if (!outfile) return false; -// -// // write header -// int n = info_->pt_num(); -// outfile << "ply\nformat ascii 1.0\nelement vertex " << n -// << "\nproperty float x\nproperty float y\nproperty float z\n" -// << "property float nx\nproperty float ny\nproperty float nz\n" -// << "element face 0\nproperty list uchar int vertex_indices\n" -// << "end_header" << std::endl; -// -// // wirte contents -// const float* pts = ptr(PointsInfo::kPoint); -// const float* normals = ptr(PointsInfo::kNormal); -// if (normals == nullptr) { -// outfile.close(); -// return false; -// } bool Points::write_ply(const string & filename) const { std::ofstream outfile(filename, std::ios::binary); diff --git a/octree/octree/points.h b/octree/octree/points.h index 98d87ff..ebf4c98 100644 --- a/octree/octree/points.h +++ b/octree/octree/points.h @@ -18,6 +18,8 @@ class Points: public PointsParser { const vector& features = vector(), const vector& labels = vector()); void set_points(vector& data); // swap data and buffer_ + + const vector& get_buffer() const { return buffer_; } bool read_points(const string& filename); bool write_points(const string& filename) const; diff --git a/octree/octree/points_parser.cpp b/octree/octree/points_parser.cpp index d26a05e..ad68ca2 100644 --- a/octree/octree/points_parser.cpp +++ b/octree/octree/points_parser.cpp @@ -84,6 +84,8 @@ void PointsParser::scale(const float* s) { } } + if (s[0] == s[1] && s[0] == s[2]) { return; } // uniform scale + if (this->info().has_property(PointsInfo::kNormal)) { float* nm = this->mutable_normal(); for (int i = 0; i < npt; ++i) { @@ -216,3 +218,36 @@ void PointsParser::normalize() { this->translate(trans); this->uniform_scale(1 / (radius + 1.0e-10f)); } + +void PointsParser::orient_normal(const string axis) { + if (!this->info().has_property(PointsInfo::kNormal)) { + return; // directly return if there is no normal + } + + int npt = info_->pt_num(); + float* normal = this->mutable_normal(); + if (axis == "x" || axis == "y" || axis == "z") { + int j = 0; + if (axis == "y") j = 1; + if (axis == "z") j = 2; + for (int i = 0; i < npt; ++i) { + int ix3 = i * 3; + if (normal[ix3 + j] < 0) { + for (int c = 0; c < 3; ++c) { + normal[ix3 + c] = -normal[ix3 + c]; + } + } + } + } else if (axis == "xyz") { + for (int i = 0; i < npt; ++i) { + int ix3 = i * 3; + for (int c = 0; c < 3; ++c) { + if (normal[ix3 + c] < 0) { + normal[ix3 + c] = -normal[ix3 + c]; + } + } + } + } else { + // do nothing + } +} diff --git a/octree/octree/points_parser.h b/octree/octree/points_parser.h index 3c9e64c..9410edb 100644 --- a/octree/octree/points_parser.h +++ b/octree/octree/points_parser.h @@ -42,6 +42,7 @@ class PointsParser { void clip(const float* bbmin, const float* bbmax); void add_noise(const float std_pt, const float std_nm); void normalize(); // translate and scale the points to unit sphere + void orient_normal(const string axis); protected: diff --git a/octree/octree/transform_octree.cpp b/octree/octree/transform_octree.cpp index 9c8ec17..66fcc4f 100644 --- a/octree/octree/transform_octree.cpp +++ b/octree/octree/transform_octree.cpp @@ -1,13 +1,14 @@ #include "transform_octree.h" -#include "math_functions.h" + +#include +#include #include #include -#include -#include -void ScanOctree::set_scale(float scale) { - scale_ = scale; -} +#include "math_functions.h" +#include "types.h" + +void ScanOctree::set_scale(float scale) { scale_ = scale; } void ScanOctree::set_axis(const float* axis, int n) { if (n == 3) { @@ -25,7 +26,7 @@ void ScanOctree::scan(vector& octree_out, const OctreeParser& octree_in, // drop_flags: 1 - drop; 0 - keep, iff the node is visible and non-empty int depth = octree_in.info().depth(); int num = octree_in.info().node_num(depth); - vector > drop_flags(depth + 1); + vector> drop_flags(depth + 1); drop_flags[depth].resize(num, 1); const int axis_channel = 3; @@ -54,7 +55,6 @@ void ScanOctree::scan(vector& octree_out, const OctreeParser& octree_in, trim_octree(octree_out, octree_in, drop_flags); } - void ScanOctree::bbox_xy(int depth) { for (int i = 0; i < 3; ++i) { bbmin_[i] = std::numeric_limits::max(); @@ -62,7 +62,7 @@ void ScanOctree::bbox_xy(int depth) { } for (int i = 0; i < 8; ++i) { - float pt[3] = { i / 4, (i / 2) % 2, i % 2 }; + float pt[3] = {i / 4, (i / 2) % 2, i % 2}; float x = dot_prod(pt, x_); if (x < bbmin_[0]) bbmin_[0] = x; if (x > bbmax_[0]) bbmax_[0] = x; @@ -73,7 +73,7 @@ void ScanOctree::bbox_xy(int depth) { float z = dot_prod(pt, z_); if (z < bbmin_[2]) bbmin_[2] = z; - if (z > bbmax_[2])bbmax_[2] = z; + if (z > bbmax_[2]) bbmax_[2] = z; } float width = -1; @@ -86,7 +86,7 @@ void ScanOctree::bbox_xy(int depth) { if (dis > width) width = dis; } - width_ = static_cast(width * scale_) + 2; // slightly larger + width_ = static_cast(width * scale_) + 2; // slightly larger } void ScanOctree::reset_buffer() { @@ -94,21 +94,22 @@ void ScanOctree::reset_buffer() { z_buffer_.assign(width_ * width_, std::numeric_limits::max()); } -void ScanOctree::z_buffer(vector& drop_flags, const OctreeParser& octree_in) { +void ScanOctree::z_buffer(vector& drop_flags, + const OctreeParser& octree_in) { // reset the buffer reset_buffer(); const int depth = octree_in.info().depth(); const int num = octree_in.info().node_num(depth); - const unsigned int* key = octree_in.key_cpu(depth); + const uintk* key = octree_in.key_cpu(depth); const int* children = octree_in.children_cpu(depth); for (int i = 0; i < num; ++i) { - if (children[i] < 0) continue; // empty node + if (children[i] < 0) continue; // empty node float normal[3]; octree_in.node_normal(normal, i, depth); - if (dot_prod(normal, z_) >= 0) continue; // invisible node + if (dot_prod(normal, z_) >= 0) continue; // invisible node float pt[3]; octree_in.key2xyz(pt, key[i], depth); @@ -143,7 +144,7 @@ void ScanOctree::generate_flags(vector>& drop_flags, const int* child_d = octree_in.children_cpu(d); for (int i = 0; i < num; ++i) { int j = child_d[i]; - if (j < 0) continue; // empty node + if (j < 0) continue; // empty node int drop = 1; for (int k = j * 8; k < j * 8 + 8; ++k) { @@ -159,7 +160,8 @@ void ScanOctree::generate_flags(vector>& drop_flags, } void ScanOctree::trim_octree(vector& octree_buffer, - const OctreeParser& octree_in, vector>& drop_flags) { + const OctreeParser& octree_in, + vector>& drop_flags) { // calculate the node number for the octree_out int depth = octree_in.info().depth(); int depth_full = octree_in.info().full_layer(); @@ -207,9 +209,9 @@ void ScanOctree::trim_octree(vector& octree_buffer, // copy children and key // !!! Caveat: currently channel_key is 1, and channel_child is 1 const int* child_in = octree_in.children_cpu(d - 1); - const unsigned int* key_in = octree_in.key_cpu(d); + const uintk* key_in = octree_in.key_cpu(d); int* child_out = octree_out.mutable_children_cpu(d); - unsigned int* key_out = octree_out.mutable_key_cpu(d); + uintk* key_out = octree_out.mutable_key_cpu(d); for (int i = 0, j = 0, id = 0; i < num; ++i) { // the node is dropped or empty if (drop[i] == 1) continue; @@ -230,7 +232,7 @@ void ScanOctree::trim_octree(vector& octree_buffer, if (location_feature == -1 || d == depth) { int nnum_in = octree_in.info().node_num(d); int nnum_out = octree_out.info().node_num(d); - const float * feature_in = octree_in.feature_cpu(d); + const float* feature_in = octree_in.feature_cpu(d); float* feature_out = octree_out.mutable_feature_cpu(d); for (int i = 0, j = 0; i < num; ++i) { // the node is dropped or empty @@ -239,8 +241,8 @@ void ScanOctree::trim_octree(vector& octree_buffer, for (int k = 8 * t; k < 8 * t + 8; ++k) { for (int c = 0; c < channel_feature; ++c) { - feature_out[c * nnum_out + j] = drop_d[k] == 0 ? - feature_in[c * nnum_in + k] : 0; + feature_out[c * nnum_out + j] = + drop_d[k] == 0 ? feature_in[c * nnum_in + k] : 0; } j++; } @@ -249,7 +251,7 @@ void ScanOctree::trim_octree(vector& octree_buffer, // copy label if ((location_label == -1 || d == depth) && channel_label != 0) { - const float * label_in = octree_in.label_cpu(d); + const float* label_in = octree_in.label_cpu(d); float* label_out = octree_out.mutable_label_cpu(d); for (int i = 0, j = 0; i < num; ++i) { // the node is dropped or empty @@ -265,7 +267,7 @@ void ScanOctree::trim_octree(vector& octree_buffer, // copy split if ((location_split == -1 || d == depth) && channel_split != 0) { - const float * split_in = octree_in.split_cpu(d); + const float* split_in = octree_in.split_cpu(d); float* split_out = octree_out.mutable_split_cpu(d); for (int i = 0, j = 0; i < num; ++i) { // the node is dropped or empty @@ -281,15 +283,13 @@ void ScanOctree::trim_octree(vector& octree_buffer, } } - - void octree_dropout(vector& octree_output, const string& octree_input, const int depth_dropout, const float threshold) { // generate the drop flag OctreeParser parser_in; parser_in.set_cpu(octree_input.c_str()); int depth = parser_in.info().depth(); - vector > drop(depth + 1); + vector> drop(depth + 1); // generate random flag for the level depth_dropout int nnum_d = parser_in.info().node_num(depth_dropout); drop[depth_dropout].resize(nnum_d, 0); @@ -305,7 +305,7 @@ void octree_dropout(vector& octree_output, const string& octree_input, drop[d].resize(nnum_d); for (int i = 0; i < nnum_dp; ++i) { int t = children_dp[i]; - if (t < 0) continue; // continue if it has no children + if (t < 0) continue; // continue if it has no children // assign the drop flag of a parent node to its children for (int j = 0; j < 8; ++j) { drop[d][t * 8 + j] = drop[d - 1][i]; @@ -338,23 +338,26 @@ void octree_dropout(vector& octree_output, const string& octree_input, // from level 0 to depth_output int num = parser_in.info().node_num_cum(depth_dropout + 1); int channel_key = parser_in.info().channel(OctreeInfo::kKey); - //CHECK_EQ(channel_key, 1) << "Currently the channel must be 1"; + // CHECK_EQ(channel_key, 1) << "Currently the channel must be 1"; std::copy_n(parser_in.key_cpu(0), num * channel_key, parser_out.mutable_key_cpu(0)); std::copy_n(parser_in.children_cpu(0), num, parser_out.mutable_children_cpu(0)); int channel_feature = parser_in.info().channel(OctreeInfo::kFeature); int location_feature = parser_in.info().locations(OctreeInfo::kFeature); if (location_feature == -1) { - std::copy_n(parser_in.feature_cpu(0), num * channel_feature, parser_out.mutable_feature_cpu(0)); + std::copy_n(parser_in.feature_cpu(0), num * channel_feature, + parser_out.mutable_feature_cpu(0)); } int channel_label = parser_in.info().channel(OctreeInfo::kLabel); int location_label = parser_in.info().locations(OctreeInfo::kLabel); if (location_label == -1) { - std::copy_n(parser_in.label_cpu(0), num * channel_label, parser_out.mutable_label_cpu(0)); + std::copy_n(parser_in.label_cpu(0), num * channel_label, + parser_out.mutable_label_cpu(0)); } int channel_split = parser_in.info().channel(OctreeInfo::kSplit); int location_split = parser_in.info().locations(OctreeInfo::kSplit); if (location_split == -1) { - std::copy_n(parser_in.split_cpu(0), num * channel_split, parser_out.mutable_split_cpu(0)); + std::copy_n(parser_in.split_cpu(0), num * channel_split, + parser_out.mutable_split_cpu(0)); } // from level depth_output+1 to depth @@ -362,9 +365,9 @@ void octree_dropout(vector& octree_output, const string& octree_input, for (int d = depth_dropout + 1; d <= depth; ++d) { int nnum_d = parser_in.info().node_num(d), id = 0; const int* child_src = parser_in.children_cpu(d); - const unsigned int* key_src = parser_in.key_cpu(d); + const uintk* key_src = parser_in.key_cpu(d); int* child_des = parser_out.mutable_children_cpu(d); - unsigned int* key_des = parser_out.mutable_key_cpu(d); + uintk* key_des = parser_out.mutable_key_cpu(d); for (int i = 0, j = 0; i < nnum_d; ++i) { if (drop[d][i] == 0) { key_des[j] = key_src[i]; @@ -377,7 +380,7 @@ void octree_dropout(vector& octree_output, const string& octree_input, if (location_feature == -1 || d == depth) { int nnum_src = parser_out.info().node_num(d); - const float * feature_src = parser_in.feature_cpu(d); + const float* feature_src = parser_in.feature_cpu(d); float* feature_des = parser_out.mutable_feature_cpu(d); for (int i = 0, j = 0; i < nnum_d; ++i) { if (drop[d][i] == 0) { @@ -390,7 +393,7 @@ void octree_dropout(vector& octree_output, const string& octree_input, } if ((location_label == -1 || d == depth) && channel_label != 0) { - const float * label_src = parser_in.label_cpu(d); + const float* label_src = parser_in.label_cpu(d); float* label_des = parser_out.mutable_label_cpu(d); for (int i = 0, j = 0; i < nnum_d; ++i) { if (drop[d][i] == 0) { @@ -401,7 +404,7 @@ void octree_dropout(vector& octree_output, const string& octree_input, } if ((location_split == -1 || d == depth) && channel_split != 0) { - const float * split_src = parser_in.split_cpu(d); + const float* split_src = parser_in.split_cpu(d); float* split_des = parser_out.mutable_split_cpu(d); for (int i = 0, j = 0; i < nnum_d; ++i) { if (drop[d][i] == 0) { @@ -417,12 +420,78 @@ void octree_dropout(vector& octree_output, const string& octree_input, const int* child_src = parser_in.children_cpu(depth_dropout); int* child_des = parser_out.mutable_children_cpu(depth_dropout); for (int i = 0; i < node_num[depth_dropout]; ++i) { - child_des[i] = (drop[depth_dropout][i] == 1 || child_src[i] < 0) ? - child_src[i] : id++; + child_des[i] = + (drop[depth_dropout][i] == 1 || child_src[i] < 0) ? child_src[i] : id++; } for (int d = 0; d < depth_dropout; ++d) { node_num_nempty[d] = parser_in.info().node_num_nempty(d); } - node_num_nempty[depth_dropout] = id; // !!! important + node_num_nempty[depth_dropout] = id; // !!! important parser_out.mutable_info().set_nempty(node_num_nempty.data()); } + +void upgrade_key64(vector& octree_out, const vector& octree_in) { + typedef typename KeyTrait::uints uints_in; + typedef typename KeyTrait::uints uints_out; + + OctreeParser parser_in, parser_out; + parser_in.set_cpu(octree_in.data()); + + + // modify the OctreeInfo, update the magic_str and ptr_dis + OctreeInfo oct_info = parser_in.info(); + oct_info.set_magic_str(OctreeInfo::kMagicStr); + oct_info.set_ptr_dis(); + + // set OctreeInfo to the output + octree_out.resize(oct_info.sizeof_octree()); + parser_out.set_cpu(octree_out.data(), &oct_info); + + // copy key + int num = oct_info.total_nnum(); + uintk* key_out = parser_out.mutable_key_cpu(0); + const uint32* key_in = reinterpret_cast(parser_in.key_cpu(0)); + for (int i = 0; i < num; ++i) { + if (oct_info.is_key2xyz()) { + uints_out* ptr_out = reinterpret_cast(key_out + i); + const uints_in* ptr_in = reinterpret_cast(key_in + i); + for (int j = 0; j < 4; ++j) { + ptr_out[j] = static_cast(ptr_in[j]); + } + } else { + key_out[i] = static_cast(key_in[i]); + } + } + + // copy children + std::copy_n(parser_in.children_cpu(0), num, parser_out.mutable_children_cpu(0)); + + // copy data + int depth = oct_info.depth(); + int feature_channel = oct_info.channel(OctreeInfo::kFeature); + int feature_location = oct_info.locations(OctreeInfo::kFeature); + int depth_start = feature_location == depth ? depth : 0; + for (int d = depth_start; d < depth + 1; ++d) { + if (!oct_info.has_property(OctreeInfo::kFeature)) break; + int n = oct_info.node_num(d) * feature_channel; + std::copy_n(parser_in.feature_cpu(d), n, parser_out.mutable_feature_cpu(d)); + } + + // copy label + int label_location = oct_info.locations(OctreeInfo::kLabel); + depth_start = label_location == depth ? depth : 0; + for (int d = depth_start; d < depth + 1; ++d) { + if (!oct_info.has_property(OctreeInfo::kLabel)) break; + int n = oct_info.node_num(d); + std::copy_n(parser_in.label_cpu(d), n, parser_out.mutable_label_cpu(d)); + } + + // copy split label + int split_location = oct_info.locations(OctreeInfo::kSplit); + depth_start = split_location == depth ? depth : 0; + for (int d = depth_start; d < depth + 1; ++d) { + if (!oct_info.has_property(OctreeInfo::kSplit)) break; + int n = oct_info.node_num(d); + std::copy_n(parser_in.split_cpu(d), n, parser_out.mutable_split_cpu(d)); + } +} diff --git a/octree/octree/transform_octree.h b/octree/octree/transform_octree.h index fb0ebe5..369c261 100644 --- a/octree/octree/transform_octree.h +++ b/octree/octree/transform_octree.h @@ -32,4 +32,6 @@ class ScanOctree { void octree_dropout(vector& octree_output, const string& octree_input, const int depth_dropout, const float threshold); +void upgrade_key64(vector& octree_out, const vector& octree_in); + #endif // _OCTREE_TRANSFORM_OCTREES_ diff --git a/octree/octree/types.h b/octree/octree/types.h new file mode 100644 index 0000000..2a33ff9 --- /dev/null +++ b/octree/octree/types.h @@ -0,0 +1,27 @@ +#ifndef _OCTREE_TYPES_ +#define _OCTREE_TYPES_ + +// #include + +// typedef uint8_t uint8; +// typedef uint16_t uint16; +// typedef uint32_t uint32; +// typedef uint64_t uint64; + +// these definations are from tensorflow +typedef unsigned char uint8; +typedef unsigned short uint16; +typedef unsigned int uint32; +typedef unsigned long long uint64; + +template struct KeyTrait {}; +template<> struct KeyTrait { typedef uint8 uints; }; +template<> struct KeyTrait { typedef uint16 uints; }; + +#ifdef KEY64 +typedef uint64 uintk; +#else +typedef uint32 uintk; +#endif // KEY64 + +#endif // _OCTREE_TYPES_ diff --git a/octree/python/pyoctree.cpp b/octree/python/pyoctree.cpp index dfc4481..e284cf2 100644 --- a/octree/python/pyoctree.cpp +++ b/octree/python/pyoctree.cpp @@ -6,6 +6,7 @@ #include #include +#include // qq add for octree #include @@ -42,51 +43,65 @@ PYBIND11_MODULE(pyoctree, m) { }); - // points interface using vectorf = vector; using vectorfc = const vector; auto Points_set_points = (bool(Points::*)(vectorfc&, vectorfc&, vectorfc&, vectorfc&))&Points::set_points; - auto Points_set_points_buffer = (void(Points::*)(vector&))&Points::set_points; + auto Mcube_compute = (void(MarchingCube::*)(vectorfc&, float, vectorfc&, + int))&MarchingCube::compute; + + // points interface py::class_(m, "Points") .def(py::init<>()) .def("read_points", &Points::read_points) .def("write_points", &Points::write_points) .def("write_ply", &Points::write_ply) .def("normalize", &Points::normalize) + .def("orient_normal", &Points::orient_normal) .def("set_points", Points_set_points) - .def("set_points_buffer", Points_set_points_buffer) +// .def("set_points_buffer", Points_set_points_buffer) .def("pts_num", [](const Points& pts) { return pts.info().pt_num(); }) // todo: fix the functions points(), normals(), labels(), // It is inefficient, since there is a memory-copy here - .def("points", [](const Points& pts) { + .def("points", [](const Points & pts) { const float* ptr = pts.points(); int num = pts.info().pt_num(); return vectorf(ptr, ptr + num * 3); }) - .def("normals", [](const Points& pts) { + .def("normals", [](const Points & pts) { const float* ptr = pts.normal(); int num = pts.info().pt_num(); return vectorf(ptr, ptr + num * 3); }) - .def("features", [](const Points& pts) { + .def("features", [](const Points & pts) { const float* ptr = pts.feature(); int num = pts.info().pt_num(); const int ch = pts.info().channel(PointsInfo::kFeature); return vectorf(ptr, ptr + num * ch); }) - .def("labels", [](const Points& pts) { + .def("labels", [](const Points & pts) { const float* ptr = pts.label(); int num = pts.info().pt_num(); return vectorf(ptr, ptr + num); }) - .def("buffer", [](const Points& pts) { + .def("buffer", [](const Points & pts) { const char* ptr = pts.data(); return py::bytes(std::string(ptr, pts.info().sizeof_points())); }); + // marching cube + py::class_(m, "MCube") + .def(py::init<>()) + .def("compute", Mcube_compute) + .def("get_vtx", [](const MarchingCube & mcube) { + return mcube.vtx_; + }) + .def("get_face", [](const MarchingCube & mcube) { + return mcube.face_; + }); + // qq: try octree interface using vectoru = vector; using vectori = vector; @@ -109,7 +124,7 @@ PYBIND11_MODULE(pyoctree, m) { }) .def("keys", [](const Octree& octree, int depth) { // int depth = octree.info().depth(); - const uint32_t* ptr = octree.key_cpu(depth); + const uintk* ptr = octree.key_cpu(depth); int num = octree.info().node_num(depth); return vectoru(ptr, ptr + num); }) diff --git a/octree/test/test_octree.cpp b/octree/test/test_octree.cpp index 14c33d3..5c6bf41 100644 --- a/octree/test/test_octree.cpp +++ b/octree/test/test_octree.cpp @@ -114,7 +114,7 @@ TEST_F(OctreeTest, TestOctreeBuild) { EXPECT_EQ(info.node_num_cum(depth + 1), nnum_cum[depth + 1]); // test the key - const unsigned int keys[] = { + const uintk keys[] = { 0, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 48, 49, 50, 51, 52, 53, 54, 55, 0, 1, 2, 3, 4, 5, 6, 7, 384, 385, 386, 387, 388, 389, 390, 391, @@ -123,7 +123,7 @@ TEST_F(OctreeTest, TestOctreeBuild) { }; for (int d = 0, j = 0; d <= depth; ++d) { const int nnum_d = info.node_num(d); - const unsigned int* key_d = octree_.key_cpu(d); + const uintk* key_d = octree_.key_cpu(d); for (int i = 0; i < nnum_d; ++i, j++) { EXPECT_EQ(key_d[i], keys[j]); } @@ -220,7 +220,7 @@ TEST_F(OctreeTest, TestOctreeTrim) { EXPECT_EQ(info.node_num_cum(depth + 1), nnum_cum[depth + 1]); // test the key - const unsigned int keys[] = { + const uintk keys[] = { 0, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 48, 49, 50, 51, 52, 53, 54, 55, 0, 1, 2, 3, 4, 5, 6, 7, 384, 385, 386, 387, 388, 389, 390, 391, @@ -229,7 +229,7 @@ TEST_F(OctreeTest, TestOctreeTrim) { }; for (int d = 0, j = 0; d <= depth; ++d) { const int nnum_d = info.node_num(d); - const unsigned int* key_d = octree_.key_cpu(d); + const uintk* key_d = octree_.key_cpu(d); for (int i = 0; i < nnum_d; ++i, j++) { EXPECT_EQ(key_d[i], keys[j]); } diff --git a/octree/test/test_octree_nn.cpp b/octree/test/test_octree_nn.cpp index 02760c4..277491f 100644 --- a/octree/test/test_octree_nn.cpp +++ b/octree/test/test_octree_nn.cpp @@ -1,7 +1,9 @@ #include +#include #include #include #include +#include TEST(VecResizeTest, TestVecResize) { @@ -18,7 +20,6 @@ TEST(VecResizeTest, TestVecResize) { ASSERT_EQ(vec2, gt2); } - TEST(BiliearNeigh, TestBiliearNeigh) { const char* octree1 = (const char*) octree::get_one_octree("octree_1"); vector buffer; @@ -36,12 +37,13 @@ TEST(BiliearNeigh, TestBiliearNeigh) { child0, nnum0, NeighHelper::get_bilinear_array().data()); // check + typedef typename KeyTrait::uints uints; const int weights[8] = { 27, 9, 9, 9, 3, 3, 3, 1 }; - const unsigned* key0 = parser.key_cpu(depth); - const unsigned* key1 = parser.key_cpu(depth + 1); + const uintk* key0 = parser.key_cpu(depth); + const uintk* key1 = parser.key_cpu(depth + 1); ASSERT_TRUE(parser.info().is_key2xyz()); - auto key_to_xyz = [](float * xyz, const unsigned * key) { - const unsigned char* ptr = (const unsigned char*)key; + auto key_to_xyz = [](float * xyz, const uintk * key) { + const uints* ptr = (const uints*)key; for (int i = 0; i < 3; ++i) { xyz[i] = (float)ptr[i] + 0.5f; } }; for (int i = 0; i < nnum1; ++i) { @@ -58,7 +60,7 @@ TEST(BiliearNeigh, TestBiliearNeigh) { int weight = 1; for (int c = 0; c < 3; ++c) { - float dis = fabs(xyz0[c] - xyz1[c]); + float dis = fabsf(xyz0[c] - xyz1[c]); ASSERT_LT(dis, 1.0f); weight *= (int)((1 - dis) * 4); } @@ -67,7 +69,7 @@ TEST(BiliearNeigh, TestBiliearNeigh) { } // check - vector xyz10(nnum1 * 8), key10(nnum1 * 8), key_octree(nnum0); + vector xyz10(nnum1 * 8), key10(nnum1 * 8), key_octree(nnum0); vector fracs(nnum1 * 3); bilinear_xyz_cpu(xyz10.data(), fracs.data(), depth, parser.key_cpu(depth + 1), depth + 1, nnum1); @@ -81,13 +83,14 @@ TEST(BiliearNeigh, TestBiliearNeigh) { } } - TEST(Coord2xyzTest, TestCoord2xyz) { + typedef typename KeyTrait::uints uints; + float coord[] = {1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8 }; - unsigned char xyz[] = { 1, 3, 5, 7, 2, 4, 6, 8 }; - unsigned int rst[2] = { 0 }; + uints xyz[] = { 1, 3, 5, 7, 2, 4, 6, 8 }; + uintk rst[2] = { 0 }; coord2xyz_cpu(rst, coord, 2, 4); - unsigned char* ptr = (unsigned char*)rst; + uints* ptr = (uints*)rst; for (int i = 0; i < 8; ++i) { ASSERT_EQ(ptr[i], xyz[i]); } diff --git a/octree/test/test_util.cpp b/octree/test/test_util.cpp index 8cde2e9..e3a0a0a 100644 --- a/octree/test/test_util.cpp +++ b/octree/test/test_util.cpp @@ -1,5 +1,6 @@ #include #include "math_functions.h" +#include #include #include diff --git a/octree/tools/.DS_Store b/octree/tools/.DS_Store deleted file mode 100644 index 6347f9d..0000000 Binary files a/octree/tools/.DS_Store and /dev/null differ diff --git a/octree/tools/adaptive_octree.cpp b/octree/tools/adaptive_octree.cpp deleted file mode 100644 index 68c6250..0000000 --- a/octree/tools/adaptive_octree.cpp +++ /dev/null @@ -1,507 +0,0 @@ -// todo: optimize the coding - -#include -#include -#include -#include -#include -#include - -#include "filenames.h" -#include "octree.h" -#include "marching_cube.h" - -using namespace std; - -int depth_start = 4; -int signal_channel = 4; -int segmentation = 0; -int split_label = 0; - -//float threshold = 0.002f; // about 2.6 degree -float threshold = 0.1f; // about 5.7 degree -float threshold_distance = 0.866; // sqrtf(3.0f) * 0.5f; -//float threshold = 0.02f; // about 8.1 degree -//float threshold = 0.05f; // about 12.8 degree -//float threshold = 0.2f; // about 12.8 degree - - -void covered_depth_nodes(vector>& dnum_, vector>& didx_, - const int* children_, const int* node_num_, const int* node_num_accu_, const int depth_) { - // init - didx_.resize(depth_ + 1); - dnum_.resize(depth_ + 1); - - //layer-depth_ - int nnum = node_num_[depth_]; - dnum_[depth_].resize(nnum, 1); - didx_[depth_].resize(nnum); - for (int i = 0; i < nnum; ++i) { - didx_[depth_][i] = i; - } - - // layer-(depth_-1) - nnum = node_num_[depth_ - 1]; - dnum_[depth_ - 1].resize(nnum, 0); - didx_[depth_ - 1].resize(nnum, -1); - const int* children_d = children_ + node_num_accu_[depth_ - 1]; - for (int i = 0; i < nnum; ++i) { - int t = children_d[i]; - if (t != -1) { - dnum_[depth_ - 1][i] = 8; - didx_[depth_ - 1][i] = t * 8; - } - } - - // layer-(depth-2) to layer-0 - for (int d = depth_ - 2; d >= 0; --d) { - nnum = node_num_[d]; - dnum_[d].resize(nnum, 0); - didx_[d].resize(nnum, -1); - const int* children_d = children_ + node_num_accu_[d]; - for (int i = 0; i < nnum; ++i) { - int t = children_d[i]; - if (t != -1) { - t *= 8; - for (int j = 0; j < 8; ++j) { - dnum_[d][i] += dnum_[d + 1][t + j]; - } - for (int j = 0; j < 8; ++j) { - if (didx_[d + 1][t + j] != -1) { - didx_[d][i] = didx_[d + 1][t + j]; - break; - } - } - } - } - } -} - - -void adaptive_octree(vector& octree_output, const vector& octree_input, - const int depth_output) { - /// const - const float mul = sqrtf(3.0f) / 2.0f; - const float imul = 2.0f / sqrtf(3.0f); - //const int signal_channel = 4; - - /// parse the octree file - Octree octree_in; - octree_in.set_octree(octree_input.data(), octree_input.size()); - bool is_key2xyz = octree_in.info().is_key2xyz(); // !!! must be true - int depth = octree_in.info().depth(); - int full_layer = octree_in.info().full_layer(); - int total_node_num = octree_in.info().total_nnum(); - int final_node_num = octree_in.info().node_num(depth); - vector nnum_vec(depth + 1, 0), nnum_accu_vec(depth + 2, 0); - for (int d = 0; d < depth + 1; ++d) { - nnum_vec[d] = octree_in.info().node_num(d); - nnum_accu_vec[d] = octree_in.info().node_num_cum(d); - } - nnum_accu_vec[depth + 1] = octree_in.info().node_num_cum(depth + 1); - const int* node_num = nnum_vec.data(); - const int* node_num_accu = nnum_accu_vec.data(); - const unsigned int* key = octree_in.key_cpu(0); - const int* children = octree_in.children_cpu(0); - const float* data = octree_in.feature_cpu(0); - const float* normal_ptr = data; // !!! channel x n - const float* dis_ptr = normal_ptr + 3 * final_node_num; - const float* label_ptr = octree_in.label_cpu(0); - - /// precompute the nodes in the depth layer covered by each octree node - vector> dnum_, didx_; - covered_depth_nodes(dnum_, didx_, children, node_num, node_num_accu, depth); - - /// precompute the points in the finest level - vector pt_depth; // !!! n x channel - if (signal_channel == 4) { - pt_depth.resize(3 * final_node_num); - const unsigned int* key_depth = key + node_num_accu[depth]; - for (int i = 0; i < final_node_num; ++i) { - const unsigned char* pt = reinterpret_cast(key_depth + i); - for (int c = 0; c < 3; ++c) { - float nc = normal_ptr[c * final_node_num + i]; - pt_depth[i * 3 + c] = static_cast(pt[c]) + 0.5f + dis_ptr[i] * nc * mul; - } - } - } - - /// the average normal & displacement and normal variation - vector > normal_avg(depth + 1), normal_err(depth + 1), distance_err(depth + 1); - vector > label_avg(depth + 1); - int nlabel = 0; - if (segmentation != 0) { - nlabel = *std::max_element(label_ptr, label_ptr + final_node_num) + 1; - } - - // initialization - for (int d = 0; d <= depth; ++d) { - normal_avg[d].resize(signal_channel * node_num[d], 0.0f); - normal_err[d].resize(node_num[d], 5.0f); // !!! initialized as 5.0f - distance_err[d].resize(node_num[d], 5.0e10f); - if (segmentation != 0) { - label_avg[d].resize(node_num[d], -1); - } - } - - // for the depth layer - memcpy(normal_avg[depth].data(), normal_ptr, normal_avg[depth].size() * sizeof(float)); - if (segmentation != 0) { - memcpy(label_avg[depth].data(), label_ptr, label_avg[depth].size() * sizeof(float)); - } - - // for the other layers - for (int d = depth - 1; d > full_layer; --d) { - vector& dnum = dnum_[d]; - vector& didx = didx_[d]; - const unsigned int* key_d = key + node_num_accu[d]; - const int* children_d = children + node_num_accu[d]; - const int* children_depth = children + node_num_accu[depth]; - const float scale = static_cast(1 << (depth - d)); - - int nnum = node_num[d]; - vector& normal_d = normal_avg[d]; - vector& normal_err_d = normal_err[d]; - vector& distance_err_d = distance_err[d]; - - vector& label_d = label_avg[d]; - for (int i = 0; i < nnum; ++i) { - if (children_d[i] == -1) continue; - - // average the normal and projection point - float count = 0.0f; - float pt_avg[3] = { 0.0f, 0.0f, 0.0f }, dis_avg = 0.0f; - float n_avg[3] = { 0.0f, 0.0f, 0.0f }; - vector l_avg(nlabel, 0); - for (int j = didx[i]; j < didx[i] + dnum[i]; ++j) { - if (children_depth[j] == -1) continue; - count += 1.0f; - for (int c = 0; c < 3; ++c) { - float nc = normal_ptr[c * final_node_num + j]; - n_avg[c] += nc; - if (signal_channel == 4) { - pt_avg[c] += pt_depth[3 * j + c]; - } - } - if (segmentation != 0) { - l_avg[label_ptr[j]] += 1; - } - } - - - float len = 1.0e-30f; - for (int c = 0; c < 3; ++c) len += n_avg[c] * n_avg[c]; - len = sqrtf(len); - - float pt_base[3]; - const unsigned char* pt = reinterpret_cast(key_d + i); - for (int c = 0; c < 3; ++c) { - n_avg[c] /= len; - if (signal_channel == 4) { - pt_avg[c] /= count * scale; // !!! note the scale - pt_base[c] = static_cast(pt[c]); - float fract_part = pt_avg[c] - pt_base[c]; - dis_avg += (fract_part - 0.5f) * n_avg[c]; - } - } - - // === version 1 - // the normal error - float nm_err = 0.0f; - for (int j = didx[i]; j < didx[i] + dnum[i]; ++j) { - if (children_depth[j] == -1) continue; - for (int c = 0; c < 3; ++c) { - float tmp = normal_ptr[c * final_node_num + j] - n_avg[c]; - nm_err += tmp * tmp; - } - } - nm_err /= count; - - // output - normal_d[i] = n_avg[0]; - normal_d[1 * nnum + i] = n_avg[1]; - normal_d[2 * nnum + i] = n_avg[2]; - if (signal_channel == 4) { - normal_d[3 * nnum + i] = dis_avg * imul; // IMPORTANT: RESCALE - } - normal_err_d[i] = nm_err; - if (segmentation != 0) { - label_d[i] = 0; - for (int j = 1, v = 0; j < nlabel; ++j) { - if (l_avg[j] > v) { - v = l_avg[j]; - label_d[i] = j; - } - } - } - - // === version 2 - if (signal_channel != 4) { - distance_err_d[i] = 0; - continue; - } - // the error from the original geometry to the averaged geometry - float distance_max1 = -1; - // !!! note the scale - float pt_avg1[3] = { pt_avg[0] * scale, pt_avg[1] * scale, pt_avg[2] * scale }; - for (int j = didx[i]; j < didx[i] + dnum[i]; ++j) { - if (children_depth[j] == -1) continue; - float dis = 0.0f; - for (int c = 0; c < 3; ++c) { - dis += (pt_depth[3 * j + c] - pt_avg1[c]) * n_avg[c]; - } - dis = abs(dis); - if (dis > distance_max1) distance_max1 = dis; - } - - // the error from the averaged geometry to the original geometry - float distance_max2 = -1; - vector vtx; - intersect_cube(vtx, pt_avg, pt_base, n_avg); - if (vtx.empty()) distance_max2 = 5.0e10f; // !!! the degenerated case, ||n_avg|| == 0 - for (auto& v : vtx) v *= scale; // !!! note the scale - for (int k = 0; k < vtx.size() / 3; ++k) { - - // min - float distance_min = 1.0e30f; - for (int j = didx[i]; j < didx[i] + dnum[i]; ++j) { - if (children_depth[j] == -1) continue; - float dis = 0.0f; - for (int c = 0; c < 3; ++c) { - float ptc = pt_depth[3 * j + c] - vtx[3 * k + c]; - dis += ptc * ptc; - } - dis = sqrtf(dis); - if (dis < distance_min) distance_min = dis; - } - - // max - if (distance_min > distance_max2) distance_max2 = distance_min; - } - - distance_err_d[i] = std::max(distance_max2, distance_max1); - } - } - - /// trim the octree according to normal_var - vector > data_output(depth + 1), label_output(depth + 1); - vector > key_output(depth + 1); - vector > children_output(depth + 1), drop(depth + 1); - for (int d = 0; d <= depth; ++d) { - drop[d].resize(node_num[d], 0); // 1 means dropping the sub-tree - } - for (int d = depth_output; d <= depth; ++d) { - int nnum_d = node_num[d]; - int nnum_dp = node_num[d - 1]; - vector& normal_err_d = normal_err[d]; - vector& dist_err_d = distance_err[d]; - const unsigned int* key_d = key + node_num_accu[d]; - const int* children_d = children + node_num_accu[d]; - const int* children_dp = children + node_num_accu[d - 1]; - vector& drop_d = drop[d]; - vector& drop_dp = drop[d - 1]; - - // generate the drop flag - bool all_drop = true; - for (int i = 0; i < nnum_dp; ++i) { - int t = children_dp[i]; - if (t == -1) continue; - for (int j = 0; j < 8; ++j) { - int idx = t * 8 + j; - drop_d[idx] = drop_dp[i] == 1 || - (normal_err_d[idx] < threshold && dist_err_d[idx] < threshold_distance); - //drop_d[idx] = drop_dp[i] == 1 ||dist_err_d[idx] < thredhold_distance; - if (all_drop && children_d[idx] != -1) { - all_drop = drop_d[idx] == 1; - } - } - } - - // make sure that there is at least one octree node in each layer - if (all_drop) { - int max_idx = 0; - float max_var = -1.0f; - for (int i = 0; i < nnum_dp; ++i) { - int t = children_dp[i]; - if (t == -1 || drop_dp[i] == 1) continue; - for (int j = 0; j < 8; ++j) { - int idx = t * 8 + j; - if (children_d[idx] != -1 && normal_err_d[idx] > max_var) { - max_var = normal_err_d[idx]; - max_idx = idx; - } - } - } - drop_d[max_idx] = 0; - } - - for (int i = 0, id = 0; i < nnum_dp; ++i) { - int t = children_dp[i]; - if (t == -1) continue; - for (int j = 0; j < 8; ++j) { - int idx = t * 8 + j; - if (drop_dp[i] == 0) { - key_output[d].push_back(key_d[idx]); - - int ch = (drop_d[idx] == 0 && children_d[idx] != -1) ? id++ : -1; - children_output[d].push_back(ch); - - for (int c = 0; c < signal_channel; ++c) { - data_output[d].push_back(normal_avg[d][c * nnum_d + idx]); - } - - if (segmentation != 0) { - label_output[d].push_back(label_avg[d][idx]); - } - } - } - } - - // transpose data - int num = key_output[d].size(); - vector data_buffer(num * signal_channel); - for (int i = 0; i < num; ++i) { - for (int c = 0; c < signal_channel; ++c) { - data_buffer[c * num + i] = data_output[d][i * signal_channel + c]; - } - } - data_output[d].swap(data_buffer); - } - - /// output - OctreeInfo info_out = octree_in.info(); - info_out.set_adaptive(true); - info_out.set_threshold_dist(threshold_distance); - info_out.set_threshold_normal(threshold); - if (split_label) { - info_out.set_property(OctreeInfo::kSplit, 1, -1); - } else { - info_out.set_property(OctreeInfo::kSplit, 0, 0); - } - info_out.set_property(OctreeInfo::kFeature, signal_channel, -1); - if (segmentation) info_out.set_property(OctreeInfo::kLabel, 1, -1); - //vector nnum_nempty_vec(depth + 1); - for (int d = 0; d <= depth; ++d) { - nnum_vec[d] = d <= depth_output ? octree_in.info().node_num(d) : key_output[d].size(); - } - info_out.set_nnum(nnum_vec.data()); - info_out.set_nnum_cum(); - info_out.set_ptr_dis(); - - // copy OctreeInfo - Octree octree_out; - octree_out.resize_octree(info_out.sizeof_octree()); - octree_out.mutable_info() = info_out; - copy(key, key + node_num_accu[depth_output], octree_out.mutable_key_cpu(0)); - for (int d = depth_output; d <= depth; ++d) { - copy(key_output[d].begin(), key_output[d].end(), octree_out.mutable_key_cpu(d)); - } - copy(children, children + node_num_accu[depth_output], octree_out.mutable_children_cpu(0)); - for (int d = depth_output; d <= depth; ++d) { - copy(children_output[d].begin(), children_output[d].end(), octree_out.mutable_children_cpu(d)); - } - for (int d = 0; d <= depth; ++d) { - vector& normal_tmp = d < depth_output ? normal_avg[d] : data_output[d]; - copy(normal_tmp.begin(), normal_tmp.end(), octree_out.mutable_feature_cpu(d)); - } - if (segmentation != 0) { - for (int d = 0; d <= depth; ++d) { - vector& label_tmp = d < depth_output ? label_avg[d] : label_output[d]; - copy(label_tmp.begin(), label_tmp.end(), octree_out.mutable_label_cpu(d)); - } - } - // update nnum_nempty - for (int d = 0; d <= depth; ++d) { - // find the last element which is not equal to -1 - int nnum_nempty = 0; - const int* children_d = octree_out.children_cpu(d); - for (int i = octree_out.info().node_num(d) - 1; i >= 0; i--) { - if (children_d[i] != -1) { - nnum_nempty = children_d[i] + 1; - break; - } - } - nnum_vec[d] = nnum_nempty; - } - octree_out.mutable_info().set_nempty(nnum_vec.data()); - - if (split_label != 0) { - // generate split label according to the children_ - vector > split_output(depth + 1); - for (int d = 0; d <= depth; ++d) { - int nnum_d = octree_out.info().node_num(d); - vector& split_d = split_output[d]; - split_d.resize(nnum_d, 1); // initialize as 1 - const int* children_d = d < depth_output ? - children + node_num_accu[d] : children_output[d].data(); - vector& data_d = data_output[d]; - for (int i = 0; i < nnum_d; ++i) { - if (children_d[i] == -1) { - split_d[i] = 0; - if (d >= depth_output) { - float t = abs(data_d[i]) + abs(data_d[nnum_d + i]) + abs(data_d[nnum_d * 2 + i]); - if (t != 0) split_d[i] = 2; - } - } - } - } - for (int d = 0; d <= depth; ++d) { - copy(split_output[d].begin(), split_output[d].end(), octree_out.mutable_split_cpu(d)); - } - } - - octree_output = octree_out.buffer(); -} - - -void adaptive_octree(const string& filename, const string& filename_output) { - // read octree - ifstream infile(filename, ios::binary); - infile.seekg(0, infile.end); - int len = infile.tellg(); - infile.seekg(0, infile.beg); - vector octree(len, 0); - infile.read(octree.data(), len); - infile.close(); - - vector octree_output; - adaptive_octree(octree_output, octree, depth_start); - - // save octree - ofstream outfile(filename_output, ios::binary); - outfile.write(octree_output.data(), octree_output.size()); - outfile.close(); -} - - -int main(int argc, char* argv[]) { - if (argc < 3) { - cout << "Usage: AdaptiveOctree.exe " - << "[depth] [signal_channel] [segmentation] [split_label]" << endl; - return 0; - } - - string input_file_path(argv[1]); - string output_file_path(argv[2]); - mkdir(output_file_path.c_str()); - if (argc > 3) depth_start = atoi(argv[3]); - if (argc > 4) signal_channel = atoi(argv[4]); - if (argc > 5) segmentation = atoi(argv[5]); - if (argc > 6) split_label = atoi(argv[6]); - - vector all_files; - get_all_filenames(all_files, input_file_path + "\\*.octree"); - - //#pragma omp parallel for - for (int i = 0; i < all_files.size(); ++i) { - string filename = extract_filename(all_files[i]); - string filename_ouput = output_file_path + filename + "_output.octree"; - - adaptive_octree(all_files[i], filename_ouput); - - cout << filename + " done!\n"; - } - - return 0; - -} diff --git a/octree/tools/bbox.cpp b/octree/tools/bbox.cpp index 83a8425..d5a86e7 100644 --- a/octree/tools/bbox.cpp +++ b/octree/tools/bbox.cpp @@ -35,7 +35,7 @@ int main(int argc, char* argv[]) { if (FLAGS_type == "box") { float bbmin[3], bbmax[3]; - bouding_box(bbmin, bbmax, pt, npt); + bounding_box(bbmin, bbmax, pt, npt); cout << bbmin[0] << ", " << bbmin[1] << ", " << bbmin[2] << ", " << bbmax[0] << ", " << bbmax[1] << ", " << bbmax[2] << endl; } else { diff --git a/octree/tools/build_octree.cpp b/octree/tools/build_octree.cpp index 7c2543c..08c8f86 100644 --- a/octree/tools/build_octree.cpp +++ b/octree/tools/build_octree.cpp @@ -21,7 +21,7 @@ DEFINE_string(axis, kOptional, "y", "The upright axis of the input model"); DEFINE_int(depth, kOptional, 6, "The maximum depth of the octree"); DEFINE_int(full_depth, kOptional, 2, "The full layer of the octree"); DEFINE_int(rot_num, kOptional, 12, "Number of poses rotated along the upright axis"); -DEFINE_float(offset, kOptional, 0.55f, "The offset value for handing thin shapes"); +DEFINE_float(offset, kOptional, 0.0f, "The offset value for handing thin shapes"); DEFINE_bool(node_dis, kOptional, false, "Output per-node displacement"); DEFINE_bool(node_feature, kOptional, false, "Compute per node feature"); DEFINE_bool(split_label, kOptional, false, "Compute per node splitting label"); @@ -61,7 +61,7 @@ class OctreeBuilder { cout << "Points number: " << npt < +#include #include #include #include @@ -16,6 +16,7 @@ DEFINE_string(filenames, kRequired, "", "The input filenames"); DEFINE_string(output_path, kOptional, ".", "The output path"); DEFINE_int(depth_start, kOptional, 0, "The starting depth"); DEFINE_int(depth_end, kOptional, 10, "The ending depth"); +DEFINE_bool(rescale, kOptional, true, "Scale the mesh according to the bbox"); DEFINE_bool(pu, kOptional, false, "Partition of Unity"); DEFINE_bool(verbose, kOptional, true, "Output logs"); @@ -59,10 +60,10 @@ int main(int argc, char* argv[]) { // convert vector V; vector F; if (!FLAGS_pu) { - octree.octree2mesh(V, F, FLAGS_depth_start, FLAGS_depth_end); + octree.octree2mesh(V, F, FLAGS_depth_start, FLAGS_depth_end, FLAGS_rescale); } else { clock_t t = clock(); - Contour contour(&octree); + Contour contour(&octree, FLAGS_rescale); contour.marching_cube(V, F); t = clock() - t; cout << "time : " << t << endl; diff --git a/octree/tools/octree2points.cpp b/octree/tools/octree2points.cpp index faacf42..0d23b33 100644 --- a/octree/tools/octree2points.cpp +++ b/octree/tools/octree2points.cpp @@ -1,4 +1,4 @@ -#include +#include #include #include @@ -14,14 +14,14 @@ DEFINE_string(output_path, kOptional, ".", "The output path"); DEFINE_string(suffix, kOptional, "points", "The output file suffix"); DEFINE_int(depth_start, kOptional, 0, "The starting depth"); DEFINE_int(depth_end, kOptional, 10, "The ending depth"); -DEFINE_int(rescale, kOptional, true, "The ending depth"); +DEFINE_bool(rescale, kOptional, true, "Scale the points according to the bbox"); DEFINE_bool(verbose, kOptional, true, "Output logs"); int main(int argc, char* argv[]) { bool succ = cflags::ParseCmd(argc, argv); if (!succ) { - cflags::PrintHelpInfo("\nUsage: octree2points.exe"); + cflags::PrintHelpInfo("\nUsage: octree2points"); return 0; } diff --git a/octree/tools/octree2pts_suncg.cpp b/octree/tools/octree2pts_suncg.cpp index 284529d..6d43611 100644 --- a/octree/tools/octree2pts_suncg.cpp +++ b/octree/tools/octree2pts_suncg.cpp @@ -1,4 +1,4 @@ -#include +#include #include #include #include @@ -22,7 +22,7 @@ void octree2pts(vector& pts, vector& labels, const Octree& octree) { const int* child = octree.children_cpu(depth); const float* label = octree.label_cpu(depth); const float* normal = octree.feature_cpu(depth); - const unsigned int* key = octree.key_cpu(depth); + const uintk* key = octree.key_cpu(depth); pts.clear(); labels.clear(); for (int i = 0; i < num; ++i) { diff --git a/octree/tools/octree_prune.cpp b/octree/tools/octree_prune.cpp index 9779d65..1663699 100644 --- a/octree/tools/octree_prune.cpp +++ b/octree/tools/octree_prune.cpp @@ -1,4 +1,4 @@ -#include +#include #include #include #include @@ -10,6 +10,7 @@ #include "octree.h" #include "contour.h" #include "cmd_flags.h" +#include "types.h" using namespace std; using cflags::Require; @@ -68,11 +69,11 @@ void prune_octree(Octree& octree_out, const Octree& octree_in) { int id = 0, j = 0; int* child_out = octree_out.mutable_children_cpu(d); const int* child_in = octree_in.children_cpu(d); - unsigned int* key_out = octree_out.mutable_key_cpu(d); + uintk* key_out = octree_out.mutable_key_cpu(d); float* split_out = octree_out.mutable_split_cpu(d); for (int i = 0; i < octree_in.info().node_num(d); ++i) { - unsigned int key = i, pt[3]; + uintk key = i, pt[3]; octree_in.key2xyz(pt, key, d); if (pt[0] < bnd0[d][0] && pt[1] < bnd0[d][1] && pt[2] < bnd0[d][2]) { @@ -104,8 +105,8 @@ void prune_octree(Octree& octree_out, const Octree& octree_in) { int* child_out = octree_out.mutable_children_cpu(d); std::copy_n(child_in, num, child_out); - const unsigned int* key_in = octree_in.key_cpu(d); - unsigned int* key_out = octree_out.mutable_key_cpu(d); + const uintk* key_in = octree_in.key_cpu(d); + uintk* key_out = octree_out.mutable_key_cpu(d); std::copy_n(key_in, num, key_out); if (location_feature == -1 || d == depth) { diff --git a/octree/tools/points_noise.cpp b/octree/tools/points_noise.cpp index 9ded8af..9490ad0 100644 --- a/octree/tools/points_noise.cpp +++ b/octree/tools/points_noise.cpp @@ -1,23 +1,25 @@ -#include -#include +#include #include -#include #include -#include +#include +#include #include "cmd_flags.h" -#include "points.h" #include "filenames.h" +#include "math_functions.h" +#include "points.h" - -using std::vector; -using std::string; +using cflags::Require; using std::cout; using std::endl; -using cflags::Require; +using std::string; +using std::vector; DEFINE_string(filenames, kRequired, "", "The input filenames"); DEFINE_string(output_path, kOptional, ".", "The output path"); +DEFINE_float(ratio, kOptional, 0.1f, "The ratio of perturbed points"); +DEFINE_float(dp, kOptional, 1.0f, "The deviation of point noise"); +DEFINE_float(dn, kOptional, 0.1f, "The deviation of normal noise"); DEFINE_bool(verbose, kOptional, true, "Output logs"); class GaussianRand { @@ -63,22 +65,27 @@ class BernoulliRand { }; - -void add_noise(Points& pts) { - BernoulliRand bernoulli(0.1f); - GaussianRand pt_noise(3.0f), normal_noise(0.1f); +void add_noise(Points& pts, GaussianRand& pt_noise, GaussianRand& normal_noise, + BernoulliRand& bernoulli) { + // bounding sphere of the points + float radius = 0, center[3] = { 0 }; + int npts = pts.info().pt_num(); + bounding_sphere(radius, center, pts.points(), npts); + radius = radius / 100.0f; // add pt noise - int npts = pts.info().pt_num(); vector noise(npts); pt_noise(noise.data(), npts); - vector mask(npts); - bernoulli(mask.data(), npts); + for (int i = 0; i < npts; ++i) { + noise[i] *= radius; // rescale the noise according to the radius + } + //vector mask(npts); + //bernoulli(mask.data(), npts); - float* ptr_pts = pts.mutable_ptr(PointsInfo::kPoint); - float* ptr_normal = pts.mutable_ptr(PointsInfo::kNormal); + float* ptr_pts = pts.mutable_points(); + float* ptr_normal = pts.mutable_normal(); for (int i = 0; i < npts; ++i) { - if (mask[i] == 0) continue; + //if (mask[i] == 0) continue; for (int c = 0; c < 3; ++c) { ptr_pts[i * 3 + c] += noise[i] * ptr_normal[i * 3 + c]; } @@ -105,32 +112,36 @@ void add_noise(Points& pts) { int main(int argc, char* argv[]) { bool succ = cflags::ParseCmd(argc, argv); if (!succ) { - cflags::PrintHelpInfo("\nUsage: simplify_points"); + cflags::PrintHelpInfo("\nUsage: points_noise"); return 0; } // file path string file_path = FLAGS_filenames; string output_path = FLAGS_output_path; - if (output_path != ".") mkdir(output_path); - else output_path = extract_path(file_path); + if (output_path != ".") { mkdir(output_path); } + else { output_path = extract_path(file_path); } output_path += "/"; vector all_files; get_all_filenames(all_files, file_path); + // declare the random number generator as global variables + BernoulliRand bernoulli(FLAGS_ratio); + GaussianRand pt_noise(FLAGS_dp), normal_noise(FLAGS_dn); + for (int i = 0; i < all_files.size(); i++) { Points pts; pts.read_points(all_files[i]); - add_noise(pts); + add_noise(pts, pt_noise, normal_noise, bernoulli); string filename = extract_filename(all_files[i]); if (FLAGS_verbose) cout << "Processing: " << filename << std::endl; - filename = output_path + filename + "_noise.points"; + filename = output_path + filename + ".points"; pts.write_points(filename); } cout << "Done: " << FLAGS_filenames << endl; return 0; -} \ No newline at end of file +} diff --git a/octree/tools/qq_octree_rotate.cpp b/octree/tools/qq_octree_rotate.cpp deleted file mode 100644 index 6868d30..0000000 --- a/octree/tools/qq_octree_rotate.cpp +++ /dev/null @@ -1,213 +0,0 @@ -// QQ add for xyz rotation - -#include -#include -#include -#include - -#include "cmd_flags.h" -#include "octree.h" -#include "filenames.h" -#include "math_functions.h" - -using std::vector; -using std::string; -using std::cout; -using std::endl; -using cflags::Require; -const float kPI = 3.14159265f; - -DEFINE_string(filenames, kRequired, "", "The input filenames"); -DEFINE_string(output_path, kOptional, ".", "The output path"); -DEFINE_string(axis, kOptional, "y", "The upright axis of the input model"); -DEFINE_int(depth, kOptional, 6, "The maximum depth of the octree"); -DEFINE_int(full_depth, kOptional, 2, "The full layer of the octree"); -DEFINE_int(rot_num, kOptional, 24, "Number of poses rotated along the upright axis"); -DEFINE_float(offset, kOptional, 0.0f, "The offset value for handing thin shapes"); -DEFINE_bool(node_dis, kOptional, false, "Output per-node displacement"); -DEFINE_bool(node_feature, kOptional, false, "Compute per node feature"); -DEFINE_bool(split_label, kOptional, false, "Compute per node splitting label"); -DEFINE_bool(adaptive, kOptional, false, "Build adaptive octree"); -DEFINE_int(adp_depth, kOptional, 4, "The starting depth of adaptive octree"); -DEFINE_float(th_distance, kOptional, 2.0f, "The threshold for simplifying octree"); -DEFINE_float(th_normal, kOptional, 0.1f, "The threshold for simplifying octree"); -DEFINE_bool(key2xyz, kOptional, false, "Convert the key to xyz when serialization"); -DEFINE_bool(extrapolate, kOptional, false, "Exptrpolate the node feature"); -DEFINE_bool(save_pts, kOptional, false, "Save the average points as signal"); -DEFINE_bool(verbose, kOptional, true, "Output logs"); - - -// OctreeBuilder shows a basic example for building an octree with a point cloud -class OctreeBuilder { - public: - bool set_point_cloud(string filename) { - // load point cloud - bool succ = point_cloud_.read_points(filename); - if (!succ) { - cout << "Can not load " << filename << endl; - return false; - } - string msg; - succ = point_cloud_.info().check_format(msg); - if (!succ) { - cout << filename << endl << msg << endl; - return false; - } - - // deal with empty points - int npt = point_cloud_.info().pt_num(); - if (npt == 0) { - cout << "This is an empty points!" << endl; - return false; - } - cout << "Points number: " << npt < 1.0e-10f) { - float offset = FLAGS_offset * 2.0f * radius_ / float(1 << FLAGS_depth); - point_cloud_.displace(offset); - radius_ += offset; - } - - return true; - } - - void set_octree_info() { - octree_info_.initialize(FLAGS_depth, FLAGS_full_depth, FLAGS_node_dis, - FLAGS_node_feature, FLAGS_split_label, FLAGS_adaptive, FLAGS_adp_depth, - FLAGS_th_distance, FLAGS_th_normal, FLAGS_key2xyz, FLAGS_extrapolate, - FLAGS_save_pts, point_cloud_); - - // the point cloud has been centralized, - // so initializing the bbmin & bbmax in the following way - float bbmin[] = { -radius_, -radius_, -radius_ }; - float bbmax[] = { radius_, radius_, radius_ }; - octree_info_.set_bbox(bbmin, bbmax); -// std::cout << octree_info_.depth() << std::endl; - } - - void build_octree() { - octree_.build(octree_info_, point_cloud_); -// std::cout << point_cloud_.info - } - - void save_octree(const string& output_filename) { - // Modify the bounding box before saving, because the center of - // the point cloud is translated to (0, 0, 0) when building the octree - octree_.mutable_info().set_bbox(radius_, center_); - octree_.write_octree(output_filename); - } - - public: - Points point_cloud_; - float radius_, center_[3]; - OctreeInfo octree_info_; - Octree octree_; -}; - - -int main(int argc, char* argv[]) { - bool succ = cflags::ParseCmd(argc, argv); - if (!succ) { - cflags::PrintHelpInfo("\nUsage: Octree.exe"); - return 0; - } - - // file path - string file_path = FLAGS_filenames; - string output_path = FLAGS_output_path; - if (output_path != ".") mkdir(output_path); - else output_path = extract_path(file_path); - output_path += "/"; - - vector all_files; - get_all_filenames(all_files, file_path); - - #pragma omp parallel for - for (int i = 0; i < all_files.size(); i++) { - OctreeBuilder builder; - bool succ = builder.set_point_cloud(all_files[i]); - - string filename = extract_filename(all_files[i]); - if (!succ) { - if (FLAGS_verbose) cout << "Warning: " + filename + " is invalid!\n"; - continue; - } - builder.set_octree_info(); - - // data augmentation -// float angle = 2.0f * kPI / float(FLAGS_rot_num); - float deg_90 = kPI * 1.0f/2; - float deg_180 = kPI; -// float axis[] = { 0.0f, 0.0f, 0.0f }; -// if (FLAGS_axis == "x") axis[0] = 1.0f; -// else if (FLAGS_axis == "y") axis[1] = 1.0f; -// else axis[2] = 1.0f; - - float x_axis[] = { 1.0f, 0.0f, 0.0f }; - float y_axis[] = { 0.0f, 1.0f, 0.0f }; - float z_axis[] = { 0.0f, 0.0f, 1.0f }; - - - if (FLAGS_verbose) cout << "Processing: " + filename + "\n"; - // QQ: get 24 augmentations - int idx = 0; - for (int v = 0; v < 4; ++v){ - builder.point_cloud_.rotate(deg_90, x_axis); - - for (int t= 0; t < 4; ++t){ - builder.point_cloud_.rotate(deg_90, z_axis); - - // output filename - char file_suffix[64]; - sprintf(file_suffix, "_%d_%d_%03d.octree", FLAGS_depth, FLAGS_full_depth, idx); - idx++; - - // build - builder.build_octree(); - builder.save_octree(output_path + filename + file_suffix); - } - } - - builder.point_cloud_.rotate(deg_90, y_axis); - - for (int t= 0; t < 4; ++t){ - builder.point_cloud_.rotate(deg_90, z_axis); - - // output filename - char file_suffix[64]; - sprintf(file_suffix, "_%d_%d_%03d.octree", FLAGS_depth, FLAGS_full_depth, idx); - idx++; - - // build - builder.build_octree(); - builder.save_octree(output_path + filename + file_suffix); - } - - builder.point_cloud_.rotate(deg_180, y_axis); - - for (int t= 0; t < 4; ++t){ - builder.point_cloud_.rotate(deg_90, z_axis); - - // output filename - char file_suffix[64]; - sprintf(file_suffix, "_%d_%d_%03d.octree", FLAGS_depth, FLAGS_full_depth, idx); - idx++; - - // build - builder.build_octree(); - builder.save_octree(output_path + filename + file_suffix); - } - } - - if (FLAGS_verbose) cout << "Done: " << FLAGS_filenames << endl; - return 0; -} - diff --git a/octree/tools/qq_octree_rotate_angle.cpp b/octree/tools/qq_octree_rotate_angle.cpp deleted file mode 100644 index d8b10a7..0000000 --- a/octree/tools/qq_octree_rotate_angle.cpp +++ /dev/null @@ -1,187 +0,0 @@ -// Not working when multiple values in angle are non-zero. - -#include -#include -#include -#include - -#include "cmd_flags.h" -#include "octree.h" -#include "filenames.h" -#include "math_functions.h" - -using std::vector; -using std::string; -using std::cout; -using std::endl; -using cflags::Require; -const float kPI = 3.14159265f; - -DEFINE_string(filenames, kRequired, "", "The input filenames"); -DEFINE_string(output_path, kOptional, ".", "The output path"); -DEFINE_string(axis, kOptional, "y", "The upright axis of the input model"); -DEFINE_int(depth, kOptional, 6, "The maximum depth of the octree"); -DEFINE_int(full_depth, kOptional, 2, "The full layer of the octree"); -DEFINE_int(rot_num, kOptional, 12, "Number of poses rotated along the upright axis"); -DEFINE_float(offset, kOptional, 0.55f, "The offset value for handing thin shapes"); -DEFINE_bool(node_dis, kOptional, false, "Output per-node displacement"); -DEFINE_bool(node_feature, kOptional, false, "Compute per node feature"); -DEFINE_bool(split_label, kOptional, false, "Compute per node splitting label"); -DEFINE_bool(adaptive, kOptional, false, "Build adaptive octree"); -DEFINE_int(adp_depth, kOptional, 4, "The starting depth of adaptive octree"); -DEFINE_float(th_distance, kOptional, 2.0f, "The threshold for simplifying octree"); -DEFINE_float(th_normal, kOptional, 0.1f, "The threshold for simplifying octree"); -DEFINE_bool(key2xyz, kOptional, false, "Convert the key to xyz when serialization"); -DEFINE_bool(extrapolate, kOptional, false, "Exptrpolate the node feature"); -DEFINE_bool(save_pts, kOptional, false, "Save the average points as signal"); -DEFINE_bool(verbose, kOptional, true, "Output logs"); - - -// OctreeBuilder shows a basic example for building an octree with a point cloud -class OctreeBuilder { - public: - bool set_point_cloud(string filename) { - // load point cloud - bool succ = point_cloud_.read_points(filename); - if (!succ) { - cout << "Can not load " << filename << endl; - return false; - } - string msg; - succ = point_cloud_.info().check_format(msg); - if (!succ) { - cout << filename << endl << msg << endl; - return false; - } - - // deal with empty points - int npt = point_cloud_.info().pt_num(); - if (npt == 0) { - cout << "This is an empty points!" << endl; - return false; - } - cout << "Points number: " << npt < 1.0e-10f) { - float offset = FLAGS_offset * 2.0f * radius_ / float(1 << FLAGS_depth); - point_cloud_.displace(offset); - radius_ += offset; - } - - return true; - } - - void set_octree_info() { - octree_info_.initialize(FLAGS_depth, FLAGS_full_depth, FLAGS_node_dis, - FLAGS_node_feature, FLAGS_split_label, FLAGS_adaptive, FLAGS_adp_depth, - FLAGS_th_distance, FLAGS_th_normal, FLAGS_key2xyz, FLAGS_extrapolate, - FLAGS_save_pts, point_cloud_); - - // the point cloud has been centralized, - // so initializing the bbmin & bbmax in the following way - float bbmin[] = { -radius_, -radius_, -radius_ }; - float bbmax[] = { radius_, radius_, radius_ }; - octree_info_.set_bbox(bbmin, bbmax); -// std::cout << octree_info_.depth() << std::endl; - } - - void build_octree() { - octree_.build(octree_info_, point_cloud_); -// std::cout << point_cloud_.info - } - - void save_octree(const string& output_filename) { - // Modify the bounding box before saving, because the center of - // the point cloud is translated to (0, 0, 0) when building the octree - octree_.mutable_info().set_bbox(radius_, center_); - octree_.write_octree(output_filename); - } - - public: - Points point_cloud_; - float radius_, center_[3]; - OctreeInfo octree_info_; - Octree octree_; -}; - - -int main(int argc, char* argv[]) { - bool succ = cflags::ParseCmd(argc, argv); - if (!succ) { - cflags::PrintHelpInfo("\nUsage: Octree.exe"); - return 0; - } - - // file path - string file_path = FLAGS_filenames; - string output_path = FLAGS_output_path; - if (output_path != ".") mkdir(output_path); - else output_path = extract_path(file_path); - output_path += "/"; - - vector all_files; - get_all_filenames(all_files, file_path); - - #pragma omp parallel for - for (int i = 0; i < all_files.size(); i++) { - OctreeBuilder builder; - bool succ = builder.set_point_cloud(all_files[i]); - - string filename = extract_filename(all_files[i]); - if (!succ) { - if (FLAGS_verbose) cout << "Warning: " + filename + " is invalid!\n"; - continue; - } - builder.set_octree_info(); - - // data augmentation -// float angle = 2.0f * kPI / float(FLAGS_rot_num); - float angle[3] = { 0 }; - angle[0] = 1; - angle[1] = 1; - angle[2] = 1; - -// float axis[] = { 0.0f, 0.0f, 0.0f }; -// if (FLAGS_axis == "x") axis[0] = 1.0f; -// else if (FLAGS_axis == "y") axis[1] = 1.0f; -// else axis[2] = 1.0f; - - if (FLAGS_verbose) cout << "Processing: " + filename + "\n"; - for (int v = 0; v < FLAGS_rot_num; ++v) { - // output filename - char file_suffix[64]; - sprintf(file_suffix, "_%d_%d_%03d.octree", FLAGS_depth, FLAGS_full_depth, v); - - - // build - builder.build_octree(); - - // save octree - builder.save_octree(output_path + filename + file_suffix); - -// char file_suffix2[64]; -// sprintf(file_suffix2, "_%d_%d_%03d.ply", FLAGS_depth, FLAGS_full_depth, v); -// builder.point_cloud_.write_ply(output_path + filename + file_suffix2); - - // rotate point for the next iteration - builder.point_cloud_.rotate(angle); - - - - // message - //cout << "Processing: " << filename.substr(filename.rfind('\\') + 1) << endl; - } - } - - if (FLAGS_verbose) cout << "Done: " << FLAGS_filenames << endl; - return 0; -} - diff --git a/octree/tools/simplify_points.cpp b/octree/tools/simplify_points.cpp deleted file mode 100644 index 279ca99..0000000 --- a/octree/tools/simplify_points.cpp +++ /dev/null @@ -1,59 +0,0 @@ -#include -#include - -#include "cmd_flags.h" -#include "filenames.h" -#include "simplify_points.h" - -using std::string; -using cflags::Require; - -DEFINE_string(filenames, kRequired, "", "The input filenames"); -DEFINE_string(output_path, kOptional, ".", "The output path"); -DEFINE_int(dim, kOptional, 256, "The maximum resolution"); -DEFINE_float(offset, kOptional, 0.55f, "The offset value for handing thin shapes"); -DEFINE_bool(avg_points, kOptional, true, "Average points as output"); -DEFINE_bool(verbose, kOptional, true, "Output logs"); - - -// The points must have normals! Can not deal with labela and feature! (TODO) - -int main(int argc, char* argv[]) { - bool succ = cflags::ParseCmd(argc, argv); - if (!succ) { - cflags::PrintHelpInfo("\nUsage: simplify_points"); - return 0; - } - - // file path - string file_path = FLAGS_filenames; - string output_path = FLAGS_output_path; - if (output_path != ".") mkdir(output_path); - else output_path = extract_path(file_path); - output_path += "/"; - - vector all_files; -// get_all_filenames(all_files, file_path); - get_all_filenames_qq(all_files, file_path); - - //#pragma omp parallel for - SimplifyPoints simplify_pts(FLAGS_dim, FLAGS_avg_points, FLAGS_offset); - for (int i = 0; i < all_files.size(); i++) { - string error_msg = simplify_pts.set_point_cloud(output_path+all_files[i]); //qq revised: was set_point_cloud(all_files[i]) - if (!error_msg.empty()) { - std::cout << error_msg << std::endl; - continue; - } - - simplify_pts.simplify(); - string path = extract_path(output_path+all_files[i]); // qq add - string filename = extract_filename(all_files[i]); - if (FLAGS_verbose) { - std::cout << "Processing: " + filename + "\n"; - } - simplify_pts.write_point_cloud(path +"/" +filename + ".smp.points"); - } - - std::cout << "Done: " << FLAGS_filenames << std::endl; - return 0; -} \ No newline at end of file diff --git a/octree/tools/upgrade_octree.cpp b/octree/tools/upgrade_octree.cpp deleted file mode 100644 index 5456a94..0000000 --- a/octree/tools/upgrade_octree.cpp +++ /dev/null @@ -1,166 +0,0 @@ -#include -#include -#include -#include -#include - -#include "cmd_flags.h" -#include "octree.h" -#include "filenames.h" - -using std::vector; -using std::string; -using std::cout; -using cflags::Require; -const float kPI = 3.14159265f; - -DEFINE_string(filenames, kRequired, "", "The input filenames"); -DEFINE_string(output_path, kOptional, ".", "The output path"); -DEFINE_bool(node_dis, kOptional, false, "Output per-node displacement"); -DEFINE_bool(node_label, kOptional, false, "Whether has node label"); -DEFINE_bool(split_label, kOptional, false, "Compute per node splitting label"); -DEFINE_bool(adaptive, kOptional, false, "Build adaptive octree"); -DEFINE_int(adp_depth, kOptional, 4, "The starting depth of adaptive octree"); -DEFINE_bool(key2xyz, kOptional, true, "Convert the key to xyz when serialization"); -DEFINE_bool(verbose, kOptional, true, "Output logs"); - - -bool load_raw_data(vector& data, const string& filename) { - std::ifstream infile(filename, std::ios::binary); - if (!infile) return false; - - infile.seekg(0, infile.end); - int len = infile.tellg(); - infile.seekg(0, infile.beg); - - data.resize(len); - infile.read(data.data(), len); - return true; -} - -int main(int argc, char* argv[]) { - bool succ = cflags::ParseCmd(argc, argv); - if (!succ) { - cflags::PrintHelpInfo("\nUsage: update_octree.exe"); - return 0; - } - if (FLAGS_node_label && FLAGS_split_label) { - cout << "The node_label and split_label can not be true at the same time\n"; - return 0; - } - - // file path - string file_path = FLAGS_filenames; - string output_path = FLAGS_output_path; - if (output_path != ".") mkdir(output_path); - else output_path = extract_path(file_path); - output_path += "/"; - - vector all_files; - get_all_filenames(all_files, file_path); - - for (int i = 0; i < all_files.size(); i++) { - vector oct_input; - bool succ = load_raw_data(oct_input, all_files[i]); - if (!succ) continue; - - /// parse the octree - int* octi = (int*)oct_input.data(); - int total_node_num = octi[0]; - int final_node_num = octi[1]; - int depth = octi[2]; - int full_layer = octi[3]; - const int* node_num = octi + 4; - const int* node_num_cum = node_num + depth + 1; - const int* key = node_num_cum + depth + 2; - const int* children = key + total_node_num; - const int* data = children + total_node_num; - const float* normal_ptr = (const float*)data; - - vector nnum_nempty(depth + 1, 0); - for (int d = 0; d <= depth; ++d) { - // find the last element which is not equal to -1 - const int* children_d = children + node_num_cum[d]; - for (int i = node_num[d] - 1; i >= 0; i--) { - if (children_d[i] != -1) { - nnum_nempty[d] = children_d[i] + 1; - break; - } - } - } - - /// set octree info - OctreeInfo octree_info; - octree_info.set_batch_size(1); - octree_info.set_depth(depth); - octree_info.set_full_layer(full_layer); - octree_info.set_adaptive_layer(FLAGS_adp_depth); - octree_info.set_adaptive(FLAGS_adaptive); - octree_info.set_node_dis(FLAGS_node_dis); - octree_info.set_key2xyz(FLAGS_key2xyz); - octree_info.set_threshold_normal(0.0f); - octree_info.set_threshold_dist(0.0f); - - float width = static_cast(1 << depth); - float bbmin[] = {0.0f, 0.0f, 0.0f }; - float bbmax[] = {width, width, width }; - octree_info.set_bbox(bbmin, bbmax); - - // by default, the octree contains Key and Child - int channel = 1; - octree_info.set_property(OctreeInfo::kKey, channel, -1); - octree_info.set_property(OctreeInfo::kChild, channel, -1); - - // set feature - int data_channel = FLAGS_node_dis ? 4 : 3; - int location = FLAGS_adaptive ? -1 : depth; - octree_info.set_property(OctreeInfo::kFeature, data_channel, location); - - // set label - if (FLAGS_node_label) { - octree_info.set_property(OctreeInfo::kLabel, 1, depth); - } - - // set split label - octree_info.set_property(OctreeInfo::kSplit, 0, 0); - if (FLAGS_split_label) { - octree_info.set_property(OctreeInfo::kSplit, 1, -1); - } - - octree_info.set_nnum(node_num); - octree_info.set_nnum_cum(); - octree_info.set_nempty(nnum_nempty.data()); - octree_info.set_ptr_dis(); - - /// output octree - vector buffer(octree_info.sizeof_octree()); - memcpy(buffer.data(), &octree_info, sizeof(OctreeInfo)); - Octree oct_parser; - oct_parser.set_octree(buffer); - int total_nnum = octree_info.total_nnum(); - int nnum_depth = octree_info.node_num(depth); - memcpy(oct_parser.mutable_key_cpu(0), key, total_nnum * sizeof(int)); - memcpy(oct_parser.mutable_children_cpu(0), children, total_nnum * sizeof(int)); - int data_size = FLAGS_adaptive ? total_nnum : nnum_depth; - memcpy(oct_parser.mutable_feature_cpu(0), data, data_size * data_channel * sizeof(float)); - if (FLAGS_node_label) { - const int* ptr = data + data_size * data_channel; - float* des = oct_parser.mutable_label_cpu(0); - for (int i = 0; i < nnum_depth; ++i) { - des[i] = static_cast(ptr[i]); - } - } - if (FLAGS_split_label) { - const int* ptr = data + data_size * data_channel; - float* des = oct_parser.mutable_split_cpu(0); - for (int i = 0; i < total_nnum; ++i) { - des[i] = static_cast(ptr[i]); - } - } - string filename = extract_filename(all_files[i]); - oct_parser.write_octree(output_path + filename + ".upgrade.octree"); - } - - return 0; -} - diff --git a/octree/tools/upgrade_points.cpp b/octree/tools/upgrade_points.cpp deleted file mode 100644 index d8641d2..0000000 --- a/octree/tools/upgrade_points.cpp +++ /dev/null @@ -1,111 +0,0 @@ -#include -#include -#include -#include - -#include "filenames.h" -#include "points.h" -#include "cmd_flags.h" - -using namespace std; - -DEFINE_string(filenames, kRequired, "", "The input filenames"); -DEFINE_string(output_path, kOptional, ".", "The output path"); -DEFINE_bool(has_label, kOptional, false, "The file contains label"); -DEFINE_bool(verbose, kOptional, true, "Output logs"); - - -void load_pointcloud(vector& pt, vector& normal, - vector& seg, const string& filename) { - const int channel = 3; - std::ifstream infile(filename, std::ios::binary); - - infile.seekg(0, infile.end); - size_t len = infile.tellg(); - infile.seekg(0, infile.beg); - - int n; - infile.read((char*)(&n), sizeof(int)); - pt.resize(3 * n); - infile.read((char*)pt.data(), sizeof(float) * 3 * n); - normal.resize(channel * n); - infile.read((char*)normal.data(), sizeof(float) * channel * n); - - if (FLAGS_has_label && - 6 * n * sizeof(float) + (n + 1) * sizeof(int) == len) { - seg.resize(n); - infile.read((char*)seg.data(), sizeof(int)*n); - } - - infile.close(); -} - -void gen_test_pointcloud(vector& pt, vector& normal, - vector& label) { - const float pt_input[] = { 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 0.0f}; - const float nm_input[] = { 1.0f, 0.0f, 0.0f, -1.0f, 0.0f, 0.0f }; - const float lb_input[] = { 0.0f, 1.0f }; - - pt.assign(pt_input, pt_input + 6); - normal.assign(nm_input, nm_input + 6); - label.assign(lb_input, lb_input + 2); -} - -int main(int argc, char* argv[]) { - bool succ = cflags::ParseCmd(argc, argv); - if (!succ) { - cflags::PrintHelpInfo("\nUsage: upgrade_points.exe"); - return 0; - } - - // file path - string file_path = FLAGS_filenames; - string output_path = FLAGS_output_path; - if (output_path != ".") mkdir(output_path); - else output_path = extract_path(file_path); - output_path += "/"; - - vector all_files; - get_all_filenames(all_files, file_path); - //#pragma omp parallel for - for (int i = 0; i < all_files.size(); ++i) { - string filename = extract_filename(all_files[i]); - - /// from the old formate to this new format - vector pts, normals, labels; - vector seg; - load_pointcloud(pts, normals, seg, all_files[i]); - if (FLAGS_has_label && seg.size() == 0) { - cout << "Error in " << filename << endl; - continue; - } - labels.assign(seg.begin(), seg.end()); - - Points points; - points.set_points(pts, normals, vector(), labels); - - points.write_points(output_path + filename + ".upgrade.points"); - - if (FLAGS_verbose) cout << "Processing: " + filename + "\n"; - } - cout << "Done: " << FLAGS_filenames << endl; - //// generate testing points - //vector pts, normals, labels; - //gen_test_pointcloud(pts, normals, labels); - //Points points; - //points.set_points(pts, normals, vector(), labels); - //points.write_points("test.points"); - - ///// backward conversion: from this new formate to the old format - //points.load_points(filename + ".points"); - //ofstream outfile(filename + "_1.points", ofstream::binary); - //int n = points.info().pt_num(); - //outfile.write((char*)(&n), sizeof(int)); - //outfile.write((const char*)points.prop_ptr(PtsInfo::kPoint), - // n * sizeof(float) * points.info().channel(PtsInfo::kPoint)); - //outfile.write((const char*)points.prop_ptr(PtsInfo::kNormal), - // n * sizeof(float) * points.info().channel(PtsInfo::kNormal)); - //outfile.close(); - - return 0; -} diff --git a/pdbbind/.DS_Store b/pdbbind/.DS_Store deleted file mode 100644 index 1106d29..0000000 Binary files a/pdbbind/.DS_Store and /dev/null differ diff --git a/pdbbind/Elements.java b/pdbbind/Elements.java deleted file mode 100755 index 070bb3a..0000000 --- a/pdbbind/Elements.java +++ /dev/null @@ -1,509 +0,0 @@ -/* - * Copyright (C) 2006-2012 Egon Willighagen - * 2014 Mark B Vine (orcid:0000-0002-7794-0426) - * - * Contact: cdk-devel@lists.sourceforge.net - * - * This program is free software; you can redistribute it and/or - * modify it under the terms of the GNU Lesser General Public License - * as published by the Free Software Foundation; either version 2.1 - * of the License, or (at your option) any later version. - * All we ask is that proper credit is given for our work, which includes - * - but is not limited to - adding the above copyright notice to the beginning - * of your source code files, and to any copyright notice that you may distribute - * with programs based on this work. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU Lesser General Public License for more details. - * - * You should have received a copy of the GNU Lesser General Public License - * along with this program; if not, write to the Free Software - * Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA. - */ -package org.openscience.cdk.config; - -import org.openscience.cdk.interfaces.IElement; - -import java.util.HashMap; -import java.util.Locale; -import java.util.Map; - -/** - * Enumeration of chemical elements. Data is taken from the Blue Obelisk Data - * Repository, version 3. This enumeration is auto-generated with utilities - * found in the 'cdk-build-utils' project. - * - * @author egonw - * @author john may - * @cdk.module core - * @cdk.githash - */ -public enum Elements { - Unknown(0, "", 0, 0, null, 0.00, null), - Hydrogen(1, "H", 1, 1, 0.37, 1.20, 2.20), - Helium(2, "He", 1, 18, 0.32, 1.40, null), - Lithium(3, "Li", 2, 1, 1.34, 2.20, 0.98), - Beryllium(4, "Be", 2, 2, 0.90, 1.90, 1.57), - Boron(5, "B", 2, 13, 0.82, 1.80, 2.04), - Carbon(6, "C", 2, 14, 0.77, 1.70, 2.55), - Nitrogen(7, "N", 2, 15, 0.75, 1.60, 3.04), - Oxygen(8, "O", 2, 16, 0.73, 1.55, 3.44), - Fluorine(9, "F", 2, 17, 0.71, 1.50, 3.98), - Neon(10, "Ne", 2, 18, 0.69, 1.54, null), - Sodium(11, "Na", 3, 1, 1.54, 2.40, 0.93), - Magnesium(12, "Mg", 3, 2, 1.30, 2.20, 1.31), - Aluminium(13, "Al", 3, 13, 1.18, 2.10, 1.61), - Silicon(14, "Si", 3, 14, 1.11, 2.10, 1.90), - Phosphorus(15, "P", 3, 15, 1.06, 1.95, 2.19), - Sulfur(16, "S", 3, 16, 1.02, 1.80, 2.58), - Chlorine(17, "Cl", 3, 17, 0.99, 1.80, 3.16), - Argon(18, "Ar", 3, 18, 0.97, 1.88, null), - Potassium(19, "K", 4, 1, 1.96, 2.80, 0.82), - Calcium(20, "Ca", 4, 2, 1.74, 2.40, 1.00), - Scandium(21, "Sc", 4, 3, 1.44, 2.30, 1.36), - Titanium(22, "Ti", 4, 4, 1.36, 2.15, 1.54), - Vanadium(23, "V", 4, 5, 1.25, 2.05, 1.63), - Chromium(24, "Cr", 4, 6, 1.27, 2.05, 1.66), - Manganese(25, "Mn", 4, 7, 1.39, 2.05, 1.55), - Iron(26, "Fe", 4, 8, 1.25, 2.05, 1.83), - Cobalt(27, "Co", 4, 9, 1.26, 2.0, 1.88), - Nickel(28, "Ni", 4, 10, 1.21, 2.0, 1.91), - Copper(29, "Cu", 4, 11, 1.38, 2.0, 1.90), - Zinc(30, "Zn", 4, 12, 1.31, 2.10, 1.65), - Gallium(31, "Ga", 4, 13, 1.26, 2.10, 1.81), - Germanium(32, "Ge", 4, 14, 1.22, 2.10, 2.01), - Arsenic(33, "As", 4, 15, 1.19, 2.05, 2.18), - Selenium(34, "Se", 4, 16, 1.16, 1.90, 2.55), - Bromine(35, "Br", 4, 17, 1.14, 1.90, 2.96), - Krypton(36, "Kr", 4, 18, 1.10, 2.02, 3.00), - Rubidium(37, "Rb", 5, 1, 2.11, 2.90, 0.82), - Strontium(38, "Sr", 5, 2, 1.92, 2.55, 0.95), - Yttrium(39, "Y", 5, 3, 1.62, 2.40, 1.22), - Zirconium(40, "Zr", 5, 4, 1.48, 2.30, 1.33), - Niobium(41, "Nb", 5, 5, 1.37, 2.15, 1.60), - Molybdenum(42, "Mo", 5, 6, 1.45, 2.10, 2.16), - Technetium(43, "Tc", 5, 7, 1.56, 2.05, 1.90), - Ruthenium(44, "Ru", 5, 8, 1.26, 2.05, 2.20), - Rhodium(45, "Rh", 5, 9, 1.35, 2.0, 2.28), - Palladium(46, "Pd", 5, 10, 1.31, 2.05, 2.20), - Silver(47, "Ag", 5, 11, 1.53, 2.10, 1.93), - Cadmium(48, "Cd", 5, 12, 1.48, 2.20, 1.69), - Indium(49, "In", 5, 13, 1.44, 2.20, 1.78), - Tin(50, "Sn", 5, 14, 1.41, 2.25, 1.96), - Antimony(51, "Sb", 5, 15, 1.38, 2.20, 2.05), - Tellurium(52, "Te", 5, 16, 1.35, 2.10, 2.10), - Iodine(53, "I", 5, 17, 1.33, 2.10, 2.66), - Xenon(54, "Xe", 5, 18, 1.30, 2.16, 2.60), - Caesium(55, "Cs", 6, 1, 2.25, 3.00, 0.79), - Barium(56, "Ba", 6, 2, 1.98, 2.70, 0.89), - Lanthanum(57, "La", 6, 3, 1.69, 2.50, 1.10), - Cerium(58, "Ce", 6, 0, null, 2.48, 1.12), - Praseodymium(59, "Pr", 6, 0, null, 2.47, 1.13), - Neodymium(60, "Nd", 6, 0, null, 2.45, 1.14), - Promethium(61, "Pm", 6, 0, null, 2.43, null), - Samarium(62, "Sm", 6, 0, null, 2.42, 1.17), - Europium(63, "Eu", 6, 0, 2.40, 2.40, null), - Gadolinium(64, "Gd", 6, 0, null, 2.38, 1.20), - Terbium(65, "Tb", 6, 0, null, 2.37, null), - Dysprosium(66, "Dy", 6, 0, null, 2.35, 1.22), - Holmium(67, "Ho", 6, 0, null, 2.33, 1.23), - Erbium(68, "Er", 6, 0, null, 2.32, 1.24), - Thulium(69, "Tm", 6, 0, null, 2.30, 1.25), - Ytterbium(70, "Yb", 6, 0, null, 2.28, null), - Lutetium(71, "Lu", 6, 0, 1.60, 2.27, 1.27), - Hafnium(72, "Hf", 6, 4, 1.50, 2.25, 1.30), - Tantalum(73, "Ta", 6, 5, 1.38, 2.20, 1.50), - Tungsten(74, "W", 6, 6, 1.46, 2.10, 2.36), - Rhenium(75, "Re", 6, 7, 1.59, 2.05, 1.90), - Osmium(76, "Os", 6, 8, 1.28, 2.0, 2.20), - Iridium(77, "Ir", 6, 9, 1.37, 2.0, 2.20), - Platinum(78, "Pt", 6, 10, 1.28, 2.05, 2.28), - Gold(79, "Au", 6, 11, 1.44, 2.10, 2.54), - Mercury(80, "Hg", 6, 12, 1.49, 2.05, 2.00), - Thallium(81, "Tl", 6, 13, 1.48, 2.20, 1.62), - Lead(82, "Pb", 6, 14, 1.47, 2.30, 2.33), - Bismuth(83, "Bi", 6, 15, 1.46, 2.30, 2.02), - Polonium(84, "Po", 6, 16, 1.46, null, 2.00), - Astatine(85, "At", 6, 17, null, null, 2.20), - Radon(86, "Rn", 6, 18, 1.45, null, null), - Francium(87, "Fr", 7, 1, null, null, 0.70), - Radium(88, "Ra", 7, 2, null, null, 0.90), - Actinium(89, "Ac", 7, 3, null, null, 1.10), - Thorium(90, "Th", 7, 0, null, 2.40, 1.30), - Protactinium(91, "Pa", 7, 0, null, null, 1.50), - Uranium(92, "U", 7, 0, null, 2.30, 1.38), - Neptunium(93, "Np", 7, 0, null, null, 1.36), - Plutonium(94, "Pu", 7, 0, null, null, 1.28), - Americium(95, "Am", 7, 0, null, null, 1.30), - Curium(96, "Cm", 7, 0, null, null, 1.30), - Berkelium(97, "Bk", 7, 0, null, null, 1.30), - Californium(98, "Cf", 7, 0, null, null, 1.30), - Einsteinium(99, "Es", 7, 0, null, null, 1.30), - Fermium(100, "Fm", 7, 0, null, null, 1.30), - Mendelevium(101, "Md", 7, 0, null, null, 1.30), - Nobelium(102, "No", 7, 0, null, null, 1.30), - Lawrencium(103, "Lr", 7, 0, null, null, null), - Rutherfordium(104, "Rf", 7, 4, null, null, null), - Dubnium(105, "Db", 7, 5, null, null, null), - Seaborgium(106, "Sg", 7, 6, null, null, null), - Bohrium(107, "Bh", 7, 7, null, null, null), - Hassium(108, "Hs", 7, 8, null, null, null), - Meitnerium(109, "Mt", 7, 9, null, null, null), - Darmstadtium(110, "Ds", 7, 10, null, null, null), - Roentgenium(111, "Rg", 7, 11, null, null, null), - Copernicium(112, "Cn", 7, 12, null, null, null), - @Deprecated - Ununtrium(113, "Uut", 7, 13, null, null, null), - Nihonium(113, "Nh", 7, 13, null, null, null), - Flerovium(114, "Fl", 7, 14, null, null, null), - @Deprecated - Ununpentium(115, "Uup", 7, 15, null, null, null), - Moscovium(115, "Mc", 7, 15, null, null, null), - Livermorium(116, "Lv", 7, 16, null, null, null), - @Deprecated - Ununseptium(117, "Uus", 7, 17, null, null, null), - Tennessine(117, "Ts", 7, 17, null, null, null), - @Deprecated - Ununoctium(118, "Uuo", 7, 18, null, null, null), - Oganesson(118, "Og", 7, 18, null, null, null); - - /** - * Atomic number, periodic table period and group. - */ - private final int number, period, group; - - /** - * The symbol of the element. - */ - private final String symbol; - - /** - * Covalent radius (rcov), van der Waals radius - * (rw) and Pauling electronegativity. - */ - private final Double rCov, rW, electronegativity; - - /** - * An {@link IElement} instance of this element. - */ - private final IElement instance; - - /** - * Lookup elements by atomic number. - */ - static final Elements[] NUMER_MAP = new Elements[119]; - - /** - * Lookup elements by symbol / name. - */ - static final Map SYMBOL_MAP = new HashMap(400); - - static { - // index elements - for (final Elements e : values()) { - NUMER_MAP[e.number] = e; - SYMBOL_MAP.put(e.symbol.toLowerCase(Locale.ENGLISH), e); - SYMBOL_MAP.put(e.name().toLowerCase(Locale.ENGLISH), e); - } - - // recently named elements - SYMBOL_MAP.put("uub", Copernicium); // 2009 - SYMBOL_MAP.put("ununbium", Copernicium); - - SYMBOL_MAP.put("uuq", Flerovium); // 2012 - SYMBOL_MAP.put("ununquadium", Flerovium); - - SYMBOL_MAP.put("uuh", Livermorium); // 2012 - SYMBOL_MAP.put("ununhexium", Livermorium); - - // 2016 - SYMBOL_MAP.put("uut", Nihonium); - SYMBOL_MAP.put("uup", Moscovium); - SYMBOL_MAP.put("uus", Tennessine); - SYMBOL_MAP.put("uuo", Oganesson); - - // alternative spellings - SYMBOL_MAP.put("sulphur", Sulfur); - SYMBOL_MAP.put("cesium", Caesium); - SYMBOL_MAP.put("aluminum", Aluminium); - - } - - /** - * Internal constructor. - * - * @param number atomic number - * @param symbol symbol - * @param period periodic table period - * @param group periodic table group - * @param rCov covalent radius - * @param rW van der Waals radius - * @param electronegativity pauling electronegativity - */ - private Elements(int number, String symbol, int period, int group, Double rCov, Double rW, Double electronegativity) { - this.number = number; - this.period = period; - this.group = group; - this.symbol = symbol; - this.rCov = rCov; - this.rW = rW; - this.electronegativity = electronegativity; - this.instance = new NaturalElement(symbol, number); - } - - /** - * The atomic number of the element. An {@link #Unknown} element - * has an atomic number of '0'. - * - * @return 0 - 116 - */ - public int number() { - return number; - } - - /** - * The element symbol, C for carbon, N for nitrogen, Na for sodium, etc. An - * {@link #Unknown} element has no symbol. - * - * @return the symbol - */ - public String symbol() { - return symbol; - } - - /** - * Return the period in the periodic table this element belongs to. If - * the element is {@link #Unknown} it's period is 0. - * - * @return a period in the periodic table - */ - public int period() { - return period; - } - - /** - * Return the group in the periodic table this element belongs to. If - * the element does not belong to a group then it's group is '0'. - * - * @return a group in the periodic table - */ - public int group() { - return group; - } - - /** - * The covalent radius, rcov, is a measure of the - * size of an atom that forms part of one covalent bond. - * - * @return covalent radius - null if not available - * @see Covalent radius - */ - public Double covalentRadius() { - return rCov; - } - - /** - * The van der Waals radius, rw, of an atom is the - * radius of an imaginary hard sphere which can be used to model the - * atom. - * - * @return van der Waals radius - null if not available - * @see Van de Waals radius - */ - public Double vdwRadius() { - return rW; - } - - /** - * Electronegativity, symbol χ, is a chemical property that describes - * the tendency of an atom or a functional group to attract electrons - * (or electron density) towards itself. This method provides access to the - * Pauling electronegativity value for a chemical element. If no value is - * available 'null' is returned. - * - * @return Pauling electronegativity - null if not available - * @see Pauling Electronegativity - */ - public Double electronegativity() { - return electronegativity; - } - - /** - * Access an {@link IElement} instance of the chemical element. - * - * @return an instance - */ - public IElement toIElement() { - return instance; - } - - /** - * Obtain the element with the specified atomic number. If no element had - * the specified atomic number then {@link #Unknown} is returned. - * - * 
-     *     // carbon
-     *     Elements e = Elements.ofNumber(6);
-     *
-     *     // oxygen
-     *     Elements e = Elements.ofNumber(8);
-     *
- * - * @param number atomic number - * @return an element, or {@link #Unknown} - */ - public static Elements ofNumber(final int number) { - if (number < 0 || number > 118) return Unknown; - return NUMER_MAP[number]; - } - - /** - * Obtain the element with the specified symbol or name. If no element had - * the specified symbol or name then {@link #Unknown} is returned. The - * input is case-insensitive. - * - * 
-     *     // carbon
-     *     Elements e = Elements.ofString("c");
-     *     Elements e = Elements.ofString("C");
-     *     Elements e = Elements.ofString("Carbon");
-     *     Elements e = Elements.ofString("carbon");
-     *
- * - * @param str input string - * @return an element, or {@link #Unknown} - */ - public static Elements ofString(final String str) { - if (str == null) return Unknown; - Elements e = SYMBOL_MAP.get(str.toLowerCase(Locale.ENGLISH)); - if (e == null) return Unknown; - return e; - } - - /** These instances are for backards compatability. */ - public final static IElement DUMMY = Unknown.toIElement(); - public final static IElement HYDROGEN = Hydrogen.toIElement(); - public final static IElement HELIUM = Helium.toIElement(); - public final static IElement LITHIUM = Lithium.toIElement(); - public final static IElement BERYLLIUM = Beryllium.toIElement(); - public final static IElement BORON = Boron.toIElement(); - public final static IElement CARBON = Carbon.toIElement(); - public final static IElement NITROGEN = Nitrogen.toIElement(); - public final static IElement OXYGEN = Oxygen.toIElement(); - public final static IElement FLUORINE = Fluorine.toIElement(); - public final static IElement NEON = Neon.toIElement(); - public final static IElement SODIUM = Sodium.toIElement(); - public final static IElement MAGNESIUM = Magnesium.toIElement(); - public final static IElement ALUMINIUM = Aluminium.toIElement(); - public final static IElement SILICON = Silicon.toIElement(); - public final static IElement PHOSPHORUS = Phosphorus.toIElement(); - public final static IElement SULFUR = Sulfur.toIElement(); - public final static IElement CHLORINE = Chlorine.toIElement(); - public final static IElement ARGON = Argon.toIElement(); - public final static IElement POTASSIUM = Potassium.toIElement(); - public final static IElement CALCIUM = Calcium.toIElement(); - public final static IElement SCANDIUM = Scandium.toIElement(); - public final static IElement TITANIUM = Titanium.toIElement(); - public final static IElement VANADIUM = Vanadium.toIElement(); - public final static IElement CHROMIUM = Chromium.toIElement(); - public final static IElement MANGANESE = Manganese.toIElement(); - public final static IElement IRON = Iron.toIElement(); - public final static IElement COBALT = Cobalt.toIElement(); - public final static IElement NICKEL = Nickel.toIElement(); - public final static IElement COPPER = Copper.toIElement(); - public final static IElement ZINC = Zinc.toIElement(); - public final static IElement GALLIUM = Gallium.toIElement(); - public final static IElement GERMANIUM = Germanium.toIElement(); - public final static IElement ARSENIC = Arsenic.toIElement(); - public final static IElement SELENIUM = Selenium.toIElement(); - public final static IElement BROMINE = Bromine.toIElement(); - public final static IElement KRYPTON = Krypton.toIElement(); - public final static IElement RUBIDIUM = Rubidium.toIElement(); - public final static IElement STRONTIUM = Strontium.toIElement(); - public final static IElement YTTRIUM = Yttrium.toIElement(); - public final static IElement ZIRCONIUM = Zirconium.toIElement(); - public final static IElement NIOBIUM = Niobium.toIElement(); - public final static IElement MOLYBDENUM = Molybdenum.toIElement(); - public final static IElement TECHNETIUM = Technetium.toIElement(); - public final static IElement RUTHENIUM = Ruthenium.toIElement(); - public final static IElement RHODIUM = Rhodium.toIElement(); - public final static IElement PALLADIUM = Palladium.toIElement(); - public final static IElement SILVER = Silver.toIElement(); - public final static IElement CADMIUM = Cadmium.toIElement(); - public final static IElement INDIUM = Indium.toIElement(); - public final static IElement TIN = Tin.toIElement(); - public final static IElement ANTIMONY = Antimony.toIElement(); - public final static IElement TELLURIUM = Tellurium.toIElement(); - public final static IElement IODINE = Iodine.toIElement(); - public final static IElement XENON = Xenon.toIElement(); - public final static IElement CAESIUM = Caesium.toIElement(); - public final static IElement BARIUM = Barium.toIElement(); - public final static IElement LANTHANUM = Lanthanum.toIElement(); - public final static IElement CERIUM = Cerium.toIElement(); - public final static IElement PRASEODYMIUM = Praseodymium.toIElement(); - public final static IElement NEODYMIUM = Neodymium.toIElement(); - public final static IElement PROMETHIUM = Promethium.toIElement(); - public final static IElement SAMARIUM = Samarium.toIElement(); - public final static IElement EUROPIUM = Europium.toIElement(); - public final static IElement GADOLINIUM = Gadolinium.toIElement(); - public final static IElement TERBIUM = Terbium.toIElement(); - public final static IElement DYSPROSIUM = Dysprosium.toIElement(); - public final static IElement HOLMIUM = Holmium.toIElement(); - public final static IElement ERBIUM = Erbium.toIElement(); - public final static IElement THULIUM = Thulium.toIElement(); - public final static IElement YTTERBIUM = Ytterbium.toIElement(); - public final static IElement LUTETIUM = Lutetium.toIElement(); - public final static IElement HAFNIUM = Hafnium.toIElement(); - public final static IElement TANTALUM = Tantalum.toIElement(); - public final static IElement TUNGSTEN = Tungsten.toIElement(); - public final static IElement RHENIUM = Rhenium.toIElement(); - public final static IElement OSMIUM = Osmium.toIElement(); - public final static IElement IRIDIUM = Iridium.toIElement(); - public final static IElement PLATINUM = Platinum.toIElement(); - public final static IElement GOLD = Gold.toIElement(); - public final static IElement MERCURY = Mercury.toIElement(); - public final static IElement THALLIUM = Thallium.toIElement(); - public final static IElement LEAD = Lead.toIElement(); - public final static IElement BISMUTH = Bismuth.toIElement(); - public final static IElement POLONIUM = Polonium.toIElement(); - public final static IElement ASTATINE = Astatine.toIElement(); - public final static IElement RADON = Radon.toIElement(); - public final static IElement FRANCIUM = Francium.toIElement(); - public final static IElement RADIUM = Radium.toIElement(); - public final static IElement ACTINIUM = Actinium.toIElement(); - public final static IElement THORIUM = Thorium.toIElement(); - public final static IElement PROTACTINIUM = Protactinium.toIElement(); - public final static IElement URANIUM = Uranium.toIElement(); - public final static IElement NEPTUNIUM = Neptunium.toIElement(); - public final static IElement PLUTONIUM = Plutonium.toIElement(); - public final static IElement AMERICIUM = Americium.toIElement(); - public final static IElement CURIUM = Curium.toIElement(); - public final static IElement BERKELIUM = Berkelium.toIElement(); - public final static IElement CALIFORNIUM = Californium.toIElement(); - public final static IElement EINSTEINIUM = Einsteinium.toIElement(); - public final static IElement FERMIUM = Fermium.toIElement(); - public final static IElement MENDELEVIUM = Mendelevium.toIElement(); - public final static IElement NOBELIUM = Nobelium.toIElement(); - public final static IElement LAWRENCIUM = Lawrencium.toIElement(); - public final static IElement RUTHERFORDIUM = Rutherfordium.toIElement(); - public final static IElement DUBNIUM = Dubnium.toIElement(); - public final static IElement SEABORGIUM = Seaborgium.toIElement(); - public final static IElement BOHRIUM = Bohrium.toIElement(); - public final static IElement HASSIUM = Hassium.toIElement(); - public final static IElement MEITNERIUM = Meitnerium.toIElement(); - public final static IElement DARMSTADTIUM = Darmstadtium.toIElement(); - public final static IElement ROENTGENIUM = Roentgenium.toIElement(); - public final static IElement UNUNBIUM = Copernicium.toIElement(); - public final static IElement UNUNTRIUM = Ununtrium.toIElement(); - public final static IElement UNUNQUADIUM = Flerovium.toIElement(); - public final static IElement FLEROVIUM = Flerovium.toIElement(); - public final static IElement UNUNPENTIUM = Ununpentium.toIElement(); - public final static IElement UNUNHEXIUM = Livermorium.toIElement(); - public final static IElement LIVERMORIUM = Livermorium.toIElement(); - - // Incorrect spelling - @Deprecated - public final static IElement PLUTOMNIUM = PLUTONIUM; -} diff --git a/pdbbind/atomic_feature.py b/pdbbind/atomic_feature.py deleted file mode 100644 index ab32324..0000000 --- a/pdbbind/atomic_feature.py +++ /dev/null @@ -1,429 +0,0 @@ -# For OctSurfNet, get the atomic features for pocket and ligand. -# OpenBabel 3.0.0 also support van der waal surface for molecules. - -import pickle - -import numpy as np -# import pybel -# import openbabel -from openbabel import pybel -from openbabel import openbabel as ob - - -# from math import ceil, sin, cos, sqrt, pi -# from itertools import combinations -import os - - -def get_cdk_vdwr_dic(file = '/home/qil15006/2020Summer/OCNN_Jun26_2020/pdbbind/Elements.java'): #/home/qil15006/2020Summer/OCNN_Jun26_2020/pdbbind/Elements.java - """extract van der waal radius from cdk Element.java file""" - cdk_vdwr_dic = {} - with open(file, 'r') as f: - lines = f.readlines() - start = False - for line in lines: - if 'public enum Elements {' in line: - start = True - continue - if start: - if '@Deprecated' in line: - continue - contents = line.split('(') - contents = contents[1] - row = contents.split(',') - atomic_number = int(row[0]) - radius = row[-3] - if 'null' in radius: - # print(line) - pass - else: - cdk_vdwr_dic[atomic_number] = float(radius) - if 'Oganesson' in line: - break - else: - continue - return cdk_vdwr_dic -cdk_vdwr_dic = get_cdk_vdwr_dic() -# print(cdk_vdwr_dic) - -class Featurizer(): - """Calcaulates atomic features for molecules. Features can encode atom type, - native pybel properties or any property defined with SMARTS patterns - - Attributes - ---------- - FEATURE_NAMES: list of strings - Labels for features (in the same order as features) - NUM_ATOM_CLASSES: int - Number of atom codes - ATOM_CODES: dict - Dictionary mapping atomic numbers to codes - NAMED_PROPS: list of string - Names of atomic properties to retrieve from pybel.Atom object - CALLABLES: list of callables - Callables used to calculcate custom atomic properties - SMARTS: list of SMARTS strings - SMARTS patterns defining additional atomic properties - """ - - def __init__(self, atom_codes=None, atom_labels=None, - named_properties=None, save_molecule_codes=True, - custom_properties=None, smarts_properties=None, - smarts_labels=None): - - """Creates Featurizer with specified types of features. Elements of a - feature vector will be in a following order: atom type encoding - (defined by atom_codes), Pybel atomic properties (defined by - named_properties), molecule code (if present), custom atomic properties - (defined `custom_properties`), and additional properties defined with - SMARTS (defined with `smarts_properties`). - - Parameters - ---------- - atom_codes: dict, optional - Dictionary mapping atomic numbers to codes. It will be used for - one-hot encoging therefore if n different types are used, codes - shpuld be from 0 to n-1. Multiple atoms can have the same code, - e.g. you can use {6: 0, 7: 1, 8: 1} to encode carbons with [1, 0] - and nitrogens and oxygens with [0, 1] vectors. If not provided, - default encoding is used. - atom_labels: list of strings, optional - Labels for atoms codes. It should have the same length as the - number of used codes, e.g. for `atom_codes={6: 0, 7: 1, 8: 1}` you - should provide something like ['C', 'O or N']. If not specified - labels 'atom0', 'atom1' etc are used. If `atom_codes` is not - specified this argument is ignored. - named_properties: list of strings, optional - Names of atomic properties to retrieve from pybel.Atom object. If - not specified ['hyb', 'heavyvalence', 'heterovalence', - 'partialcharge'] is used. - save_molecule_codes: bool, optional (default True) - If set to True, there will be an additional feature to save - molecule code. It is usefeul when saving molecular complex in a - single array. - custom_properties: list of callables, optional - Custom functions to calculate atomic properties. Each element of - this list should be a callable that takes pybel.Atom object and - returns a float. If callable has `__name__` property it is used as - feature label. Otherwise labels 'func' etc are used, where i is - the index in `custom_properties` list. - smarts_properties: list of strings, optional - Additional atomic properties defined with SMARTS patterns. These - patterns should match a single atom. If not specified, deafult - patterns are used. - smarts_labels: list of strings, optional - Labels for properties defined with SMARTS. Should have the same - length as `smarts_properties`. If not specified labels 'smarts0', - 'smarts1' etc are used. If `smarts_properties` is not specified - this argument is ignored. - """ - - # Remember namse of all features in the correct order - self.FEATURE_NAMES = [] - - if atom_codes is not None: - if not isinstance(atom_codes, dict): - raise TypeError('Atom codes should be dict, got %s instead' - % type(atom_codes)) - codes = set(atom_codes.values()) - for i in range(len(codes)): - if i not in codes: - raise ValueError('Incorrect atom code %s' % i) - - self.NUM_ATOM_CLASSES = len(codes) - self.ATOM_CODES = atom_codes - if atom_labels is not None: - if len(atom_labels) != self.NUM_ATOM_CLASSES: - raise ValueError('Incorrect number of atom labels: ' - '%s instead of %s' - % (len(atom_labels), self.NUM_ATOM_CLASSES)) - else: - atom_labels = ['atom%s' % i for i in range(self.NUM_ATOM_CLASSES)] - self.FEATURE_NAMES += atom_labels - else: - self.ATOM_CODES = {} - - metals = ([3, 4, 11, 12, 13] + list(range(19, 32)) - + list(range(37, 51)) + list(range(55, 84)) - + list(range(87, 104))) - - # List of tuples (atomic_num, class_name) with atom types to encode. - atom_classes = [ - (1, 'H'), # QQ add: H also considered in surface. - (5, 'B'), - (6, 'C'), - (7, 'N'), - (8, 'O'), - (15, 'P'), - (16, 'S'), - (34, 'Se'), - ([9, 17, 35, 53], 'halogen'), - (metals, 'metal') - ] - - for code, (atom, name) in enumerate(atom_classes): - if type(atom) is list: - for a in atom: - self.ATOM_CODES[a] = code - else: - self.ATOM_CODES[atom] = code - self.FEATURE_NAMES.append(name) - - self.NUM_ATOM_CLASSES = len(atom_classes) - - if named_properties is not None: - if not isinstance(named_properties, (list, tuple, np.ndarray)): - raise TypeError('named_properties must be a list') - allowed_props = [prop for prop in dir(pybel.Atom) - if not prop.startswith('__')] - for prop_id, prop in enumerate(named_properties): - if prop not in allowed_props: - raise ValueError( - 'named_properties must be in pybel.Atom attributes,' - ' %s was given at position %s' % (prop_id, prop) - ) - self.NAMED_PROPS = named_properties - else: - # pybel.Atom properties to save - self.NAMED_PROPS = ['hyb', 'heavyvalence', 'heterovalence', - 'partialcharge', 'radius'] - self.FEATURE_NAMES += self.NAMED_PROPS - - if not isinstance(save_molecule_codes, bool): - raise TypeError('save_molecule_codes should be bool, got %s ' - 'instead' % type(save_molecule_codes)) - self.save_molecule_codes = save_molecule_codes - if save_molecule_codes: - # Remember if an atom belongs to the ligand or to the protein - self.FEATURE_NAMES.append('molcode') - - self.CALLABLES = [] - if custom_properties is not None: - for i, func in enumerate(custom_properties): - if not callable(func): - raise TypeError('custom_properties should be list of' - ' callables, got %s instead' % type(func)) - name = getattr(func, '__name__', '') - if name == '': - name = 'func%s' % i - self.CALLABLES.append(func) - self.FEATURE_NAMES.append(name) - - if smarts_properties is None: - # SMARTS definition for other properties - self.SMARTS = [ - '[#6+0!$(*~[#7,#8,F]),SH0+0v2,s+0,S^3,Cl+0,Br+0,I+0]', - '[a]', - '[!$([#1,#6,F,Cl,Br,I,o,s,nX3,#7v5,#15v5,#16v4,#16v6,*+1,*+2,*+3])]', - '[!$([#6,H0,-,-2,-3]),$([!H0;#7,#8,#9])]', - '[r]' - ] - smarts_labels = ['hydrophobic', 'aromatic', 'acceptor', 'donor', - 'ring'] - elif not isinstance(smarts_properties, (list, tuple, np.ndarray)): - raise TypeError('smarts_properties must be a list') - else: - self.SMARTS = smarts_properties - - if smarts_labels is not None: - if len(smarts_labels) != len(self.SMARTS): - raise ValueError('Incorrect number of SMARTS labels: %s' - ' instead of %s' - % (len(smarts_labels), len(self.SMARTS))) - else: - smarts_labels = ['smarts%s' % i for i in range(len(self.SMARTS))] - - # Compile patterns - self.compile_smarts() - self.FEATURE_NAMES += smarts_labels - - def compile_smarts(self): - self.__PATTERNS = [] - for smarts in self.SMARTS: - self.__PATTERNS.append(pybel.Smarts(smarts)) - - def encode_num(self, atomic_num): - """Encode atom type with a binary vector. If atom type is not included in - the `atom_classes`, its encoding is an all-zeros vector. - - Parameters - ---------- - atomic_num: int - Atomic number - - Returns - ------- - encoding: np.ndarray - Binary vector encoding atom type (one-hot or null). - """ - - if not isinstance(atomic_num, int): - raise TypeError('Atomic number must be int, %s was given' - % type(atomic_num)) - - encoding = np.zeros(self.NUM_ATOM_CLASSES) - try: - encoding[self.ATOM_CODES[atomic_num]] = 1.0 - except: - pass - return encoding - - def find_smarts(self, molecule): - """Find atoms that match SMARTS patterns. - - Parameters - ---------- - molecule: pybel.Molecule - - Returns - ------- - features: np.ndarray - NxM binary array, where N is the number of atoms in the `molecule` - and M is the number of patterns. `features[i, j]` == 1.0 if i'th - atom has j'th property - """ - - if not isinstance(molecule, pybel.Molecule): - raise TypeError('molecule must be pybel.Molecule object, %s was given' - % type(molecule)) - - features = np.zeros((len(molecule.atoms), len(self.__PATTERNS))) - - for (pattern_id, pattern) in enumerate(self.__PATTERNS): - atoms_with_prop = np.array(list(*zip(*pattern.findall(molecule))), - dtype=int) - 1 - features[atoms_with_prop, pattern_id] = 1.0 - return features - - def get_features(self, molecule, molcode=None): - """Get coordinates and features for all heavy atoms in the molecule. - - Parameters - ---------- - molecule: pybel.Molecule - molcode: float, optional - Molecule type. You can use it to encode whether an atom belongs to - the ligand (1.0) or to the protein (-1.0) etc. - - Returns - ------- - coords: np.ndarray, shape = (N, 3) - Coordinates of all heavy atoms in the `molecule`. - features: np.ndarray, shape = (N, F) - Features of all heavy atoms in the `molecule`: atom type - (one-hot encoding), pybel.Atom attributes, type of a molecule - (e.g protein/ligand distinction), and other properties defined with - SMARTS patterns - """ - - if not isinstance(molecule, pybel.Molecule): - raise TypeError('molecule must be pybel.Molecule object,' - ' %s was given' % type(molecule)) - if molcode is None: - if self.save_molecule_codes is True: - raise ValueError('save_molecule_codes is set to True,' - ' you must specify code for the molecule') - elif not isinstance(molcode, (float, int)): - raise TypeError('motlype must be float, %s was given' - % type(molcode)) - - coords = [] - features = [] - heavy_atoms = [] - - for i, atom in enumerate(molecule): - # ignore hydrogens and dummy atoms (they have atomicnum set to 0) - # if atom.atomicnum > 1: - # heavy_atoms.append(i) - # coords.append(atom.coords) - # - # features.append(np.concatenate(( - # self.encode_num(atom.atomicnum), - # [atom.__getattribute__(prop) for prop in self.NAMED_PROPS], - # [func(atom) for func in self.CALLABLES], - # ))) - - heavy_atoms.append(i) - coords.append(atom.coords) - - # ['hyb', 'heavyvalence', 'heterovalence', - # 'partialcharge'] - atom_attributes = [] - for prop in self.NAMED_PROPS: - if prop == 'hyb': - atom_attributes.append(atom.hyb) - if prop == 'heavyvalence': - atom_attributes.append(atom.heavydegree) - if prop == 'heterovalence': - atom_attributes.append(atom.heterodegree) - if prop == 'partialcharge': - atom_attributes.append(atom.partialcharge) - if prop == 'radius': - temp = atom.atomicnum - # temp2 = ob.GetVdwRad(temp) - # if abs(temp2 - 1.1) < 0.01: - # temp2 = 1.2 - temp2 = cdk_vdwr_dic[temp] - atom_attributes.append(temp2) - pass - - features.append(np.concatenate(( - self.encode_num(atom.atomicnum), - # [atom.__getattribute__(prop) for prop in self.NAMED_PROPS], - atom_attributes, - [func(atom) for func in self.CALLABLES], - ))) - - coords = np.array(coords, dtype=np.float32) - features = np.array(features, dtype=np.float32) - if self.save_molecule_codes: - features = np.hstack((features, - molcode * np.ones((len(features), 1)))) - - features = np.hstack([features, - self.find_smarts(molecule)[heavy_atoms]]) - - if np.isnan(features).any(): - raise RuntimeError('Got NaN when calculating features') - - return coords, features - -def write_feature_file(folder): - contents = folder.split('/') - id = contents[-1] - ligand_file = folder + '/{}_ligand.mol2'.format(id) - pocket_file = folder + '/{}_pocket.pdb'.format(id) - - ligand = next(pybel.readfile('mol2', ligand_file)) - featurizer = Featurizer() - ligand_coords, ligand_feature = featurizer.get_features(ligand, molcode=0) - with open('{}/{}_ligand_feature.txt'.format(folder,id), 'w') as f: - for i in range(ligand_feature.shape[0]): - # f.writelines('{} {}\n'.format(i+1, ligand_feature[i].tolist())) - f.writelines('{}'.format(i+1)) - for j in ligand_coords[i]: - f.writelines(' {}'.format(j)) - for j in ligand_feature[i]: - f.writelines(' {}'.format(j)) - f.writelines('\n') - - pocket = next(pybel.readfile('pdb', pocket_file)) - featurizer = Featurizer() - pocket_coords, pocket_feature = featurizer.get_features(pocket, molcode=1) - with open('{}/{}_pocket_feature.txt'.format(folder, id), 'w') as f: - for i in range(pocket_feature.shape[0]): - # f.writelines('{} {}\n'.format(i+1, pocket_feature[i].tolist())) - f.writelines('{}'.format(i+1)) - for j in pocket_coords[i]: - f.writelines(' {}'.format(j)) - for j in pocket_feature[i]: - f.writelines(' {}'.format(j)) - f.writelines('\n') - pass - -if __name__ == "__main__": - path = os.getcwd() - write_feature_file(path) - -# path = '../test_set/4oem' #'/media/data/data_share/pdbbind/v2018-other-PL/3oc0' #'/media/data/data_share/pdbbind/CASF-2016/coreset/3wtj'# #'../test_set/1a4r' -# write_feature_file(path) diff --git a/pdbbind/bash_scripts/data_prepare_points.sh b/pdbbind/bash_scripts/data_prepare_points.sh deleted file mode 100644 index 1da6fe6..0000000 --- a/pdbbind/bash_scripts/data_prepare_points.sh +++ /dev/null @@ -1,13 +0,0 @@ -for D in ./*/; -do - echo "${D}"; - cd "${D}"; - java -cp /home/qil15006/2020Summer/OCNN_Jun26_2020/pdbbind/java/cdk-2.3-SNAPSHOT.jar:/home/qil15006/2020Summer/OCNN_Jun26_2020/pdbbind/java Surface_for_single 6 ico pdbbind; - /home/qil15006/.conda/envs/tf-OCNN/bin/python /home/qil15006/2020Summer/OCNN_Jun26_2020/pdbbind/write_complex.py - /home/qil15006/.conda/envs/tf-OCNN/bin/python /home/qil15006/2020Summer/OCNN_Jun26_2020/pdbbind/atomic_feature.py - /home/qil15006/2020Summer/OCNN_Jun26_2020/octree/build/pdb_to_points --complex_id file_list.txt - # uncomment if need octree - #mkdir octree_folder - #/home/qil15006/2020Summer/OCNN_Jun26_2020/octree/build/qq_octree_rotate --depth 5 --filenames point_list.txt --output_path ./octree_folder - cd ..; -done \ No newline at end of file diff --git a/pdbbind/check_points_file.py b/pdbbind/check_points_file.py deleted file mode 100644 index fd903ff..0000000 --- a/pdbbind/check_points_file.py +++ /dev/null @@ -1,29 +0,0 @@ -import os - -cwd = os.getcwd() -folders = os.listdir(cwd) - -def check_name_points(filename): - if 'points.points' in filename: - return True - -count = 0 -for folder in folders: - current_folder = os.path.join(cwd, folder) - if os.path.isfile(current_folder): - continue - subfolders = os.listdir(current_folder) - find = False - for sub in subfolders: - if check_name_points(sub): - size = os.stat(os.path.join(current_folder, sub)).st_size - if size < 10000000: - print('File size too small: {}'.format(folder)) - find = True - break - if not find: - print('Not find points in: {}'.format(folder)) - count += 1 - -print('Total not found: {}'.format(count)) - diff --git a/pdbbind/chesk_make_grids.py b/pdbbind/chesk_make_grids.py deleted file mode 100644 index 73b6e01..0000000 --- a/pdbbind/chesk_make_grids.py +++ /dev/null @@ -1,23 +0,0 @@ -import numpy as np - -size = 64 -channel = 24 -a = np.zeros(size**3*channel) - - -j = 12 -x,y,z = 15,26,27 - - -idx = j * size **3 + x * size**2 + y * size + z -a[idx] = 1 - -b = a.reshape(channel, size, size, size) -b.nonzero() - -import tensorflow as tf -c = tf.convert_to_tensor(a) -c = tf.reshape(c, (channel, size, size, size)) -sess = tf.InteractiveSession() -c[j, x, y, z].eval() - diff --git a/pdbbind/data_download.sh b/pdbbind/data_download.sh deleted file mode 100644 index 5d271e0..0000000 --- a/pdbbind/data_download.sh +++ /dev/null @@ -1,28 +0,0 @@ -mkdir data_folder -cd data_folder - -wget "http://www.pdbbind.org.cn/download/pdbbind_v2018_refined.tar.gz" -wget "http://www.pdbbind.org.cn/download/pdbbind_v2018_other_PL.tar.gz" -wget "http://www.pdbbind-cn.org/download/CASF-2016.tar.gz" - -tar -xzvf pdbbind_v2018_refined.tar.gz -tar -xzvf pdbbind_v2018_other_PL.tar.gz -tar -xzvf CASF-2016.tar.gz - -cp ../bash_scripts/* v2018-other-PL -cp ../bash_scripts/* refined-set -cp ../bash_scripts/* CASF-2016/coreset - -cd v2018-other-PL -bash correct_mol2.sh -bash data_clean_index0.sh -#bash data_prepare_points.sh -# -cd ../refined-set -bash data_clean_index0.sh -#bash data_prepare_points.sh -# -cd ../CASF-2016/coreset -bash data_clean_index0.sh -#bash data_prepare_points.sh - diff --git a/pdbbind/generate_octree_list.py b/pdbbind/generate_octree_list.py deleted file mode 100644 index 23a615d..0000000 --- a/pdbbind/generate_octree_list.py +++ /dev/null @@ -1,383 +0,0 @@ -import re -import math -import numpy as np -import random -import os -import pandas as pd - - - -def filter(folders, feature_need_sdf): - results = [] - for folder in folders: - if len(folder) != 4: - continue - if folder in feature_need_sdf: - continue - if os.path.isfile(folder): - print(folder) - continue - results.append(folder) - return results - -def check_duplicate(folder1, folder2): - results = [] - for folder in folder1: - if folder in folder2: - # print('folder1 subfolder {} also in folder2'.format(folder)) - continue - results.append(folder) - for folder in folder2: - if folder in folder1: - # print('folder2 subfolder {} also in folder1'.format(folder)) - pass - return results - -# use to check if all the compelx show in PL_data include pdb and mol2 file. Answer is NO. -def check_dic_id(dic, general_folder, refine_folder, core_folder): - for key in dic: - if key not in general_folder and key not in refine_folder and key not in core_folder: - print(key) - - -def read_affinity(file_name, mode): - """ - read affinity data from pdbbind index file, refers to log Kd/Ki - Benefit is: in general set, there are some <,>,~ in Kd/Ki, directly use log Kd/Ki might be better. - """ - record_dic = {} - with open(file_name, 'r') as data_fid: - for line in data_fid: - if '#' in line or '===' in line or len(line) == 0: - continue - line = re.sub('\s+', ' ', line).strip() - contents = line.split(' ') - id = contents[0] - affinity = float(contents[3]) - if mode == 'reg': - record_dic[id] = affinity - else: - if affinity > 6 + math.log10(5): - record_dic[id] = 1 - elif affinity < 6 - math.log10(5): - record_dic[id] = 0 - else: - print('skip {}, affinity is {}.'.format(id, affinity)) - return record_dic - -# train/test/val split, with all pdbbind data. -def write_octree_list(general_dic, core_folders, refined_folders, depth = 8, mode = 'reg'): - # test only include core set - test_file_name = root_folder + 'octree_list_test_{}_{}.txt'.format(depth, mode) - test_affinity = [] - with open(test_file_name, 'w') as f: - for id in core_folders: - if id not in general_dic: - continue - for v in range(0, view_num): - path = 'CASF-2016/coreset/{0}/octree_folder/{1}_points_{2}_2_{3:03d}.octree'.format(id, id, depth, v) - line = '{} {}\n'.format(path, general_dic[id]) - f.write(line) - test_affinity.append(general_dic[id]) - - # validation is part of refined set - random.seed(2020) - val_id = random.sample(refined_folders, k=600) - val_file_name = root_folder + 'octree_list_val_{}_{}.txt'.format(depth, mode) - val_affinity = [] - with open(val_file_name, 'w') as f: - for id in refined_folders: - if id not in val_id: - continue - if id not in general_dic: - print('id {} not in general_dic'.format(id)) - continue - for v in range(0, view_num): - path = refined_set + '/{0}/octree_folder/{1}_points_{2}_2_{3:03d}.octree'.format(id, id, depth, v) - line = '{} {}\n'.format(path, general_dic[id]) - f.write(line) - val_affinity.append(general_dic[id]) - - train_file_name = root_folder + 'octree_list_train_{}_{}.txt'.format(depth, mode) - train_affinity = [] - with open(train_file_name, 'w') as f: - train_total = general_folders + refined_folders - for id in train_total: - if id in val_id: - continue - if id not in general_dic: - continue - for v in range(0, view_num): - if id in refined_folders: - path = refined_set + '/{0}/octree_folder/{1}_points_{2}_2_{3:03d}.octree'.format(id, id, depth, v) - else: - path = 'v2018-other-PL/{0}/octree_folder/{1}_points_{2}_2_{3:03d}.octree'.format(id, id, depth, v) - line = '{} {}\n'.format(path, general_dic[id]) - f.write(line) - train_affinity.append(general_dic[id]) - - train_affinity = np.array(train_affinity) - val_affinity = np.array(val_affinity) - test_affinity = np.array(test_affinity) - print('train_affinity size: {}, mean: {}, std: {}'.format(train_affinity.shape[0], np.mean(train_affinity), np.std(train_affinity))) - print('val_affinity size: {}, mean: {}, std: {}'.format(val_affinity.shape[0], np.mean(val_affinity), np.std(val_affinity))) - print('test_affinity size: {}, mean: {}, std: {}'.format(test_affinity.shape[0], np.mean(test_affinity), np.std(test_affinity))) - -def write_points_list(general_dic, core_folders, refined_folders, mode = 'reg', density = None): - # test only include core set - test_file_name = root_folder + 'points_list_test_{}.txt'.format(mode) - test_affinity = [] - with open(test_file_name, 'w') as f: - for id in core_folders: - if id not in general_dic: - continue - if density is None: - path = 'CASF-2016/coreset/{0}/{1}_points.points'.format(id, id) - else: - path = 'CASF-2016/coreset/{0}/{1}_points_{2}.points'.format(id, id, density) - # print(path) - line = '{} {}\n'.format(path, general_dic[id]) - f.write(line) - test_affinity.append(general_dic[id]) - - # validation is part of refined set - random.seed(2020) - # val_id = random.choices(refined_folders, k=1000) - val_id = random.sample(refined_folders, k=600) - val_file_name = root_folder + 'points_list_val_{}.txt'.format(mode) - val_affinity = [] - with open(val_file_name, 'w') as f: - for id in refined_folders: - if id not in val_id or id not in general_dic: - continue - if density is None: - path = refined_set + '/{0}/{1}_points.points'.format(id, id) - else: - path = refined_set + '/{0}/{1}_points_{2}.points'.format(id, id, density) - - line = '{} {}\n'.format(path, general_dic[id]) - f.write(line) - val_affinity.append(general_dic[id]) - - - - train_file_name = root_folder + 'points_list_train_{}.txt'.format(mode) - train_affinity = [] - with open(train_file_name, 'w') as f: - train_total = general_folders + refined_folders - for id in train_total: - if id in val_id: - continue - if id not in general_dic: - continue - if id in refined_folders: - if density is None: - path = refined_set + '/{0}/{1}_points.points'.format(id, id) - else: - path = refined_set + '/{0}/{1}_points_{2}.points'.format(id, id, density) - else: - if density is None: - path = 'v2018-other-PL/{0}/{1}_points.points'.format(id, id) - else: - path = 'v2018-other-PL/{0}/{1}_points_{2}.points'.format(id, id, density) - - # print(path) - line = '{} {}\n'.format(path, general_dic[id]) - f.write(line) - train_affinity.append(general_dic[id]) - - train_affinity = np.array(train_affinity) - val_affinity = np.array(val_affinity) - test_affinity = np.array(test_affinity) - print('train_affinity size: {}, mean: {}, std: {}'.format(train_affinity.shape[0], np.mean(train_affinity), np.std(train_affinity))) - print('val_affinity size: {}, mean: {}, std: {}'.format(val_affinity.shape[0], np.mean(val_affinity), np.std(val_affinity))) - print('test_affinity size: {}, mean: {}, std: {}'.format(test_affinity.shape[0], np.mean(test_affinity), np.std(test_affinity))) - - -def write_points_list_refine(general_dic, core_folders, refined_folders, mode='reg', density = None): - # test only include core set - test_file_name = root_folder + 'points_list_test_refine_{}.txt'.format(mode) - test_affinity = [] - with open(test_file_name, 'w') as f: - for id in core_folders: - if id not in general_dic: - continue - if density is None: - path = 'CASF-2016/coreset/{0}/{1}_points.points'.format(id, id) - else: - path = 'CASF-2016/coreset/{0}/{1}_points_{2}.points'.format(id, id, density) - line = '{} {}\n'.format(path, general_dic[id]) - f.write(line) - test_affinity.append(general_dic[id]) - - # validation is part of refined set - random.seed(2020) - # val_id = random.choices(refined_folders, k=1000) - val_id = random.sample(refined_folders, k=300) - val_file_name = root_folder + 'points_list_val_refine_{}.txt'.format(mode) - val_affinity = [] - with open(val_file_name, 'w') as f: - for id in refined_folders: - if id not in val_id or id not in general_dic: - continue - if density is None: - path = refined_set + '/{0}/{1}_points.points'.format(id, id) - else: - path = refined_set + '/{0}/{1}_points_{2}.points'.format(id, id, density) - - line = '{} {}\n'.format(path, general_dic[id]) - f.write(line) - val_affinity.append(general_dic[id]) - - train_file_name = root_folder + 'points_list_train_refine_{}.txt'.format(mode) - train_affinity = [] - with open(train_file_name, 'w') as f: - train_total = refined_folders - for id in train_total: - if id not in general_dic: - continue - if id in val_id: - continue - if id in refined_folders: - if density is None: - path = refined_set + '/{0}/{1}_points.points'.format(id, id) - else: - path = refined_set + '/{0}/{1}_points_{2}.points'.format(id, id, density) - line = '{} {}\n'.format(path, general_dic[id]) - f.write(line) - train_affinity.append(general_dic[id]) - - train_affinity = np.array(train_affinity) - val_affinity = np.array(val_affinity) - test_affinity = np.array(test_affinity) - print('train shape: ', train_affinity.shape) - print('val shape: ', val_affinity.shape) - print('test shape: ', test_affinity.shape) - print('train_affinity mean: {}, std: {}'.format(np.mean(train_affinity), np.std(train_affinity))) - print('val_affinity mean: {}, std: {}'.format(np.mean(val_affinity), np.std(val_affinity))) - print('test_affinity mean: {}, std: {}'.format(np.mean(test_affinity), np.std(test_affinity))) - - -def write_octree_list_refine(general_dic, core_folders, refined_folders, depth = 8, mode='reg'): - # test only include core set - test_file_name = root_folder + 'octree_list_core_{}_{}.txt'.format(depth, mode) - test_affinity = [] - with open(test_file_name, 'w') as f: - for id in core_folders: - if id not in general_dic: - continue - # for v in range(0, view_num): - for v in range(15, 16): - path = 'CASF-2016/coreset/{0}/octree_folder/{1}_points_{2}_2_{3:03d}.octree'.format(id, id, depth, v) - line = '{} {}\n'.format(path, general_dic[id]) - f.write(line) - test_affinity.append(general_dic[id]) - - train_file_name = root_folder + 'octree_list_refine_{}_{}.txt'.format(depth, mode) - train_affinity = [] - with open(train_file_name, 'w') as f: - train_total = refined_folders - for id in train_total: - if id not in general_dic: - continue - # for v in range(0, view_num): - for v in range(15, 16): - if id in refined_folders: - path = refined_set + '/{0}/octree_folder/{1}_points_{2}_2_{3:03d}.octree'.format(id, id, depth, v) - # else: - # path = 'v2018-other-PL/{0}/{1}_points_{2}_2_{3:03d}.octree'.format(id, id, depth, v) - line = '{} {}\n'.format(path, general_dic[id]) - f.write(line) - train_affinity.append(general_dic[id]) - - train_affinity = np.array(train_affinity) - test_affinity = np.array(test_affinity) - print('train shape: ', train_affinity.shape) - print('test shape: ', test_affinity.shape) - print('train_affinity mean: {}, std: {}'.format(np.mean(train_affinity), np.std(train_affinity))) - print('test_affinity mean: {}, std: {}'.format(np.mean(test_affinity), np.std(test_affinity))) - -def list_astex_complex(general_dic): - folder = '/media/data/data_share/qil15006/astex_diverse_set' - complexes = os.listdir(folder) - complexes = [item for item in complexes if len(item)==4 ] - - # filted_complexes = [] - # for complex in complexes: - # if complex in general_folders: - # print('Complex {} in general folder'.format(complex)) - # continue - # if complex in refined_folders: - # print('Complex {} in refine folder'.format(complex)) - # continue - # if complex in core_folders: - # print('Complex {} in core folder'.format(complex)) - # continue - # filted_complexes.append(complex) - # complexes = filted_complexes - # print(len(complexes)) - - pafnucy_affinity = pd.read_csv('./pdbbind/affinity_data.csv', names = ['pdbid', 'affinity']) - deepatom_affinity = pd.read_csv('./pdbbind/Supplement_INDEX_Benchmark.csv', header = None, names = ['pdbid', 'affinity']) - - affinity_dic = {} - for complex in complexes: - if complex in general_dic: - affinity_dic[complex] = general_dic[complex] - if complex in pafnucy_affinity['pdbid'].unique(): - affinity_dic[complex] = pafnucy_affinity.loc[pafnucy_affinity['pdbid'] == complex, 'affinity'] - if complex in deepatom_affinity['pdbid'].unique(): - affinity_dic[complex] = deepatom_affinity.loc[deepatom_affinity['pdbid'] == complex, 'affinity'] - - print(len(affinity_dic)) - - -if __name__ == "__main__": - root_folder = '/media/data/data_share/pdbbind/' - refined_set = 'refined_set_v2018' - - # root_folder = '/scratch/qil15006/pdbbind/' - # refined_set = 'refined-set' - mode = 'reg' - - general_file = root_folder + 'v2018-other-PL/index/INDEX_general_PL_data.2018' - view_num = 24 - general_folders = os.listdir(root_folder + 'v2018-other-PL') - refined_folders = os.listdir(root_folder + refined_set) - core_folders = os.listdir(root_folder + 'CASF-2016/coreset') - - feature_need_sdf = ['3qlb', '4oem', '4oel', '2fov', '2fou', '2foy'] - - - general_folders = filter(general_folders, feature_need_sdf) - refined_folders = filter(refined_folders, feature_need_sdf) - core_folders = filter(core_folders, feature_need_sdf) - - print(len(general_folders), len(refined_folders), len(core_folders)) - print('Check general and core') - general_folders = check_duplicate(general_folders, core_folders) - print('Check general and refine') - general_folders = check_duplicate(general_folders, refined_folders) - print('Check refine and core') - refined_folders = check_duplicate(refined_folders, core_folders) - print(len(general_folders), len(refined_folders), len(core_folders)) - - core_id_list = core_folders #get_core_id(core_path) - general_dic = read_affinity(general_file, mode = mode) - print(len(general_dic)) - - # check_dic_id(general_dic, general_folders, refined_folders, core_folders) - - # write_octree_list(general_dic, core_folders, refined_folders, depth = 5, mode = mode) - # write_octree_list_refine(general_dic, core_folders, refined_folders, depth=5, mode=mode) - write_points_list(general_dic, core_folders, refined_folders, mode=mode, density = 5) - # write_points_list_refine(general_dic, core_folders, refined_folders, mode=mode, density = 5) - - - -# print(np.mean(affinities)) -# print(np.std(affinities)) -# import matplotlib.pyplot as plt -# import numpy as np -# n, bins, patches = plt.hist(affinities, 30, facecolor='blue', alpha=0.5) -# plt.savefig('histagram.png') diff --git a/pdbbind/java/AdaptiveSurface.java b/pdbbind/java/AdaptiveSurface.java deleted file mode 100644 index ef09184..0000000 --- a/pdbbind/java/AdaptiveSurface.java +++ /dev/null @@ -1,320 +0,0 @@ -import java.io.BufferedWriter; -import java.io.File; -import java.io.FileInputStream; -import java.io.FileWriter; -import java.io.IOException; -import java.io.InputStream; -import java.util.ArrayList; -import java.util.Arrays; -import java.util.List; - -import javax.vecmath.Point3d; - -import org.openscience.cdk.ChemFile; -import org.openscience.cdk.geometry.surface.AdaptiveNumericalSurface; -import org.openscience.cdk.geometry.surface.NumericalSurface; -import org.openscience.cdk.geometry.surface.Point_Type; -import org.openscience.cdk.interfaces.IAtomContainer; -import org.openscience.cdk.interfaces.IChemObjectBuilder; -import org.openscience.cdk.io.Mol2Reader; -import org.openscience.cdk.io.PDBReader; -import org.openscience.cdk.io.iterator.IteratingSDFReader; -import org.openscience.cdk.silent.SilentChemObjectBuilder; -import org.openscience.cdk.io.MDLV2000Reader; -import org.openscience.cdk.tools.manipulator.ChemFileManipulator; - -public class AdaptiveSurface { - //public javax.vecmath.Point3d[] getAllSurfacePoints(); - - public static String get_id(String filename) { - String[] parts = filename.split("."); - String part1 = parts[0]; - String[] parts1 = part1.split("_"); - String id = parts1[0]; - return id; - } - - public static void handle_single_mol2(String filename, String path, String id, int tess_level, String tess_type, boolean append) { - //String id = get_id(filename); -// String[] parts1 = filename.split("_"); -// String id = parts1[0]; -// System.out.println("Test"); - //return id; - - try { - File file = new File(path + "/" + id + "/" + filename); - Mol2Reader reader = new Mol2Reader(new FileInputStream(file)); - ChemFile crambin = reader.read(new ChemFile()); - // ChemFile crambin = reader.read(new ChemFile()); generate warning: org.openscience.cdk.config.atomtypes.AtomTypeHandle WARN: Unrecognized hybridization in config file: tetrahedral - // refer to https://github.com/johnmay/cdk/blob/master/base/core/src/main/java/org/openscience/cdk/config/atomtypes/AtomTypeHandler.java - // Another warning: org.openscience.cdk.io.Mol2Reader WARN: Not reading molecule qualifiers - // refer to https://github.com/cdk/cdk/blob/master/storage/io/src/main/java/org/openscience/cdk/io/Mol2Reader.java - List containers= ChemFileManipulator.getAllAtomContainers(crambin); - String write_filename = path + "/" + id + "/" + id + "_cdk_ligand.xyz"; - String atom_filename = path + "/" + id + "/" + id + "_cdk_ligand.txt"; - //FileOutputStream outputStream = new FileOutputStream(write_filename); - BufferedWriter writer = new BufferedWriter(new FileWriter(write_filename, append)); - BufferedWriter writer1 = new BufferedWriter(new FileWriter(atom_filename, append)); - System.out.println("container size: " + containers.size()); - for (int i = 0; i< containers.size(); i++) { - IAtomContainer container = containers.get(i); - String description = container.toString(); - - AdaptiveNumericalSurface new_surface = new AdaptiveNumericalSurface(container, 0, tess_level, tess_type); -// System.out.println(container); -// System.out.println(description); -// System.out.println(new_surface); - try { - //new_surface.calculateSurface(); - //Point3d[] points= new_surface.getAllSurfacePoints(); - ArrayList point_types = new_surface.getAllPointswithAtomType(); - -// System.out.println(points); - - System.out.println(point_types.size()); - for (int j = 0; j < point_types.size(); j++) { - org.openscience.cdk.geometry.surface.Point_Type point_type = point_types.get(j); - Point3d coord = (point_type).getCoord(); - int atom = ((Point_Type) point_type).getAtom(); - String str = coord.x + " " + coord.y + " " + coord.z + "\n"; -// if (j == 1){System.out.println(str);} - writer.write(str); - writer1.write(atom + "\n"); - }} catch (Exception ex) {System.out.println(ex);} - } - /*IChemObject pdb_mol = reader.read();*/ - reader.close(); - - writer.close(); - writer1.close(); - System.out.println("Finished " + write_filename); - } catch (Exception ex) {System.out.println(ex);} - //return container; - } - - public static void handle_single_sdf(String filename, String path, String id, int tess_level, String tess_type, boolean append) { - //String id = get_id(filename); -// String[] parts1 = filename.split("_"); -// String id = parts1[0]; -// System.out.println("Test"); - //return id; - - try { -// File file = new File(path + "/" + id + "/" + filename); - final boolean noStopOnError = true; - final IChemObjectBuilder builder = SilentChemObjectBuilder.getInstance(); - String write_filename = path + "/" + id + "/" + id + "_cdk_ligand.xyz"; - String atom_filename = path + "/" + id + "/" + id + "_cdk_ligand.txt"; - //FileOutputStream outputStream = new FileOutputStream(write_filename); - BufferedWriter writer = new BufferedWriter(new FileWriter(write_filename, append)); - BufferedWriter writer1 = new BufferedWriter(new FileWriter(atom_filename, append)); - -// List containers = new ArrayList(); - - try (InputStream in = new FileInputStream(path + "/" + id + "/" + filename); // .mol also fine! - IteratingSDFReader sdfr = new IteratingSDFReader(in, builder, noStopOnError)) { - while (sdfr.hasNext()) { - IAtomContainer mol = sdfr.next(); -// containers.add(mol); - AdaptiveNumericalSurface new_surface = new AdaptiveNumericalSurface(mol, 0, tess_level, tess_type); - // process molecule here - ArrayList point_types = new_surface.getAllPointswithAtomType(); - System.out.println(point_types.size()); - for (int j = 0; j < point_types.size(); j++) { - org.openscience.cdk.geometry.surface.Point_Type point_type = point_types.get(j); - Point3d coord = (point_type).getCoord(); - int atom = ((Point_Type) point_type).getAtom(); - String str = coord.x + " " + coord.y + " " + coord.z + "\n"; - writer.write(str); - writer1.write(atom + "\n"); - } - } - } catch (IOException e) { - System.err.println("Error: " + e.getMessage()); - } - -// MDLV2000Reader reader = new MDLV2000Reader(new FileInputStream(file)); -// ChemFile crambin = reader.read(new ChemFile()); -// List containers= ChemFileManipulator.getAllAtomContainers(crambin); - writer.close(); - writer1.close(); - System.out.println("Finished " + write_filename); - } catch (Exception ex) {System.out.println(ex);} - //return container; - } - - public static void handle_single_pdb(String filename, String path, String id, int tess_level, String tess_type, String file_type) { - //String id = get_id(filename); -// System.out.println(filename); -// String[] parts = filename.split("_"); - //System.out.println(parts); -// String id = parts[0]; -// System.out.println(id); - - - try { - File file = new File(path + "/" + id + "/" + filename); - PDBReader reader = new PDBReader(new FileInputStream(file)); - //IAtomContainer container = reader.read(SilentChemObjectBuilder.getInstance().newInstance(IAtomContainer.class)); - ChemFile crambin = reader.read(new ChemFile()); - List containers= ChemFileManipulator.getAllAtomContainers(crambin); - String write_filename = path + "/" + id + "/" + id + "_cdk_"+ file_type + ".xyz"; - String atom_filename = path + "/" + id + "/" + id + "_cdk_"+ file_type + ".txt"; - //FileOutputStream outputStream = new FileOutputStream(write_filename); - BufferedWriter writer = new BufferedWriter(new FileWriter(write_filename)); - BufferedWriter writer1 = new BufferedWriter(new FileWriter(atom_filename)); - for (int i = 0; i< containers.size(); i++) { - IAtomContainer container = containers.get(i); -// String description = container.toString(); - - AdaptiveNumericalSurface new_surface = new AdaptiveNumericalSurface(container, 0, tess_level, tess_type); -// System.out.println(container); -// System.out.println(description); -// System.out.println(new_surface); - try { - ArrayList point_types = new_surface.getAllPointswithAtomType(); -// System.out.println(points); - System.out.println(point_types.size()); - for (int j = 0; j < point_types.size(); j++) { - org.openscience.cdk.geometry.surface.Point_Type point_type = point_types.get(j); - Point3d coord = (point_type).getCoord(); - int atom = ((Point_Type) point_type).getAtom(); - String str = coord.x + " " + coord.y + " " + coord.z + "\n"; -// if (j == 1){System.out.println(str);} - writer.write(str); - writer1.write(atom + "\n"); - }} catch (Exception ex) {System.out.println(ex);} - } - /*IChemObject pdb_mol = reader.read();*/ - reader.close(); - - writer.close(); - writer1.close(); - System.out.println("Finished " + write_filename); - } catch (Exception ex) {System.out.println(ex);} - //return container; - } - - public static void handle_single_complex(String filename, String path, String id, int tess_level, String tess_type, String file_type) { - // need revise: to separate ligand/protein. - try { - File file = new File(path + "/" + id + "/" + filename); - PDBReader reader = new PDBReader(new FileInputStream(file)); - ChemFile crambin = reader.read(new ChemFile()); - List containers= ChemFileManipulator.getAllAtomContainers(crambin); - String write_filename = path + "/" + id + "/" + id + "_cdk_"+ file_type + ".xyz"; - String atom_filename = path + "/" + id + "/" + id + "_cdk_"+ file_type + ".txt"; - BufferedWriter writer = new BufferedWriter(new FileWriter(write_filename)); - BufferedWriter writer1 = new BufferedWriter(new FileWriter(atom_filename)); - for (int i = 0; i< containers.size(); i++) { - IAtomContainer container = containers.get(i); - AdaptiveNumericalSurface new_surface = new AdaptiveNumericalSurface(container, 0, tess_level, tess_type); - try { - ArrayList point_types = new_surface.getAllPointswithAtomType(); - System.out.println(point_types.size()); - for (int j = 0; j < point_types.size(); j++) { - org.openscience.cdk.geometry.surface.Point_Type point_type = point_types.get(j); - Point3d coord = (point_type).getCoord(); - int atom = ((Point_Type) point_type).getAtom(); - String str = coord.x + " " + coord.y + " " + coord.z + "\n"; - writer.write(str); - writer1.write(atom + "\n"); - }} catch (Exception ex) {System.out.println(ex);} - } - reader.close(); - - writer.close(); - writer1.close(); - System.out.println("Finished " + write_filename); - } catch (Exception ex) {System.out.println(ex);} - } - - public static void handle_single_id(File complex_id, String path, int tess_level, String tess_type, Boolean protein_flag, String source) { - //File folder = new File("your/path"); - String id_string = complex_id.getName(); - File[] listOfFiles = complex_id.listFiles(); - - if (source.equals("pdbbind")) { - boolean append = false; - for (int i = 0; i < listOfFiles.length; i++) { - String file_name = listOfFiles[i].getName(); - - if (file_name.endsWith(".mol2")){ - System.out.println("Start work on: " + file_name); -// handle_single_mol2(file_name, path, id_string, tess_level, tess_type, append); - System.out.println("Finished: " + file_name); - append = false; - } -// else if (file_name.endsWith("_ligand.sdf")) { -// System.out.println("Start work on: " + file_name); -// handle_single_sdf(file_name, path, id_string, tess_level, tess_type, append); -// System.out.println("Finished: " + file_name); -// append = false; -// } - else if(file_name.endsWith("pocket.pdb")) { - System.out.println("Start work on: " + file_name); - handle_single_pdb(file_name, path, id_string, tess_level, tess_type, "pocket"); - System.out.println("Finished: " + file_name); - } - else if(protein_flag && file_name.endsWith("protein.pdb")) { - System.out.println("Start work on: " + file_name); - handle_single_pdb(file_name, path, id_string, tess_level-2, tess_type, "protein"); - System.out.println("Finished: " + file_name); - } - } // end for - - } // end if source - else if (source.equals("pdbbank")) { - boolean append = false; - for (int i = 0; i < listOfFiles.length; i++) { - String file_name = listOfFiles[i].getName(); - if(file_name.endsWith("_withHs.pdb")) { - handle_single_complex(file_name, path, id_string, tess_level-2, tess_type, "protein"); - } - }// end for - } - else { - System.out.println("This File Source is not supported: " + source); - } - - } - - public static void main(String[] args) { - String path = args[0]; - int tess_level = Integer.parseInt(args[1]); - String tess_type = args[2]; - Boolean protein_flag = Boolean.parseBoolean(args[3]); - String source = args[4]; - //System.out.println(path); - File folder = new File(path); - File[] listOfIDs = folder.listFiles(); - //System.out.println(listOfIDs); - try { - for (int i = 0; i < listOfIDs.length; i++) { - if (listOfIDs[i].isFile()) { - continue; - } - else if (listOfIDs[i].isDirectory()) { - File complex_id = listOfIDs[i]; - String id_string = complex_id.getName(); - System.out.println(id_string); - List already = Arrays.asList("1a1e", "1a4r", - "2pjt", "2phb", "1a4k", "3avj", "1x7a"); - List skip = Arrays.asList("4g17", "3v2w", - "2y80", "4prg", "2phb", "1a1e", "1a4r", - "2pjt", "1a4k"); - -// List already = Arrays.asList(); -// List skip = Arrays.asList(); - if(already.contains(id_string)|| skip.contains(id_string)) { - System.out.println("Skip " + id_string + "."); - continue; - } - handle_single_id(complex_id, path, tess_level, tess_type, protein_flag, source); - System.out.println("\n"); - } - } - } catch (Exception ex) {System.out.println(ex);} - System.out.println("All Finish"); - } -} diff --git a/pdbbind/java/Surface_for_single.class b/pdbbind/java/Surface_for_single.class deleted file mode 100644 index 7660871..0000000 Binary files a/pdbbind/java/Surface_for_single.class and /dev/null differ diff --git a/pdbbind/java/Surface_for_single.java b/pdbbind/java/Surface_for_single.java deleted file mode 100644 index c3906dc..0000000 --- a/pdbbind/java/Surface_for_single.java +++ /dev/null @@ -1,302 +0,0 @@ -import java.io.BufferedWriter; -import java.io.File; -import java.io.FileInputStream; -import java.io.FileWriter; -import java.io.IOException; -import java.io.InputStream; -import java.util.ArrayList; -import java.util.Arrays; -import java.util.List; -import java.nio.file.Path; -import java.nio.file.Paths; - - -import javax.vecmath.Point3d; - -import org.openscience.cdk.ChemFile; -import org.openscience.cdk.geometry.surface.AdaptiveNumericalSurface; -import org.openscience.cdk.geometry.surface.NumericalSurface; -import org.openscience.cdk.geometry.surface.Point_Type; -import org.openscience.cdk.interfaces.IAtomContainer; -import org.openscience.cdk.interfaces.IChemObjectBuilder; -import org.openscience.cdk.io.Mol2Reader; -import org.openscience.cdk.io.PDBReader; -import org.openscience.cdk.io.iterator.IteratingSDFReader; -import org.openscience.cdk.silent.SilentChemObjectBuilder; -import org.openscience.cdk.io.MDLV2000Reader; -import org.openscience.cdk.tools.manipulator.ChemFileManipulator; - -import java.io.BufferedWriter; -import java.io.File; -import java.io.FileInputStream; -import java.io.FileWriter; -import java.io.IOException; -import java.io.InputStream; -import java.util.ArrayList; -import java.util.Arrays; -import java.util.List; - -import javax.vecmath.Point3d; - -import org.openscience.cdk.ChemFile; -import org.openscience.cdk.geometry.surface.AdaptiveNumericalSurface; -import org.openscience.cdk.geometry.surface.NumericalSurface; -import org.openscience.cdk.geometry.surface.Point_Type; -import org.openscience.cdk.interfaces.IAtomContainer; -import org.openscience.cdk.interfaces.IChemObjectBuilder; -import org.openscience.cdk.io.Mol2Reader; -import org.openscience.cdk.io.PDBReader; -import org.openscience.cdk.io.iterator.IteratingSDFReader; -import org.openscience.cdk.silent.SilentChemObjectBuilder; -import org.openscience.cdk.io.MDLV2000Reader; -import org.openscience.cdk.tools.manipulator.ChemFileManipulator; - -public class Surface_for_single { - //public javax.vecmath.Point3d[] getAllSurfacePoints(); - - public static String get_id(String filename) { - String[] parts = filename.split("."); - String part1 = parts[0]; - String[] parts1 = part1.split("_"); - String id = parts1[0]; - return id; - } - - public static void handle_single_mol2(String filename, String path, String id, int tess_level, String tess_type, String file_type, boolean append) { - - try { - File file = new File(path + "/" + id + "/" + filename); - Mol2Reader reader = new Mol2Reader(new FileInputStream(file)); - ChemFile crambin = reader.read(new ChemFile()); - // ChemFile crambin = reader.read(new ChemFile()); generate warning: org.openscience.cdk.config.atomtypes.AtomTypeHandle WARN: Unrecognized hybridization in config file: tetrahedral - // refer to https://github.com/johnmay/cdk/blob/master/base/core/src/main/java/org/openscience/cdk/config/atomtypes/AtomTypeHandler.java - // Another warning: org.openscience.cdk.io.Mol2Reader WARN: Not reading molecule qualifiers - // refer to https://github.com/cdk/cdk/blob/master/storage/io/src/main/java/org/openscience/cdk/io/Mol2Reader.java - List containers= ChemFileManipulator.getAllAtomContainers(crambin); - String write_filename = path + "/" + id + "/" + id + "_cdk_"+ file_type + ".xyz"; - String atom_filename = path + "/" + id + "/" + id + "_cdk_"+ file_type + ".txt"; - //FileOutputStream outputStream = new FileOutputStream(write_filename); - BufferedWriter writer = new BufferedWriter(new FileWriter(write_filename, append)); - BufferedWriter writer1 = new BufferedWriter(new FileWriter(atom_filename, append)); - System.out.println("container size: " + containers.size()); - for (int i = 0; i< containers.size(); i++) { - IAtomContainer container = containers.get(i); -// String description = container.toString(); - -// AdaptiveNumericalSurface new_surface = new AdaptiveNumericalSurface(container, 0, tess_level, tess_type); - NumericalSurface new_surface = new NumericalSurface(container, 0, tess_level, tess_type); -// System.out.println(container); -// System.out.println(description); -// System.out.println(new_surface); - try { - //new_surface.calculateSurface(); - //Point3d[] points= new_surface.getAllSurfacePoints(); - ArrayList point_types = new_surface.getAllPointswithAtomType(); - -// System.out.println(points); - - System.out.println(point_types.size()); - for (int j = 0; j < point_types.size(); j++) { - org.openscience.cdk.geometry.surface.Point_Type point_type = point_types.get(j); - Point3d coord = (point_type).getCoord(); - int atom = ((Point_Type) point_type).getAtom(); - int atom_index = ((Point_Type) point_type).getIndex(); - String str = coord.x + " " + coord.y + " " + coord.z + "\n"; -// if (j == 1){System.out.println(str);} - writer.write(str); -// writer1.write(atom + "\n"); - writer1.write(atom + " " + atom_index + "\n"); - }} catch (Exception ex) {System.out.println("remind QQ Error: " + ex);} - } - /*IChemObject pdb_mol = reader.read();*/ - reader.close(); - - writer.close(); - writer1.close(); - System.out.println("Finished " + write_filename); - } catch (Exception ex) {System.out.println(ex);} - //return container; - } - - public static void handle_single_pdb(String filename, String path, String id, int tess_level, String tess_type, String file_type) { - //String id = get_id(filename); -// System.out.println(filename); -// String[] parts = filename.split("_"); - //System.out.println(parts); -// String id = parts[0]; -// System.out.println(id); - - - try { - File file = new File(path + "/" + id + "/" + filename); - PDBReader reader = new PDBReader(new FileInputStream(file)); - //IAtomContainer container = reader.read(SilentChemObjectBuilder.getInstance().newInstance(IAtomContainer.class)); - ChemFile crambin = reader.read(new ChemFile()); - List containers= ChemFileManipulator.getAllAtomContainers(crambin); - String write_filename = path + "/" + id + "/" + id + "_cdk_"+ file_type + ".xyz"; - String atom_filename = path + "/" + id + "/" + id + "_cdk_"+ file_type + ".txt"; - //FileOutputStream outputStream = new FileOutputStream(write_filename); - BufferedWriter writer = new BufferedWriter(new FileWriter(write_filename)); - BufferedWriter writer1 = new BufferedWriter(new FileWriter(atom_filename)); - for (int i = 0; i< containers.size(); i++) { - IAtomContainer container = containers.get(i); -// String description = container.toString(); - System.out.println(containers.size()); - -// AdaptiveNumericalSurface new_surface = new AdaptiveNumericalSurface(container, 0, tess_level, tess_type); - NumericalSurface new_surface = new NumericalSurface(container, 0, tess_level, tess_type); -// System.out.println(container); -// System.out.println(description); -// System.out.println(new_surface); - try { - ArrayList point_types = new_surface.getAllPointswithAtomType(); -// System.out.println(points); - System.out.println(point_types.size()); - for (int j = 0; j < point_types.size(); j++) { - org.openscience.cdk.geometry.surface.Point_Type point_type = point_types.get(j); - Point3d coord = (point_type).getCoord(); - int atom = ((Point_Type) point_type).getAtom(); - int atom_index = ((Point_Type) point_type).getIndex(); - String str = coord.x + " " + coord.y + " " + coord.z + "\n"; -// if (j == 1){System.out.println(str);} - writer.write(str); -// writer1.write(atom + "\n"); - writer1.write(atom + " " + atom_index + "\n"); - }} catch (Exception ex) {System.out.println(ex);} - } - /*IChemObject pdb_mol = reader.read();*/ - reader.close(); - - writer.close(); - writer1.close(); - System.out.println("Finished " + write_filename); - } catch (Exception ex) {System.out.println(ex);} - //return container; - } - - public static void handle_single_complex(String filename, String path, String id, int tess_level, String tess_type, String file_type) { - // need revise: to separate ligand/protein. - try { - File file = new File(path + "/" + id + "/" + filename); - PDBReader reader = new PDBReader(new FileInputStream(file)); - ChemFile crambin = reader.read(new ChemFile()); - List containers= ChemFileManipulator.getAllAtomContainers(crambin); - String write_filename = path + "/" + id + "/" + id + "_cdk_"+ file_type + ".xyz"; - String atom_filename = path + "/" + id + "/" + id + "_cdk_"+ file_type + ".txt"; - BufferedWriter writer = new BufferedWriter(new FileWriter(write_filename)); - BufferedWriter writer1 = new BufferedWriter(new FileWriter(atom_filename)); - for (int i = 0; i< containers.size(); i++) { - IAtomContainer container = containers.get(i); - AdaptiveNumericalSurface new_surface = new AdaptiveNumericalSurface(container, 0, tess_level, tess_type); - try { - ArrayList point_types = new_surface.getAllPointswithAtomType(); - System.out.println(point_types.size()); - for (int j = 0; j < point_types.size(); j++) { - org.openscience.cdk.geometry.surface.Point_Type point_type = point_types.get(j); - Point3d coord = (point_type).getCoord(); - int atom = ((Point_Type) point_type).getAtom(); - int atom_index = ((Point_Type) point_type).getIndex(); - String str = coord.x + " " + coord.y + " " + coord.z + "\n"; - writer.write(str); -// writer1.write(atom + "\n"); - writer1.write(atom + " " + atom_index + "\n"); - }} catch (Exception ex) {System.out.println(ex);} - } - reader.close(); - - writer.close(); - writer1.close(); - System.out.println("Finished " + write_filename); - } catch (Exception ex) {System.out.println(ex);} - } - - public static void handle_single_id(File complex_id, String path, int tess_level, String tess_type, Boolean protein_flag, String source) { - //File folder = new File("your/path"); - String id_string = complex_id.getName(); - File[] listOfFiles = complex_id.listFiles(); - - if (source.equals("pdbbind")) { - boolean append = false; - for (int i = 0; i < listOfFiles.length; i++) { - String file_name = listOfFiles[i].getName(); - - if (file_name.endsWith("ligand.mol2")){ - System.out.println("Start work on: " + file_name); - handle_single_mol2(file_name, path, id_string, tess_level, tess_type, "ligand", append); - System.out.println("Finished: " + file_name); - append = false; - } - else if(file_name.endsWith("pocket.pdb")) { - System.out.println("Start work on: " + file_name); - handle_single_pdb(file_name, path, id_string, tess_level, tess_type, "pocket"); - System.out.println("Finished: " + file_name); - } - else if(protein_flag && file_name.endsWith("protein.pdb")) { - System.out.println("Start work on: " + file_name); - handle_single_pdb(file_name, path, id_string, tess_level-2, tess_type, "protein"); - System.out.println("Finished: " + file_name); - } - } // end for - - } // end if source - else if (source.equals("pdbbank")) { - boolean append = false; - for (int i = 0; i < listOfFiles.length; i++) { - String file_name = listOfFiles[i].getName(); - if(file_name.endsWith("_withHs.pdb")) { - handle_single_complex(file_name, path, id_string, tess_level-2, tess_type, "protein"); - } - }// end for - } - else if (source.equals("astex")) { - boolean append = false; - for (int i = 0; i < listOfFiles.length; i++) { - String file_name = listOfFiles[i].getName(); - - if (file_name.endsWith("ligand.mol2")){ - System.out.println("Start work on: " + file_name); - handle_single_mol2(file_name, path, id_string, tess_level, tess_type, "ligand", append); - System.out.println("Finished: " + file_name); - append = false; - } - else if(file_name.endsWith("protein.mol2")) { - System.out.println("Start work on: " + file_name); - handle_single_mol2(file_name, path, id_string, tess_level, tess_type, "pocket", append); - System.out.println("Finished: " + file_name); - } - } // end for - - } - - else { - System.out.println("This File Source is not supported: " + source); - } - - } - public static void main(String[] args) { - - int tess_level = Integer.parseInt(args[0]); - String tess_type = args[1]; - String source = args[2]; - - Path currentRelativePath = Paths.get(""); - String s = currentRelativePath.toAbsolutePath().toString(); - String[] array = s.split("/"); - -// String complex_id = array[array.length-1]; - array = Arrays.copyOf(array, array.length - 1); - String dataset_path = String.join("/", array); - - File complex_file = new File(s); - - if (complex_file.isDirectory()) { - String id_string = complex_file.getName(); - handle_single_id(complex_file, dataset_path, tess_level, tess_type, false, source); - } - else { - System.out.println(s + "is not a folder"); - } - - } -} - diff --git a/pdbbind/java/Surface_from_Mol2.java b/pdbbind/java/Surface_from_Mol2.java deleted file mode 100644 index 9bdcf4a..0000000 --- a/pdbbind/java/Surface_from_Mol2.java +++ /dev/null @@ -1,177 +0,0 @@ -import java.io.BufferedWriter; -import java.io.File; -import java.io.FileInputStream; -import java.io.FileOutputStream; -import java.io.FileWriter; -import java.util.List; - -import javax.vecmath.Point3d; - -import org.openscience.cdk.geometry.surface.NumericalSurface; -import org.openscience.cdk.interfaces.IAtomContainer; -import org.openscience.cdk.io.*; -import org.openscience.cdk.silent.SilentChemObjectBuilder; -import org.openscience.cdk.tools.manipulator.ChemFileManipulator; -import org.apache.commons.io.FileUtils; -import org.openscience.cdk.*; - - -public class Surface_from_Mol2 { - - //public javax.vecmath.Point3d[] getAllSurfacePoints(); - - public static String get_id(String filename) { - String[] parts = filename.split("."); - String part1 = parts[0]; - String[] parts1 = part1.split("_"); - String id = parts1[0]; - return id; - } - - public static void handle_single_mol2(String filename, String path) { - //String id = get_id(filename); - String[] parts1 = filename.split("_"); - String id = parts1[0]; - System.out.println("Test"); - //return id; - - try { - File file = new File(path + "/" + id + "/" + filename); - Mol2Reader reader = new Mol2Reader(new FileInputStream(file)); - //IAtomContainer container = reader.read(SilentChemObjectBuilder.getInstance().newInstance(IAtomContainer.class)); - ChemFile crambin = reader.read(new ChemFile()); - - List containers= ChemFileManipulator.getAllAtomContainers(crambin); - String write_filename = path + "/" + id + "/" + id + "_cdk_ligand.xyz"; - //FileOutputStream outputStream = new FileOutputStream(write_filename); - BufferedWriter writer = new BufferedWriter(new FileWriter(write_filename)); - for (int i = 0; i< containers.size(); i++) { - IAtomContainer container = containers.get(i); - String description = container.toString(); - - NumericalSurface new_surface = new NumericalSurface(container, 0, 6); - System.out.println(container); - System.out.println(description); - System.out.println(new_surface); - // Next line is wrong. - try { - new_surface.calculateSurface(); - Point3d[] points= new_surface.getAllSurfacePoints(); -// System.out.println(points); - System.out.println(points.length); - for (int j = 0; j < points.length; j++) { - String str = points[j].x + " " + points[j].y + " " + points[j].z + "\n"; - if (j == 1){System.out.println(str);} - writer.write(str); - }} catch (Exception ex) {System.out.println(ex);} - } - /*IChemObject pdb_mol = reader.read();*/ - reader.close(); - - writer.close(); - System.out.println("Finished " + write_filename); - } catch (Exception ex) {System.out.println(ex);} - //return container; - } - - public static void handle_single_pdb(String filename, String path) { - //String id = get_id(filename); - System.out.println(filename); - String[] parts = filename.split("_"); - //System.out.println(parts); - String id = parts[0]; - System.out.println(id); - - try { - File file = new File(path + "/" + id + "/" + filename); - PDBReader reader = new PDBReader(new FileInputStream(file)); - //IAtomContainer container = reader.read(SilentChemObjectBuilder.getInstance().newInstance(IAtomContainer.class)); - ChemFile crambin = reader.read(new ChemFile()); - List containers= ChemFileManipulator.getAllAtomContainers(crambin); - String write_filename = path + "/" + id + "/" + id + "_cdk_pocket.xyz"; - //FileOutputStream outputStream = new FileOutputStream(write_filename); - BufferedWriter writer = new BufferedWriter(new FileWriter(write_filename)); -// writer.write("ply\n" + -// "format ascii 1.0\n" + -// "element vertex 107812\n" + -// "property float x\n" + -// "property float y\n" + -// "property float z\n" + -// "property float nx\n" + -// "property float ny\n" + -// "property float nz\n" + -// "element face 0\n" + -// "property list uchar int vertex_indices\n" + -// "end_header"); - for (int i = 0; i< containers.size(); i++) { - IAtomContainer container = containers.get(i); - String description = container.toString(); - - NumericalSurface new_surface = new NumericalSurface(container, 0, 6); - System.out.println(container); - System.out.println(description); - System.out.println(new_surface); - // Next line is wrong. - try { - new_surface.calculateSurface(); - Point3d[] points= new_surface.getAllSurfacePoints(); -// System.out.println(points); - System.out.println(points.length); - for (int j = 0; j < points.length; j++) { - String str = points[j].x + " " + points[j].y + " " + points[j].z + "\n"; - if (j == 1){System.out.println(str);} - writer.write(str); - }} catch (Exception ex) {System.out.println(ex);} - } - /*IChemObject pdb_mol = reader.read();*/ - reader.close(); - - writer.close(); - System.out.println("Finished " + write_filename); - } catch (Exception ex) {System.out.println(ex);} - //return container; - - } - - public static void handle_single_id(File complex_id, String path) { - //File folder = new File("your/path"); - File[] listOfFiles = complex_id.listFiles(); - for (int i = 0; i < listOfFiles.length; i++) { - String file_name = listOfFiles[i].getName(); - - if (file_name.endsWith(".mol2")){ - System.out.println("Start work on: " + file_name); - handle_single_mol2(file_name, path); - System.out.println("Finished: " + file_name); - - } - else if(file_name.endsWith("pocket.pdb")) { - System.out.println("Start work on: " + file_name); - handle_single_pdb(file_name, path); - System.out.println("Finished: " + file_name); - } - } - } - - public static void main(String[] args) { - String path = args[0]; - //System.out.println(path); - File folder = new File(path); - File[] listOfIDs = folder.listFiles(); - //System.out.println(listOfIDs); - try { - for (int i = 0; i < listOfIDs.length; i++) { - if (listOfIDs[i].isFile()) { - //System.out.println("File " + listOfFiles[i].getName()); - continue; - } - else if (listOfIDs[i].isDirectory()) { - File complex_id = listOfIDs[i]; - //String str = complex_id.getPath(); - //System.out.println(str); - handle_single_id(complex_id, path); - } - } - } catch (Exception ex) {System.out.println(ex);} - } -} diff --git a/pdbbind/java/Surface_with_Type.class b/pdbbind/java/Surface_with_Type.class deleted file mode 100644 index a4bdb7f..0000000 Binary files a/pdbbind/java/Surface_with_Type.class and /dev/null differ diff --git a/pdbbind/java/Surface_with_Type.java b/pdbbind/java/Surface_with_Type.java deleted file mode 100644 index 6d43b80..0000000 --- a/pdbbind/java/Surface_with_Type.java +++ /dev/null @@ -1,259 +0,0 @@ -// The updated version for Adaptive surface. -// Reduced the number of points that would be generated. -// Also generate txt file to describe the points. e.g. atom type, index. - - -import java.io.BufferedWriter; -import java.io.File; -import java.io.FileInputStream; -import java.io.FileWriter; -import java.util.ArrayList; -import java.util.Arrays; -import java.util.List; - -import javax.vecmath.Point3d; - -import org.openscience.cdk.ChemFile; -import org.openscience.cdk.geometry.surface.NumericalSurface; -import org.openscience.cdk.geometry.surface.Point_Type; -import org.openscience.cdk.interfaces.IAtomContainer; -import org.openscience.cdk.io.Mol2Reader; -import org.openscience.cdk.io.PDBReader; -import org.openscience.cdk.tools.manipulator.ChemFileManipulator; - -public class Surface_with_Type { - //public javax.vecmath.Point3d[] getAllSurfacePoints(); - - public static String get_id(String filename) { - String[] parts = filename.split("."); - String part1 = parts[0]; - String[] parts1 = part1.split("_"); - String id = parts1[0]; - return id; - } - - public static void handle_single_mol2(String filename, String path) { - //String id = get_id(filename); - String[] parts1 = filename.split("_"); - String id = parts1[0]; -// System.out.println("Test"); - //return id; - - try { - File file = new File(path + "/" + id + "/" + filename); - Mol2Reader reader = new Mol2Reader(new FileInputStream(file)); - //IAtomContainer container = reader.read(SilentChemObjectBuilder.getInstance().newInstance(IAtomContainer.class)); - ChemFile crambin = reader.read(new ChemFile()); - - List containers= ChemFileManipulator.getAllAtomContainers(crambin); - String write_filename = path + "/" + id + "/" + id + "_cdk_ligand.xyz"; - String atom_filename = path + "/" + id + "/" + id + "_cdk_ligand.txt"; - //FileOutputStream outputStream = new FileOutputStream(write_filename); - BufferedWriter writer = new BufferedWriter(new FileWriter(write_filename)); - BufferedWriter writer1 = new BufferedWriter(new FileWriter(atom_filename)); - for (int i = 0; i< containers.size(); i++) { - IAtomContainer container = containers.get(i); -// String description = container.toString(); - - NumericalSurface new_surface = new NumericalSurface(container, 0, 6); -// System.out.println(container); -// System.out.println(description); -// System.out.println(new_surface); - // Next line is wrong. - try { - //new_surface.calculateSurface(); - //Point3d[] points= new_surface.getAllSurfacePoints(); - ArrayList point_types = new_surface.getAllPointswithAtomType(); - -// System.out.println(points); - - System.out.println(point_types.size()); - for (int j = 0; j < point_types.size(); j++) { - org.openscience.cdk.geometry.surface.Point_Type point_type = point_types.get(j); - Point3d coord = (point_type).getCoord(); - int atom = ((Point_Type) point_type).getAtom(); - int atom_index = ((Point_Type) point_type).getIndex(); - String str = coord.x + " " + coord.y + " " + coord.z + "\n"; - if (j == 1){System.out.println(str);} - writer.write(str); - writer1.write(atom + " " + atom_index + "\n"); - }} catch (Exception ex) {System.out.println(ex);} - } - /*IChemObject pdb_mol = reader.read();*/ - reader.close(); - - writer.close(); - writer1.close(); - System.out.println("Finished " + write_filename); - } catch (Exception ex) {System.out.println(ex);} - //return container; - } - - public static void handle_single_pdb(String filename, String path) { - //String id = get_id(filename); -// System.out.println(filename); - String[] parts = filename.split("_"); - //System.out.println(parts); - String id = parts[0]; -// System.out.println(id); - - try { - File file = new File(path + "/" + id + "/" + filename); - PDBReader reader = new PDBReader(new FileInputStream(file)); - //IAtomContainer container = reader.read(SilentChemObjectBuilder.getInstance().newInstance(IAtomContainer.class)); - ChemFile crambin = reader.read(new ChemFile()); - List containers= ChemFileManipulator.getAllAtomContainers(crambin); - String write_filename = path + "/" + id + "/" + id + "_cdk_pocket.xyz"; - String atom_filename = path + "/" + id + "/" + id + "_cdk_pocket.txt"; - //FileOutputStream outputStream = new FileOutputStream(write_filename); - BufferedWriter writer = new BufferedWriter(new FileWriter(write_filename)); - BufferedWriter writer1 = new BufferedWriter(new FileWriter(atom_filename)); - for (int i = 0; i< containers.size(); i++) { - IAtomContainer container = containers.get(i); -// String description = container.toString(); - - NumericalSurface new_surface = new NumericalSurface(container, 0, 6); -// System.out.println(container); -// System.out.println(description); -// System.out.println(new_surface); - // Next line is wrong. - try { - ArrayList point_types = new_surface.getAllPointswithAtomType(); -// System.out.println(points); - System.out.println(point_types.size()); - for (int j = 0; j < point_types.size(); j++) { - org.openscience.cdk.geometry.surface.Point_Type point_type = point_types.get(j); - Point3d coord = (point_type).getCoord(); - int atom = ((Point_Type) point_type).getAtom(); - int index = ((Point_Type) point_type).getIndex(); - String str = coord.x + " " + coord.y + " " + coord.z + "\n"; - if (j == 1){System.out.println(str);} - writer.write(str); - writer1.write(atom + " " + index + "\n"); - }} catch (Exception ex) {System.out.println(ex);} - } - /*IChemObject pdb_mol = reader.read();*/ - reader.close(); - - writer.close(); - writer1.close(); - System.out.println("Finished " + write_filename); - } catch (Exception ex) {System.out.println(ex);} - //return container; - - } - - public static void handle_single_protein(String filename, String path) { - //String id = get_id(filename); -// System.out.println(filename); - String[] parts = filename.split("_"); - //System.out.println(parts); - String id = parts[0]; -// System.out.println(id); - - try { - File file = new File(path + "/" + id + "/" + filename); - PDBReader reader = new PDBReader(new FileInputStream(file)); - //IAtomContainer container = reader.read(SilentChemObjectBuilder.getInstance().newInstance(IAtomContainer.class)); - ChemFile crambin = reader.read(new ChemFile()); - List containers= ChemFileManipulator.getAllAtomContainers(crambin); - String write_filename = path + "/" + id + "/" + id + "_cdk_protein.xyz"; - String atom_filename = path + "/" + id + "/" + id + "_cdk_protein.txt"; - //FileOutputStream outputStream = new FileOutputStream(write_filename); - BufferedWriter writer = new BufferedWriter(new FileWriter(write_filename)); - BufferedWriter writer1 = new BufferedWriter(new FileWriter(atom_filename)); - for (int i = 0; i< containers.size(); i++) { - IAtomContainer container = containers.get(i); -// String description = container.toString(); - - NumericalSurface new_surface = new NumericalSurface(container, 0, 6); -// System.out.println(container); -// System.out.println(description); -// System.out.println(new_surface); - // Next line is wrong. - try { - ArrayList point_types = new_surface.getAllPointswithAtomType(); -// System.out.println(points); - System.out.println(point_types.size()); - for (int j = 0; j < point_types.size(); j++) { - org.openscience.cdk.geometry.surface.Point_Type point_type = point_types.get(j); - Point3d coord = (point_type).getCoord(); - int atom = ((Point_Type) point_type).getAtom(); - int index = ((Point_Type) point_type).getIndex(); - String str = coord.x + " " + coord.y + " " + coord.z + "\n"; - if (j == 1){System.out.println(str);} - writer.write(str); - writer1.write(atom + " " + index + "\n"); - }} catch (Exception ex) {System.out.println(ex);} - } - /*IChemObject pdb_mol = reader.read();*/ - reader.close(); - - writer.close(); - writer1.close(); - System.out.println("Finished " + write_filename); - } catch (Exception ex) {System.out.println(ex);} - //return container; - - } - - public static void handle_single_id(File complex_id, String path) { - //File folder = new File("your/path"); - File[] listOfFiles = complex_id.listFiles(); - for (int i = 0; i < listOfFiles.length; i++) { - String file_name = listOfFiles[i].getName(); - - if (file_name.endsWith(".mol2")){ - System.out.println("Start work on: " + file_name); - handle_single_mol2(file_name, path); - System.out.println("Finished: " + file_name); - - } - else if(file_name.endsWith("pocket.pdb")) { - System.out.println("Start work on: " + file_name); - handle_single_pdb(file_name, path); - System.out.println("Finished: " + file_name); - } -// else if(file_name.endsWith("protein.pdb")) { -// System.out.println("Start work on: " + file_name); -// handle_single_protein(file_name, path); -// System.out.println("Finished: " + file_name); -// } - - - } - } - - public static void main(String[] args) { - String path = args[0]; - //System.out.println(path); - File folder = new File(path); - File[] listOfIDs = folder.listFiles(); - //System.out.println(listOfIDs); - try { - for (int i = 0; i < listOfIDs.length; i++) { - if (listOfIDs[i].isFile()) { - continue; - } - else if (listOfIDs[i].isDirectory()) { - File complex_id = listOfIDs[i]; - String id_string = complex_id.getName(); - System.out.println(id_string); -// List already = Arrays.asList("1a1e", "1a4r", -// "2pjt", "2phb", "1a4k", "3avj", "1x7a", -// "4tvj", "4crf", "4PJT", "1i1e", "3huc"); -// List skip = Arrays.asList("4g17", "3v2w", -// "2y80", "4prg", "2hmu", "4m0f"); - List already = Arrays.asList(); - List skip = Arrays.asList(); - if(already.contains(id_string)|| skip.contains(id_string)) { - System.out.println("Skip " + id_string + "."); - continue; - } - handle_single_id(complex_id, path); - } - } - } catch (Exception ex) {System.out.println(ex);} - System.out.println("All Finish"); - } -} diff --git a/pdbbind/java/Tess_Val.java b/pdbbind/java/Tess_Val.java deleted file mode 100644 index b4640ae..0000000 --- a/pdbbind/java/Tess_Val.java +++ /dev/null @@ -1,18 +0,0 @@ -import java.util.Arrays; -import java.util.HashSet; -import java.util.Set; - -import javax.vecmath.Point3d; - -import org.openscience.cdk.geometry.surface.Tessellate; - -public class Tess_Val { - public static void main(String[] args) { - Tessellate tess = new Tessellate("ico", 4); - tess.doTessellate(); -// System.out.println(tess.getNumberOfTriangles()); - Point3d[] point_list = tess.getTessAsPoint3ds(); - Set point_set = new HashSet(Arrays.asList(point_list)); - System.out.println(point_set.size()); - } -} diff --git a/pdbbind/java/cdk-2.3-SNAPSHOT.jar b/pdbbind/java/cdk-2.3-SNAPSHOT.jar deleted file mode 100644 index 1c01c94..0000000 Binary files a/pdbbind/java/cdk-2.3-SNAPSHOT.jar and /dev/null differ diff --git a/pdbbind/write_complex.py b/pdbbind/write_complex.py deleted file mode 100644 index ff5dc06..0000000 --- a/pdbbind/write_complex.py +++ /dev/null @@ -1,19 +0,0 @@ -import os - -path = os.getcwd() -complex_id = path.split('/')[-1] - -# id_list_file = path + '/file_list.txt' -# with open(id_list_file, 'w') as fw: -# fw.write(complex_id) - - -# file_list is used to get .points from pdb points. -id_list_file = path + '/file_list.txt' -with open(id_list_file, 'w') as fw: - fw.write(complex_id) - -# point_list is used to get .octrees from .points -id_list_file = path + '/point_list.txt' -with open(id_list_file, 'w') as fw: - fw.write(path + '/' + complex_id + '_points.points') \ No newline at end of file diff --git a/tensorflow/.DS_Store b/tensorflow/.DS_Store index d6e1183..daa977a 100644 Binary files a/tensorflow/.DS_Store and b/tensorflow/.DS_Store differ diff --git a/tensorflow/data/cls_modelnet.py b/tensorflow/data/cls_modelnet.py deleted file mode 100644 index c8d6809..0000000 --- a/tensorflow/data/cls_modelnet.py +++ /dev/null @@ -1,236 +0,0 @@ -import os -import sys -import argparse - - -parser = argparse.ArgumentParser() -parser.add_argument('--run', type=str, required=True, - help='The command to run.') -parser.add_argument('--converter', type=str, required=False, - default='util/convert_tfrecords.py', - help='The path of the convert_tfrecords') -parser.add_argument('--scanner', type=str, required=False, - help='The path of the virtual_scanner') -parser.add_argument('--octree', type=str, required=False, default='octree', - help='The path of the octree') -parser.add_argument('--simplify_points', type=str, required=False, - default='simplify_points', - help='The path of the simplify_points') - - -abs_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) -root_folder = os.path.join(abs_path, 'script/dataset/ModelNet40') - - -args = parser.parse_args() -octree = args.octree -converter = os.path.join(abs_path, args.converter) -virtual_scanner = args.scanner -simplify = args.simplify_points - - -def download_m40(): - # download via wget - if not os.path.exists(root_folder): - os.makedirs(root_folder) - url = 'http://modelnet.cs.princeton.edu/ModelNet40.zip' - cmd = 'wget %s -O %s/ModelNet40.zip' % (url, root_folder) - print(cmd) - os.system(cmd) - - # unzip - cmd = 'unzip %s/ModelNet40.zip -d %s' % (root_folder, root_folder) - print(cmd) - os.system(cmd) - - -def download_m40_points(): - # download via wget - if not os.path.exists(root_folder): - os.makedirs(root_folder) - url = 'https://www.dropbox.com/s/m233s9eza3acj2a/ModelNet40.points.zip?dl=0' - cmd = 'wget %s -O %s/ModelNet40.points.zip' % (url, root_folder) - print(cmd) - os.system(cmd) - - # unzip - cmd = 'unzip %s/ModelNet40.points.zip -d %s/ModelNet40.points' % ( - root_folder, root_folder) - print(cmd) - os.system(cmd) - - -def clean_off_file(filename): - # read the contents of the file - with open(filename) as fid: - file_str = fid.read() - # fix the file - if file_str[0:3] != 'OFF': - print('Error: not an OFF file: ' + filename) - elif file_str[0:4] != 'OFF\n': - print('Info: fix an OFF file: ' + filename) - new_str = file_str[0:3] + '\n' + file_str[3:] - with open(filename, 'w') as f_rewrite: - f_rewrite.write(new_str) - - -def m40_get_filelist(root_folder, train=True, suffix='off'): - filelist, category = [], [] - folders = sorted(os.listdir(root_folder)) - assert(len(folders) == 40) - for idx, folder in enumerate(folders): - subfolder = 'train' if train else 'test' - current_folder = os.path.join(root_folder, folder, subfolder) - filenames = sorted(os.listdir(current_folder)) - for filename in filenames: - if filename.endswith(suffix): - filelist.append(os.path.join(folder, subfolder, filename)) - category.append(idx) - return filelist, category - - -def m40_move_files(src_folder, des_folder, suffix): - folders = os.listdir(src_folder) - for folder in folders: - for subfolder in ['train', 'test']: - curr_src_folder = os.path.join(src_folder, folder, subfolder) - curr_des_folder = os.path.join(des_folder, folder, subfolder) - if not os.path.exists(curr_des_folder): - os.makedirs(curr_des_folder) - filenames = os.listdir(curr_src_folder) - for filename in filenames: - if filename.endswith('.points'): - os.rename(os.path.join(curr_src_folder, filename), - os.path.join(curr_des_folder, filename)) - - -def m40_convert_mesh_to_points(): - mesh_folder = os.path.join(root_folder, 'ModelNet40') - # Delete 3 files since the virtualscanner can not well deal with them - filelist = ['cone/train/cone_0117.off', - 'curtain/train/curtain_0066.off', - 'car/train/car_0021.off.off'] - for filename in filelist: - filename = os.path.join(mesh_folder, filename) - if os.path.exists(filename): - os.remove(filename) - - # clean the off files - train_list, _ = m40_get_filelist(mesh_folder, train=True, suffix='off') - test_list, _ = m40_get_filelist(mesh_folder, train=False, suffix='off') - filelist = train_list + test_list - for filename in filelist: - clean_off_file(os.path.join(mesh_folder, filename)) - - # run virtualscanner - folders = os.listdir(mesh_folder) - for folder in folders: - for subfolder in ['train', 'test']: - curr_folder = os.path.join(mesh_folder, folder, subfolder) - cmd = '%s %s 14' % (virtual_scanner, curr_folder) - print(cmd) - os.system(cmd) - - # move points - m40_move_files(mesh_folder, mesh_folder + '.points', 'points') - - -def m40_convert_points_to_octree(depth=5, adaptive=0, node_dis=0): - points_folder = os.path.join(root_folder, 'ModelNet40.points') - folders = os.listdir(points_folder) - for folder in folders: - for subfolder in ['train', 'test']: - curr_folder = os.path.join(points_folder, folder, subfolder) - # write filelist to disk - filenames = os.listdir(curr_folder) - filelist_name = os.path.join(curr_folder, 'list.txt') - with open(filelist_name, 'w') as fid: - for filename in filenames: - if filename.endswith('.points'): - fid.write(os.path.join(curr_folder, filename) + '\n') - # run octree - octree_folder = points_folder[:-6] + 'octree.%d' % depth - if adaptive == 1: - octree_folder = octree_folder + '.adaptive' - output_path = os.path.join(octree_folder, folder, subfolder) - if not os.path.exists(output_path): - os.makedirs(output_path) - cmd = '%s --filenames %s --output_path %s --depth %d --adaptive %d --node_dis %d --axis z' % \ - (octree, filelist_name, output_path, depth, adaptive, node_dis) - print(cmd) - os.system(cmd) - - -def m40_simplify_points(resolution=64): - # rename and backup the original folders - points_folder = os.path.join(root_folder, 'ModelNet40.points') - original_folder = points_folder + ".dense" - if os.path.exists(points_folder): - os.rename(points_folder, original_folder) - - folders = os.listdir(original_folder) - for folder in folders: - for subfolder in ['train', 'test']: - curr_folder = os.path.join(original_folder, folder, subfolder) - # write filelist to disk - filenames = os.listdir(curr_folder) - filelist_name = os.path.join(curr_folder, 'list.txt') - with open(filelist_name, 'w') as fid: - for filename in filenames: - if filename.endswith('.points'): - fid.write(os.path.join(curr_folder, filename) + '\n') - # run simplify_points - output_path = os.path.join(points_folder, folder, subfolder) - if not os.path.exists(output_path): - os.makedirs(output_path) - cmd = '%s --filenames %s --output_path %s --dim %d' % \ - (simplify, filelist_name, output_path, resolution) - print(cmd) - os.system(cmd) - os.remove(filelist_name) - - -def m40_generate_points_tfrecords(): - points_folder = os.path.join(root_folder, 'ModelNet40.points') - for folder in ['train', 'test']: - train = folder == 'train' - shuffle = '--shuffle true' if folder == 'train' else '' - filelist, idx = m40_get_filelist( - points_folder, train=train, suffix='points') - filename = os.path.join(root_folder, 'm40_%s_points_list.txt' % folder) - with open(filename, 'w') as fid: - for i in range(len(filelist)): - fid.write('%s %d\n' % (filelist[i], idx[i])) - tfrecords_name = os.path.join( - root_folder, 'm40_%s_points.tfrecords' % folder) - cmd = 'python %s %s --file_dir %s --list_file %s --records_name %s' % \ - (converter, shuffle, points_folder, filename, tfrecords_name) - print(cmd) - os.system(cmd) - - -def m40_generate_octree_tfrecords(depth=5): - # generate octree - m40_convert_points_to_octree(depth, adaptive=0, node_dis=0) - - # generate tfrecords - octree_folder = os.path.join(root_folder, 'ModelNet40.octree.%d' % depth) - for folder in ['train', 'test']: - train = folder == 'train' - shuffle = '--shuffle true' if folder == 'train' else '' - filelist, idx = m40_get_filelist( - octree_folder, train=train, suffix='octree') - filename = os.path.join(root_folder, 'm40_%s_octree_list.txt' % folder) - with open(filename, 'w') as fid: - for i in range(len(filelist)): - fid.write('%s %d\n' % (filelist[i], idx[i])) - tfname = os.path.join( - root_folder, 'm40_%d_2_12_%s_octree.tfrecords' % (depth, folder)) - cmd = 'python %s %s --file_dir %s --list_file %s --records_name %s' % \ - (converter, shuffle, octree_folder, filename, tfname) - print(cmd) - os.system(cmd) - - -if __name__ == '__main__': - eval('%s()' % args.run) diff --git a/tensorflow/data/completion.py b/tensorflow/data/completion.py deleted file mode 100644 index 61673bb..0000000 --- a/tensorflow/data/completion.py +++ /dev/null @@ -1,188 +0,0 @@ -import os -import sys -import argparse - - -parser = argparse.ArgumentParser() -parser.add_argument('--run', type=str, required=True) -parser.add_argument('--octree', type=str, required=False, - default='script/logs/completion/skip_connections_test') -args = parser.parse_args() - -abs_path = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) -root_folder = os.path.join(abs_path, 'script/dataset/ocnn_completion') - -points2ply, ply2points, octree2pts = 'points2ply', 'ply2points', 'octree2points' -converter = os.path.join(abs_path, 'util/convert_tfrecords.py') - - -def download_point_clouds(): - # download via wget - if not os.path.exists(root_folder): - os.makedirs(root_folder) - url = 'https://www.dropbox.com/s/y5ljm2bs8649j3p/ocnn_completion.zip?dl=0' - cmd = 'wget %s -O %s.zip' % (url, root_folder) - print(cmd) - os.system(cmd) - - # unzip - cmd = 'unzip %s.zip -d %s' % (root_folder, root_folder) - print(cmd) - os.system(cmd) - - -def _convert_ply_to_points(prefix='shape'): - ply_folder = os.path.join(root_folder, prefix + '.ply') - points_folder = os.path.join(root_folder, prefix + '.points') - - folders = os.listdir(ply_folder) - for folder in folders: - curr_folder = os.path.join(ply_folder, folder) - - # write filelist to disk - filenames = os.listdir(curr_folder) - filelist_name = os.path.join(curr_folder, 'filelist.txt') - with open(filelist_name, 'w') as fid: - for filename in filenames: - if filename.endswith('.ply'): - fid.write(os.path.join(curr_folder, filename) + '\n') - - # run points2ply - output_path = os.path.join(points_folder, folder) - if not os.path.exists(output_path): - os.makedirs(output_path) - cmd = '%s --filenames %s --output_path %s --verbose 0' % \ - (ply2points, filelist_name, output_path) - print(cmd) - os.system(cmd) - os.remove(filelist_name) - - -def convert_ply_to_points(): - _convert_ply_to_points('shape') - _convert_ply_to_points('test.scans') - - -def _convert_points_to_ply(prefix='shape'): - ply_folder = os.path.join(root_folder, prefix + '.ply') - points_folder = os.path.join(root_folder, prefix + '.points') - - folders = os.listdir(points_folder) - for folder in folders: - curr_folder = os.path.join(points_folder, folder) - - # write filelist to disk - filenames = os.listdir(curr_folder) - filelist_name = os.path.join(curr_folder, 'filelist.txt') - with open(filelist_name, 'w') as fid: - for filename in filenames: - if filename.endswith('.points'): - fid.write(os.path.join(curr_folder, filename) + '\n') - - # run points2ply - output_path = os.path.join(ply_folder, folder) - if not os.path.exists(output_path): - os.makedirs(output_path) - cmd = '%s --filenames %s --output_path %s --verbose 0' % \ - (points2ply, filelist_name, output_path) - print(cmd) - os.system(cmd) - os.remove(filelist_name) - - -def convert_points_to_ply(): - _convert_points_to_ply('shape') - _convert_points_to_ply('test.scans') - - -def generate_points_tfrecords(): - # tfrecords for training - points_folder = os.path.join(root_folder, 'shape.points') - filelist = os.path.join(root_folder, 'filelist_train.txt') - records_name = os.path.join(root_folder, 'completion_train_points.tfrecords') - cmd = 'python %s --file_dir %s --list_file %s --records_name %s' % \ - (converter, points_folder, filelist, records_name) - print(cmd) - os.system(cmd) - - # tfrecords for testing - points_folder = os.path.join(root_folder, 'shape.points') - filelist = os.path.join(root_folder, 'filelist_test.txt') - records_name = os.path.join(root_folder, 'completion_test_points.tfrecords') - cmd = 'python %s --file_dir %s --list_file %s --records_name %s' % \ - (converter, points_folder, filelist, records_name) - print(cmd) - os.system(cmd) - - # tfrecords for testing scans - points_folder = os.path.join(root_folder, 'test.scans.points') - filelist = os.path.join(root_folder, 'filelist_test_scans.txt') - records_name = os.path.join(root_folder, 'completion_test_scans_points.tfrecords') - cmd = 'python %s --file_dir %s --list_file %s --records_name %s' % \ - (converter, points_folder, filelist, records_name) - print(cmd) - os.system(cmd) - - -def generate_dataset(): - download_point_clouds() - convert_ply_to_points() - generate_points_tfrecords() - - -def rename_output_octree(): - filelist = os.path.join(root_folder, 'filelist_test_scans.txt') - filenames = [] - with open(filelist, 'r') as fid: - for line in fid: - filename = line.split()[0] - filenames.append(filename[:-6] + 'octree') - - idx = 0 - folder_in = args.octree - octree_in = os.listdir(folder_in) - octree_in.sort() - folder_out = os.path.join(root_folder, 'output.octree') - for o in octree_in: - if o.endswith('output.octree'): - name_in = os.path.join(folder_in, o) - name_out = os.path.join(folder_out, filenames[idx]) - os.renames(name_in, name_out) - idx += 1 - assert (idx == 1200) - - -def _convert_octree_to_points(suffix='ply'): - octree_folder = os.path.join(root_folder, 'output.octree') - points_folder = os.path.join(root_folder, 'output.' + suffix) - - folders = os.listdir(octree_folder) - for folder in folders: - curr_folder = os.path.join(octree_folder, folder) - - # write filelist to disk - filenames = os.listdir(curr_folder) - filelist_name = os.path.join(curr_folder, 'filelist.txt') - with open(filelist_name, 'w') as fid: - for filename in filenames: - if filename.endswith('.octree'): - fid.write(os.path.join(curr_folder, filename) + '\n') - - # run octree2points - output_path = os.path.join(points_folder, folder) - if not os.path.exists(output_path): - os.makedirs(output_path) - cmd = '%s --filenames %s --output_path %s --verbose 0 --suffix %s' % \ - (octree2pts, filelist_name, output_path, suffix) - print(cmd) - os.system(cmd) - os.remove(filelist_name) - - -def convert_octree_to_points(): - _convert_octree_to_points('points') - _convert_octree_to_points('ply') - - -if __name__ == '__main__': - eval('%s()' % args.run) diff --git a/tensorflow/data/seg_shapenet.py b/tensorflow/data/seg_shapenet.py deleted file mode 100644 index e8f740d..0000000 --- a/tensorflow/data/seg_shapenet.py +++ /dev/null @@ -1,128 +0,0 @@ -import os -import json - -current_path = os.path.dirname(os.path.realpath(__file__)) -root_folder = os.path.join(current_path, '../script/dataset/shapenet_segmentation') -ply2points = 'ply2points' -convert_tfrecords = os.path.join(current_path, '../util/convert_tfrecords.py') -zip_name = 'shapenetcore_partanno_segmentation_benchmark_v0_normal' - -txt_folder = os.path.join(root_folder, zip_name) -ply_folder = os.path.join(root_folder, 'ply') -points_folder = os.path.join(root_folder, 'points') -dataset_folder = os.path.join(root_folder, 'datasets') - -categories= ['02691156', '02773838', '02954340', '02958343', - '03001627', '03261776', '03467517', '03624134', - '03636649', '03642806', '03790512', '03797390', - '03948459', '04099429', '04225987', '04379243'] -names = ['Aero', 'Bag', 'Cap', 'Car', - 'Chair', 'EarPhone', 'Guitar', 'Knife', - 'Lamp', 'Laptop', 'Motor', 'Mug', - 'Pistol', 'Rocket', 'Skate', 'Table'] -seg_num = [4, 2, 2, 4, 4, 3, 3, 2, 4, 2, 6, 2, 3, 3, 3, 3 ] -dis = [0, 4, 6, 8, 12, 16, 19, 22, 24, 28, 30, 36, 38, 41, 44, 47] - -def download_and_unzip(): - print('Downloading and unzipping ...') - if not os.path.exists(root_folder): os.makedirs(root_folder) - url = 'https://shapenet.cs.stanford.edu/media/%s.zip' % zip_name - os.system('wget %s -P %s' % (url, root_folder)) - os.system('unzip %s.zip -d %s' % (os.path.join(root_folder, zip_name), root_folder)) - -def txt_to_ply(): - print('Convert txt files to ply files ...') - header = 'ply\nformat ascii 1.0\nelement vertex %d\n' + \ - 'property float x\nproperty float y\nproperty float z\n' + \ - 'property float nx\nproperty float ny\nproperty float nz\n' + \ - 'property float label\nelement face 0\n' + \ - 'property list uchar int vertex_indices\nend_header' - for i, c in enumerate(categories): - src_folder = os.path.join(txt_folder, c) - des_folder = os.path.join(ply_folder, c) - if not os.path.exists(des_folder): os.makedirs(des_folder) - - filenames = os.listdir(src_folder) - for filename in filenames: - filename_txt = os.path.join(src_folder, filename) - filename_ply = os.path.join(des_folder, filename[:-4] + '.ply') - with open(filename_txt, 'r') as fid: - lines = [] - for line in fid: - if line == '\n': continue - nums = line.split() - nums[-1] = str(float(nums[-1]) - dis[i]) - lines.append(' '.join(nums)) - - ply_header = header % len(lines) - ply_content = '\n'.join([ply_header] + lines) - with open(filename_ply, 'w') as fid: - fid.write(ply_content) - -def ply_to_points(): - print('Convert ply files to points files ...') - for c in categories: - src_folder = os.path.join(ply_folder, c) - des_folder = os.path.join(points_folder, c) - list_folder = os.path.join(ply_folder, 'fileliet') - if not os.path.exists(des_folder): os.makedirs(des_folder) - if not os.path.exists(list_folder): os.makedirs(list_folder) - - list_filename = os.path.join(list_folder, c + '.txt') - filenames = [os.path.join(src_folder, filename) for filename in os.listdir(src_folder)] - with open(list_filename, 'w') as fid: - fid.write('\n'.join(filenames)) - - cmds = [ply2points, - '--filenames', list_filename, - '--output_path', des_folder, - '--verbose', '0'] - cmd = ' '.join(cmds) - # print(cmd + '\n') - os.system(cmd) - -def points_to_tfrecords(): - print('Convert points files to tfrecords files ...') - if not os.path.exists(dataset_folder): os.makedirs(dataset_folder) - list_folder = os.path.join(txt_folder, 'train_test_split') - train_list_name = os.path.join(list_folder, 'shuffled_train_file_list.json') - val_list_name = os.path.join(list_folder, 'shuffled_val_file_list.json') - test_list_name = os.path.join(list_folder, 'shuffled_test_file_list.json') - with open(train_list_name) as fid: train_list = json.load(fid) - with open(val_list_name) as fid: val_list = json.load(fid) - with open(test_list_name) as fid: test_list = json.load(fid) - for i, c in enumerate(categories): - filelist_name = os.path.join(list_folder, c + '_train_val.txt') - filelist = ['%s.points %d' % (line[11:], i) for line in train_list if c in line] + \ - ['%s.points %d' % (line[11:], i) for line in val_list if c in line] - with open(filelist_name, 'w') as fid: - fid.write('\n'.join(filelist)) - - dataset_name = os.path.join(dataset_folder, c + '_train_val.tfrecords') - cmds = ['python', convert_tfrecords, - '--file_dir', points_folder, - '--list_file', filelist_name, - '--records_name', dataset_name] - cmd = ' '.join(cmds) - print(cmd + '\n') - os.system(cmd) - - filelist_name = os.path.join(list_folder, c + '_test.txt') - filelist = ['%s.points %d' % (line[11:], i) for line in test_list if c in line] - with open(filelist_name, 'w') as fid: - fid.write('\n'.join(filelist)) - - dataset_name = os.path.join(dataset_folder, c + '_test.tfrecords') - cmds = ['python', convert_tfrecords, - '--file_dir', points_folder, - '--list_file', filelist_name, - '--records_name', dataset_name] - cmd = ' '.join(cmds) - print(cmd + '\n') - os.system(cmd) - -if __name__ == '__main__': - download_and_unzip() - txt_to_ply() - ply_to_points() - points_to_tfrecords() diff --git a/tensorflow/libs/.DS_Store b/tensorflow/libs/.DS_Store deleted file mode 100644 index 1ff9a70..0000000 Binary files a/tensorflow/libs/.DS_Store and /dev/null differ diff --git a/tensorflow/libs/Makefile b/tensorflow/libs/Makefile deleted file mode 100644 index de6d75d..0000000 --- a/tensorflow/libs/Makefile +++ /dev/null @@ -1,92 +0,0 @@ -TF_CFLAGS := -I/home/qil15006/.conda/envs/tf-OCNN/lib/python3.7/site-packages/tensorflow/include -D_GLIBCXX_USE_CXX11_ABI=1 -TF_LFLAGS := -L/home/qil15006/.conda/envs/tf-OCNN/lib/python3.7/site-packages/tensorflow -l:libtensorflow_framework.so.1 -OCT_CFLAGS := -I../../octree/octree -OCT_LFLAGS := -L../../octree/build -loctree_lib - -NVCC_FLAGS1 := -std=c++11 -O2 -c -NVCC_FLAGS2 := $(TF_CFLAGS) $(OCT_CFLAGS) -I /usr/local/cuda-10.1/include -x cu -Xcompiler -fPIC -D GOOGLE_CUDA=1 -I /usr/local -expt-relaxed-constexpr -DNDEBUG -CC_FLAGS1 := -std=c++11 -O2 -CC_FLAGS2 := $(TF_CFLAGS) $(OCT_CFLAGS) -I /usr/local/cuda-10.1/include -L /usr/local/cuda-10.1/lib64 -D GOOGLE_CUDA=1 $(TF_LFLAGS) $(OCT_LFLAGS) -fPIC -lcudart - -CC := g++ -NVCC := /usr/local/cuda-10.1/bin/nvcc - -cpu_objects := object/octree2col_op.cc.o object/octree_align_op.cc.o object/octree_batch_op.cc.o object/octree_bilinear_op.cc.o object/octree_conv_op.cc.o object/octree_gather_op.cc.o object/octree_grow_op.cc.o object/octree_key_op.cc.o object/octree_mask_op.cc.o object/octree_max_pool_op.cc.o object/octree_new_op.cc.o object/octree_pad_op.cc.o object/octree_property_op.cc.o object/octree_samples.cc.o object/octree_search_op.cc.o object/octree_set_property_op.cc.o object/octree_update_op.cc.o object/points2octree_op.cc.o object/points_property_op.cc.o object/transform_octree_op.cc.o object/transform_points_op.cc.o -gpu_objects := object/tensorflow_gpu_gemm.cu.cc.o - -.PHONY : all clean - -all : libocnn.so - -libocnn.so : $(cpu_objects) $(gpu_objects) - $(CC) $(CC_FLAGS1) -shared -o libocnn.so object/*.o $(CC_FLAGS2) - -object/octree2col_op.cc.o : octree2col_op.cc - $(CC) $(CC_FLAGS1) -c octree2col_op.cc $(CC_FLAGS2) -o object/octree2col_op.cc.o - -object/octree_align_op.cc.o : octree_align_op.cc - $(CC) $(CC_FLAGS1) -c octree_align_op.cc $(CC_FLAGS2) -o object/octree_align_op.cc.o - -object/octree_batch_op.cc.o : octree_batch_op.cc - $(CC) $(CC_FLAGS1) -c octree_batch_op.cc $(CC_FLAGS2) -o object/octree_batch_op.cc.o - -object/octree_bilinear_op.cc.o : octree_bilinear_op.cc - $(CC) $(CC_FLAGS1) -c octree_bilinear_op.cc $(CC_FLAGS2) -o object/octree_bilinear_op.cc.o - -object/octree_conv_op.cc.o : tensorflow_gpu_gemm.h octree_conv_op.cc - $(CC) $(CC_FLAGS1) -c octree_conv_op.cc $(CC_FLAGS2) -o object/octree_conv_op.cc.o - -object/octree_gather_op.cc.o : octree_gather_op.cc - $(CC) $(CC_FLAGS1) -c octree_gather_op.cc $(CC_FLAGS2) -o object/octree_gather_op.cc.o - -object/octree_grow_op.cc.o : octree_grow_op.cc - $(CC) $(CC_FLAGS1) -c octree_grow_op.cc $(CC_FLAGS2) -o object/octree_grow_op.cc.o - -object/octree_key_op.cc.o : octree_key_op.cc - $(CC) $(CC_FLAGS1) -c octree_key_op.cc $(CC_FLAGS2) -o object/octree_key_op.cc.o - -object/octree_mask_op.cc.o : octree_mask_op.cc - $(CC) $(CC_FLAGS1) -c octree_mask_op.cc $(CC_FLAGS2) -o object/octree_mask_op.cc.o - -object/octree_max_pool_op.cc.o : octree_max_pool_op.cc - $(CC) $(CC_FLAGS1) -c octree_max_pool_op.cc $(CC_FLAGS2) -o object/octree_max_pool_op.cc.o - -object/octree_new_op.cc.o : octree_new_op.cc - $(CC) $(CC_FLAGS1) -c octree_new_op.cc $(CC_FLAGS2) -o object/octree_new_op.cc.o - -object/octree_pad_op.cc.o : octree_pad_op.cc - $(CC) $(CC_FLAGS1) -c octree_pad_op.cc $(CC_FLAGS2) -o object/octree_pad_op.cc.o - -object/octree_property_op.cc.o : octree_property_op.cc - $(CC) $(CC_FLAGS1) -c octree_property_op.cc $(CC_FLAGS2) -o object/octree_property_op.cc.o - -object/octree_samples.cc.o : octree_samples.cc - $(CC) $(CC_FLAGS1) -c octree_samples.cc $(CC_FLAGS2) -o object/octree_samples.cc.o - -object/octree_search_op.cc.o : octree_search_op.cc - $(CC) $(CC_FLAGS1) -c octree_search_op.cc $(CC_FLAGS2) -o object/octree_search_op.cc.o - -object/octree_set_property_op.cc.o : octree_set_property_op.cc - $(CC) $(CC_FLAGS1) -c octree_set_property_op.cc $(CC_FLAGS2) -o object/octree_set_property_op.cc.o - -object/octree_update_op.cc.o : octree_update_op.cc - $(CC) $(CC_FLAGS1) -c octree_update_op.cc $(CC_FLAGS2) -o object/octree_update_op.cc.o - -object/points2octree_op.cc.o : points2octree_op.cc - $(CC) $(CC_FLAGS1) -c points2octree_op.cc $(CC_FLAGS2) -o object/points2octree_op.cc.o - -object/points_property_op.cc.o : points_property_op.cc - $(CC) $(CC_FLAGS1) -c points_property_op.cc $(CC_FLAGS2) -o object/points_property_op.cc.o - -object/transform_octree_op.cc.o : transform_octree_op.cc - $(CC) $(CC_FLAGS1) -c transform_octree_op.cc $(CC_FLAGS2) -o object/transform_octree_op.cc.o - -object/transform_points_op.cc.o : transform_points_op.cc - $(CC) $(CC_FLAGS1) -c transform_points_op.cc $(CC_FLAGS2) -o object/transform_points_op.cc.o - -object/tensorflow_gpu_gemm.cu.cc.o : tensorflow_gpu_gemm.h tensorflow_gpu_gemm.cu.cc - $(NVCC) $(NVCC_FLAGS1) tensorflow_gpu_gemm.cu.cc $(NVCC_FLAGS2) -o object/tensorflow_gpu_gemm.cu.cc.o - -clean : - rm object/*.o - diff --git a/tensorflow/libs/__init__.py b/tensorflow/libs/__init__.py index 71f9927..e36fa52 100644 --- a/tensorflow/libs/__init__.py +++ b/tensorflow/libs/__init__.py @@ -3,7 +3,22 @@ from tensorflow.python.framework import ops -_current_path = os.path.dirname(os.path.realpath(__file__)) + +if 'OCTREE_KEY' in os.environ and os.environ['OCTREE_KEY'] == '64': + print('INFO from ocnn: The octree key is 64 bits') + octree_key64 = True + tf_uintk = tf.uint64 + tf_uints = tf.uint16 + tf_intk = tf.int64 +else: + print('INFO from ocnn: The octree key is 32 bits, ' + 'the octree depth should be smaller than 10.') + octree_key64 = False + tf_uintk = tf.uint32 + tf_uints = tf.uint8 + tf_intk = tf.int32 + +_current_path = os.path.dirname(os.path.realpath(__file__)) _tf_ocnn_module = tf.load_op_library(os.path.join(_current_path, 'libocnn.so')) # check the modules @@ -12,6 +27,9 @@ bounding_sphere = _tf_ocnn_module.bounding_sphere points_property = _tf_ocnn_module.points_property transform_points = _tf_ocnn_module.transform_points +normalize_points = _tf_ocnn_module.normalize_points +points_new = _tf_ocnn_module.points_new +points_set_property = _tf_ocnn_module.points_set_property octree_drop = _tf_ocnn_module.octree_drop octree_scan = _tf_ocnn_module.octree_scan octree_cast = _tf_ocnn_module.octree_cast @@ -22,7 +40,7 @@ octree_depad = _tf_ocnn_module.octree_depad octree2col = _tf_ocnn_module.octree_to_col col2octree = _tf_ocnn_module.col_to_octree -octree_grow = _tf_ocnn_module.octree_grow +# octree_grow = _tf_ocnn_module.octree_grow octree_new = _tf_ocnn_module.octree_new octree_update = _tf_ocnn_module.octree_update octree_align = _tf_ocnn_module.octree_align @@ -52,6 +70,9 @@ ops.NotDifferentiable('OctreeSetProperty') ops.NotDifferentiable('OctreeBatch') ops.NotDifferentiable('TransformPoints') +ops.NotDifferentiable('NormalizePoints') +ops.NotDifferentiable('PointsNew') +ops.NotDifferentiable('PointsSetProperty') ops.NotDifferentiable('PointsToOctree') ops.NotDifferentiable('OctreeProperty') ops.NotDifferentiable('OctreeNew') @@ -101,22 +122,22 @@ def _ColToOctreeGrad(op, grad): @ops.RegisterGradient('OctreeMaxPool') def _OctreeMaxPoolGrad(op, *grad): - grad_out = _octree_max_unpool(grad[0], op.outputs[1], op.inputs[1], + grad_out = _octree_max_unpool(grad[0], op.outputs[1], op.inputs[1], op.get_attr('depth')) return [grad_out, None] @ops.RegisterGradient('OctreeMaxUnpool') def _OctreeMaxUnpoolGrad(op, grad): - grad_out = _octree_mask_pool(grad, op.inputs[1], op.inputs[2], + grad_out = _octree_mask_pool(grad, op.inputs[1], op.inputs[2], op.get_attr('depth')) return [grad_out, None, None] @ops.RegisterGradient('OctreeMaskPool') def _OctreeMaskPoolGrad(op, grad): - grad_out = _octree_max_unpool(grad, op.inputs[1], op.inputs[2], - op.get_attr('depth')) + grad_out = _octree_max_unpool(grad, op.inputs[1], op.inputs[2], + op.get_attr('depth')) return [grad_out, None, None] @@ -132,7 +153,7 @@ def _OctreeConvGrad(op, grad): def _OctreeDeconvGrad(op, grad): grad_out = _octree_deconv_grad(op.inputs[0], op.inputs[1], op.inputs[2], grad, op.get_attr('depth'), op.get_attr('num_output'), - op.get_attr('kernel_size'), op.get_attr('stride')) + op.get_attr('kernel_size'), op.get_attr('stride')) return grad_out + (None, ) @@ -163,7 +184,7 @@ def octree_max_pool(data, octree, depth): def octree_max_unpool(data, mask, octree, depth): - with tf.variable_scope('octree_max_unpool'): + with tf.variable_scope('octree_max_unpool'): data = octree_depad(data, octree, depth) # !!! depth data = _octree_max_unpool(data, mask, octree, depth) # the bottom data depth return data @@ -180,12 +201,12 @@ def octree_avg_pool(data, octree, depth): # todo: merge octree_conv_fast and octree_conv_memory to reduce code redundancy def octree_conv_fast(data, octree, depth, channel, kernel_size=[3], stride=1): assert(type(kernel_size) is list and len(kernel_size) < 4) - for i in range(len(kernel_size), 3): + for i in range(len(kernel_size), 3): kernel_size.append(kernel_size[-1]) - with tf.variable_scope('octree_conv'): + with tf.variable_scope('octree_conv'): dim = int(data.shape[1]) * kernel_size[0] * kernel_size[1] * kernel_size[2] - kernel = tf.get_variable('weights', shape=[channel, dim], dtype=tf.float32, + kernel = tf.get_variable('weights', shape=[channel, dim], dtype=tf.float32, initializer=tf.contrib.layers.xavier_initializer()) col = octree2col(data, octree, depth, kernel_size, stride) col = tf.reshape(col, [dim, -1]) @@ -193,17 +214,17 @@ def octree_conv_fast(data, octree, depth, channel, kernel_size=[3], stride=1): conv = tf.expand_dims(tf.expand_dims(conv, 0), -1) # [C, H] -> [1, C, H, 1] if stride == 2: conv = octree_pad(conv, octree, depth-1, 0) - return conv + return conv def octree_conv_memory(data, octree, depth, channel, kernel_size=[3], stride=1): assert(type(kernel_size) is list and len(kernel_size) < 4) - for i in range(len(kernel_size), 3): + for i in range(len(kernel_size), 3): kernel_size.append(kernel_size[-1]) - with tf.variable_scope('octree_conv'): + with tf.variable_scope('octree_conv'): dim = int(data.shape[1]) * kernel_size[0] * kernel_size[1] * kernel_size[2] - kernel = tf.get_variable('weights', shape=[channel, dim], dtype=tf.float32, + kernel = tf.get_variable('weights', shape=[channel, dim], dtype=tf.float32, initializer=tf.contrib.layers.xavier_initializer()) conv = _octree_conv(data, kernel, octree, depth, channel, kernel_size, stride) if stride == 2: @@ -213,14 +234,14 @@ def octree_conv_memory(data, octree, depth, channel, kernel_size=[3], stride=1): def octree_deconv_fast(data, octree, depth, channel, kernel_size=[3], stride=1): assert(type(kernel_size) is list and len(kernel_size) < 4) - for i in range(len(kernel_size), 3): + for i in range(len(kernel_size), 3): kernel_size.append(kernel_size[-1]) with tf.variable_scope('octree_deconv'): kernel_sdim = kernel_size[0] * kernel_size[1] * kernel_size[2] dim = channel * kernel_sdim - kernel = tf.get_variable('weights', shape=[int(data.shape[1]), dim], dtype=tf.float32, - initializer=tf.contrib.layers.xavier_initializer()) + kernel = tf.get_variable('weights', shape=[int(data.shape[1]), dim], dtype=tf.float32, + initializer=tf.contrib.layers.xavier_initializer()) if stride == 2: data = octree_depad(data, octree, depth) depth = depth + 1 @@ -233,14 +254,14 @@ def octree_deconv_fast(data, octree, depth, channel, kernel_size=[3], stride=1): def octree_deconv_memory(data, octree, depth, channel, kernel_size=[3], stride=1): assert(type(kernel_size) is list and len(kernel_size) < 4) - for i in range(len(kernel_size), 3): + for i in range(len(kernel_size), 3): kernel_size.append(kernel_size[-1]) - with tf.variable_scope('octree_deconv'): + with tf.variable_scope('octree_deconv'): kernel_sdim = kernel_size[0] * kernel_size[1] * kernel_size[2] dim = channel * kernel_sdim - kernel = tf.get_variable('weights', shape=[int(data.shape[1]), dim], dtype=tf.float32, - initializer=tf.contrib.layers.xavier_initializer()) + kernel = tf.get_variable('weights', shape=[int(data.shape[1]), dim], dtype=tf.float32, + initializer=tf.contrib.layers.xavier_initializer()) if stride == 2: data = octree_depad(data, octree, depth) deconv = _octree_deconv(data, kernel, octree, depth, channel, kernel_size, stride) @@ -250,7 +271,7 @@ def octree_deconv_memory(data, octree, depth, channel, kernel_size=[3], stride=1 def octree_full_voxel(data, depth): height = 2 ** (3 * depth) channel = int(data.shape[1]) - with tf.variable_scope('octree_full_voxel'): + with tf.variable_scope('octree_full_voxel'): data = tf.reshape(data, [channel, -1, height]) # (1, C, H, 1) -> (C, batch_size, H1) data = tf.transpose(data, perm=[1, 0, 2]) return data @@ -300,12 +321,12 @@ def octree_bilinear_legacy(data, octree, depth, target_depth): def octree_bilinear_v1(pts, data, octree, depth): with tf.variable_scope('octree_bilinear'): mask = tf.constant( - [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], - [1, 0, 0], [1, 0, 1], [1, 1, 0], [1, 1, 1]], dtype=tf.float32) - - xyzf, ids = tf.split(pts, [3, 1], 1) + [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], + [1, 0, 0], [1, 0, 1], [1, 1, 0], [1, 1, 1]], dtype=tf.float32) + + xyzf, ids = tf.split(pts, [3, 1], 1) xyzf = xyzf - 0.5 # since the value is defined on the center of each voxel - xyzi = tf.floor(xyzf) # the integer part + xyzi = tf.floor(xyzf) # the integer part frac = xyzf - xyzi # the fraction part feat = tf.transpose(tf.squeeze(data, [0, 3])) # (1, C, H, 1) -> (H, C) @@ -316,14 +337,14 @@ def octree_bilinear_v1(pts, data, octree, depth): maski = mask[i, :] maskc = 1.0 - maski xyzm = xyzi + maski - xyzm = tf.cast(tf.concat([xyzm, ids], axis=1), dtype=tf.uint8) + xyzm = tf.cast(tf.concat([xyzm, ids], axis=1), dtype=tf_uints) idxi = octree_search_key(octree_encode_key(xyzm), octree, depth, is_xyz=True) - + weight = tf.abs(tf.reduce_prod(maskc - frac, axis=1, keepdims=True)) - output += weight * tf.gather(feat, idxi) - norm += weight * tf.expand_dims(tf.cast(idxi > -1, dtype=tf.float32), -1) + output += weight * tf.gather(feat, idxi) + norm += weight * tf.expand_dims(tf.cast(idxi > -1, dtype=tf.float32), -1) output = tf.div(output, norm) - + output = tf.expand_dims(tf.expand_dims(tf.transpose(output), 0), -1) frac = tf.expand_dims(tf.expand_dims(tf.transpose(frac), 0), -1) @@ -334,14 +355,14 @@ def octree_bilinear_v1(pts, data, octree, depth): def octree_bilinear_v2(pts, data, octree, depth): with tf.variable_scope('octree_bilinear'): mask = tf.constant( - [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], - [1, 0, 0], [1, 0, 1], [1, 1, 0], [1, 1, 1]], dtype=tf.float32) - - xyzf, ids = tf.split(pts, [3, 1], 1) + [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], + [1, 0, 0], [1, 0, 1], [1, 1, 0], [1, 1, 1]], dtype=tf.float32) + + xyzf, ids = tf.split(pts, [3, 1], 1) xyzf = xyzf - 0.5 # since the value is defined on the center of each voxel - xyzi = tf.floor(xyzf) # the integer part + xyzi = tf.floor(xyzf) # the integer part frac = xyzf - xyzi # the fraction part - + output = tf.zeros([1, tf.shape(data)[1], tf.shape(xyzi)[0], 1], dtype=tf.float32) norm = tf.zeros([tf.shape(xyzi)[0], 1], dtype=tf.float32) @@ -349,60 +370,66 @@ def octree_bilinear_v2(pts, data, octree, depth): maski = mask[i, :] maskc = 1.0 - maski xyzm = xyzi + maski - xyzm = tf.cast(tf.concat([xyzm, ids], axis=1), dtype=tf.uint8) + xyzm = tf.cast(tf.concat([xyzm, ids], axis=1), dtype=tf_uints) # !!! Note some elements of idxi may be -1 idxi = octree_search_key(octree_encode_key(xyzm), octree, depth, is_xyz=True) - + weight = tf.abs(tf.reduce_prod(maskc - frac, axis=1, keepdims=True)) - # output += weight * tf.gather(data, idxi, axis=2) + # output += weight * tf.gather(data, idxi, axis=2) output += weight * octree_gather(data, idxi) norm += weight * tf.expand_dims(tf.cast(idxi > -1, dtype=tf.float32), -1) - output = tf.div(output, norm) + output = tf.div(output, norm) return output -# pts: (N, 4), i.e. N x (x, y, z, id). +# pts: (N, 4), i.e. N x (x, y, z, id). # data: (1, C, H, 1) # !!! Note: the pts should be scaled into [0, 2^depth] def octree_bilinear_v3(pts, data, octree, depth): with tf.variable_scope('octree_linear'): mask = tf.constant( - [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], + [[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], [1, 0, 0], [1, 0, 1], [1, 1, 0], [1, 1, 1]], dtype=tf.float32) - masku = tf.constant([0, 65536, 256, 65792, 1, 65537, 257, 65793], dtype=tf.int32) + if octree_key64: + masku = tf.constant([0, 4294967296, 65536, 4295032832, + 1, 4294967297, 65537, 4295032833], dtype=tf.int64) + else: + masku = tf.constant([0, 65536, 256, 65792, + 1, 65537, 257, 65793], dtype=tf.int32) + maskc = 1 - mask - - xyzf, ids = tf.split(pts, [3, 1], 1) + + xyzf, ids = tf.split(pts, [3, 1], 1) xyzf = xyzf - 0.5 # since the value is defined on the center of each voxel xyzi = tf.floor(xyzf) # the integer part (N, 3) frac = xyzf - xyzi # the fraction part (N, 3) - - key = tf.cast(tf.concat([xyzi, ids], axis=1), dtype=tf.uint8) - key = tf.cast(octree_encode_key(key), dtype=tf.int32) - # Cast the key to `int32` since the `add` below does not support `uint32` + + key = tf.cast(tf.concat([xyzi, ids], axis=1), dtype=tf_uints) + key = tf.cast(octree_encode_key(key), dtype=tf_intk) + # Cast the key to `int32` since the `add` below does not support `uint64` # The size effect is that the batch_size must be smaller than 128 - key = tf.expand_dims(key, 1) + masku # (N, 8), - key = tf.cast(tf.reshape(key, [-1]), dtype=tf.uint32) - + key = tf.expand_dims(key, 1) + masku # (N, 8), + key = tf.cast(tf.reshape(key, [-1]), dtype=tf_uintk) + idx = octree_search_key(key, octree, depth) # (N*8,) flgs = idx > -1 # filtering flags idx = tf.boolean_mask(idx, flgs) - + npt = tf.shape(xyzi)[0] ids = tf.reshape(tf.range(npt), [-1, 1]) - ids = tf.reshape(tf.tile(ids, [1, 8]), [-1]) # (N*8,) + ids = tf.reshape(tf.tile(ids, [1, 8]), [-1]) # (N*8,) ids = tf.boolean_mask(ids, flgs) frac = maskc - tf.expand_dims(frac, axis=1) weight = tf.abs(tf.reshape(tf.reduce_prod(frac, axis=2), [-1])) weight = tf.boolean_mask(weight, flgs) - indices = tf.concat([tf.expand_dims(ids, 1), tf.expand_dims(idx, 1)], 1) + indices = tf.concat([tf.expand_dims(ids, 1), tf.expand_dims(idx, 1)], 1) indices = tf.cast(indices, tf.int64) data = tf.squeeze(data, [0, 3]) # (C, H) h = tf.shape(data)[1] mat = tf.SparseTensor(indices=indices, values=weight, dense_shape=[npt, h]) - + # channel, max_channel = int(data.shape[0]), 512 # if channel > max_channel: # num = channel // max_channel @@ -431,16 +458,17 @@ def octree_bilinear_v3(pts, data, octree, depth): def octree_bilinear(data, octree, depth, target_depth, mask=None): with tf.name_scope('Octree_bilinear'): xyz = octree_property(octree, property_name='xyz', depth=target_depth, - channel=1, dtype=tf.uint32) + channel=1, dtype=tf_uintk) xyz = tf.reshape(xyz, [-1]) - if mask is not None: xyz = tf.boolean_mask(xyz, mask) + if mask is not None: + xyz = tf.boolean_mask(xyz, mask) xyz = tf.cast(octree_decode_key(xyz), dtype=tf.float32) - - # Attention: displacement 0.5, scale + + # Attention: displacement 0.5, scale scale = 2.0**(depth-target_depth) xyz += tf.constant([0.5, 0.5, 0.5, 0.0], dtype=tf.float32) xyz *= tf.constant([scale, scale, scale, 1.0], dtype=tf.float32) - + output = octree_bilinear_v3(xyz, data, octree, depth) return output @@ -450,16 +478,51 @@ def octree_bilinear(data, octree, depth, target_depth, mask=None): def octree_nearest_interp(pts, data, octree, depth): with tf.variable_scope('octree_nearest_interp'): # The value is defined on the center of each voxel, - # so we can get the closest grid point by simply casting the value to uint8 - pts = tf.cast(pts, dtype=tf.uint8) + # so we can get the closest grid point by simply casting the value to tf_uints + pts = tf.cast(pts, dtype=tf_uints) key = tf.reshape(octree_encode_key(pts), [-1]) idx = octree_search_key(key, octree, depth) # !!! Note that some of idx may be -1 or over-bound - # Use tf.gather may be problematic with some version of tensorflow - # according to my experiments. So I implemented octree_gather to - # replace the original tf.gather. If you encounter errors, please + # Use tf.gather may be problematic with some version of tensorflow + # according to my experiments. So I implemented octree_gather to + # replace the original tf.gather. If you encounter errors, please # use the octree_gather # output = tf.gather(data, idx, axis=2) output = octree_gather(data, idx) return output + + + +def octree_signal(octree, depth, channel): + with tf.name_scope('octree_signal'): + signal = octree_property(octree, property_name='feature', dtype=tf.float32, + depth=depth, channel=channel) + signal = tf.reshape(signal, [1, channel, -1, 1]) + return signal + + +def octree_xyz(octree, depth, decode=True): + with tf.name_scope('octree_xyz'): + xyz = octree_property(octree, property_name='xyz', dtype=tf_uintk, + depth=depth, channel=1) + xyz = tf.reshape(xyz, [-1]) # uint32, N + if decode: + xyz = octree_decode_key(xyz) # uint8, Nx4 + return xyz + + +def octree_child(octree, depth): + with tf.name_scope('octree_child'): + child = octree_property(octree, property_name='child', dtype=tf.int32, + depth=depth, channel=1) + child = tf.reshape(child, [-1]) + return child + + +def octree_split(octree, depth): + with tf.name_scope('octree_split'): + split = octree_property(octree, property_name='split', dtype=tf.float32, + depth=depth, channel=1) + split = tf.reshape(split, [-1]) + return split \ No newline at end of file diff --git a/tensorflow/libs/build.py b/tensorflow/libs/build.py index 20862ff..ede055a 100644 --- a/tensorflow/libs/build.py +++ b/tensorflow/libs/build.py @@ -2,15 +2,26 @@ import sys import tensorflow as tf import subprocess +import argparse -OCTREE_DIR = '../../octree' -CUDA_DIR = '/usr/local/cuda-10.1' -if len(sys.argv) > 1: OCTREE_DIR = sys.argv[1] -if len(sys.argv) > 2: CUDA_DIR = sys.argv[2] +parser = argparse.ArgumentParser() +parser.add_argument("--octree", type=str, required=False, + default='../../octree') +parser.add_argument("--cuda", type=str, required=False, + default='/usr/local/cuda-10.1') +parser.add_argument('--key64', type=str, required=False, + default='false') +parser.add_argument('--cc', type=str, required=False, + default='g++') +args = parser.parse_args() +OCTREE_DIR = args.octree +CUDA_DIR = args.cuda +CC = args.cc +KEY64 = '-DKEY64' if args.key64.lower() == 'true' else '' -lines = [] +lines = [] TF_CFLAGS = " ".join(tf.sysconfig.get_compile_flags()) TF_LFLAGS = " ".join(tf.sysconfig.get_link_flags()) lines.append("TF_CFLAGS := %s" % TF_CFLAGS) @@ -20,12 +31,16 @@ lines.append("") lines.append("NVCC_FLAGS1 := -std=c++11 -O2 -c") -lines.append("NVCC_FLAGS2 := $(TF_CFLAGS) $(OCT_CFLAGS) -I %s/include -x cu -Xcompiler -fPIC -D GOOGLE_CUDA=1 -I /usr/local -expt-relaxed-constexpr -DNDEBUG" % CUDA_DIR) +lines.append("NVCC_FLAGS2 := $(TF_CFLAGS) $(OCT_CFLAGS) -I %s/include " \ + "-x cu -Xcompiler -fPIC -D GOOGLE_CUDA=1 -I /usr/local " \ + "-expt-relaxed-constexpr -DNDEBUG %s" % (CUDA_DIR, KEY64)) lines.append("CC_FLAGS1 := -std=c++11 -O2") -lines.append("CC_FLAGS2 := $(TF_CFLAGS) $(OCT_CFLAGS) -I %s/include -L %s/lib64 -D GOOGLE_CUDA=1 $(TF_LFLAGS) $(OCT_LFLAGS) -fPIC -lcudart" % (CUDA_DIR, CUDA_DIR)) +lines.append("CC_FLAGS2 := $(TF_CFLAGS) $(OCT_CFLAGS) -I %s/include " \ + "-L %s/lib64 -D GOOGLE_CUDA=1 $(TF_LFLAGS) $(OCT_LFLAGS) " \ + "-fPIC -lcudart %s" % (CUDA_DIR, CUDA_DIR, KEY64)) lines.append("") -lines.append("CC := g++") +lines.append("CC := %s" % CC) lines.append("NVCC := %s/bin/nvcc" % CUDA_DIR) lines.append("") @@ -35,16 +50,16 @@ gpu_sources = [] all_headers = [] for filename in sorted(os.listdir(".")): - if filename.endswith(".cu.cc") or filename.endswith(".cu"): - targetname = filename + ".o" - gpu_sources.append(filename) - gpu_objects.append(os.path.join("object", targetname)) - elif filename.endswith(".cc") or filename.endswith(".cpp"): - targetname = filename + ".o" - cpu_sources.append(filename) - cpu_objects.append(os.path.join("object", targetname)) - elif filename.endswith(".h"): - all_headers.append(filename) + if filename.endswith(".cu.cc") or filename.endswith(".cu"): + targetname = filename + ".o" + gpu_sources.append(filename) + gpu_objects.append(os.path.join("object", targetname)) + elif filename.endswith(".cc") or filename.endswith(".cpp"): + targetname = filename + ".o" + cpu_sources.append(filename) + cpu_objects.append(os.path.join("object", targetname)) + elif filename.endswith(".h"): + all_headers.append(filename) lines.append("cpu_objects := %s" % " ".join(cpu_objects)) lines.append("gpu_objects := %s" % " ".join(gpu_objects)) @@ -60,27 +75,38 @@ lines.append("\t$(CC) $(CC_FLAGS1) -shared -o libocnn.so object/*.o $(CC_FLAGS2)") lines.append("") + for i in range(len(cpu_objects)): - dependency = b" ".join(subprocess.Popen("g++ -std=c++11 -MM -MG %s" % cpu_sources[i], stdin=subprocess.PIPE, stdout=subprocess.PIPE, shell=True).stdout.readlines()).decode("utf-8") - headers = [] - for h in all_headers: - if dependency.find(h) > 0: - headers.append(h) - headers = " ".join(headers) - lines.append("%s : %s %s" % (cpu_objects[i], headers, cpu_sources[i])) - lines.append("\t$(CC) $(CC_FLAGS1) -c %s $(CC_FLAGS2) -o %s" % (cpu_sources[i], cpu_objects[i])) - lines.append("") + dependency = b" ".join( + subprocess.Popen("%s -std=c++11 -MM -MG %s" % (CC, cpu_sources[i]), + stdin=subprocess.PIPE, + stdout=subprocess.PIPE, + shell=True).stdout.readlines()).decode("utf-8") + headers = [] + for h in all_headers: + if dependency.find(h) > 0: + headers.append(h) + headers = " ".join(headers) + lines.append("%s : %s %s" % (cpu_objects[i], headers, cpu_sources[i])) + lines.append("\t$(CC) $(CC_FLAGS1) -c %s $(CC_FLAGS2) -o %s" % \ + (cpu_sources[i], cpu_objects[i])) + lines.append("") for i in range(len(gpu_objects)): - dependency = b" ".join(subprocess.Popen("g++ -std=c++11 -MM -MG %s" % gpu_sources[i], stdin=subprocess.PIPE, stdout=subprocess.PIPE, shell=True).stdout.readlines()).decode("utf-8") - headers = [] - for h in all_headers: - if dependency.find(h) > 0: - headers.append(h) - headers = " ".join(headers) - lines.append("%s : %s %s" % (gpu_objects[i], headers, gpu_sources[i])) - lines.append("\t$(NVCC) $(NVCC_FLAGS1) %s $(NVCC_FLAGS2) -o %s" % (gpu_sources[i], gpu_objects[i])) - lines.append("") + dependency = b" ".join( + subprocess.Popen("%s -std=c++11 -MM -MG %s" % (CC, gpu_sources[i]), + stdin=subprocess.PIPE, + stdout=subprocess.PIPE, + shell=True).stdout.readlines()).decode("utf-8") + headers = [] + for h in all_headers: + if dependency.find(h) > 0: + headers.append(h) + headers = " ".join(headers) + lines.append("%s : %s %s" % (gpu_objects[i], headers, gpu_sources[i])) + lines.append("\t$(NVCC) $(NVCC_FLAGS1) %s $(NVCC_FLAGS2) -o %s" % \ + (gpu_sources[i], gpu_objects[i])) + lines.append("") lines.append("clean :") lines.append("\t rm %s" % os.path.join("object", "*.o")) @@ -89,8 +115,8 @@ lines = [line + "\n" for line in lines] with open("Makefile", "w") as f: - f.writelines(lines) + f.writelines(lines) if not os.path.exists("object"): - os.mkdir("object") + os.mkdir("object") os.system("make -j all") diff --git a/tensorflow/libs/octree2col_op.cc b/tensorflow/libs/octree2col_op.cc index a079682..b1da354 100644 --- a/tensorflow/libs/octree2col_op.cc +++ b/tensorflow/libs/octree2col_op.cc @@ -62,8 +62,6 @@ class Octree2ColBase : public OpKernel { vector& ni_cpu = NeighHelper::Get().get_ni(kernel_size_); int count = ni_cpu.size(); OP_REQUIRES_OK(ctx, ctx->allocate_temp(DT_INT32, TensorShape({ count }), &ni_gpu)); - // int* ni_ptr = ni_gpu.flat().data(); - // cudaMemcpy(ni_ptr, ni_cpu.data(), sizeof(int) * count, cudaMemcpyHostToDevice); memcpy_gpu(count, ni_cpu.data(), ni_gpu.flat().data()); } diff --git a/tensorflow/libs/octree_align_op.cc b/tensorflow/libs/octree_align_op.cc index a9b151c..66f9af3 100644 --- a/tensorflow/libs/octree_align_op.cc +++ b/tensorflow/libs/octree_align_op.cc @@ -1,4 +1,3 @@ -#include #include #include #include @@ -57,17 +56,17 @@ class OctreeAlignOp : public OpKernel { CHECK_EQ(src_octree.info().node_num(curr_depth_), src_h); // get key - const uint32* src_key = src_octree.key_gpu(curr_depth_); + const uintk* src_key = src_octree.key_gpu(curr_depth_); Tensor src_key_tensor; if (src_octree.info().is_key2xyz()) { xyz2key_gpu_op(context, &src_key_tensor, src_key, src_h, curr_depth_); - src_key = src_key_tensor.flat().data(); + src_key = src_key_tensor.flat().data(); } - const uint32* des_key = des_octree.key_gpu(curr_depth_); + const uintk* des_key = des_octree.key_gpu(curr_depth_); Tensor des_key_tensor; if (des_octree.info().is_key2xyz()) { xyz2key_gpu_op(context, &des_key_tensor, des_key, des_h, curr_depth_); - des_key = des_key_tensor.flat().data(); + des_key = des_key_tensor.flat().data(); } // binary search @@ -89,10 +88,11 @@ class OctreeAlignOp : public OpKernel { protected: void xyz2key_gpu_op(OpKernelContext* context, Tensor* key_tensor, - const uint32* xyz, const int num, const int depth) { - OP_REQUIRES_OK(context, context->allocate_temp( - DT_UINT32, TensorShape({num}), key_tensor)); - auto ptr = key_tensor->flat().data(); + const uintk* xyz, const int num, const int depth) { + auto dtype = std::is_same::value ? DT_UINT32 : DT_UINT64; + OP_REQUIRES_OK( + context, context->allocate_temp(dtype, TensorShape({num}), key_tensor)); + auto ptr = key_tensor->flat().data(); xyz2key_gpu(ptr, xyz, num, depth); } diff --git a/tensorflow/libs/octree_batch_op.cc b/tensorflow/libs/octree_batch_op.cc index d424180..d5f840c 100644 --- a/tensorflow/libs/octree_batch_op.cc +++ b/tensorflow/libs/octree_batch_op.cc @@ -27,9 +27,9 @@ class OctreeBatchOp : public OpKernel { const Tensor& data_in = context->input(0); auto octree_buffer = data_in.flat(); // int batch_size = data_in.shape().dim_size(0); - int batch_size = data_in.shape().num_elements(); + int batch_size = data_in.shape().num_elements(); // qq: TODO: long? vector octrees_in; - for (int i = 0; i < batch_size; ++i) { + for (int i = 0; i < batch_size; ++i) { // qq: TODO: long? octrees_in.push_back(octree_buffer(i).data()); } diff --git a/tensorflow/libs/octree_bilinear_op.cc b/tensorflow/libs/octree_bilinear_op.cc index 8ec89c7..2f096e0 100644 --- a/tensorflow/libs/octree_bilinear_op.cc +++ b/tensorflow/libs/octree_bilinear_op.cc @@ -1,10 +1,10 @@ -#include "octree_nn.h" -#include "octree_parser.h" - #include #include #include +#include "octree_nn.h" +#include "octree_parser.h" + namespace tensorflow { REGISTER_OP("OctreeBilinear") @@ -14,21 +14,20 @@ REGISTER_OP("OctreeBilinear") .Output("index: int32") .Output("fracs: float") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { - c->set_output(0, c->MakeShape({ c->UnknownDim(), 8 })); - c->set_output(1, c->MakeShape({ c->UnknownDim(), 3 })); + c->set_output(0, c->MakeShape({c->UnknownDim(), 8})); + c->set_output(1, c->MakeShape({c->UnknownDim(), 3})); return Status::OK(); }) .Doc(R"doc(Octree bilinear operator.)doc"); - class OctreeBilinearOp : public OpKernel { public: explicit OctreeBilinearOp(OpKernelConstruction* context) : OpKernel(context) { OP_REQUIRES_OK(context, context->GetAttr("curr_depth", &curr_depth_)); OP_REQUIRES_OK(context, context->GetAttr("target_depth", &target_depth_)); - CHECK_GT(curr_depth_, 0) - << "The curr_depth should be larger than 0"; - CHECK_GT(target_depth_, curr_depth_) + CHECK_GT(curr_depth_, 0) + << "The curr_depth should be larger than 0"; + CHECK_GT(target_depth_, curr_depth_) << "The target_depth should be larger than curr_depth"; } @@ -42,41 +41,41 @@ class OctreeBilinearOp : public OpKernel { // get key & xyz Tensor src_buffer; int src_h = octree_.info().node_num(curr_depth_); - const uint32* src_key = octree_.key_gpu(curr_depth_); + const uintk* src_key = octree_.key_gpu(curr_depth_); if (octree_.info().is_key2xyz()) { xyz2key_gpu_op(context, &src_buffer, src_key, src_h, curr_depth_); - src_key = src_buffer.flat().data(); + src_key = src_buffer.flat().data(); } Tensor des_buffer; int des_h = octree_.info().node_num(target_depth_); - const uint32* des_xyz = octree_.key_gpu(target_depth_); + const uintk* des_xyz = octree_.key_gpu(target_depth_); if (!octree_.info().is_key2xyz()) { key2xyz_gpu_op(context, &des_buffer, des_xyz, des_h, target_depth_); - des_xyz = des_buffer.flat().data(); + des_xyz = des_buffer.flat().data(); } // out data Tensor* idx_tensor = nullptr; - TensorShape idx_shape({ des_h, 8}); + TensorShape idx_shape({des_h, 8}); OP_REQUIRES_OK(context, context->allocate_output(0, idx_shape, &idx_tensor)); auto idx_ptr = idx_tensor->flat().data(); - + Tensor* frac_tensor = nullptr; - TensorShape frac_shape({ des_h, 3}); + TensorShape frac_shape({des_h, 3}); OP_REQUIRES_OK(context, context->allocate_output(1, frac_shape, &frac_tensor)); auto frac_ptr = frac_tensor->flat().data(); // calc bilinear xyz Tensor buf_xyz; - TensorShape rst_shape({ des_h, 8}); - OP_REQUIRES_OK(context, context->allocate_temp(DT_UINT32, rst_shape, &buf_xyz)); - auto rst_xyz = buf_xyz.flat().data(); + TensorShape rst_shape({des_h, 8}); + OP_REQUIRES_OK(context, context->allocate_temp(get_dtype(), rst_shape, &buf_xyz)); + auto rst_xyz = buf_xyz.flat().data(); bilinear_xyz_gpu(rst_xyz, frac_ptr, curr_depth_, des_xyz, target_depth_, des_h); - Tensor buf_key; + Tensor buf_key; xyz2key_gpu_op(context, &buf_key, rst_xyz, des_h * 8, target_depth_); - auto rst_key = buf_key.flat().data(); + auto rst_key = buf_key.flat().data(); // binary search search_key_gpu(idx_ptr, src_key, src_h, rst_key, des_h * 8); @@ -84,28 +83,28 @@ class OctreeBilinearOp : public OpKernel { protected: void xyz2key_gpu_op(OpKernelContext* context, Tensor* key_tensor, - const uint32* xyz, const int num, const int depth) { - OP_REQUIRES_OK(context, - context->allocate_temp(DT_UINT32, TensorShape({ num }), key_tensor)); - auto ptr = key_tensor->flat().data(); + const uintk* xyz, const int num, const int depth) { + OP_REQUIRES_OK(context, + context->allocate_temp(get_dtype(), TensorShape({num}), key_tensor)); + auto ptr = key_tensor->flat().data(); xyz2key_gpu(ptr, xyz, num, depth); } void key2xyz_gpu_op(OpKernelContext* context, Tensor* xyz_tensor, - const uint32* key, const int num, const int depth) { - OP_REQUIRES_OK(context, - context->allocate_temp(DT_UINT32, TensorShape({ num }), xyz_tensor)); - auto ptr = xyz_tensor->flat().data(); + const uintk* key, const int num, const int depth) { + OP_REQUIRES_OK(context, + context->allocate_temp(get_dtype(), TensorShape({num}), xyz_tensor)); + auto ptr = xyz_tensor->flat().data(); key2xyz_gpu(ptr, key, num, depth); } + DataType get_dtype() { return sizeof(uintk) == 4 ? DT_UINT32 : DT_UINT64; } private: int curr_depth_; int target_depth_; }; - REGISTER_KERNEL_BUILDER(Name("OctreeBilinear").Device(DEVICE_GPU), OctreeBilinearOp); } // namespace tensorflow diff --git a/tensorflow/libs/octree_conv_op.cc b/tensorflow/libs/octree_conv_op.cc index 24bbfa3..897db1f 100644 --- a/tensorflow/libs/octree_conv_op.cc +++ b/tensorflow/libs/octree_conv_op.cc @@ -119,7 +119,7 @@ class OctreeConvTF : public OpKernel, public OctreeBaseConv { Tensor* data_buffer, Tensor* result_buffer, Tensor* ni_gpu) { OctreeBaseConv::reshape(); - int count = num_elements(this->workspace_shape_); + int count = num_elements(this->workspace_shape_); // qq: TODO: long? OP_REQUIRES_OK(ctx, ctx->allocate_temp(DT_FLOAT, TensorShape({ count }), workspace)); this->workspace_ = workspace->flat().data(); diff --git a/tensorflow/libs/octree_grow_op.cc b/tensorflow/libs/octree_grow_op.cc deleted file mode 100644 index 168075a..0000000 --- a/tensorflow/libs/octree_grow_op.cc +++ /dev/null @@ -1,140 +0,0 @@ -#include "octree_nn.h" -#include "octree_parser.h" - -#include -#include -#include -#include - -namespace tensorflow { - -REGISTER_OP("OctreeGrow") - .Input("in_octree: int8") - .Attr("target_depth: int") - .Attr("full_octree: bool = false") - .Output("out_octree: int8") - .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { - c->set_output(0, c->MakeShape({ c->UnknownDim() })); - return Status::OK(); - }) - .Doc(R"doc(Octree grow operator.)doc"); - - -class OctreeGrowOp : public OpKernel { - public: - explicit OctreeGrowOp(OpKernelConstruction* context) : OpKernel(context) { - OP_REQUIRES_OK(context, context->GetAttr("target_depth", &target_depth_)); - OP_REQUIRES_OK(context, context->GetAttr("full_octree", &full_octree_)); - } - - void Compute(OpKernelContext* context) override { - // in octree - OctreeParser octree_in; - octree_in.set_gpu(context->input(0).flat().data()); - - // out info - batch_size_ = octree_in.info().batch_size(); - node_num_ = octree_in.info().node_num_nempty(target_depth_ - 1) << 3; - OctreeInfo oct_info_; - oct_info_ = octree_in.info(); - update_octreeinfo(oct_info_); - - // out octree - Tensor* tensor_out = nullptr; - TensorShape shape_out({ oct_info_.sizeof_octree() }); - OP_REQUIRES_OK(context, context->allocate_output(0, shape_out, &tensor_out)); - auto* ptr_out = tensor_out->flat().data(); - // memset_gpu(tensor_out->NumElements(), 0, ptr_out); - cudaMemset(ptr_out, 0, tensor_out->NumElements()); - - // copy octree - OctreeParser octree_out; - octree_out.set_gpu(ptr_out, &oct_info_); - copy_octree_gpu(octree_out, octree_in); - - // grow octree - if (full_octree_) { - calc_neigh_gpu(octree_out.mutable_neighbor_gpu(target_depth_), - target_depth_, batch_size_); - generate_key_gpu(octree_out.mutable_key_gpu(target_depth_), - target_depth_, batch_size_); - sequence_gpu(octree_out.mutable_children_gpu(target_depth_), node_num_); - } else { - Tensor displacement, parent; - init_neigh_ptrs(context, parent, displacement); - const int* label_ptr = octree_out.children_gpu(target_depth_ - 1); - calc_neigh_gpu(octree_out.mutable_neighbor_gpu(target_depth_), - octree_out.neighbor_gpu(target_depth_ - 1), label_ptr, - octree_out.info().node_num(target_depth_ - 1), ptr_parent_, ptr_dis_); - generate_key_gpu(octree_out.mutable_key_gpu(target_depth_), - octree_out.key_gpu(target_depth_ - 1), label_ptr, - octree_out.info().node_num(target_depth_ - 1)); - sequence_gpu(octree_out.mutable_children_gpu(target_depth_), node_num_); - } - } - - private: - void update_octreeinfo(OctreeInfo& oct_info_) { - oct_info_.set_depth(target_depth_); - if (full_octree_) { - oct_info_.set_full_layer(target_depth_); - } - float width = 1 << target_depth_; - float bbmin[] = { 0, 0, 0 }; - float bbmax[] = { width, width, width }; - oct_info_.set_bbox(bbmin, bbmax); - oct_info_.set_nnum(target_depth_, node_num_); - // Just set the non-empty node number as node_num_, - // it needs to be updated by the new node-splitting label - oct_info_.set_nempty(target_depth_, node_num_); - oct_info_.set_nnum_cum(); - oct_info_.set_ptr_dis(); - } - - // todo: replace the cudaMemcpy with the wrapper function memcpy_gpu - - void copy_octree_gpu(OctreeParser& octree_out, const OctreeParser& octree_in) { - int node_num_cum = octree_in.info().node_num_cum(target_depth_); - int key_channel = octree_in.info().channel(OctreeInfo::kKey); - int child_channel = octree_in.info().channel(OctreeInfo::kChild); - int neigh_channel = octree_in.info().channel(OctreeInfo::kNeigh); - int feature_channel = octree_in.info().channel(OctreeInfo::kFeature); - cudaMemcpy(octree_out.mutable_key_gpu(0), octree_in.key_gpu(0), - key_channel * node_num_cum * sizeof(int), cudaMemcpyDeviceToDevice); - cudaMemcpy(octree_out.mutable_children_gpu(0), octree_in.children_gpu(0), - child_channel * node_num_cum * sizeof(int), cudaMemcpyDeviceToDevice); - cudaMemcpy(octree_out.mutable_neighbor_gpu(0), octree_in.neighbor_gpu(0), - neigh_channel * node_num_cum * sizeof(int), cudaMemcpyDeviceToDevice); - cudaMemcpy(octree_out.mutable_feature_gpu(0), octree_in.feature_gpu(0), - feature_channel * node_num_cum * sizeof(float), cudaMemcpyDeviceToDevice); - } - - void init_neigh_ptrs(OpKernelContext* ctx, Tensor& parent, Tensor& dis) { - const vector& dis_cpu = NeighHelper::Get().get_dis_array(); - TensorShape dshape({ (long long int) dis_cpu.size() }); - OP_REQUIRES_OK(ctx, ctx->allocate_temp(DT_INT32, dshape, &dis)); - ptr_dis_ = dis.flat().data(); - cudaMemcpy(ptr_dis_, dis_cpu.data(), dis_cpu.size() * sizeof(int), - cudaMemcpyHostToDevice); - - const vector& parent_cpu = NeighHelper::Get().get_parent_array(); - TensorShape pshape({ (long long int) parent_cpu.size() }); - OP_REQUIRES_OK(ctx, ctx->allocate_temp(DT_INT32, pshape, &parent)); - ptr_parent_ = parent.flat().data(); - cudaMemcpy(ptr_parent_, parent_cpu.data(), parent_cpu.size() * sizeof(int), - cudaMemcpyHostToDevice); - } - - private: - int batch_size_; - int target_depth_; - int node_num_; - bool full_octree_; - int* ptr_parent_; - int* ptr_dis_; -}; - - -REGISTER_KERNEL_BUILDER(Name("OctreeGrow").Device(DEVICE_GPU), OctreeGrowOp); - -} // namespace tensorflow diff --git a/tensorflow/libs/octree_key_op.cc b/tensorflow/libs/octree_key_op.cc index 53e17e0..6dd7b9c 100644 --- a/tensorflow/libs/octree_key_op.cc +++ b/tensorflow/libs/octree_key_op.cc @@ -1,35 +1,48 @@ -#include "octree_nn.h" -#include "octree_parser.h" - #include #include #include #include +#include +#include "octree_nn.h" +#include "octree_parser.h" + namespace tensorflow { +inline string uintk_modifier(string str) { + string dtype = std::is_same::value ? "uint32" : "uint64"; + size_t pos = str.find("uintk"); + return str.replace(pos, 5, dtype); +} + +inline string uints_modifier(string str) { + string dtype = std::is_same::value ? "uint8" : "uint16"; + size_t pos = str.find("uints"); + return str.replace(pos, 5, dtype); +} + REGISTER_OP("OctreeEncodeKey") - .Input("xyz: uint8") - .Output("key: uint32") + .Input(uints_modifier("xyz: uints")) + .Output(uintk_modifier("key: uintk")) .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { c->set_output(0, c->MakeShape({c->Dim(c->input(0), 0)})); return Status::OK(); }) - .Doc(R"doc(Encode the (x, y, z, id) to key in uint32)")doc"); + .Doc(R"doc(Encode the (x, y, z, id) to key in uintk)")doc"); REGISTER_OP("OctreeDecodeKey") - .Input("key: uint32") - .Output("xyz: uint8") + .Input(uintk_modifier("key: uintk")) + .Output(uints_modifier("xyz: uints")) .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { c->set_output(0, c->MakeShape({c->Dim(c->input(0), 0), 4})); return Status::OK(); }) - .Doc(R"doc(Decode the key to (x, y, z, id) in uint8)")doc"); + .Doc(R"doc(Decode the key to (x, y, z, id) in uints)")doc"); REGISTER_OP("OctreeKeyToXyz") - .Input("key: uint32") + .Input(uintk_modifier("key: uintk")) .Attr("depth: int = 8") - .Output("xyz: uint32") + .Output(uintk_modifier("xyz: uintk")) .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { c->set_output(0, c->input(0)); return Status::OK(); @@ -37,9 +50,9 @@ REGISTER_OP("OctreeKeyToXyz") .Doc(R"doc(Convert the key to xyz)")doc"); REGISTER_OP("OctreeXyzToKey") - .Input("xyz: uint32") + .Input(uintk_modifier("xyz: uintk")) .Attr("depth: int = 8") - .Output("key: uint32") + .Output(uintk_modifier("key: uintk")) .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { c->set_output(0, c->input(0)); return Status::OK(); @@ -47,27 +60,28 @@ REGISTER_OP("OctreeXyzToKey") .Doc(R"doc(Convert the xyz to key)")doc"); REGISTER_OP("OctreeSearchKey") - .Input("key: uint32") + .Input(uintk_modifier("key: uintk")) .Input("octree: int8") .Attr("depth: int") .Attr("is_xyz: bool = True") .Output("kidx: int32") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { - c->set_output(0, c->MakeShape({ c->UnknownDim() })); + c->set_output(0, c->MakeShape({c->UnknownDim()})); return Status::OK(); }) .Doc(R"doc(Octree search operator.)doc"); - class OctreeEncodeKeyOp : public OpKernel { public: explicit OctreeEncodeKeyOp(OpKernelConstruction* context) - : OpKernel(context) {} + : OpKernel(context) {} void Compute(OpKernelContext* context) override { + typedef typename KeyTrait::uints uints; + // in data const Tensor& data_in = context->input(0); - auto ptr_in = data_in.flat().data(); + auto ptr_in = data_in.flat().data(); int num = data_in.dim_size(0); int channel = data_in.dim_size(1); CHECK_EQ(data_in.dims(), 2) << "The dim of input tensor must be 2."; @@ -75,24 +89,27 @@ class OctreeEncodeKeyOp : public OpKernel { // out data Tensor* data_out = nullptr; - TensorShape shape_out({ num }); + TensorShape shape_out({num}); OP_REQUIRES_OK(context, context->allocate_output(0, shape_out, &data_out)); - auto ptr_out = data_out->flat().data(); + auto ptr_out = data_out->flat().data(); // copy data - cudaMemcpy(ptr_out, ptr_in, sizeof(uint8) * channel * num, cudaMemcpyDeviceToDevice); + int n = channel * num; + cudaMemcpy(ptr_out, ptr_in, sizeof(uints) * n, cudaMemcpyDeviceToDevice); } }; class OctreeDecodeKeyOp : public OpKernel { public: explicit OctreeDecodeKeyOp(OpKernelConstruction* context) - : OpKernel(context) {} + : OpKernel(context) {} void Compute(OpKernelContext* context) override { + typedef typename KeyTrait::uints uints; + // in data const Tensor& data_in = context->input(0); - auto ptr_in = data_in.flat().data(); + auto ptr_in = data_in.flat().data(); int num = data_in.dim_size(0); CHECK_EQ(data_in.dims(), 1) << "The dim of input tensor must be 1."; @@ -100,10 +117,10 @@ class OctreeDecodeKeyOp : public OpKernel { Tensor* data_out = nullptr; TensorShape shape_out({num, 4}); OP_REQUIRES_OK(context, context->allocate_output(0, shape_out, &data_out)); - auto ptr_out = data_out->flat().data(); + auto ptr_out = data_out->flat().data(); // copy data - cudaMemcpy(ptr_out, ptr_in, sizeof(uint32) * num, cudaMemcpyDeviceToDevice); + cudaMemcpy(ptr_out, ptr_in, sizeof(uintk) * num, cudaMemcpyDeviceToDevice); } }; @@ -117,14 +134,14 @@ class OctreeKeyToXyzOp : public OpKernel { // in data const Tensor& data_in = context->input(0); const TensorShape& shape_in = data_in.shape(); - auto ptr_in = data_in.flat().data(); + auto ptr_in = data_in.flat().data(); int num = shape_in.num_elements(); CHECK_GE(num, 1) << "The element number of input tensor must be 1."; // out data Tensor* data_out = nullptr; OP_REQUIRES_OK(context, context->allocate_output(0, shape_in, &data_out)); - auto ptr_out = data_out->flat().data(); + auto ptr_out = data_out->flat().data(); // convert key2xyz_gpu(ptr_out, ptr_in, num, depth_); @@ -136,8 +153,7 @@ class OctreeKeyToXyzOp : public OpKernel { class OctreeXyzToKeyOp : public OpKernel { public: - explicit OctreeXyzToKeyOp(OpKernelConstruction* context) - : OpKernel(context) { + explicit OctreeXyzToKeyOp(OpKernelConstruction* context) : OpKernel(context) { OP_REQUIRES_OK(context, context->GetAttr("depth", &depth_)); } @@ -145,14 +161,14 @@ class OctreeXyzToKeyOp : public OpKernel { // in data const Tensor& data_in = context->input(0); const TensorShape& shape_in = data_in.shape(); - auto ptr_in = data_in.flat().data(); + auto ptr_in = data_in.flat().data(); int num = shape_in.num_elements(); CHECK_GE(num, 1) << "The element number of input tensor must be 1."; // out data Tensor* data_out = nullptr; OP_REQUIRES_OK(context, context->allocate_output(0, shape_in, &data_out)); - auto ptr_out = data_out->flat().data(); + auto ptr_out = data_out->flat().data(); // convert xyz2key_gpu(ptr_out, ptr_in, num, depth_); @@ -165,7 +181,7 @@ class OctreeXyzToKeyOp : public OpKernel { class OctreeSearchKeyOp : public OpKernel { public: explicit OctreeSearchKeyOp(OpKernelConstruction* context) - : OpKernel(context) { + : OpKernel(context) { OP_REQUIRES_OK(context, context->GetAttr("depth", &depth_)); OP_REQUIRES_OK(context, context->GetAttr("is_xyz", &is_xyz_)); } @@ -174,7 +190,7 @@ class OctreeSearchKeyOp : public OpKernel { // input const Tensor& data_in = context->input(0); const TensorShape& shape_in = data_in.shape(); - const uint32* src_key = data_in.flat().data(); + const uintk* src_key = data_in.flat().data(); int src_h = shape_in.num_elements(); CHECK_GE(src_h, 1) << "The element number of input tensor must be 1."; @@ -182,18 +198,18 @@ class OctreeSearchKeyOp : public OpKernel { Tensor src_key_tensor; if (is_xyz_) { xyz2key_gpu_op(context, &src_key_tensor, src_key, src_h, depth_); - src_key = src_key_tensor.flat().data(); + src_key = src_key_tensor.flat().data(); } // octree OctreeParser octree_; octree_.set_gpu(context->input(1).flat().data()); int des_h = octree_.info().node_num(depth_); - const uint32* des_key = octree_.key_gpu(depth_); + const uintk* des_key = octree_.key_gpu(depth_); Tensor des_key_tensor; if (octree_.info().is_key2xyz()) { xyz2key_gpu_op(context, &des_key_tensor, des_key, des_h, depth_); - des_key = des_key_tensor.flat().data(); + des_key = des_key_tensor.flat().data(); } // output @@ -206,12 +222,12 @@ class OctreeSearchKeyOp : public OpKernel { } protected: - // todo: isolated the following functions out of this file void xyz2key_gpu_op(OpKernelContext* context, Tensor* key_tensor, - const uint32* xyz, const int num, const int depth) { - OP_REQUIRES_OK(context, - context->allocate_temp(DT_UINT32, TensorShape({ num }), key_tensor)); - auto ptr = key_tensor->flat().data(); + const uintk* xyz, const int num, const int depth) { + auto dtype = std::is_same::value ? DT_UINT32 : DT_UINT64; + OP_REQUIRES_OK( + context, context->allocate_temp(dtype, TensorShape({num}), key_tensor)); + auto ptr = key_tensor->flat().data(); xyz2key_gpu(ptr, xyz, num, depth); } @@ -220,7 +236,6 @@ class OctreeSearchKeyOp : public OpKernel { bool is_xyz_; }; - REGISTER_KERNEL_BUILDER(Name("OctreeDecodeKey").Device(DEVICE_GPU), OctreeDecodeKeyOp); REGISTER_KERNEL_BUILDER(Name("OctreeEncodeKey").Device(DEVICE_GPU), OctreeEncodeKeyOp); REGISTER_KERNEL_BUILDER(Name("OctreeKeyToXyz").Device(DEVICE_GPU), OctreeKeyToXyzOp); diff --git a/tensorflow/libs/octree_new_op.cc b/tensorflow/libs/octree_new_op.cc index e025f7a..54b4a89 100644 --- a/tensorflow/libs/octree_new_op.cc +++ b/tensorflow/libs/octree_new_op.cc @@ -1,4 +1,3 @@ -#include #include #include #include @@ -47,7 +46,7 @@ class OctreeNewOp : public OpKernel { oct_info_.set_adaptive(false); } oct_info_.set_key2xyz(true); - oct_info_.set_property(OctreeInfo::kKey, 2, -1); + oct_info_.set_property(OctreeInfo::kKey, 1, -1); oct_info_.set_property(OctreeInfo::kChild, 1, -1); oct_info_.set_property(OctreeInfo::kNeigh, 8, -1); oct_info_.set_property(OctreeInfo::kFeature, channel_, -1); @@ -63,14 +62,14 @@ class OctreeNewOp : public OpKernel { Tensor* tensor_out = nullptr; TensorShape shape_out({oct_info_.sizeof_octree()}); OP_REQUIRES_OK(context, context->allocate_output(0, shape_out, &tensor_out)); - auto* ptr_out = tensor_out->flat().data(); - cudaMemset(ptr_out, 0, tensor_out->NumElements()); + int8* ptr_out = tensor_out->flat().data(); + memset(ptr_out, 0, tensor_out->NumElements()); // set octree, skip the propoerties neigh and feature OctreeParser octree_out; - octree_out.set_gpu(ptr_out, &oct_info_); - sequence_gpu(octree_out.mutable_key_gpu(depth_), node_num_); - sequence_gpu(octree_out.mutable_children_gpu(depth_), node_num_); + octree_out.set_cpu(ptr_out, &oct_info_); + sequence_cpu(octree_out.mutable_key_cpu(depth_), node_num_); + sequence_cpu(octree_out.mutable_children_cpu(depth_), node_num_); } private: @@ -80,6 +79,6 @@ class OctreeNewOp : public OpKernel { bool has_displace_; }; -REGISTER_KERNEL_BUILDER(Name("OctreeNew").Device(DEVICE_GPU), OctreeNewOp); +REGISTER_KERNEL_BUILDER(Name("OctreeNew").Device(DEVICE_CPU), OctreeNewOp); } // namespace tensorflow diff --git a/tensorflow/libs/octree_property_op.cc b/tensorflow/libs/octree_property_op.cc index 123dd56..10f1b1c 100644 --- a/tensorflow/libs/octree_property_op.cc +++ b/tensorflow/libs/octree_property_op.cc @@ -1,11 +1,11 @@ -#include "octree_nn.h" -#include "octree_parser.h" - #include #include #include #include +#include "octree_nn.h" +#include "octree_parser.h" + namespace tensorflow { REGISTER_OP("OctreeProperty") @@ -13,7 +13,7 @@ REGISTER_OP("OctreeProperty") .Attr("property_name: string") .Attr("depth: int") .Attr("channel: int") - .Attr("dtype: {int32,float32,uint32}") + .Attr("dtype: {int32,float32,uint32,uint64}") .Output("out_property: dtype") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { int channel; @@ -23,11 +23,9 @@ REGISTER_OP("OctreeProperty") }) .Doc(R"doc(Octree property operator.)doc"); - class OctreePropertyOp : public OpKernel { public: - explicit OctreePropertyOp(OpKernelConstruction* context) : - OpKernel(context) { + explicit OctreePropertyOp(OpKernelConstruction* context) : OpKernel(context) { OP_REQUIRES_OK(context, context->GetAttr("property_name", &property_name_)); OP_REQUIRES_OK(context, context->GetAttr("depth", &depth_)); OP_REQUIRES_OK(context, context->GetAttr("channel", &channel_)); @@ -42,35 +40,28 @@ class OctreePropertyOp : public OpKernel { Tensor buf0, buf1; const void* property_ptr = nullptr; int length = octree_.info().node_num(depth_), channel = 1; + bool key32 = std::is_same::value; + DataType key_dtype = key32 ? DataType::DT_UINT32 : DataType::DT_UINT64; if (property_name_ == "key") { property_ptr = octree_.key_gpu(depth_); channel = octree_.info().channel(OctreeInfo::kKey); - CHECK_EQ(dtype_, DataType::DT_UINT32); + CHECK_EQ(dtype_, key_dtype); } else if (property_name_ == "xyz") { - // key2xzy property_ptr = octree_.key_gpu(depth_); channel = octree_.info().channel(OctreeInfo::kKey); if (!octree_.info().is_key2xyz()) { - OP_REQUIRES_OK(context, - context->allocate_temp(DT_UINT32, TensorShape({ length }), &buf0)); - uint32* ptr = buf0.flat().data(); - key2xyz_gpu(ptr, (const uint32*)property_ptr, length, depth_); + OP_REQUIRES_OK(context, context->allocate_temp( + key_dtype, TensorShape({length}), &buf0)); + uintk* ptr = buf0.flat().data(); + key2xyz_gpu(ptr, (const uintk*)property_ptr, length, depth_); property_ptr = ptr; } - CHECK_EQ(dtype_, DataType::DT_UINT32); - //// xyz to float - //channel = channel_; - //OP_REQUIRES_OK(context, - // context->allocate_temp(DT_FLOAT, TensorShape({ length * channel }), &buf1)); - //CHECK_EQ(dtype_, DataType::DT_FLOAT); - //float* ptr = buf1.flat().data(); - //xyz2coord_gpu(ptr, xyz, length, channel); - //property_ptr = ptr; + CHECK_EQ(dtype_, key_dtype); } else if (property_name_ == "index") { - const unsigned int* key_ptr = octree_.key_gpu(depth_); + const uintk* key_ptr = octree_.key_gpu(depth_); channel = octree_.info().channel(OctreeInfo::kKey); - OP_REQUIRES_OK(context, - context->allocate_temp(DT_INT32, TensorShape({ length }), &buf0)); + OP_REQUIRES_OK(context, context->allocate_temp( + DT_INT32, TensorShape({length}), &buf0)); int* idx_ptr = buf0.flat().data(); key2idx_gpu(idx_ptr, key_ptr, length); property_ptr = idx_ptr; @@ -98,31 +89,38 @@ class OctreePropertyOp : public OpKernel { } else { LOG(FATAL) << "Unsupported Octree Property"; } - CHECK_EQ(channel_, channel) << "The specified channel_ is wrong."; + CHECK_EQ(channel_, channel) << " The specified channel_ is wrong." + << " Property name: " << property_name_; Tensor* out_tensor; - TensorShape out_shape({ channel, length }); - OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &out_tensor)); + TensorShape out_shape({channel, length}); + OP_REQUIRES_OK(context, + context->allocate_output(0, out_shape, &out_tensor)); int num = channel * length; switch (dtype_) { - case DataType::DT_UINT32: { - auto ptr = out_tensor->flat().data(); - cudaMemcpy(ptr, property_ptr, sizeof(uint32) * num, cudaMemcpyDeviceToDevice); - } - break; - case DataType::DT_INT32: { - auto ptr = out_tensor->flat().data(); - cudaMemcpy(ptr, property_ptr, sizeof(int) * num, cudaMemcpyDeviceToDevice); - } - break; - case DataType::DT_FLOAT: { - auto ptr = out_tensor->flat().data(); - cudaMemcpy(ptr, property_ptr, sizeof(float) * num, cudaMemcpyDeviceToDevice); - } - break; - default: - LOG(FATAL) << "Invalid DataType"; + case DataType::DT_UINT32: { + auto ptr = out_tensor->flat().data(); + cudaMemcpy(ptr, property_ptr, sizeof(uint32) * num, + cudaMemcpyDeviceToDevice); + } break; + case DataType::DT_UINT64: { + auto ptr = out_tensor->flat().data(); + cudaMemcpy(ptr, property_ptr, sizeof(uint64) * num, + cudaMemcpyDeviceToDevice); + } break; + case DataType::DT_INT32: { + auto ptr = out_tensor->flat().data(); + cudaMemcpy(ptr, property_ptr, sizeof(int) * num, + cudaMemcpyDeviceToDevice); + } break; + case DataType::DT_FLOAT: { + auto ptr = out_tensor->flat().data(); + cudaMemcpy(ptr, property_ptr, sizeof(float) * num, + cudaMemcpyDeviceToDevice); + } break; + default: + LOG(FATAL) << "Invalid DataType"; } } @@ -133,6 +131,7 @@ class OctreePropertyOp : public OpKernel { int channel_; }; -REGISTER_KERNEL_BUILDER(Name("OctreeProperty").Device(DEVICE_GPU), OctreePropertyOp); +REGISTER_KERNEL_BUILDER(Name("OctreeProperty").Device(DEVICE_GPU), + OctreePropertyOp); } // namespace tensorflow diff --git a/tensorflow/libs/octree_search_op.cc b/tensorflow/libs/octree_search_op.cc index 5c51215..9ebdb68 100644 --- a/tensorflow/libs/octree_search_op.cc +++ b/tensorflow/libs/octree_search_op.cc @@ -1,30 +1,27 @@ -#include "octree_nn.h" -#include "octree_parser.h" - #include #include #include +#include "octree_nn.h" +#include "octree_parser.h" + namespace tensorflow { REGISTER_OP("OctreeSearch") - .Input("xyz: float") // C * H + .Input("xyz: float") // C * H .Input("octree: int8") .Attr("depth: int") - //.Attr("dtype: {int32,float32,uint32}") .Output("kidx: int32") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { - c->set_output(0, c->MakeShape({ c->UnknownDim() })); + c->set_output(0, c->MakeShape({c->UnknownDim()})); return Status::OK(); }) .Doc(R"doc(Octree search operator.)doc"); - class OctreeSearchOp : public OpKernel { public: explicit OctreeSearchOp(OpKernelConstruction* context) : OpKernel(context) { OP_REQUIRES_OK(context, context->GetAttr("depth", &depth_)); - //OP_REQUIRES_OK(context, context->GetAttr("dtype", &dtype_)); } void Compute(OpKernelContext* context) override { @@ -38,27 +35,27 @@ class OctreeSearchOp : public OpKernel { // coord2xyz Tensor src_xyz_tensor; coord2xyz_gpu_op(context, &src_xyz_tensor, src_ptr, src_h, channel); - const uint32* src_xyz = src_xyz_tensor.flat().data(); + const uintk* src_xyz = src_xyz_tensor.flat().data(); // xyz2key Tensor src_key_tensor; xyz2key_gpu_op(context, &src_key_tensor, src_xyz, src_h, depth_); - const uint32* src_key = src_key_tensor.flat().data(); + const uintk* src_key = src_key_tensor.flat().data(); // octree OctreeParser octree_; octree_.set_gpu(context->input(1).flat().data()); int des_h = octree_.info().node_num(depth_); - const uint32* des_key = octree_.key_gpu(depth_); + const uintk* des_key = octree_.key_gpu(depth_); Tensor des_key_tensor; if (octree_.info().is_key2xyz()) { xyz2key_gpu_op(context, &des_key_tensor, des_key, des_h, depth_); - des_key = des_key_tensor.flat().data(); + des_key = des_key_tensor.flat().data(); } // output Tensor* des_tensor = nullptr; - TensorShape des_shape({ src_h }); + TensorShape des_shape({src_h}); OP_REQUIRES_OK(context, context->allocate_output(0, des_shape, &des_tensor)); auto idx_ptr = des_tensor->flat().data(); @@ -69,27 +66,28 @@ class OctreeSearchOp : public OpKernel { protected: // todo: isolated the following functions out of this file void xyz2key_gpu_op(OpKernelContext* context, Tensor* key_tensor, - const uint32* xyz, const int num, const int depth) { - OP_REQUIRES_OK(context, - context->allocate_temp(DT_UINT32, TensorShape({ num }), key_tensor)); - auto ptr = key_tensor->flat().data(); + const uintk* xyz, const int num, const int depth) { + auto dtype = std::is_same::value ? DT_UINT32 : DT_UINT64; + OP_REQUIRES_OK( + context, context->allocate_temp(dtype, TensorShape({num}), key_tensor)); + auto ptr = key_tensor->flat().data(); xyz2key_gpu(ptr, xyz, num, depth); } void coord2xyz_gpu_op(OpKernelContext* context, Tensor* xyz_tensor, - const float* coord, const int num, const int channel) { - OP_REQUIRES_OK(context, - context->allocate_temp(DT_UINT32, TensorShape({ num }), xyz_tensor)); - auto xyz = xyz_tensor->flat().data(); + const float* coord, const int num, const int channel) { + auto dtype = std::is_same::value ? DT_UINT32 : DT_UINT64; + OP_REQUIRES_OK( + context, context->allocate_temp(dtype, TensorShape({num}), xyz_tensor)); + auto xyz = xyz_tensor->flat().data(); coord2xyz_gpu(xyz, coord, num, channel); } private: int depth_; - //DataType dtype_; + // DataType dtype_; }; - REGISTER_KERNEL_BUILDER(Name("OctreeSearch").Device(DEVICE_GPU), OctreeSearchOp); } // namespace tensorflow diff --git a/tensorflow/libs/octree_set_property_op.cc b/tensorflow/libs/octree_set_property_op.cc index 19aeeb5..2e3a0e6 100644 --- a/tensorflow/libs/octree_set_property_op.cc +++ b/tensorflow/libs/octree_set_property_op.cc @@ -1,20 +1,20 @@ -#include "octree_parser.h" -#include "octree_info.h" - #include #include #include #include +#include "octree_info.h" +#include "octree_parser.h" + namespace tensorflow { REGISTER_OP("OctreeSetProperty") - .Input("in_octree: int8") - .Input("in_property: dtype") + .Input("octree_in: int8") + .Input("property_in: dtype") .Attr("property_name: string") .Attr("depth: int") - .Attr("dtype: {int32,float32,uint32}") - .Output("out_octree: int8") + .Attr("dtype: {int32,float32,uint32,uint64}") + .Output("octree_out: int8") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { c->set_output(0, c->input(0)); return Status::OK(); @@ -23,31 +23,33 @@ REGISTER_OP("OctreeSetProperty") class OctreeSetPropertyOp : public OpKernel { public: - explicit OctreeSetPropertyOp(OpKernelConstruction* context) : - OpKernel(context) { + explicit OctreeSetPropertyOp(OpKernelConstruction* context) + : OpKernel(context) { OP_REQUIRES_OK(context, context->GetAttr("property_name", &property_name_)); OP_REQUIRES_OK(context, context->GetAttr("depth", &depth_)); OP_REQUIRES_OK(context, context->GetAttr("dtype", &dtype_)); } void Compute(OpKernelContext* context) override { - const Tensor& in_octree = context->input(0); - const Tensor& in_property = context->input(1); - auto in_ptr = in_octree.flat().data(); + const Tensor& octree_in = context->input(0); + const Tensor& property_in = context->input(1); + auto ptr_in = octree_in.flat().data(); - Tensor* out_octree; - OP_REQUIRES_OK(context, context->allocate_output(0, in_octree.shape(), &out_octree)); - auto out_ptr = out_octree->flat().data(); - cudaMemcpy(out_ptr, in_ptr, in_octree.NumElements(), cudaMemcpyDeviceToDevice); + Tensor* octree_out; + OP_REQUIRES_OK(context, context->allocate_output(0, octree_in.shape(), &octree_out)); + auto ptr_out = octree_out->flat().data(); + cudaMemcpy(ptr_out, ptr_in, octree_in.NumElements(), cudaMemcpyDeviceToDevice); OctreeParser oct_parser; - int length; + oct_parser.set_gpu(ptr_out); void* property_ptr = nullptr; - oct_parser.set_gpu(out_ptr); - length = oct_parser.info().node_num(depth_); + int length = oct_parser.info().node_num(depth_); if (property_name_ == "key") { + bool key32 = std::is_same::value; + DataType key_dtype = key32 ? DataType::DT_UINT32 : DataType::DT_UINT64; property_ptr = oct_parser.mutable_key_gpu(depth_); - CHECK_EQ(dtype_, DataType::DT_UINT32); + length *= oct_parser.info().channel(OctreeInfo::kKey); + CHECK_EQ(dtype_, key_dtype); } else if (property_name_ == "child") { property_ptr = oct_parser.mutable_children_gpu(depth_); length *= oct_parser.info().channel(OctreeInfo::kChild); @@ -71,28 +73,31 @@ class OctreeSetPropertyOp : public OpKernel { } else { LOG(FATAL) << "Unsupported Octree Property"; } - CHECK_EQ(length, in_property.NumElements()) << "Wrong Property Size"; + + CHECK_EQ(length, property_in.NumElements()) << "Wrong Property Size"; switch (dtype_) { - case DataType::DT_UINT32: { - auto in_property_ptr = in_property.flat().data(); - cudaMemcpy(property_ptr, in_property_ptr, sizeof(uint32) * length, - cudaMemcpyDeviceToDevice); - } - break; - case DataType::DT_INT32: { - auto in_property_ptr = in_property.flat().data(); - cudaMemcpy(property_ptr, in_property_ptr, sizeof(int) * length, - cudaMemcpyDeviceToDevice); - } - break; - case DataType::DT_FLOAT: { - auto in_property_ptr = in_property.flat().data(); - cudaMemcpy(property_ptr, in_property_ptr, sizeof(float) * length, - cudaMemcpyDeviceToDevice); - } - break; - default: - LOG(FATAL) << "Wrong DataType"; + case DataType::DT_UINT32: { + auto property_in_ptr = property_in.flat().data(); + cudaMemcpy(property_ptr, property_in_ptr, sizeof(uint32) * length, + cudaMemcpyDeviceToDevice); + } break; + case DataType::DT_UINT64: { + auto property_in_ptr = property_in.flat().data(); + cudaMemcpy(property_ptr, property_in_ptr, sizeof(uint64) * length, + cudaMemcpyDeviceToDevice); + } break; + case DataType::DT_INT32: { + auto property_in_ptr = property_in.flat().data(); + cudaMemcpy(property_ptr, property_in_ptr, sizeof(int) * length, + cudaMemcpyDeviceToDevice); + } break; + case DataType::DT_FLOAT: { + auto property_in_ptr = property_in.flat().data(); + cudaMemcpy(property_ptr, property_in_ptr, sizeof(float) * length, + cudaMemcpyDeviceToDevice); + } break; + default: + LOG(FATAL) << "Wrong DataType"; } } @@ -100,7 +105,6 @@ class OctreeSetPropertyOp : public OpKernel { string property_name_; DataType dtype_; int depth_; - }; REGISTER_KERNEL_BUILDER(Name("OctreeSetProperty").Device(DEVICE_GPU), OctreeSetPropertyOp); diff --git a/tensorflow/libs/points_property_op.cc b/tensorflow/libs/points_property_op.cc index 7956ae4..1286925 100644 --- a/tensorflow/libs/points_property_op.cc +++ b/tensorflow/libs/points_property_op.cc @@ -1,9 +1,9 @@ -#include "points_parser.h" - #include #include #include +#include "points_parser.h" + namespace tensorflow { REGISTER_OP("PointsProperty") @@ -14,15 +14,14 @@ REGISTER_OP("PointsProperty") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { int channel; TF_RETURN_IF_ERROR(c->GetAttr("channel", &channel)); - c->set_output(0, c->MakeShape({ c->UnknownDim(), channel })); + c->set_output(0, c->MakeShape({c->UnknownDim(), channel})); return Status::OK(); }) .Doc(R"doc(Points property operator.)doc"); class PointsPropertyOp : public OpKernel { public: - explicit PointsPropertyOp(OpKernelConstruction* context) : - OpKernel(context) { + explicit PointsPropertyOp(OpKernelConstruction* context) : OpKernel(context) { OP_REQUIRES_OK(context, context->GetAttr("property_name", &property_name_)); OP_REQUIRES_OK(context, context->GetAttr("channel", &channel_)); } @@ -46,46 +45,58 @@ class PointsPropertyOp : public OpKernel { } // channel - int channel = 0; + PointsInfo::PropType ptype; if (property_name_ == "xyz") { - channel = 4; // x, y, z, id + ptype = PointsInfo::kPoint; } else if (property_name_ == "label") { - channel = 1; + ptype = PointsInfo::kLabel; + } else if (property_name_ == "normal") { + ptype = PointsInfo::kNormal; + } else if (property_name_ == "feature") { + ptype = PointsInfo::kFeature; } else { LOG(FATAL) << "Unsupported Property: " << property_name_; } - CHECK_EQ(channel_, channel) << "The specified channel_ is wrong."; + int channel = channel_; // `channel` is the actual channel saved in points + if (ptype == PointsInfo::kPoint) { + CHECK(channel_ == 3 || channel_ == 4) + << "The specified channel_ for xyz is wrong."; + channel = 3; + } + for (int i = 0; i < batch_size; ++i) { + int ch = points_in[i].info().channel(ptype); + CHECK_EQ(ch, channel) << "The specified channel_ is wrong."; + const float* ptr = points_in[i].ptr(ptype); + CHECK_NE(ptr, nullptr) << "The specified property does not exist."; + } // init output Tensor* out_tensor; - TensorShape out_shape({ total_num, channel }); - OP_REQUIRES_OK(context, context->allocate_output(0, out_shape, &out_tensor)); + TensorShape out_shp({total_num, channel_}); + OP_REQUIRES_OK(context, context->allocate_output(0, out_shp, &out_tensor)); float* out_ptr = out_tensor->flat().data(); // copy output - if (property_name_ == "xyz") { - for (int i = 0; i < batch_size; ++i) { - const float *in_ptr = points_in[i].points(); - for (int j = 0; j < npts[i]; ++j) { - int jx3 = j * 3, jx4 = j * 4; - for (int c = 0; c < 3; ++c) { - out_ptr[jx4 + c] = in_ptr[jx3 + c]; - } - out_ptr[jx4 + 3] = static_cast(i); // id + for (int i = 0; i < batch_size; ++i) { + const float* in_ptr = points_in[i].ptr(ptype); + for (int j = 0; j < npts[i]; ++j) { + int jxc_in = j * channel, jxc_out = j * channel_; + for (int c = 0; c < channel; ++c) { + out_ptr[jxc_out + c] = in_ptr[jxc_in + c]; } - out_ptr += npts[i] * channel; } - } else if (property_name_ == "label") { + out_ptr += npts[i] * channel_; + } + + // output point index if channel == 4 + if (ptype == PointsInfo::kPoint && channel_ == 4) { + out_ptr = out_tensor->flat().data(); for (int i = 0; i < batch_size; ++i) { - const float *in_ptr = points_in[i].label(); - CHECK(in_ptr != nullptr) << "The points have on labels"; for (int j = 0; j < npts[i]; ++j) { - out_ptr[j] = in_ptr[j]; + out_ptr[j * channel_ + 3] = static_cast(i); // id } - out_ptr += npts[i]; + out_ptr += npts[i] * channel_; } - } else { - LOG(FATAL) << "Unsupported Property: " << property_name_; } } diff --git a/tensorflow/libs/points_set_property_op.cc b/tensorflow/libs/points_set_property_op.cc new file mode 100644 index 0000000..f1c6583 --- /dev/null +++ b/tensorflow/libs/points_set_property_op.cc @@ -0,0 +1,132 @@ +#include +#include +#include + +#include "points.h" + +namespace tensorflow { + +REGISTER_OP("PointsNew") + .Input("pts: float") + .Input("normals: float") + .Input("features: float") + .Input("labels: float") + .Output("points: string") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + c->set_output(0, c->MakeShape({1})); + return Status::OK(); + }) + .Doc(R"doc(Create a point cloud.)doc"); + +REGISTER_OP("PointsSetProperty") + .Input("points: string") + .Input("data: float") + .Attr("property_name: string") + .Output("points_out: string") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + c->set_output(0, c->MakeShape({1})); + return Status::OK(); + }) + .Doc(R"doc(Set points property operator.)doc"); + +class PointsNewOp : public OpKernel { + public: + explicit PointsNewOp(OpKernelConstruction* context) : OpKernel(context) {} + + void Compute(OpKernelContext* context) override { + // input + auto get_data = [&](vector& vec, int idx) { + const Tensor& data_in = context->input(idx); + const int64 num = data_in.NumElements(); + if (num > 0) { + const float* ptr = data_in.flat().data(); + vec.assign(ptr, ptr + num); + } + }; + vector pts, normals, features, labels; + get_data(pts, 0); + get_data(normals, 1); + get_data(features, 2); + get_data(labels, 3); + + // create the point cloud + Points point_cloud; + bool succ = point_cloud.set_points(pts, normals, features, labels); + CHECK(succ) << "Error occurs when setting points"; + + // output + Tensor* tsr = nullptr; + OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape{1}, &tsr)); + string& out_str = tsr->flat()(0); + const vector& points_buf = point_cloud.get_buffer(); + out_str.assign(points_buf.begin(), points_buf.end()); + } +}; + +class PointsSetPropertyOp : public OpKernel { + public: + explicit PointsSetPropertyOp(OpKernelConstruction* context) + : OpKernel(context) { + OP_REQUIRES_OK(context, context->GetAttr("property_name", &property_name_)); + } + + void Compute(OpKernelContext* context) override { + // input points + const Tensor& data_in = context->input(0); + CHECK_EQ(data_in.NumElements(), 1); + const Tensor& data = context->input(1); + CHECK_EQ(data.dims(), 2); + int num = data.dim_size(0); + int channel = data.dim_size(1); + + // copy the data out of the input tensor + auto points_array = data_in.flat(); + vector points_buf(points_array(0).begin(), points_array(0).end()); + + // init the points + Points pts; + pts.set(points_buf.data()); + + // check the points + string msg; + bool succ = pts.info().check_format(msg); + CHECK(succ) << msg; + + // get prop + PointsInfo::PropType ptype; + if (property_name_ == "xyz") { + ptype = PointsInfo::kPoint; + } else if (property_name_ == "label") { + ptype = PointsInfo::kLabel; + } else if (property_name_ == "normal") { + ptype = PointsInfo::kNormal; + } else if (property_name_ == "feature") { + ptype = PointsInfo::kFeature; + } else { + LOG(FATAL) << "Unsupported Property: " << property_name_; + } + + // set the property + float* ptr = pts.mutable_ptr(ptype); + CHECK(ptr != nullptr) << "The property does not exist."; + CHECK_EQ(channel, pts.info().channel(ptype)) << "The channel is wrong."; + CHECK_EQ(num, pts.info().pt_num()) << "The point number is wrong."; + memcpy(ptr, data.flat().data(), num * channel * sizeof(float)); + + // output + Tensor* out_data = nullptr; + const TensorShape& shape = data_in.shape(); + OP_REQUIRES_OK(context, context->allocate_output(0, shape, &out_data)); + string& out_str = out_data->flat()(0); + out_str.assign(points_buf.begin(), points_buf.end()); + } + + private: + string property_name_; + int channel_; +}; + +REGISTER_KERNEL_BUILDER(Name("PointsSetProperty").Device(DEVICE_CPU), PointsSetPropertyOp); +REGISTER_KERNEL_BUILDER(Name("PointsNew").Device(DEVICE_CPU), PointsNewOp); + +} // namespace tensorflow diff --git a/tensorflow/libs/transform_points_op.cc b/tensorflow/libs/transform_points_op.cc index 2226e1c..7eaf2dc 100644 --- a/tensorflow/libs/transform_points_op.cc +++ b/tensorflow/libs/transform_points_op.cc @@ -23,9 +23,9 @@ REGISTER_OP("TransformPoints") .Input("ratio: float") .Input("dim: int32") .Input("stddev: float") - .Attr("axis: string='y'") // todo: delete this attribute + .Attr("axis: string='y'") // todo: delete this attribute .Attr("depth: int=6") - .Attr("offset: float=0.55") + .Attr("offset: float=0.0") .Output("points_out: string") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { c->set_output(0, c->input(0)); @@ -33,13 +33,25 @@ REGISTER_OP("TransformPoints") }) .Doc(R"doc(Transform points.)doc"); +REGISTER_OP("NormalizePoints") + .Input("points: string") + .Input("radius: float") + .Input("center: float") + .Output("points_out: string") + .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { + c->set_output(0, c->input(0)); + return Status::OK(); + }) + .Doc(R"doc(Move the center to the origin, and normalize input to [-1, 1].)doc"); + REGISTER_OP("BoundingSphere") .Input("points: string") + .Attr("method: string='sphere'") .Output("radius: float") .Output("center: float") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { - c->set_output(0, c->MakeShape({ 1 })); - c->set_output(1, c->MakeShape({ 3 })); + c->set_output(0, c->MakeShape({1})); + c->set_output(1, c->MakeShape({3})); return Status::OK(); }) .Doc(R"doc(Compute the bounding sphere of a point cloud.)doc"); @@ -63,7 +75,7 @@ REGISTER_OP("MakeGrids") class TransformPointsOp : public OpKernel { public: explicit TransformPointsOp(OpKernelConstruction* context) - : OpKernel(context) { + : OpKernel(context) { OP_REQUIRES_OK(context, context->GetAttr("axis", &axis_)); OP_REQUIRES_OK(context, context->GetAttr("depth", &depth_)); OP_REQUIRES_OK(context, context->GetAttr("offset", &offset_)); @@ -71,29 +83,27 @@ class TransformPointsOp : public OpKernel { void Compute(OpKernelContext* context) override { // input - auto extract_param = [](float * vec, const Tensor & ts) { + auto extract_param = [](float* vec, const Tensor& ts) { for (int i = 0; i < 3 && i < ts.NumElements(); ++i) { vec[i] = ts.flat()(i); } }; const Tensor& data_in = context->input(0); // float rotate = context->input(1).flat()(0); - float angle[3] = { 0 }; + float angle[3] = {0}; extract_param(angle, context->input(1)); - float scales[3] = { 1.0f, 1.0f, 1.0f }; + float scales[3] = {1.0f, 1.0f, 1.0f}; extract_param(scales, context->input(2)); - float jitter[3] = { 0 }; + float jitter[3] = {0}; extract_param(jitter, context->input(3)); float radius = context->input(4).flat()(0); - float center[3] = { 0 }; + float center[3] = {0}; extract_param(center, context->input(5)); float ratio = context->input(6).flat()(0); int dim = context->input(7).flat()(0); - float stddev[3] = { 0 }; // std_points, std_normals, std_features + float stddev[3] = {0}; // std_points, std_normals, std_features extract_param(stddev, context->input(8)); -// std::cout << angle[0] << " " << scales[0] << " " << jitter[0] << " " << radius[0] << " " << center[0] << " " << stddev[0] << std::endl; - // check CHECK_EQ(data_in.NumElements(), 1); for (int i = 0; i < 3; ++i) { @@ -113,126 +123,35 @@ class TransformPointsOp : public OpKernel { bool succ = pts.info().check_format(msg); CHECK(succ) << msg; - int N = pts.info().pt_num(); -// std::cout << "N in transorm points: " << N << std::endl; + // centralize + float dis[3] = {-center[0], -center[1], -center[2]}; + if (dis[0] != 0.0f || dis[1] != 0.0f || dis[2] != 0.0f) { + pts.translate(dis); + } - // centralize & displacement // QQ revise: we do not want to add offset. and adjust the radius + // displacement const float kEPS = 1.0e-10f; - // float dis[3] = { -center[0], -center[1], -center[2] }; - // pts.translate(dis); - // if (offset_ > kEPS) { - // // !!! rescale the offset, relative to the octree node size - // float offset = offset_ * 2.0f * radius / float(1 << depth_); - // pts.displace(offset); - // radius += offset; - // } + if (offset_ > kEPS) { + // !!! rescale the offset, relative to the octree node size + float offset = offset_ * 2.0f * radius / float(1 << depth_); + pts.displace(offset); + radius += offset; + } // data augmentation: rotate the point cloud if (fabs(angle[0]) > kEPS || fabs(angle[1]) > kEPS || fabs(angle[2]) > kEPS) { - // float axes[] = { 0.0f, 0.0f, 0.0f }; - // if (axis_ == "x") axes[0] = 1.0f; - // else if (axis_ == "y") axes[1] = 1.0f; - // else axes[2] = 1.0f; - // pts.rotate(rotate, axes); - - if (axis_ == "x") { angle[1] = angle[2] = 0; pts.rotate(angle);} - else if (axis_ == "y") { angle[0] = angle[2] = 0; pts.rotate(angle);} - else if (axis_ == "z") { angle[0] = angle[1] = 0; pts.rotate(angle); } - else if (axis_ == "xyz") //QQ revise: randomly along xyz. - { - int rand_num = rand()%6; - - if (rand_num == 0){ - float x_angle[3] = { 0 }; - x_angle[0] = angle[0]; - pts.rotate(x_angle); - - float y_angle[3] = { 0 }; - y_angle[1] = angle[1]; - pts.rotate(y_angle); - - float z_angle[3] = { 0 }; - z_angle[2] = angle[2]; - pts.rotate(z_angle); - } - - if (rand_num == 1){ - float x_angle[3] = { 0 }; - x_angle[0] = angle[0]; - pts.rotate(x_angle); - - float z_angle[3] = { 0 }; - z_angle[2] = angle[2]; - pts.rotate(z_angle); - - float y_angle[3] = { 0 }; - y_angle[1] = angle[1]; - pts.rotate(y_angle); - } - - if (rand_num == 2){ - float y_angle[3] = { 0 }; - y_angle[1] = angle[1]; - pts.rotate(y_angle); - - float x_angle[3] = { 0 }; - x_angle[0] = angle[0]; - pts.rotate(x_angle); - - float z_angle[3] = { 0 }; - z_angle[2] = angle[2]; - pts.rotate(z_angle); - } - - if (rand_num == 3){ - float y_angle[3] = { 0 }; - y_angle[1] = angle[1]; - pts.rotate(y_angle); - - float z_angle[3] = { 0 }; - z_angle[2] = angle[2]; - pts.rotate(z_angle); - - float x_angle[3] = { 0 }; - x_angle[0] = angle[0]; - pts.rotate(x_angle); - } - - if (rand_num == 4){ - - float z_angle[3] = { 0 }; - z_angle[2] = angle[2]; - pts.rotate(z_angle); - - float x_angle[3] = { 0 }; - x_angle[0] = angle[0]; - pts.rotate(x_angle); - - float y_angle[3] = { 0 }; - y_angle[1] = angle[1]; - pts.rotate(y_angle); - } - - if (rand_num == 5){ - float z_angle[3] = { 0 }; - z_angle[2] = angle[2]; - pts.rotate(z_angle); - - float y_angle[3] = { 0 }; - y_angle[1] = angle[1]; - pts.rotate(y_angle); - - float x_angle[3] = { 0 }; - x_angle[0] = angle[0]; - pts.rotate(x_angle); - } - + if (axis_ == "x") { + angle[1] = angle[2] = 0; + } else if (axis_ == "y") { + angle[0] = angle[2] = 0; + } else if (axis_ == "z") { + angle[0] = angle[1] = 0; + } else { } - else {} -// pts.rotate(angle); + pts.rotate(angle); } - // jitter // QQ revise: we can keep the jitter, in our case, + // jitter float max_jitter = -1.0; for (int i = 0; i < 3; i++) { // !!! rescale the jitter, relative to the radius @@ -243,54 +162,109 @@ class TransformPointsOp : public OpKernel { } if (fabs(max_jitter) > kEPS) { pts.translate(jitter); - //radius += max_jitter; + // radius += max_jitter; } - // scale to [-1, 1]^3 + // scale to [-1, 1]^3 if (radius == 0) radius = kEPS; - float max_scale = -1.0f; - for (int i = 0; i < 3; ++i) { - scales[i] /= radius; - if (max_scale < scales[i]) { max_scale = scales[i]; } + for (int i = 0; i < 3; ++i) { scales[i] /= radius; } + if (scales[0] != 1.0f || scales[1] != 1.0f || scales[2] != 1.0f) { + pts.scale(scales); } - pts.scale(scales); // add noise if (stddev[0] > 0 || stddev[1] > 0 || stddev[2] > 0) { pts.add_noise(stddev[0], stddev[1]); } - // clip the points to the box[-1, 1] ^ 3, - const float bbmin[] = { -1.0f, -1.0f, -1.0f }; - const float bbmax[] = { 1.0f, 1.0f, 1.0f }; - if (max_scale > 1.0f || max_jitter > kEPS) { - pts.clip(bbmin, bbmax); - } + // clip the points to the box[-1, 1] ^ 3, + const float bbmin[] = {-1.0f, -1.0f, -1.0f}; + const float bbmax[] = {1.0f, 1.0f, 1.0f}; + pts.clip(bbmin, bbmax); + // float max_scale = -1.0f; + // for (int i = 0; i < 3; ++i) { + // if (max_scale < scales[i]) { max_scale = scales[i]; } + // } + // if (max_scale > 1.0f || max_jitter > kEPS) { + // pts.clip(bbmin, bbmax); + // } - // dropout points // qq: omit -// if (dim > 0 && ratio > 0) { -// DropPoints drop_points(dim, ratio, bbmin, bbmax); -// drop_points.dropout(pts); -// } + // dropout points + if (dim > 0 && ratio > 0) { + DropPoints drop_points(dim, ratio, bbmin, bbmax); + drop_points.dropout(pts); + } // output Tensor* out_data = nullptr; - OP_REQUIRES_OK(context, context->allocate_output(0, data_in.shape(), &out_data)); + const TensorShape& shape = data_in.shape(); + OP_REQUIRES_OK(context, context->allocate_output(0, shape, &out_data)); string& out_str = out_data->flat()(0); out_str.assign(points_buf.begin(), points_buf.end()); } - private: string axis_; int depth_; float offset_; }; +class NormalizePointsOp : public OpKernel { + public: + explicit NormalizePointsOp(OpKernelConstruction* context) + : OpKernel(context) {} + + void Compute(OpKernelContext* context) override { + // input + const string& data_in = context->input(0).flat()(0); + const float radius = context->input(1).flat()(0); + const float* center = context->input(2).flat().data(); + + // check + CHECK_EQ(context->input(0).NumElements(), 1); + CHECK_EQ(context->input(1).NumElements(), 1); + CHECK_EQ(context->input(2).NumElements(), 3); + + // copy the data out of the input tensor + vector points_buf(data_in.begin(), data_in.end()); + + // init the points + Points pts; + pts.set(points_buf.data()); + + // check the points + string msg; + bool succ = pts.info().check_format(msg); + CHECK(succ) << msg; + + // centralize + const float dis[3] = {-center[0], -center[1], -center[2]}; + if (dis[0] != 0.0f || dis[1] != 0.0f || dis[2] != 0.0f) { + pts.translate(dis); + } + + // scale to [-1, 1] + CHECK_GE(radius, 0.0f); + const float inv_radius = 1.0f / radius; + const float scales[3] = {inv_radius, inv_radius, inv_radius}; + if (scales[0] != 1.0f || scales[1] != 1.0f || scales[2] != 1.0f) { + pts.scale(scales); + } + + // output + Tensor* out_data = nullptr; + const TensorShape& shape = context->input(0).shape(); + OP_REQUIRES_OK(context, context->allocate_output(0, shape, &out_data)); + string& out_str = out_data->flat()(0); + out_str.assign(points_buf.begin(), points_buf.end()); + } +}; + class BoundingSphereOp : public OpKernel { public: - explicit BoundingSphereOp(OpKernelConstruction* context) - : OpKernel(context) {} + explicit BoundingSphereOp(OpKernelConstruction* context) : OpKernel(context) { + OP_REQUIRES_OK(context, context->GetAttr("method", &method_)); + } void Compute(OpKernelContext* context) override { const Tensor& data_in = context->input(0); @@ -306,20 +280,34 @@ class BoundingSphereOp : public OpKernel { CHECK(succ) << msg; // bounding sphere - float radius, center[3]; - bounding_sphere(radius, center, pts.points(), pts.info().pt_num()); + float radius = 0.0f, center[3] = {0.0f}; + if (method_ == "sphere") { + bounding_sphere(radius, center, pts.points(), pts.info().pt_num()); + } else { + float bbmin[3] = {0.0f}, bbmax[3] = {0.0f}; + bounding_box(bbmin, bbmax, pts.points(), pts.info().pt_num()); + for (int j = 0; j < 3; ++j) { + center[j] = (bbmax[j] + bbmin[j]) / 2.0f; + float width = (bbmax[j] - bbmin[j]) / 2.0f; + radius += width * width; + } + radius = sqrtf(radius + 1.0e-20f); + } // output Tensor* out0 = nullptr; - OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape({ 1 }), &out0)); + OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape({1}), &out0)); float* ptr0 = out0->flat().data(); ptr0[0] = radius; Tensor* out1 = nullptr; - OP_REQUIRES_OK(context, context->allocate_output(1, TensorShape({ 3 }), &out1)); + OP_REQUIRES_OK(context, context->allocate_output(1, TensorShape({3}), &out1)); float* ptr1 = out1->flat().data(); for (int i = 0; i < 3; ++i) { ptr1[i] = center[i]; } } + + private: + string method_; }; class MakeGridsOp : public OpKernel { @@ -425,9 +413,8 @@ class MakeGridsOp : public OpKernel { int out_size_; }; - - REGISTER_KERNEL_BUILDER(Name("TransformPoints").Device(DEVICE_CPU), TransformPointsOp); +REGISTER_KERNEL_BUILDER(Name("NormalizePoints").Device(DEVICE_CPU), NormalizePointsOp); REGISTER_KERNEL_BUILDER(Name("BoundingSphere").Device(DEVICE_CPU), BoundingSphereOp); REGISTER_KERNEL_BUILDER(Name("MakeGrids").Device(DEVICE_CPU), MakeGridsOp); diff --git a/tensorflow/script/.DS_Store b/tensorflow/script/.DS_Store index 2f74965..53c5fb4 100644 Binary files a/tensorflow/script/.DS_Store and b/tensorflow/script/.DS_Store differ diff --git a/tensorflow/script/config.py b/tensorflow/script/config.py index 1949b24..a1acf5f 100644 --- a/tensorflow/script/config.py +++ b/tensorflow/script/config.py @@ -15,7 +15,7 @@ _C.SOLVER.type = 'adam' # Choose from sgd or adam _C.SOLVER.max_iter = 160000 # Maximum training iterations _C.SOLVER.test_iter = 100 # Test steps in testing phase -_C.SOLVER.test_every_iter = 1000 # Test model every n training steps +_C.SOLVER.test_every_iter = 4000 # Test model every n training steps _C.SOLVER.lr_type = 'step' # Learning rate type: step or cos _C.SOLVER.learning_rate = 0.01 # Initial learning rate _C.SOLVER.gamma = 0.1 # Learning rate step-wise decay @@ -24,7 +24,7 @@ _C.SOLVER.var_name = ('_name',)# Variable names used for finetuning _C.SOLVER.ignore_var_name = ('_name',)# Ignore variable names when loading ckpt _C.SOLVER.verbose = False # Whether to output some messages -_C.SOLVER.task = 'class' +_C.SOLVER.task = 'reg' # DATA related parameters @@ -59,7 +59,7 @@ _C.DATA.train.batch_size = 32 # Training data batch size _C.DATA.train.mask_ratio = 0.0 # Mask out some point features -_C.DATA.train.return_iter= False # Return the data iterator +# _C.DATA.train.return_iter= False # Return the data iterator _C.DATA.train.return_pts = False # Also return points @@ -86,6 +86,10 @@ _C.LOSS = CN() _C.LOSS.num_class = 40 # The class number for the cross-entropy loss _C.LOSS.weight_decay = 0.0005 # The weight decay on model weights +_C.LOSS.sigma = 0.1 # Use for MID training +_C.LOSS.momentum = 0.5 # Use for MID training +_C.LOSS.inst_num = 57449 # The object number in MID training +_C.LOSS.seg_num = 100 # The clustering number in MID training _C.LOSS.weights = (1.0, 1.0) # The weight factors for different losses _C.LOSS.label_smoothing = 0.0 # The factor of label smoothing diff --git a/tensorflow/script/logs/pdbbind/resnet_point2grid_20200806_reg_6/.DS_Store b/tensorflow/script/configs/.DS_Store similarity index 92% rename from tensorflow/script/logs/pdbbind/resnet_point2grid_20200806_reg_6/.DS_Store rename to tensorflow/script/configs/.DS_Store index f7d19a6..5008ddf 100644 Binary files a/tensorflow/script/logs/pdbbind/resnet_point2grid_20200806_reg_6/.DS_Store and b/tensorflow/script/configs/.DS_Store differ diff --git a/tensorflow/script/configs/ae_resnet.yaml b/tensorflow/script/configs/ae_resnet.yaml deleted file mode 100644 index 52153a3..0000000 --- a/tensorflow/script/configs/ae_resnet.yaml +++ /dev/null @@ -1,39 +0,0 @@ -SOLVER: - gpu: 0, - logdir: logs/ae/0609_resnet - run: train - max_iter: 320000 - test_iter: 336 - test_every_iter: 2000 - step_size: (80000,) - ckpt_num: 20 - -DATA: - train: - dtype: points - depth: 6 - location: dataset/ocnn_completion/completion_train_points.tfrecords - batch_size: 16 - distort: False - offset: 0.0 - node_dis: True - split_label: True - - test: - dtype: points - depth: 6 - location: dataset/ocnn_completion/completion_test_points.tfrecords - batch_size: 16 - distort: False - offset: 0.0 - node_dis: True - split_label: True - -MODEL: - name: resnet - channel: 4 - nout: 32 # The channel of the hidden code, the code length is 4*4*4*32 (2048) - depth: 6 - -LOSS: - weight_decay: 0.0005 \ No newline at end of file diff --git a/tensorflow/script/configs/ae_resnet_decode.yaml b/tensorflow/script/configs/ae_resnet_decode.yaml deleted file mode 100644 index d75ca5e..0000000 --- a/tensorflow/script/configs/ae_resnet_decode.yaml +++ /dev/null @@ -1,26 +0,0 @@ -SOLVER: - gpu: 0, - logdir: logs/ae/0609_resnet_decode_shape - ckpt: logs/ae/0609_resnet/model/iter_320000.ckpt - run: decode_shape - test_iter: 200 - -DATA: - test: - dtype: points - depth: 6 - location: dataset/ocnn_completion/completion_test_points.tfrecords - batch_size: 1 - distort: False - offset: 0.0 - node_dis: True - split_label: True - -MODEL: - name: resnet - channel: 4 - nout: 32 # The channel of the hidden code - depth: 6 - -LOSS: - weight_decay: 0.0005 \ No newline at end of file diff --git a/tensorflow/script/configs/ae_resnet_test.yaml b/tensorflow/script/configs/ae_resnet_test.yaml deleted file mode 100644 index 7811a30..0000000 --- a/tensorflow/script/configs/ae_resnet_test.yaml +++ /dev/null @@ -1,26 +0,0 @@ -SOLVER: - gpu: 0, - logdir: logs/ae/0609_resnet_test - ckpt: logs/ae/0609_resnet/model/iter_320000.ckpt - run: test - test_iter: 336 - -DATA: - test: - dtype: points - depth: 6 - location: dataset/ocnn_completion/completion_test_points.tfrecords - batch_size: 16 - distort: False - offset: 0.0 - node_dis: True - split_label: True - -MODEL: - name: resnet - channel: 4 - nout: 32 # The channel of the hidden code, the code length is 4*4*4*32 (2048) - depth: 6 - -LOSS: - weight_decay: 0.0005 \ No newline at end of file diff --git a/tensorflow/script/configs/cls_octree.yaml b/tensorflow/script/configs/cls_octree.yaml deleted file mode 100644 index ac511ae..0000000 --- a/tensorflow/script/configs/cls_octree.yaml +++ /dev/null @@ -1,35 +0,0 @@ -SOLVER: - gpu: 0, - logdir: logs/m40/0322_ocnn_octree - run: train - max_iter: 160000 - test_iter: 925 - test_every_iter: 2000 - step_size: (40000,) - -DATA: - train: - dtype: octree - distort: True - depth: 5 - location: dataset/ModelNet40/m40_5_2_12_train_octree.tfrecords - batch_size: 32 - x_alias: data - test: - dtype: octree - distort: False - depth: 5 - location: dataset/ModelNet40/m40_5_2_12_test_octree.tfrecords - shuffle: 0 - batch_size: 32 - x_alias: data - -MODEL: - name: ocnn - channel: 3 - nout: 40 - depth: 5 - -LOSS: - num_class: 40 - weight_decay: 0.0005 diff --git a/tensorflow/script/configs/cls_octree_test.yaml b/tensorflow/script/configs/cls_octree_test.yaml deleted file mode 100644 index f375889..0000000 --- a/tensorflow/script/configs/cls_octree_test.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# To test the model with the tensorflow checkpoint - -SOLVER: - gpu: 0, - logdir: logs/m40/0322_ocnn_octree_test - ckpt: logs/m40/0322_ocnn_octree/model/iter_160000.ckpt - run: test - test_iter: 925 - -DATA: - test: - dtype: octree - depth: 5 - location: dataset/ModelNet40/m40_5_2_12_test_octree.tfrecords - shuffle: 0 - batch_size: 32 - x_alias: data - -MODEL: - name: ocnn - channel: 3 - nout: 40 - depth: 5 - -LOSS: - num_class: 40 - weight_decay: 0.0005 diff --git a/tensorflow/script/configs/cls_pdbbind_points.yaml b/tensorflow/script/configs/cls_pdbbind_points.yaml deleted file mode 100644 index 1e4f875..0000000 --- a/tensorflow/script/configs/cls_pdbbind_points.yaml +++ /dev/null @@ -1,50 +0,0 @@ -SOLVER: - gpu: 0,1 - logdir: logs/pdbbind/ocnn_points_20200716_reg_5 - run: train -# ckpt: logs/pdbbind/ocnn_points_20200704_class_7/model/iter_120000.ckpt - max_iter: 120000 - test_iter: 17 # 689 = 13*53, 221 = 13 * 17 - test_every_iter: 1000 - step_size: (40000,) - learning_rate: 0.001 - task: reg - -DATA: - train: - dtype: points - distort: True - depth: 5 - axis: xyz - angle: (6, 6, 6) - interval: (1, 1, 1) - scale: 0.0 - jitter: 0.0 - location: /media/data/data_share/pdbbind/tfrecords/test_reg_points_den5.tfrecords - batch_size: 30 - x_alias: data - shuffle: 200 - test: - dtype: points - distort: False - depth: 5 - axis: z - angle: (5, 5, 5) - interval: (1, 1, 1) - scale: 0.0 - jitter: 0.0 - location: /media/data/data_share/pdbbind/tfrecords/test_reg_points_den5.tfrecords - shuffle: 1 - batch_size: 13 - x_alias: data - -MODEL: - name: ocnn #ocnn_input_logit - channel: 24 - nout: 1 - depth: 5 - resblock_num: 3 - -LOSS: - num_class: 1 - weight_decay: 0.0005 diff --git a/tensorflow/script/configs/cls_pdbbind_points_resnet.yaml b/tensorflow/script/configs/cls_pdbbind_points_resnet.yaml deleted file mode 100644 index 780c858..0000000 --- a/tensorflow/script/configs/cls_pdbbind_points_resnet.yaml +++ /dev/null @@ -1,50 +0,0 @@ -SOLVER: - gpu: 0,1 - logdir: logs/pdbbind/cnn_points_20200722_reg_5 - run: train - #ckpt: logs/pdbbind/ocnn_points_20200630_class/model/iter_003800.ckpt -# ckpt: logs/pdbbind/ocnn_points_20200630_class_7/model/iter_000400.ckpt - max_iter: 16000 - test_iter: 53 # 689 = 13*53, 221 = 13 * 17 - test_every_iter: 1000 - step_size: (40000,) - learning_rate: 0.01 - -DATA: - train: - dtype: point2grid #points, point2grid, octree, grids - distort: True - depth: 6 - axis: xyz - angle: (5, 5, 5) - interval: (1, 1, 1) - scale: 0.0 - jitter: 4.0 - location: /media/data/data_share/pdbbind/tfrecords/test_reg_points_den5.tfrecords #train_class_points_records - batch_size: 30 - x_alias: data - shuffle: 500 - test: - dtype: point2grid - distort: False - depth: 6 - axis: z - angle: (5, 5, 5) - interval: (1, 1, 1) - scale: 0.0 - jitter: 4.0 - location: /media/data/data_share/pdbbind/tfrecords/test_reg_points_den5.tfrecords #val_class_points.tfrecords - shuffle: 0 - batch_size: 13 - x_alias: data - -MODEL: - name: cnn #ocnn, cnn, resnet - channel: 24 - nout: 1 - depth: 6 - resblock_num: 3 - -LOSS: - num_class: 2 - weight_decay: 0.0005 \ No newline at end of file diff --git a/tensorflow/script/configs/cls_points.yaml b/tensorflow/script/configs/cls_points.yaml deleted file mode 100644 index 582b341..0000000 --- a/tensorflow/script/configs/cls_points.yaml +++ /dev/null @@ -1,49 +0,0 @@ -# Resent with small rotations with testing accuarcy 92.5% - -SOLVER: - gpu: 0, - logdir: logs/m40/0608_resnet_points - run: train - max_iter: 160000 - test_iter: 77 - test_every_iter: 2000 - step_size: (40000,) - ckpt_num: 20 - -DATA: - train: - dtype: points - distort: True - depth: 8 - axis: z - angle: (5, 5, 5) - interval: (1, 1, 1) # small rotation - scale: 0.25 - jitter: 0.125 - location: dataset/ModelNet40/m40_train_points.tfrecords - batch_size: 32 - x_alias: data - test: - dtype: points - distort: False - depth: 8 - axis: z - angle: (5, 5, 5) - interval: (1, 1, 1) - scale: 0.25 - jitter: 0.125 - location: dataset/ModelNet40/m40_test_points.tfrecords - shuffle: 0 - batch_size: 32 - x_alias: data - -MODEL: - name: resnet - channel: 3 - nout: 40 - depth: 8 - resblock_num: 3 - -LOSS: - num_class: 40 - weight_decay: 0.0005 \ No newline at end of file diff --git a/tensorflow/script/configs/completion_test.yaml b/tensorflow/script/configs/completion_test.yaml deleted file mode 100644 index 881ac83..0000000 --- a/tensorflow/script/configs/completion_test.yaml +++ /dev/null @@ -1,29 +0,0 @@ -SOLVER: - gpu: 0, - logdir: logs/completion/skip_connections_test - ckpt: logs/completion/skip_connectinos/model/iter_320000.ckpt - run: decode_shape - test_iter: 1200 - - -DATA: - test: - dtype: points - depth: 6 - distort: False - location: dataset/ocnn_completion/completion_test_scans_points.tfrecords - batch_size: 1 - depth: 6 - offset: 0.0 - node_dis: True - split_label: True - x_alias: data - y_alias: index - shuffle: 0 - - -MODEL: - name: resnet - channel: 4 - depth: 6 - skip_connections: True diff --git a/tensorflow/script/configs/completion_train.yaml b/tensorflow/script/configs/completion_train.yaml deleted file mode 100644 index 330e956..0000000 --- a/tensorflow/script/configs/completion_train.yaml +++ /dev/null @@ -1,44 +0,0 @@ -SOLVER: - gpu: 0, - logdir: logs/completion/skip_connectinos - run: train - max_iter: 320000 - test_iter: 100 - test_every_iter: 2000 - step_size: (80000,) - ckpt_num: 20 - - -DATA: - train: - dtype: points - depth: 6 - distort: False - location: dataset/ocnn_completion/completion_train_points.tfrecords - camera: dataset/ocnn_completion/completion_train_points.camera_path.dict - batch_size: 16 - depth: 6 - offset: 0.0 - node_dis: True - split_label: True - - test: - dtype: points - depth: 6 - distort: False - location: dataset/ocnn_completion/completion_train_points.tfrecords - batch_size: 16 - depth: 6 - offset: 0.0 - node_dis: True - split_label: True - - -MODEL: - name: resnet - channel: 4 - depth: 6 - - -LOSS: - weight_decay: 0.0005 diff --git a/tensorflow/script/configs/reg_pdbbind_general_points.yaml b/tensorflow/script/configs/reg_pdbbind_general_points.yaml deleted file mode 100644 index 161dfd3..0000000 --- a/tensorflow/script/configs/reg_pdbbind_general_points.yaml +++ /dev/null @@ -1,49 +0,0 @@ -SOLVER: - gpu: 0, - logdir: logs/pdbbind/resnet_points_20200811_general_reg_6 - run: train - max_iter: 190437 # 190437 # 100 epochs for batch_size=8 - test_iter: 120 - test_every_iter: 4000 - step_size: (40000,) - learning_rate: 0.001 - task: reg - -DATA: - train: - dtype: points - distort: True - depth: 6 - axis: xyz - angle: (180, 180, 180) - interval: (1, 1, 1) - scale: 0.0 - jitter: 0.125 - location: /media/data/data_share/pdbbind/tfrecords/train_reg_points_den3.tfrecords - batch_size: 8 - x_alias: data - shuffle: 100 - test: - dtype: points - distort: False - depth: 6 - axis: xyz - angle: (12, 12, 12) - interval: (1, 1, 1) - scale: 0.0 - jitter: 0.0 - location: /media/data/data_share/pdbbind/tfrecords/val_reg_points_den3.tfrecords - shuffle: 1 - batch_size: 5 - x_alias: data - -MODEL: - name: ocnn #ocnn #resnet - channel: 24 - nout: 1 - depth: 6 - resblock_num: 3 - -LOSS: - num_class: 1 - weight_decay: 0.001 \ No newline at end of file diff --git a/tensorflow/script/configs/reg_pdbbind_general_test_points.yaml b/tensorflow/script/configs/reg_pdbbind_general_test_points.yaml deleted file mode 100644 index f779692..0000000 --- a/tensorflow/script/configs/reg_pdbbind_general_test_points.yaml +++ /dev/null @@ -1,32 +0,0 @@ -SOLVER: - gpu: 0, - run: test - ckpt: logs/pdbbind/ocnn_points_20200811_general_reg_6/model/iter_188000.ckpt - test_iter: 2280 # 689 = 13*53, 221 = 13 * 17 - task: reg - -DATA: - test: - dtype: points - distort: True - depth: 6 - axis: xyz - angle: (180, 180, 180) - interval: (10, 10, 10) - scale: 0.0 - jitter: 0.125 - location: /media/data/data_share/pdbbind/tfrecords/test_refine_reg_points_den3.tfrecords - shuffle: 1 - batch_size: 5 - x_alias: data - -MODEL: - name: ocnn #resnet - channel: 24 - nout: 1 - depth: 6 - resblock_num: 3 - -LOSS: - num_class: 1 - weight_decay: 0.001 \ No newline at end of file diff --git a/tensorflow/script/configs/reg_pdbbind_grids.yaml b/tensorflow/script/configs/reg_pdbbind_grids.yaml deleted file mode 100644 index e8f268d..0000000 --- a/tensorflow/script/configs/reg_pdbbind_grids.yaml +++ /dev/null @@ -1,50 +0,0 @@ -SOLVER: - gpu: 0, - logdir: logs/pdbbind/resnet_grids_20200728_reg_5 - run: train -# ckpt: logs/pdbbind/ocnn_points_20200704_class_7/model/iter_120000.ckpt - max_iter: 10000 - test_iter: 20 # 689 = 13*53, 221 = 13 * 17 - test_every_iter: 20000 - step_size: (40000,) - learning_rate: 0.001 - task: reg - -DATA: - train: - dtype: grids - distort: True - depth: 6 - axis: xyz - angle: (6, 6, 6) - interval: (1, 1, 1) - scale: 0.0 - jitter: 0.0 - location: /media/data/data_share/pdbbind/tfrecords/refine_reg_grids_dep5.tfrecords - batch_size: 8 # 285 = 15 * 19 - x_alias: data - shuffle: 50 - test: - dtype: grids - distort: False - depth: 6 - axis: z - angle: (6, 6, 6) - interval: (1, 1, 1) - scale: 0.0 - jitter: 0.0 - location: /media/data/data_share/pdbbind/tfrecords/test_reg_grids_dep5.tfrecords - shuffle: 1 - batch_size: 16 - x_alias: data - -MODEL: - name: resnet_grids #resnet_grids - channel: 24 - nout: 1 - depth: 6 - resblock_num: 3 - -LOSS: - num_class: 1 - weight_decay: 0.0005 diff --git a/tensorflow/script/configs/reg_pdbbind_octree.yaml b/tensorflow/script/configs/reg_pdbbind_octree.yaml deleted file mode 100644 index 8726112..0000000 --- a/tensorflow/script/configs/reg_pdbbind_octree.yaml +++ /dev/null @@ -1,50 +0,0 @@ -SOLVER: - gpu: 0, - logdir: logs/pdbbind/ocnn_octree_20200716_reg_5 - run: train -# ckpt: logs/pdbbind/ocnn_points_20200704_class_7/model/iter_120000.ckpt - max_iter: 10000 - test_iter: 20 # 689 = 13*53, 221 = 13 * 17 - test_every_iter: 20000 - step_size: (40000,) - learning_rate: 0.001 - task: reg - -DATA: - train: - dtype: octree - distort: True - depth: 5 - axis: xyz - angle: (6, 6, 6) - interval: (1, 1, 1) - scale: 0.0 - jitter: 0.0 - location: /media/data/data_share/pdbbind/tfrecords/refine_reg_octree_dep5.tfrecords - batch_size: 16 # 285 = 15 * 19 - x_alias: data - shuffle: 200 - test: - dtype: octree - distort: False - depth: 5 - axis: z - angle: (6, 6, 6) - interval: (1, 1, 1) - scale: 0.0 - jitter: 0.0 - location: /media/data/data_share/pdbbind/tfrecords/test_reg_octree_dep5.tfrecords - shuffle: 1 - batch_size: 16 - x_alias: data - -MODEL: - name: ocnn #ocnn_input_logit - channel: 24 - nout: 1 - depth: 5 - resblock_num: 3 - -LOSS: - num_class: 1 - weight_decay: 0.0005 diff --git a/tensorflow/script/configs/reg_pdbbind_point2grid.yaml b/tensorflow/script/configs/reg_pdbbind_point2grid.yaml deleted file mode 100644 index dd37d97..0000000 --- a/tensorflow/script/configs/reg_pdbbind_point2grid.yaml +++ /dev/null @@ -1,49 +0,0 @@ -SOLVER: - gpu: 0, - logdir: logs/pdbbind/resnet_point2grid_20200806_reg_6 - run: train - max_iter: 160000 - test_iter: 20 # 689 = 13*53, 221 = 13 * 17 - test_every_iter: 10 - step_size: (40000,) - learning_rate: 0.00001 - task: reg - -DATA: - train: - dtype: point2grid - distort: True - depth: 6 - axis: xyz - angle: (12, 12, 12) - interval: (1, 1, 1) - scale: 0.0 - jitter: 0.125 #0.125 - location: /media/data/data_share/pdbbind/tfrecords/train_reg_points_den3.tfrecords - batch_size: 32 - x_alias: data - shuffle: 100 - test: - dtype: point2grid - distort: False - depth: 6 - axis: xyz - angle: (6, 6, 6) - interval: (1, 1, 1) - scale: 0.0 - jitter: 0.0 - location: /media/data/data_share/pdbbind/tfrecords/test_refine_reg_points_den3.tfrecords #val_reg_points_den3.tfrecords - shuffle: 1 - batch_size: 8 - x_alias: data - -MODEL: - name: resnet_grids # cnn_grids OR resnet_grids - channel: 24 - nout: 1 - depth: 6 - resblock_num: 3 - -LOSS: - num_class: 1 - weight_decay: 0.001 diff --git a/tensorflow/script/configs/reg_pdbbind_points.yaml b/tensorflow/script/configs/reg_pdbbind_points.yaml deleted file mode 100644 index e4827c9..0000000 --- a/tensorflow/script/configs/reg_pdbbind_points.yaml +++ /dev/null @@ -1,50 +0,0 @@ -SOLVER: - gpu: 0, - logdir: logs/pdbbind/resnet_points_20200831_reg_6 -# ckpt: logs/pdbbind/resnet_points_20200728_reg_6/model/iter_000200.ckpt - run: train - max_iter: 160000 - test_iter: 38 # 689 = 13*53, 221 = 13 * 17 - test_every_iter: 200 - step_size: (40000,) - learning_rate: 0.001 - task: reg - -DATA: - train: - dtype: points - distort: True - depth: 6 - axis: xyz - angle: (12, 12, 12) - interval: (1, 1, 1) - scale: 0.0 - jitter: 0.0 #0.125 - location: /media/data/data_share/pdbbind/tfrecords/train_reg_points_den3.tfrecords - batch_size: 32 - x_alias: data - shuffle: 100 - test: - dtype: points - distort: True - depth: 6 - axis: xyz - angle: (12, 12, 12) - interval: (1, 1, 1) - scale: 0.0 - jitter: 0.0 - location: /media/data/data_share/pdbbind/tfrecords/val_refine_reg_points_den3.tfrecords - shuffle: 0 - batch_size: 15 - x_alias: data - -MODEL: - name: resnet # resnet - channel: 24 - nout: 1 - depth: 6 - resblock_num: 3 - -LOSS: - num_class: 1 - weight_decay: 0.001 diff --git a/tensorflow/script/configs/reg_pdbbind_refine_test_points.yaml b/tensorflow/script/configs/reg_pdbbind_refine_test_points.yaml deleted file mode 100644 index e0eebbe..0000000 --- a/tensorflow/script/configs/reg_pdbbind_refine_test_points.yaml +++ /dev/null @@ -1,50 +0,0 @@ -SOLVER: - gpu: 1, - logdir: logs/pdbbind/resnet_points_20200728_refine_reg_6 - run: test -# ckpt: logs/pdbbind/ocnn_points_20200704_class_7/model/iter_120000.ckpt - max_iter: 10000 - test_iter: 20 #689 = 13*53, 221 = 13 * 17 - test_every_iter: 20000 - step_size: (40000,) - learning_rate: 0.001 - task: reg - -DATA: - train: - dtype: points - distort: True - depth: 6 - axis: xyz - angle: (12, 12, 12) - interval: (1, 1, 1) - scale: 0.0 - jitter: 0.0 #0.125 - location: /media/data/data_share/pdbbind/tfrecords/train_reg_points_den3.tfrecords - batch_size: 16 - x_alias: data - shuffle: 100 - test: - dtype: points - distort: False - depth: 6 - axis: z - angle: (6, 6, 6) - interval: (1, 1, 1) - scale: 0.0 - jitter: 0.0 - location: /scratch/qil15006/pdbbind/tfrecords/test_refine_reg_points_den3.tfrecords - shuffle: 1 - batch_size: 16 - x_alias: data - -MODEL: - name: resnet #ocnn_input_logit - channel: 24 - nout: 1 - depth: 6 - resblock_num: 3 - -LOSS: - num_class: 1 - weight_decay: 0.001 diff --git a/tensorflow/script/dataset.py b/tensorflow/script/dataset.py index 61560e0..9be89d8 100644 --- a/tensorflow/script/dataset.py +++ b/tensorflow/script/dataset.py @@ -1,17 +1,11 @@ import sys import tensorflow as tf sys.path.append("..") -from libs import bounding_sphere, points2octree, transform_points, octree_batch, make_grids +from libs import bounding_sphere, points2octree, \ + transform_points, octree_batch, normalize_points, make_grids import numpy as np -import sys -# sys.path.append('../') -# sys.path.insert(0, '/home/qil15006/2020Summer/OCNN_Jun26_2020/octree/build/python') -# import pyoctree -# from util import make_voxel - - class ParseExample: def __init__(self, x_alias='data', y_alias='label', y_type = 'int', **kwargs): self.x_alias = x_alias @@ -50,6 +44,14 @@ def __call__(self, points): save_pts=self.save_pts) return octree +class NormalizePoints: + def __init__(self): + pass # normalize with bounding_sphere + + def __call__(self, points): + radius, center = bounding_sphere(points) + points = normalize_points(points, radius, center) + return points class MakeGrids: def __init__(self, out_size, feature_num, **kwargs): @@ -63,8 +65,7 @@ def __call__(self, points): class TransformPoints: def __init__(self, distort, depth, offset=0.55, axis='xyz', scale=0.25, jitter=8, drop_dim=[8, 32], angle=[20, 180, 20], dropout=[0, 0], - stddev=[0, 0, 0], uniform=False, interval=[1, 1, 1], - bounding_sphere=bounding_sphere, **kwargs): + stddev=[0, 0, 0], uniform=False, interval=[1, 1, 1], **kwargs): self.distort = distort self.axis = axis self.scale = scale @@ -77,7 +78,6 @@ def __init__(self, distort, depth, offset=0.55, axis='xyz', scale=0.25, self.stddev = stddev self.uniform_scale = uniform self.interval = interval - self.bounding_sphere = bounding_sphere def __call__(self, points): angle, scale, jitter, ratio, dim, angle, stddev = 0.0, 1.0, 0.0, 0.0, 0, 0, 0 @@ -109,9 +109,7 @@ def __call__(self, points): stddev = [tf.random.uniform(shape=[], minval=0, maxval=s) for s in self.stddev] stddev = tf.stack(stddev) - # radius, center = self.bounding_sphere(points) #// QQ revise: we have fixed radius and center. - radius = 32 - center = [0,0,0] + radius, center = tf.constant(32.0), tf.constant([0.0, 0.0, 0.0]) points = transform_points(points, angle=angle, scale=scale, jitter=jitter, radius=radius, center=center, axis=self.axis, depth=self.depth, offset=self.offset, @@ -121,8 +119,9 @@ def __call__(self, points): class PointDataset: - def __init__(self, parse_example, transform_points, points2octree): + def __init__(self, parse_example, normalize_points, transform_points, points2octree): self.parse_example = parse_example + self.normalize_points = normalize_points self.transform_points = transform_points self.points2octree = points2octree @@ -131,6 +130,7 @@ def __call__(self, record_names, batch_size, shuffle_size=1000, with tf.name_scope('points_dataset'): def preprocess(record): points, label = self.parse_example(record) + # points = self.normalize_points(points) points = self.transform_points(points) octree = self.points2octree(points) outputs= (octree, label) @@ -142,64 +142,19 @@ def merge_octrees(octrees, *args): return (octree,) + args dataset = tf.data.TFRecordDataset(record_names).take(take).repeat() - if shuffle_size > 1: - dataset = dataset.shuffle(shuffle_size) - print('shuffle_size: {}'.format(shuffle_size)) - itr = dataset.map(preprocess, num_parallel_calls=12) \ - .batch(batch_size).map(merge_octrees, num_parallel_calls=12) \ - .prefetch(12).make_one_shot_iterator() + if shuffle_size > 1: dataset = dataset.shuffle(shuffle_size) + itr = dataset.map(preprocess, num_parallel_calls=16) \ + .batch(batch_size).map(merge_octrees, num_parallel_calls=8) \ + .prefetch(8).make_one_shot_iterator() return itr if return_iter else itr.get_next() + class Point2GridDataset: def __init__(self, parse_example, transform_points, make_grids): self.parse_example = parse_example self.transform_points = transform_points self.make_grids = make_grids - # def parse_points(self, pts): - # # parse .points file - # pts_num = pts.pts_num() - # coords = pts.points() - # normals = pts.normals() - # features = pts.features() - # - # # transform into numpy arrays - # coords = np.array(coords).reshape((pts_num, -1)) - # normals = np.array(normals).reshape((pts_num, -1)) - # features = np.array(features).reshape((pts_num, -1)) - # features = np.concatenate((normals, features), axis = 1) - # - # return coords, features - # - # def parse_coords(self, pts): - # pts_num = pts.pts_num() - # coords = pts.points() - # # normals = pts.normals() - # # features = pts.features() - # - # # transform into numpy arrays - # coords = np.array(coords).reshape((pts_num, -1)) - # # normals = np.array(normals).reshape((pts_num, -1)) - # # features = np.array(features).reshape((pts_num, -1)) - # # features = np.concatenate((normals, features), axis=1) - # - # return coords - # - # def parse_features(self, pts): - # pts_num = pts.pts_num() - # # coords = pts.points() - # normals = pts.normals() - # features = pts.features() - # - # # transform into numpy arrays - # # coords = np.array(coords).reshape((pts_num, -1)) - # normals = np.array(normals).reshape((pts_num, -1)) - # features = np.array(features).reshape((pts_num, -1)) - # features = np.concatenate((normals, features), axis=1) - # - # return features - - def __call__(self, record_names, batch_size, shuffle_size=1000, return_iter=False, take=-1, return_pts=False, **kwargs): with tf.name_scope('points2grids_dataset'): @@ -232,7 +187,6 @@ def wrap_preprocess(record): #https://www.tensorflow.org/api_docs/python/tf/data/Dataset#prefetch return itr if return_iter else itr.get_next() - class OctreeDataset: def __init__(self, parse_example): self.parse_example = parse_example @@ -277,14 +231,12 @@ def preprocess(record): return itr if return_iter else itr.get_next() class DatasetFactory: - def __init__(self, flags, y_type = 'int', bounding_sphere=bounding_sphere, - point_dataset=PointDataset): + def __init__(self, flags, y_type = 'int', normalize_points=NormalizePoints, + point_dataset=PointDataset, transform_points=TransformPoints): self.flags = flags if flags.dtype == 'points': - print(flags) - self.dataset = point_dataset(ParseExample(y_type = y_type, **flags), - TransformPoints(**flags, bounding_sphere=bounding_sphere), - Points2Octree(**flags)) + self.dataset = point_dataset(ParseExample(y_type = y_type, **flags), normalize_points(), + transform_points(**flags), Points2Octree(**flags)) elif flags.dtype == 'octree': self.dataset = OctreeDataset(ParseExample(y_type = y_type, **flags)) elif flags.dtype == 'grids': @@ -298,13 +250,8 @@ def __init__(self, flags, y_type = 'int', bounding_sphere=bounding_sphere, else: print('Error: unsupported datatype ' + flags.dtype) - def __call__(self): + def __call__(self, return_iter=False): return self.dataset( record_names=self.flags.location, batch_size=self.flags.batch_size, - shuffle_size=self.flags.shuffle, return_iter=self.flags.return_iter, - take=self.flags.take, return_pts=self.flags.return_pts, - depth = self.flags.depth) - - -# Put tfrecords, o.w. the disk io will be bottleneck. -# Big CPU memory, can support large tfrecords. \ No newline at end of file + shuffle_size=self.flags.shuffle, return_iter=return_iter, + take=self.flags.take, return_pts=self.flags.return_pts, depth = self.flags.depth) diff --git a/tensorflow/script/dataset/README.md b/tensorflow/script/dataset/README.md deleted file mode 100644 index 428c8b6..0000000 --- a/tensorflow/script/dataset/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# Dataset - -This folder contains the data used in the experiments. diff --git a/tensorflow/script/detail.txt b/tensorflow/script/detail.txt deleted file mode 100644 index f6752df..0000000 --- a/tensorflow/script/detail.txt +++ /dev/null @@ -1,39 +0,0 @@ -1. Tensorboard is available -https://stackoverflow.com/questions/37987839/how-can-i-run-tensorboard-on-a-remote-server - -depth=8 sometimes stop -2020-08-03 11:16:59.926547: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7 - 10%|███▋ | 4151/40720 [48:24<7:15:29, 1.40it/s]2020-08-03 12:05:10.841355: F tensorflow/core/framework/tensor_shape.cc:324] Check failed: size >= 0 (-269897832 vs. 0) -Aborted (core dumped) -Question: shape溢出？check - -2020-08-05 08:59:35.437257: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7 - 7%|██▌ | 1325/19044 [15:41<3:20:12, 1.47it/s]2020-08-05 09:15:02.874012: F tensorflow/core/framework/tensor_shape.cc:324] Check failed: size >= 0 (-268584336 vs. 0) -Aborted (core dumped) - - - 19%|██████▋ | 3550/19044 [32:33<1:38:42, 2.62it/s]2020-08-13 18:16:04.658965: F tensorflow/core/framework/tensor_shape.cc:324] Check failed: size >= 0 (-275657904 vs. 0) -Aborted (core dumped) -Check failed: size >= 0 (-1872442624 vs. 0) - -depth=8: -den5 vs den6 in 1a1e: octree node: 206744 vs 215912. -Question: will density affect the performance? -try den3 and den4 on depth=6 octree, compare. - - -astex: protein.mol2 ligand.mol -This Mol2 file is non-standard. Problem with molecule: A_1G9V_PROTEIN Cannot interpret atom types correctly, instead attempting to interpret atom type: C as elements instead. - -for D in /media/data/data_share/qil15006/astex_diverse_set/*/; -do - echo "${D}"; - cd "${D}"; - obabel ligand.mol -O ligand.mol2; - obabel protein.mol2 -O pocket.pdb; - java -cp /home/qil15006/2020Summer/OCNN_Jun26_2020/pdbbind/java/cdk-2.3-SNAPSHOT.jar:/home/qil15006/2020Summer/OCNN_Jun26_2020/pdbbind/java Surface_for_single 5 ico pdbbind; - /home/qil15006/.conda/envs/tf-OCNN/bin/python /home/qil15006/2020Summer/OCNN_Jun26_2020/pdbbind/write_complex.py - /home/qil15006/.conda/envs/tf-OCNN/bin/python /home/qil15006/2020Summer/OCNN_Jun26_2020/pdbbind/atomic_feature_astex.py - /home/qil15006/2020Summer/OCNN_Jun26_2020/octree/build/pdb_to_points --complex_id file_list.txt - cd ..; -done \ No newline at end of file diff --git a/tensorflow/script/logs/.DS_Store b/tensorflow/script/logs/.DS_Store deleted file mode 100644 index 48a8af9..0000000 Binary files a/tensorflow/script/logs/.DS_Store and /dev/null differ diff --git a/tensorflow/script/logs/pdbbind/.DS_Store b/tensorflow/script/logs/pdbbind/.DS_Store deleted file mode 100644 index 5051dbe..0000000 Binary files a/tensorflow/script/logs/pdbbind/.DS_Store and /dev/null differ diff --git a/tensorflow/script/logs/pdbbind/resnet_point2grid_20200806_reg_6/timeline.json b/tensorflow/script/logs/pdbbind/resnet_point2grid_20200806_reg_6/timeline.json deleted file mode 100644 index 022b34c..0000000 --- a/tensorflow/script/logs/pdbbind/resnet_point2grid_20200806_reg_6/timeline.json +++ /dev/null @@ -1,47367 +0,0 @@ -{ - 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"name": "solver/global_step", - "pid": 5, - "tid": 1, - "ts": 1596804868487229, - "id": 109 - }, - { - "ph": "t", - "cat": "DataFlow", - "name": "solver/global_step", - "pid": 5, - "tid": 0, - "ts": 1596804868598883, - "id": 109 - } - ] -} \ No newline at end of file diff --git a/tensorflow/script/network_ae.py b/tensorflow/script/network_ae.py deleted file mode 100644 index 188767a..0000000 --- a/tensorflow/script/network_ae.py +++ /dev/null @@ -1,234 +0,0 @@ -import tensorflow as tf -from ocnn import * - - -class AutoEncoderOcnn: - def __init__(self, flags): - self.flags = flags - - def octree_encoder(self, octree, training, reuse=False): - flags = self.flags - depth, nout = flags.depth, flags.nout - channel = [4, nout, 128, 64, 32, 16, 8] - with tf.variable_scope('ocnn_encoder', reuse=reuse): - with tf.variable_scope('signal_gt'): - data = octree_property(octree, property_name="feature", dtype=tf.float32, - depth=depth, channel=flags.channel) - data = tf.reshape(data, [1, flags.channel, -1, 1]) - - for d in range(depth, 1, -1): - with tf.variable_scope('depth_%d' % d): - data = octree_conv_bn_relu(data, octree, d, channel[d], training) - data, _ = octree_max_pool(data, octree, d) - - with tf.variable_scope('depth_1'): - data = downsample(data, channel[1], training) - - with tf.variable_scope('code'): - code = conv2d_bn(data, channel[1], kernel_size=1, stride=1, training=training) - code = tf.nn.tanh(code) - return code - - def octree_decoder(self, code, octree, training, reuse=False): - flags = self.flags - depth = flags.depth - channel = [512, 256, 128, 64, 32, 16, 8] - with tf.variable_scope('ocnn_decoder', reuse=reuse): - label_gt = [None]*10 - with tf.variable_scope('label_gt'): - for d in range(2, depth + 1): - label = octree_property(octree, property_name="split", dtype=tf.float32, - depth=d, channel=1) - label_gt[d] = tf.reshape(tf.cast(label, dtype=tf.int32), [-1]) - - with tf.variable_scope('signal_gt'): - signal_gt = octree_property(octree, property_name="feature", dtype=tf.float32, - depth=depth, channel=flags.channel) - signal_gt = tf.reshape(signal_gt, [1, flags.channel, -1, 1]) - - data = code - with tf.variable_scope('depth_1'): - data = upsample(data, channel[1], training) - - loss = []; accu = []; - for d in range(2, depth + 1): - with tf.variable_scope('depth_%d' % d): - data = octree_upsample(data, octree, d-1, channel[d], training) - data = octree_conv_bn_relu(data, octree, d, channel[d], training) - - with tf.variable_scope('predict_%d' % d): - logit, label = predict_label(data, 2, 32, training) - - with tf.variable_scope('loss_%d' % d): - logit = tf.transpose(tf.squeeze(logit, [0,3])) # (1, C, H, 1) -> (H, C) - loss.append(softmax_loss(logit, label_gt[d], num_class=2)) - accu.append(label_accuracy(label, label_gt[d])) - - if d == depth: - with tf.variable_scope('regress_%d' % d): - signal = predict_signal(data, flags.channel, 32, training) - loss.append(regress_loss(signal, signal_gt)) - - return loss, accu - - def octree_decode_shape(self, code, training, reuse=False): - flags = self.flags - depth = flags.depth - channel = [512, 256, 128, 64, 32, 16, 8] - with tf.variable_scope('ocnn_decoder', reuse=reuse): - with tf.variable_scope('octree_0'): - displace = False if flags.channel < 4 else True - octree = octree_new(batch_size=1, channel=flags.channel, has_displace=displace) - with tf.variable_scope('octree_1'): - octree = octree_grow(octree, target_depth=1, full_octree=True) - with tf.variable_scope('octree_2'): - octree = octree_grow(octree, target_depth=2, full_octree=True) - - data = code - with tf.variable_scope('depth_1'): - data = upsample(data, channel[1], training) - - for d in range(2, depth + 1): - with tf.variable_scope('depth_%d' % d): - data = octree_upsample(data, octree, d-1, channel[d], training) - data = octree_conv_bn_relu(data, octree, d, channel[d], training) - - with tf.variable_scope('predict_%d' % d): - _, label = predict_label(data, 2, 32, training) - - with tf.variable_scope('octree_%d' % d, reuse=True): - octree = octree_update(octree, label, depth=d, mask=1) - # octree = octree_update(octree, label_gt[d], depth=d, mask=1) - if d < depth: - with tf.variable_scope('octree_%d' % (d+1)): - octree = octree_grow(octree, target_depth=d+1, full_octree=False) - else: - with tf.variable_scope('regress_%d' % d): - signal = predict_signal(data, flags.channel, 32, training) - signal = normalize_signal(signal) - signal = octree_mask(signal, label, mask=0) - with tf.variable_scope('octree_%d' % d, reuse=True): - octree = octree_set_property(octree, signal, property_name="feature", depth=depth) - return octree - -class AutoEncoderResnet: - def __init__(self, flags): - self.flags = flags - - def octree_encoder(self, octree, training, reuse=False): - flags = self.flags - depth, nout = flags.depth, flags.nout - channels = [4, nout, 256, 256, 128, 64, 32, 16] - with tf.variable_scope('ocnn_encoder', reuse=reuse): - with tf.variable_scope('signal_gt'): - data = octree_property(octree, property_name="feature", dtype=tf.float32, - depth=depth, channel=flags.channel) - data = tf.reshape(data, [1, flags.channel, -1, 1]) - - with tf.variable_scope("front"): - data = octree_conv_bn_relu(data, octree, depth, channels[depth], training) - - for d in range(depth, 2, -1): - for i in range(0, flags.resblock_num): - with tf.variable_scope('resblock_%d_%d' % (d, i)): - data = octree_resblock(data, octree, d, channels[d], 1, training) - with tf.variable_scope('down_%d' % d): - data = octree_conv_bn_relu(data, octree, d, channels[d-1], training, - stride=2, kernel_size=[2]) - - with tf.variable_scope('code'): - # code = conv2d_bn(data, channels[1], kernel_size=1, stride=1, training=training) - code = octree_conv1x1_bn(data, flags.nout, training=training) - code = tf.nn.tanh(code) - return code - - def octree_decoder(self, code, octree, training, reuse=False): - flags = self.flags - depth = flags.depth - channels = [4, 64, 256, 256, 128, 64, 32, 16] - with tf.variable_scope('ocnn_decoder', reuse=reuse): - data = code - loss, accu = [], [] - for d in range(2, depth + 1): - for i in range(0, flags.resblock_num): - with tf.variable_scope('resblock_%d_%d' % (d, i)): - data = octree_resblock(data, octree, d, channels[d], 1, training) - - with tf.variable_scope('predict_%d' % d): - logit, label = predict_label(data, 2, 32, training) - logit = tf.transpose(tf.squeeze(logit, [0,3])) # (1, C, H, 1) -> (H, C) - - with tf.variable_scope('loss_%d' % d): - with tf.variable_scope('label_gt'): - label_gt = octree_property(octree, property_name="split", - dtype=tf.float32, depth=d, channel=1) - label_gt = tf.reshape(tf.cast(label_gt, dtype=tf.int32), [-1]) - loss.append(softmax_loss(logit, label_gt, num_class=2)) - accu.append(label_accuracy(label, label_gt)) - - if d == depth: - with tf.variable_scope('regress_%d' % d): - signal = predict_signal(data, flags.channel, 32, training) - - with tf.variable_scope('loss_regress'): - with tf.variable_scope('signal_gt'): - signal_gt = octree_property(octree, property_name="feature", - dtype=tf.float32, depth=depth, channel=flags.channel) - signal_gt = tf.reshape(signal_gt, [1, flags.channel, -1, 1]) - loss.append(regress_loss(signal, signal_gt)) - - if d < depth: - with tf.variable_scope('up_%d' % d): - data = octree_deconv_bn_relu(data, octree, d, channels[d-1], training, - stride=2, kernel_size=[2]) - return loss, accu - - def octree_decode_shape(self, code, training, reuse=False): - flags = self.flags - depth = flags.depth - channels = [4, 64, 256, 256, 128, 64, 32, 16] - with tf.variable_scope('ocnn_decoder', reuse=reuse): - with tf.variable_scope('octree_0'): - displace = False if flags.channel < 4 else True - octree = octree_new(batch_size=1, channel=flags.channel, has_displace=displace) - with tf.variable_scope('octree_1'): - octree = octree_grow(octree, target_depth=1, full_octree=True) - with tf.variable_scope('octree_2'): - octree = octree_grow(octree, target_depth=2, full_octree=True) - - data = code - for d in range(2, depth + 1): - for i in range(0, flags.resblock_num): - with tf.variable_scope('resblock_%d_%d' % (d, i)): - data = octree_resblock(data, octree, d, channels[d], 1, training) - - with tf.variable_scope('predict_%d' % d): - _, label = predict_label(data, 2, 32, training) - - with tf.variable_scope('octree_%d' % d, reuse=True): - octree = octree_update(octree, label, depth=d, mask=1) - if d < depth: - with tf.variable_scope('octree_%d' % (d+1)): - octree = octree_grow(octree, target_depth=d+1, full_octree=False) - else: - with tf.variable_scope('regress_%d' % d): - signal = predict_signal(data, flags.channel, 32, training) - signal = normalize_signal(signal) - signal = octree_mask(signal, label, mask=0) - with tf.variable_scope('octree_%d' % d, reuse=True): - octree = octree_set_property(octree, signal, property_name="feature", depth=depth) - - if d < depth: - with tf.variable_scope('up_%d' % d): - data = octree_deconv_bn_relu(data, octree, d, channels[d-1], training, - stride=2, kernel_size=[2]) - return octree - - -def make_autoencoder(flags): - if flags.name == 'ocnn': - return AutoEncoderOcnn(flags) - elif flags.name == 'resnet': - return AutoEncoderResnet(flags) - else: - pass diff --git a/tensorflow/script/network_cls.py b/tensorflow/script/network_cls.py index 6998788..8bf7279 100644 --- a/tensorflow/script/network_cls.py +++ b/tensorflow/script/network_cls.py @@ -5,8 +5,7 @@ # octree-based resnet55 def network_resnet(octree, flags, training=True, reuse=None): depth = flags.depth - full_depth = 1 - channels = [2048, 1024, 512, 256, 128, 64, 32, 16, 8] #[2048, 1024, 512, 256, 256, 128, 64, 32, 16] + channels = [2048, 1024, 512, 256, 128, 64, 32, 16, 8] with tf.variable_scope("ocnn_resnet", reuse=reuse): data = octree_property(octree, property_name="feature", dtype=tf.float32, depth=depth, channel=flags.channel) @@ -15,7 +14,7 @@ def network_resnet(octree, flags, training=True, reuse=None): with tf.variable_scope("conv1"): data = octree_conv_bn_relu(data, octree, depth, channels[depth], training) - for d in range(depth, full_depth, -1): + for d in range(depth, 2, -1): for i in range(0, flags.resblock_num): with tf.variable_scope('resblock_%d_%d' % (d, i)): data = octree_resblock(data, octree, d, channels[d], 1, training) @@ -23,10 +22,9 @@ def network_resnet(octree, flags, training=True, reuse=None): data, _ = octree_max_pool(data, octree, d) with tf.variable_scope("global_average"): - data = octree_full_voxel(data, depth=full_depth) + data = octree_full_voxel(data, depth=2) data = tf.reduce_mean(data, 2) - - + if flags.dropout[0]: data = tf.layers.dropout(data, rate=0.5, training=training) @@ -112,40 +110,10 @@ def network_cnn_grids(grids, flags, training=True, reuse=None): return logit - -# # qq add for test -# def network_ocnn_with_input(octree, flags, training=True, reuse=None): -# depth = flags.depth -# channels = [2048, 1024, 512, 256, 128, 64, 32, 16, 8] -# with tf.variable_scope("ocnn", reuse=reuse): -# data = octree_property(octree, property_name="feature", dtype=tf.float32, -# depth=depth, channel=flags.channel) -# data = tf.reshape(data, [1, flags.channel, -1, 1]) -# feature = data -# -# for d in range(depth, 2, -1): -# with tf.variable_scope('depth_%d' % d): -# data = octree_conv_bn_relu(data, octree, d, channels[d], training) -# data, _ = octree_max_pool(data, octree, d) -# -# with tf.variable_scope("full_voxel"): -# data = octree_full_voxel(data, depth=2) -# data = tf.layers.dropout(data, rate=0.5, training=training) -# -# with tf.variable_scope("fc1"): -# data = fc_bn_relu(data, channels[2], training=training) -# data = tf.layers.dropout(data, rate=0.5, training=training) -# -# with tf.variable_scope("fc2"): -# logit = dense(data, flags.nout, use_bias=True) -# -# return logit, feature - - -# def cls_network(octree, flags, training, reuse=False): -# if flags.name.lower() == 'ocnn': -# return network_ocnn(octree, flags, training, reuse) -# elif flags.name.lower() == 'resnet': -# return network_resnet(octree, flags, training, reuse) -# else: -# print('Error, no network: ' + flags.name) +def cls_network(octree, flags, training, reuse=False): + if flags.name.lower() == 'ocnn': + return network_ocnn(octree, flags, training, reuse) + elif flags.name.lower() == 'resnet': + return network_resnet(octree, flags, training, reuse) + else: + print('Error, no network: ' + flags.name) diff --git a/tensorflow/script/network_completion.py b/tensorflow/script/network_completion.py deleted file mode 100644 index ae1a9a3..0000000 --- a/tensorflow/script/network_completion.py +++ /dev/null @@ -1,138 +0,0 @@ -import tensorflow as tf -from ocnn import * - - -def get_input_signal(octree, depth, channel): - signal = octree_property( - octree, property_name='feature', dtype=tf.float32, - depth=depth, channel=channel) - signal = tf.reshape(signal, [1, channel, -1, 1]) - return signal - - -def get_split_label(octree, depth): - label = octree_property( - octree, property_name='split', dtype=tf.float32, - depth=depth, channel=1) - label = tf.reshape(tf.cast(label, dtype=tf.int32), [-1]) - return label - - -class CompletionResnet: - def __init__(self, flags): - self.flags = flags - self.channels = [4, 512, 512, 256, 128, 64, 32, 16] - - def octree_encoder(self, octree, training, reuse=False): - flags, channels = self.flags, self.channels - depth, convd = flags.depth, [None] * 10 - - with tf.variable_scope('ocnn_encoder', reuse=reuse): - with tf.variable_scope('signal_gt'): - data = get_input_signal(octree, depth, flags.channel) - - with tf.variable_scope("front_%d" % depth): - convd[depth] = octree_conv_bn_relu( - data, octree, depth, channels[depth], training) - - for d in range(depth, 1, -1): - for i in range(0, flags.resblock_num): - with tf.variable_scope('resblock_%d_%d' % (d, i)): - convd[d] = octree_resblock( - convd[d], octree, d, channels[d], 1, training) - - if d > 2: - with tf.variable_scope('down_%d' % d): - convd[d-1] = octree_conv_bn_relu( - convd[d], octree, d, channels[d-1], training, - stride=2, kernel_size=[2]) - return convd - - def octree_decoder(self, convd, octree0, octree1, training, reuse=False): - flags, channels = self.flags, self.channels - depth, deconv = flags.depth, convd[2] - loss, accu = [], [] - - with tf.variable_scope('ocnn_decoder', reuse=reuse): - for d in range(2, depth + 1): - if d > 2: - with tf.variable_scope('up_%d' % d): - deconv = octree_deconv_bn_relu( - deconv, octree1, d-1, channels[d], training, - stride=2, kernel_size=[2]) - if flags.skip_connections: - skip, _ = octree_align(convd[d], octree0, octree1, d) - deconv = deconv + skip - - for i in range(0, flags.resblock_num): - with tf.variable_scope('resblock_%d_%d' % (d, i)): - deconv = octree_resblock( - deconv, octree1, d, channels[d], 1, training) - - with tf.variable_scope('predict_%d' % d): - logit, label = predict_label(deconv, 2, 32, training) - # (1, C, H, 1) -> (H, C) - logit = tf.transpose(tf.squeeze(logit, [0, 3])) - - with tf.variable_scope('loss_%d' % d): - with tf.variable_scope('label_gt'): - label_gt = get_split_label(octree1, d) - loss.append(softmax_loss(logit, label_gt, num_class=2)) - accu.append(label_accuracy(label, label_gt)) - - if d == depth: - with tf.variable_scope('regress_%d' % d): - signal = predict_signal(deconv, flags.channel, 32, training) - - with tf.variable_scope('loss_regress'): - with tf.variable_scope('signal_gt'): - signal_gt = get_input_signal(octree1, depth, flags.channel) - loss.append(regress_loss(signal, signal_gt)) - return loss, accu - - def decode_shape(self, convd, octree0, training, reuse=False): - flags, channels = self.flags, self.channels - depth, deconv = flags.depth, convd[2] - - with tf.variable_scope('ocnn_decoder', reuse=reuse): - # init the octree - with tf.variable_scope('octree_0'): - dis = False if flags.channel < 4 else True - octree = octree_new(1, channel=flags.channel, has_displace=dis) - with tf.variable_scope('octree_1'): - octree = octree_grow(octree, target_depth=1, full_octree=True) - with tf.variable_scope('octree_2'): - octree = octree_grow(octree, target_depth=2, full_octree=True) - - for d in range(2, depth + 1): - if d > 2: - with tf.variable_scope('up_%d' % d): - deconv = octree_deconv_bn_relu( - deconv, octree, d-1, channels[d], training, - stride=2, kernel_size=[2]) - if flags.skip_connections: - skip, _ = octree_align(convd[d], octree0, octree, d) - deconv = deconv + skip - - for i in range(0, flags.resblock_num): - with tf.variable_scope('resblock_%d_%d' % (d, i)): - deconv = octree_resblock( - deconv, octree, d, channels[d], 1, training) - - with tf.variable_scope('predict_%d' % d): - _, label = predict_label(deconv, 2, 32, training) - - with tf.variable_scope('octree_%d' % d): - octree = octree_update(octree, label, depth=d, mask=1) - if d < depth: - with tf.variable_scope('octree_%d' % (d+1)): - octree = octree_grow(octree, target_depth=d+1, full_octree=False) - else: - with tf.variable_scope('regress_%d' % d): - signal = predict_signal(deconv, flags.channel, 32, training) - signal = normalize_signal(signal) - signal = octree_mask(signal, label, mask=0) - with tf.variable_scope('octree_%d' % d): - octree = octree_set_property( - octree, signal, property_name="feature", depth=depth) - return octree diff --git a/tensorflow/script/network_factory.py b/tensorflow/script/network_factory.py index 9e39293..265c6f5 100644 --- a/tensorflow/script/network_factory.py +++ b/tensorflow/script/network_factory.py @@ -1,7 +1,8 @@ import tensorflow as tf from network_cls import network_ocnn, network_resnet, network_cnn_grids, network_resnet_grids -# from network_cls import network_ocnn_with_input # qq add for test - +from network_unet import network_unet +from network_hrnet import HRNet +# from network_unet_scannet import network_unet34 def cls_network(octree, flags, training, reuse=False): if flags.name.lower() == 'ocnn': @@ -12,12 +13,5 @@ def cls_network(octree, flags, training, reuse=False): return network_cnn_grids(octree, flags, training, reuse) elif flags.name.lower() == 'resnet_grids': return network_resnet_grids(octree, flags, training, reuse) - - # elif flags.name.lower() == 'ocnn_input_logit': - # return network_ocnn_with_input(octree, flags, training, reuse) else: print('Error, no network: ' + flags.name) - - - - \ No newline at end of file diff --git a/tensorflow/script/ocnn.py b/tensorflow/script/ocnn.py index b0ce49c..3c9effb 100644 --- a/tensorflow/script/ocnn.py +++ b/tensorflow/script/ocnn.py @@ -24,8 +24,8 @@ def get_variables_with_name(name=None, without=None, train_only=True, verbose=Fa def dense(inputs, nout, use_bias=False): inputs = tf.layers.flatten(inputs) - fc = tf.layers.dense(inputs, nout, use_bias=use_bias, - kernel_initializer=tf.contrib.layers.xavier_initializer()) + fc = tf.layers.dense(inputs, nout, use_bias=use_bias, + kernel_initializer=tf.contrib.layers.xavier_initializer()) return fc @@ -38,11 +38,10 @@ def fc_bn_relu(inputs, nout, training): bn = batch_norm(fc, training) return tf.nn.relu(bn) - def conv2d(inputs, nout, kernel_size, stride, padding='SAME', data_format='channels_first'): return tf.layers.conv2d(inputs, nout, kernel_size=kernel_size, strides=stride, - padding=padding, data_format=data_format, use_bias=False, - kernel_initializer=tf.contrib.layers.xavier_initializer()) + padding=padding, data_format=data_format, use_bias=False, + kernel_initializer=tf.contrib.layers.xavier_initializer()) def conv3d(inputs, nout, kernel_size, stride, padding='SAME', data_format='channels_first'): return tf.layers.conv3d(inputs, nout, kernel_size=kernel_size, strides=stride, @@ -52,36 +51,35 @@ def conv3d(inputs, nout, kernel_size, stride, padding='SAME', data_format='chann def octree_conv1x1(inputs, nout, use_bias=False): outputs = tf.layers.conv2d(inputs, nout, kernel_size=1, strides=1, - data_format='channels_first', use_bias=use_bias, - kernel_initializer=tf.contrib.layers.xavier_initializer()) + data_format='channels_first', use_bias=use_bias, + kernel_initializer=tf.contrib.layers.xavier_initializer()) return outputs def octree_conv1x1(inputs, nout, use_bias=False): with tf.variable_scope('conv2d_1x1'): inputs = tf.squeeze(inputs, axis=[0, 3]) # (1, C, H, 1) -> (C, H) - weights = tf.get_variable('weights', shape=[nout, int(inputs.shape[0])], - dtype=tf.float32, initializer=tf.contrib.layers.xavier_initializer()) + weights = tf.get_variable('weights', shape=[nout, int(inputs.shape[0])], + dtype=tf.float32, initializer=tf.contrib.layers.xavier_initializer()) outputs = tf.matmul(weights, inputs) # (C, H) -> (nout, H) if use_bias: - bias = tf.get_variable('bias', shape=[nout, 1], dtype=tf.float32, - initializer=tf.contrib.layers.xavier_initializer()) + bias = tf.get_variable('bias', shape=[nout, 1], dtype=tf.float32, + initializer=tf.contrib.layers.xavier_initializer()) outputs = bias + outputs outputs = tf.expand_dims(tf.expand_dims(outputs, axis=0), axis=-1) return outputs -def octree_conv1x1_bn(inputs, nout, training, bn=True): +def octree_conv1x1_bn(inputs, nout, training): conv = octree_conv1x1(inputs, nout, use_bias=False) - if bn: - return batch_norm(conv, training) - else: - return conv + return batch_norm(conv, training) + -def octree_conv1x1_bn_relu(inputs, nout, training, bn = True): - conv = octree_conv1x1_bn(inputs, nout, training, bn) +def octree_conv1x1_bn_relu(inputs, nout, training): + conv = octree_conv1x1_bn(inputs, nout, training) return tf.nn.relu(conv) + def conv2d_bn(inputs, nout, kernel_size, stride, training): conv = conv2d(inputs, nout, kernel_size, stride) return batch_norm(conv, training) @@ -105,22 +103,23 @@ def conv3d_bn_relu(inputs, nout, kernel_size, stride, training): def upsample(data, channel, training): deconv = tf.layers.conv2d_transpose(data, channel, kernel_size=[8, 1], - strides=[8, 1], data_format='channels_first', use_bias=False, - kernel_initializer=tf.contrib.layers.xavier_initializer()) + strides=[8, 1], data_format='channels_first', use_bias=False, + kernel_initializer=tf.contrib.layers.xavier_initializer()) bn = tf.layers.batch_normalization(deconv, axis=1, training=training) return tf.nn.relu(bn) + def downsample(data, channel, training): deconv = tf.layers.conv2d(data, channel, kernel_size=[8, 1], - strides=[8, 1], data_format='channels_first', use_bias=False, - kernel_initializer=tf.contrib.layers.xavier_initializer()) + strides=[8, 1], data_format='channels_first', use_bias=False, + kernel_initializer=tf.contrib.layers.xavier_initializer()) bn = tf.layers.batch_normalization(deconv, axis=1, training=training) return tf.nn.relu(bn) def avg_pool2d(inputs, data_format='NCHW'): return tf.nn.avg_pool2d(inputs, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], - padding='SAME', data_format=data_format) + padding='SAME', data_format=data_format) def max_pool3d(inputs, data_format='NCDHW'): return tf.nn.max_pool3d(inputs, ksize=[1,1,2,2,2], strides=[1,1,2,2,2], @@ -134,7 +133,7 @@ def avg_pool3d(inputs, data_format='NCDHW'): def global_pool(inputs, data_format='channels_first'): axis = [2, 3] if data_format == 'channels_first' else [1, 2] return tf.reduce_mean(inputs, axis=axis) - + # !!! Deprecated def octree_upsample(data, octree, depth, channel, training): @@ -143,8 +142,9 @@ def octree_upsample(data, octree, depth, channel, training): up = upsample(depad, channel, training) return up + def octree_upsample(data, octree, depth, channel, training): - up = octree_deconv_bn_relu(data, octree, depth, channel, training, + up = octree_deconv_bn_relu(data, octree, depth, channel, training, kernel_size=[2], stride=2, fast_mode=False) return up @@ -155,51 +155,51 @@ def octree_downsample(data, octree, depth, channel, training): pad = octree_pad(down, octree, depth) return pad - def octree_conv_bn(data, octree, depth, channel, training, kernel_size=[3], - stride=1, fast_mode=False, bn=True): + stride=1, fast_mode=False): if fast_mode == True: conv = octree_conv_fast(data, octree, depth, channel, kernel_size, stride) else: - conv = octree_conv_memory(data, octree, depth, channel, kernel_size, stride) - if bn: - return tf.layers.batch_normalization(conv, axis=1, training=training) - else: - return conv + conv = octree_conv_memory( + data, octree, depth, channel, kernel_size, stride) + return tf.layers.batch_normalization(conv, axis=1, training=training) + def octree_conv_bn_relu(data, octree, depth, channel, training, kernel_size=[3], - stride=1, fast_mode=False, bn = True): + stride=1, fast_mode=False): with tf.variable_scope('conv_bn_relu'): conv_bn = octree_conv_bn(data, octree, depth, channel, training, kernel_size, - stride, fast_mode, bn) + stride, fast_mode) rl = tf.nn.relu(conv_bn) return rl def octree_conv_bn_leakyrelu(data, octree, depth, channel, training): - cb = octree_conv_bn(data, octree, depth, channel, training) + cb = octree_conv_bn(data, octree, depth, channel, training) return tf.nn.leaky_relu(cb, alpha=0.2) def octree_deconv_bn(data, octree, depth, channel, training, kernel_size=[3], stride=1, fast_mode=False): if fast_mode == True: - conv = octree_deconv_fast(data, octree, depth, channel, kernel_size, stride) + conv = octree_deconv_fast( + data, octree, depth, channel, kernel_size, stride) else: - conv = octree_deconv_memory(data, octree, depth, channel, kernel_size, stride) + conv = octree_deconv_memory( + data, octree, depth, channel, kernel_size, stride) return tf.layers.batch_normalization(conv, axis=1, training=training) def octree_deconv_bn_relu(data, octree, depth, channel, training, kernel_size=[3], stride=1, fast_mode=False): with tf.variable_scope('deconv_bn_relu'): - conv_bn = octree_deconv_bn(data, octree, depth, channel, training, kernel_size, + conv_bn = octree_deconv_bn(data, octree, depth, channel, training, kernel_size, stride, fast_mode) rl = tf.nn.relu(conv_bn) return rl -def octree_resblock(data, octree, depth, num_out, stride, training, bottleneck=4, bn=True): +def octree_resblock(data, octree, depth, num_out, stride, training, bottleneck=4): num_in = int(data.shape[1]) channelb = int(num_out / bottleneck) if stride == 2: @@ -207,22 +207,20 @@ def octree_resblock(data, octree, depth, num_out, stride, training, bottleneck=4 depth = depth - 1 with tf.variable_scope("1x1x1_a"): - block = octree_conv1x1_bn_relu(data, channelb, training=training, bn=bn) + block1 = octree_conv1x1_bn_relu(data, channelb, training=training) with tf.variable_scope("3x3x3"): - block = octree_conv_bn_relu(block, octree, depth, channelb, training, bn=bn) + block2 = octree_conv_bn_relu(block1, octree, depth, channelb, training) with tf.variable_scope("1x1x1_b"): - block = octree_conv1x1_bn(block, num_out, training=training, bn=bn) - + block3 = octree_conv1x1_bn(block2, num_out, training=training) + block4 = data if num_in != num_out: with tf.variable_scope("1x1x1_c"): - block4 = octree_conv1x1_bn(data, num_out, training=training, bn=bn) - return tf.nn.relu(block + block4) - else: - return tf.nn.relu(block + data) + block4 = octree_conv1x1_bn(data, num_out, training=training) + return tf.nn.relu(block3 + block4) def grids_resblock(grids, num_out, stride, training, bottleneck=4): num_in = int(grids.shape[1]) @@ -248,21 +246,28 @@ def grids_resblock(grids, num_out, stride, training, bottleneck=4): return tf.nn.relu(block + grids) - def octree_resblock2(data, octree, depth, num_out, training): + num_in = int(data.shape[1]) with tf.variable_scope("conv_1"): conv = octree_conv_bn_relu(data, octree, depth, num_out/4, training) with tf.variable_scope("conv_2"): conv = octree_conv_bn(conv, octree, depth, num_out, training) - out = tf.nn.relu(conv + data) + + link = data + if num_in != num_out: + with tf.variable_scope("conv_1x1"): + link = octree_conv1x1_bn(data, num_out, training=training) + + out = tf.nn.relu(conv + link) return out + def predict_module(data, num_output, num_hidden, training): # MLP with one hidden layer with tf.variable_scope('conv1'): conv = octree_conv1x1_bn_relu(data, num_hidden, training) with tf.variable_scope('conv2'): - logit= octree_conv1x1(conv, num_output, use_bias=True) + logit = octree_conv1x1(conv, num_output, use_bias=True) return logit @@ -270,7 +275,7 @@ def predict_label(data, num_output, num_hidden, training): logit = predict_module(data, num_output, num_hidden, training) # prob = tf.nn.softmax(logit, axis=1) # logit (1, num_output, ?, 1) label = tf.argmax(logit, axis=1, output_type=tf.int32) # predict (1, ?, 1) - label = tf.reshape(label, [-1]) # flatten + label = tf.reshape(label, [-1]) # flatten return logit, label @@ -278,11 +283,12 @@ def predict_signal(data, num_output, num_hidden, training): return tf.nn.tanh(predict_module(data, num_output, num_hidden, training)) -def softmax_loss(logit, label_gt, num_class, label_smoothing=0.0): +def softmax_loss(logit, label_gt, num_class, label_smoothing=0.0): with tf.name_scope('softmax_loss'): label_gt = tf.cast(label_gt, tf.int32) onehot = tf.one_hot(label_gt, depth=num_class) - loss = tf.losses.softmax_cross_entropy(onehot, logit, label_smoothing=label_smoothing) + loss = tf.losses.softmax_cross_entropy( + onehot, logit, label_smoothing=label_smoothing) return loss @@ -323,9 +329,20 @@ def normalize_signal(data): output = tf.nn.l2_normalize(data, axis=1) return output + +def average_tensors(tower_tensors): + avg_tensors = [] + with tf.name_scope('avg_tensors'): + for tensors in tower_tensors: + tensors = [tf.expand_dims(tensor, 0) for tensor in tensors] + avg_tensor = tf.concat(tensors, axis=0) + avg_tensor = tf.reduce_mean(avg_tensor, 0) + avg_tensors.append(avg_tensor) + return avg_tensors + -def build_solver(total_loss, learning_rate_handle): - with tf.name_scope('solver'): +def solver_single_gpu(total_loss, learning_rate_handle, gpu_num=1): + with tf.variable_scope('solver'): update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): global_step = tf.Variable(0, trainable=False, name='global_step') @@ -335,6 +352,42 @@ def build_solver(total_loss, learning_rate_handle): return solver, lr +def solver_multiple_gpus(total_loss, learning_rate_handle, gpu_num): + tower_grads, variables = [], [] + with tf.device('/cpu:0'): + with tf.variable_scope('solver'): + global_step = tf.Variable(0, trainable=False, name='global_step') + lr = learning_rate_handle(global_step) + opt = tf.train.MomentumOptimizer(lr, 0.9) + + for i in range(gpu_num): + with tf.device('/gpu:%d' % i): + with tf.name_scope('device_b%d' % i): + grads_and_vars = opt.compute_gradients(total_loss[i]) + grads, variables = zip(*grads_and_vars) + tower_grads.append(grads) + + update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + # !!! Only get the update_ops defined on `device_0` to avoid the sync + # between different GPUs to speed up the training process. !!! + update_ops = [op for op in update_ops if 'device_0' in op.name] + assert update_ops, 'The update ops of BN are empty, check the namescope \'device_0\'' + with tf.device('/cpu:0'): + with tf.name_scope('sync_and_apply_grad'): + with tf.control_dependencies(update_ops): + tower_grads = list(zip(*tower_grads)) + avg_grads = average_tensors(tower_grads) + grads_and_vars = list(zip(avg_grads, variables)) + solver = opt.apply_gradients(grads_and_vars, global_step=global_step) + return solver, lr + + +def build_solver(total_loss, learning_rate_handle, gpu_num=1): + assert (gpu_num > 0) + the_solver = solver_single_gpu if gpu_num == 1 else solver_multiple_gpus + return the_solver(total_loss, learning_rate_handle, gpu_num) + + def summary_train(names, tensors): with tf.name_scope('summary_train'): summaries = [] @@ -378,7 +431,7 @@ def loss_functions_reg(logit, label_gt, num_class, weight_decay, var_name, label return [loss, accu, regularizer] def loss_functions_seg(logit, label_gt, num_class, weight_decay, var_name, mask=-1): - with tf.name_scope('loss_seg'): + with tf.name_scope('loss_seg'): label_mask = label_gt > mask # filter label -1 masked_logit = tf.boolean_mask(logit, label_mask) masked_label = tf.boolean_mask(label_gt, label_mask) @@ -392,10 +445,11 @@ def loss_functions_seg(logit, label_gt, num_class, weight_decay, var_name, mask= def get_seg_label(octree, depth): with tf.name_scope('seg_label'): label = octree_property(octree, property_name='label', dtype=tf.float32, - depth=depth, channel=1) + depth=depth, channel=1) label = tf.reshape(tf.cast(label, tf.int32), [-1]) return label + def run_k_iterations(sess, k, tensors): num = len(tensors) avg_results = [0] * num @@ -403,7 +457,7 @@ def run_k_iterations(sess, k, tensors): iter_results = sess.run(tensors) for j in range(num): avg_results[j] += iter_results[j] - + for j in range(num): avg_results[j] /= k return avg_results @@ -418,7 +472,7 @@ def tf_IoU_per_shape(pred, label, class_num, mask=-1): IoU, valid_part_num, esp = 0.0, 0.0, 1.0e-10 for k in range(class_num): pk, lk = tf.equal(pred, k), tf.equal(label, k) - # pk, lk = pred == k, label == k # why can this not output the right results? + # pk, lk = pred == k, label == k # why can this not output the right results? intsc = tf.reduce_sum(tf.cast(pk & lk, dtype=tf.float32)) union = tf.reduce_sum(tf.cast(pk | lk, dtype=tf.float32)) valid = tf.cast(tf.reduce_any(lk), dtype=tf.float32) @@ -430,8 +484,8 @@ def tf_IoU_per_shape(pred, label, class_num, mask=-1): class Optimizer: def __init__(self, stype='SGD', var_list=None, mul=1.0): - self.stype = stype # TODO: support more optimizers - self.mul = mul # used to modulate the global learning rate + self.stype = stype # TODO: support more optimizers + self.mul = mul # used to modulate the global learning rate self.var_list = var_list def __call__(self, total_loss, learning_rate): @@ -444,3 +498,26 @@ def __call__(self, total_loss, learning_rate): .minimize(total_loss, global_step=global_step, var_list=self.var_list) return solver, lr + + + +def octree2points(octree, depth, pts_channel=4, output_normal=False): + with tf.name_scope('octree2points'): + signal = octree_signal(octree, depth, 4) # normal and displacement + signal = tf.transpose(tf.squeeze(signal, [0, 3])) # (1, C, H, 1) -> (H, C) + xyz = octree_xyz(octree, depth) + xyz = tf.cast(xyz, dtype=tf.float32) + + mask = octree_child(octree, depth) > -1 + signal = tf.boolean_mask(signal, mask) + xyz = tf.boolean_mask(xyz, mask) + + c = 3.0 ** 0.5 / 2.0 + normal, dis = tf.split(signal, [3, 1], axis=1) + pts, idx = tf.split(xyz, [3, 1], axis=1) + pts = (pts + 0.5) + normal * (dis * c) + if pts_channel == 4: + pts = tf.concat([pts, idx], axis=1) + output = pts if not output_normal else (pts, normal) + return output + diff --git a/tensorflow/script/ocnn_lrp_results/.DS_Store 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5.538940730451159117e-02 5.538940730451159117e-02 -5.468750000000000000e-01 1.562500000000000000e-02 1.562500000000000000e-02 1.196541671183774941e-01 1.196541671183774941e-01 1.196541671183774941e-01 -5.468750000000000000e-01 1.562500000000000000e-02 4.687500000000000000e-02 9.208342066686933669e-02 9.208342066686933669e-02 9.208342066686933669e-02 -5.156250000000000000e-01 7.812500000000000000e-02 1.562500000000000000e-02 6.659943761097349146e-02 6.659943761097349146e-02 6.659943761097349146e-02 -5.781250000000000000e-01 1.562500000000000000e-02 1.562500000000000000e-02 1.067361258861275197e-01 1.067361258861275197e-01 1.067361258861275197e-01 diff --git a/tensorflow/script/run_ae.py b/tensorflow/script/run_ae.py deleted file mode 100644 index 8449521..0000000 --- a/tensorflow/script/run_ae.py +++ /dev/null @@ -1,70 +0,0 @@ -import os -import tensorflow as tf -from tqdm import tqdm - -from config import parse_args -from tfsolver import TFSolver -from dataset import DatasetFactory -from network_ae import make_autoencoder -from ocnn import l2_regularizer - - -tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) - -FLAGS = parse_args() - -# get the autoencoder -autoencoder = make_autoencoder(FLAGS.MODEL) - -# define the graph -def compute_graph(dataset='train', training=True, reuse=False): - flags_data = FLAGS.DATA.train if dataset=='train' else FLAGS.DATA.test - octree, label = DatasetFactory(flags_data)() - code = autoencoder.octree_encoder(octree, training, reuse) - loss, accu = autoencoder.octree_decoder(code, octree, training, reuse) - - with tf.name_scope('total_loss'): - reg = l2_regularizer('ocnn', FLAGS.LOSS.weight_decay) - total_loss = tf.add_n(loss + [reg]) - tensors = loss + [reg] + accu + [total_loss] - depth = FLAGS.MODEL.depth - names = ['loss%d' % d for d in range(2, depth + 1)] + ['normal', 'reg'] + \ - ['accu%d' % d for d in range(2, depth + 1)] + ['total_loss'] - return tensors, names - -# define the solver -class AeTFSolver(TFSolver): - def __init__(self, flags, compute_graph): - super(AeTFSolver, self).__init__(flags, compute_graph) - - def decode_shape(self): - # build graph - octree, label = DatasetFactory(FLAGS.DATA.test)() - code = autoencoder.octree_encoder(octree, training=False, reuse=False) - octree_pred = autoencoder.octree_decode_shape(code, training=False, reuse=False) - - # checkpoint - assert(self.flags.ckpt) # the self.flags.ckpt should be provided - tf_saver = tf.train.Saver(max_to_keep=20) - - # start - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - with tf.Session(config=config) as sess: - # restore and initialize - self.initialize(sess) - tf_saver.restore(sess, self.flags.ckpt) - logdir = self.flags.logdir - tf.summary.FileWriter(logdir, sess.graph) - - print('Start testing ...') - for i in tqdm(range(0, self.flags.test_iter)): - origin, reconstructed = sess.run([octree, octree_pred]) - with open(logdir + ('/%04d_input.octree' % i), "wb") as f: - f.write(origin.tobytes()) - with open(logdir + ('/%04d_output.octree' % i), "wb") as f: - f.write(reconstructed.tobytes()) - -# run the experiments -solver = AeTFSolver(FLAGS.SOLVER, compute_graph) -solver.run() diff --git a/tensorflow/script/run_cls.py b/tensorflow/script/run_cls.py index c667e5e..44f8248 100644 --- a/tensorflow/script/run_cls.py +++ b/tensorflow/script/run_cls.py @@ -6,32 +6,45 @@ from network_factory import cls_network from ocnn import loss_functions, loss_functions_reg -tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) - -# configs -FLAGS = parse_args() # define the graph -def compute_graph(dataset='train', training=True, reuse=False, task = 'class'): - flags_data = FLAGS.DATA.train if dataset=='train' else FLAGS.DATA.test - y_type = 'int' if task == 'class' else 'float' - octree, label = DatasetFactory(flags_data, y_type = y_type)() - logit = cls_network(octree, FLAGS.MODEL, training, reuse) - if task == 'class': - losses = loss_functions(logit, label, FLAGS.LOSS.num_class, - FLAGS.LOSS.weight_decay, 'ocnn', FLAGS.LOSS.label_smoothing) - names = ['loss', 'accu', 'regularizer', 'total_loss'] - else: - losses = loss_functions_reg(logit, label, FLAGS.LOSS.num_class, - FLAGS.LOSS.weight_decay, 'ocnn', FLAGS.LOSS.label_smoothing) - names = ['loss', 'rmse', 'regularizer', 'total_loss'] - losses.append(losses[0] + losses[2]) # total loss - return losses, names +class ComputeGraph: + def __init__(self, flags): + self.flags = flags + + def __call__(self, dataset='train', training=True, reuse=False, gpu_num=1): + FLAGS = self.flags + with tf.device('/cpu:0'): + flags_data = FLAGS.DATA.train if dataset == 'train' else FLAGS.DATA.test + y_type = 'int' if FLAGS.SOLVER.task == 'class' else 'float' + data_iter = DatasetFactory(flags_data, y_type = y_type)(return_iter=True) + + tower_tensors = [] + for i in range(gpu_num): + with tf.device('/gpu:%d' % i): + with tf.name_scope('device_%d' % i): + octree, label = data_iter.get_next() + logit = cls_network(octree, FLAGS.MODEL, training, reuse) + if FLAGS.SOLVER.task == 'class': + losses = loss_functions(logit, label, FLAGS.LOSS.num_class, + FLAGS.LOSS.weight_decay, 'ocnn', + FLAGS.LOSS.label_smoothing) + names = ['loss', 'accu', 'regularizer', 'total_loss'] + else: + losses = loss_functions_reg(logit, label, FLAGS.LOSS.num_class, + FLAGS.LOSS.weight_decay, 'ocnn', FLAGS.LOSS.label_smoothing) + names = ['loss', 'rmse', 'regularizer', 'total_loss'] + losses.append(losses[0] + losses[2]) # total loss + tower_tensors.append(losses) + reuse = True + + tensors = tower_tensors[0] if gpu_num == 1 else list(zip(*tower_tensors)) + return tensors, names # run the experiments if __name__ == '__main__': - solver = TFSolver(FLAGS.SOLVER, compute_graph) - # solver.timeline() + FLAGS = parse_args() + compute_graph = ComputeGraph(FLAGS) + solver = TFSolver(FLAGS, compute_graph) solver.run() - diff --git a/tensorflow/script/run_completion.py b/tensorflow/script/run_completion.py deleted file mode 100644 index 8c949d5..0000000 --- a/tensorflow/script/run_completion.py +++ /dev/null @@ -1,133 +0,0 @@ -import os -import sys -import pickle -import numpy as np -import tensorflow as tf -from tqdm import tqdm - -from config import parse_args, FLAGS -from tfsolver import TFSolver -from dataset import DatasetFactory -from network_completion import CompletionResnet -from ocnn import l2_regularizer -sys.path.append('..') -from libs import octree_scan, octree_batch - - -# flags -FLAGS.DATA.train.camera = '_' # used to generate partial scans -FLAGS.MODEL.skip_connections = True -FLAGS = parse_args() - - -# the dataset -def bounding_sphere(points): - radius = 64.0 - center = (64.0, 64.0, 64.0) - return radius, center - - -class PointDataset: - def __init__(self, parse_example, transform_points, points2octree): - self.parse_example = parse_example - self.transform_points = transform_points - self.points2octree = points2octree - with open(FLAGS.DATA.train.camera, 'rb') as fid: - self.camera_path = pickle.load(fid) - - def gen_scan_axis(self, i): - j = np.random.randint(0, 8) - key = '%d_%d' % (i, j) - axes = np.array(self.camera_path[key]) - # perturb the axes - rnd = np.random.random(axes.shape) * 0.4 - 0.2 - axes = np.reshape(axes + rnd, (-1, 3)) - axes = axes / np.sqrt(np.sum(axes ** 2, axis=1, keepdims=True) + 1.0e-6) - axes = np.reshape(axes, (-1)) - return axes.astype(np.float32) - - def __call__(self, record_names, batch_size, shuffle_size=1000, - return_iter=False, take=-1, **kwargs): - with tf.name_scope('points_dataset'): - def preprocess(record): - points, label = self.parse_example(record) - points = self.transform_points(points) - octree1 = self.points2octree(points) # the complete octree - scan_axis = tf.py_func(self.gen_scan_axis, [label], tf.float32) - octree0 = octree_scan(octree1, scan_axis) # the transformed octree - return octree0, octree1 - - def merge_octrees(octrees0, octrees1, *args): - octree0 = octree_batch(octrees0) - octree1 = octree_batch(octrees1) - return (octree0, octree1) + args - - dataset = tf.data.TFRecordDataset(record_names).take(take).repeat() - if shuffle_size > 1: - dataset = dataset.shuffle(shuffle_size) - itr = dataset.map(preprocess, num_parallel_calls=8) \ - .batch(batch_size).map(merge_octrees, num_parallel_calls=8) \ - .prefetch(8).make_one_shot_iterator() - return itr if return_iter else itr.get_next() - - -# get the network -network = CompletionResnet(FLAGS.MODEL) - - -# define the graph -def compute_graph(dataset='train', training=True, reuse=False): - flags_data = FLAGS.DATA.train if dataset == 'train' else FLAGS.DATA.test - octree0, octree1 = DatasetFactory(flags_data, bounding_sphere, PointDataset)() - convd = network.octree_encoder(octree0, training, reuse) - loss, accu = network.octree_decoder(convd, octree0, octree1, training, reuse) - - with tf.name_scope('total_loss'): - reg = l2_regularizer('ocnn', FLAGS.LOSS.weight_decay) - total_loss = tf.add_n(loss + [reg]) - tensors = loss + [reg] + accu + [total_loss] - depth = FLAGS.MODEL.depth - names = ['loss%d' % d for d in range(2, depth + 1)] + ['normal', 'reg'] + \ - ['accu%d' % d for d in range(2, depth + 1)] + ['total_loss'] - return tensors, names - - -# define the solver -class CompletionSolver(TFSolver): - def __init__(self, flags, compute_graph): - super(CompletionSolver, self).__init__(flags, compute_graph) - - def decode_shape(self): - # build graph - octree_in, _ = DatasetFactory(FLAGS.DATA.test, bounding_sphere)() - convd = network.octree_encoder(octree_in, training=False, reuse=False) - octree_out = network.decode_shape(convd, octree_in, training=False, reuse=False) - - # checkpoint - assert(self.flags.ckpt) - tf_saver = tf.train.Saver(max_to_keep=20) - - # start - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - with tf.Session(config=config) as sess: - # restore and initialize - self.initialize(sess) - print('Load check point: ' + self.flags.ckpt) - tf_saver.restore(sess, self.flags.ckpt) - logdir = self.flags.logdir - tf.summary.FileWriter(logdir, sess.graph) - - print('Start testing ...') - for i in tqdm(range(0, self.flags.test_iter), ncols=80): - o0, o1 = sess.run([octree_in, octree_out]) - with open(logdir + ('/%04d_input.octree' % i), "wb") as f: - f.write(o0.tobytes()) - with open(logdir + ('/%04d_output.octree' % i), "wb") as f: - f.write(o1.tobytes()) - - -# run the experiments -if __name__ == '__main__': - solver = CompletionSolver(FLAGS.SOLVER, compute_graph) - solver.run() diff --git a/tensorflow/script/tensorflow_lrp.py b/tensorflow/script/tensorflow_lrp.py deleted file mode 100644 index 83f4387..0000000 --- a/tensorflow/script/tensorflow_lrp.py +++ /dev/null @@ -1,211 +0,0 @@ -import numpy as np -import os -import sys - -# sys.path.append("../..") - -import tensorflow as tf -from dataset import * -from ocnn import * -from learning_rate import LRFactory -from tensorflow.python.client import timeline - - -# np.set_printoptions(threshold=sys.maxsize) -# tf.enable_eager_execution() # for save input.octree - -depth=6 -channel=24 -nout=2 - -def network_ocnn_original(octree, training=False, reuse=False): - channels = [512, 256, 128, 64, 32, 16, 12, 8, 6]# [512, 256, 128, 64, 32, 16, 8, 4] - - with tf.variable_scope("ocnn", reuse=reuse): - data = octree_property(octree, property_name="feature", dtype=tf.float32, - depth=depth, channel=channel) - data = tf.reshape(data, [1, channel, -1, 1]) - for d in range(depth, 2, -1): - with tf.variable_scope('depth_%d' % d): - data = octree_conv_bn_relu(data, octree, d, channels[d], training) - data, _ = octree_max_pool(data, octree, d) - - with tf.variable_scope("full_voxel"): - data = octree_full_voxel(data, depth=2) - data = tf.layers.dropout(data, rate=0.5, training=training) - - with tf.variable_scope("fc1"): - data = fc_bn_relu(data, channels[2], training=training) - data = tf.layers.dropout(data, rate=0.5, training=training) - - with tf.variable_scope("fc2"): - logit = dense(data, nout, use_bias=True) - return logit - -#Function to call octree_max_pool and only return the first value instead of a tuple -octree_max_pool_first = lambda x, *args, **kwargs: octree_max_pool(x, *args, **kwargs)[0] - -class network_ocnn_class(): - def __init__(self, training=False): - self.channels = [2048, 1024, 512, 256, 128, 64, 32, 32, 32] # [512, 256, 128, 64, 32, 16, 8, 4] - - self.layer_list = [ - ('ocnn', octree_property, [], {'property_name': 'feature', 'dtype': tf.float32, 'depth': depth, 'channel': channel}, 'octree_property'), - ('ocnn', tf.reshape, [[1, channel, -1, 1]], {}, 'reshape'), - ] - - for d in range(depth, 2, -1): - self.layer_list.append(('depth_%d'%d, octree_conv_memory, [], {'depth':d, 'channel':self.channels[d]}, 'conv_depth_%d'%d)) - self.layer_list.append(('depth_%d'%d, tf.layers.batch_normalization, [], {'axis':1, 'training':training}, 'batch_norm_depth_%d'%d)) - self.layer_list.append(('depth_%d'%d, tf.nn.relu, [], {}, 'relu_depth_%d'%d)) - self.layer_list.append(['depth_%d'%d, octree_max_pool_first, [], {'depth': d}, 'conv_pooling_depth_%d'%d]) - - self.layer_list.extend([ - ('full_voxel', octree_full_voxel, [], {'depth': 2}, 'full voxel'), - ('full_voxel', tf.layers.dropout, [], {'rate': 0.5, 'training': training}, 'dropout 1'), - - ('fc1', dense, [self.channels[2]], {}, 'dense'), - ('fc1', batch_norm, [], {'training': training}, 'batch_norm'), - ('fc1', tf.nn.relu, [], {}, 'relu'), - - - ('fc1', tf.layers.dropout, [], {'rate': 0.5, 'training': training} , 'dropout 2'), - ('fc2', dense, [nout], {'use_bias': False}, 'fully connected'), - ]) - - def call(self, octree, reuse=tf.AUTO_REUSE): - print('Test call') - data = octree - with tf.variable_scope("ocnn", reuse=reuse): - for layer in self.layer_list: - with tf.variable_scope(layer[0], reuse=reuse): - args = layer[2] - kwargs = layer[3] - if layer[4][:4]=='conv': - kwargs['octree']=octree - if 'depth' in layer[4]: - with tf.variable_scope('conv_bn_relu'): - data = layer[1](data, *args, **kwargs) - else: - data = layer[1](data, *args, **kwargs) - return data - - def LRP(self, octree, analyze_index=0): - layer_list = self.layer_list - L=len(layer_list) - A=[0]*(L+1) - - A[0] = octree - with tf.variable_scope("ocnn"): - #Forward Pass - for i in range(0,L): - with tf.variable_scope(layer_list[i][0], reuse=tf.AUTO_REUSE): - args = layer_list[i][2] - kwargs = layer_list[i][3] - if layer_list[i][4][:4]=='conv': - kwargs['octree']=octree - if 'depth' in layer_list[i][4]: - with tf.variable_scope('conv_bn_relu'): - A[i+1] = layer_list[i][1](A[i], *args, **kwargs) - else: - A[i+1] = layer_list[i][1](A[i], *args, **kwargs) - - #Create a mask for the outputs - T = A[-1]/tf.reduce_max(A[-1]) - T = tf.floor(tf.nn.relu(T)) - #Create a list to store relevance values, append output layer with mask - R = [None]*L + [(A[-1]*T)] - - Cs = [0]*L - result_table=[] - - #Backward Pass - for i in range(1,L)[::-1]: - if layer_list[i][4][:10] == 'batch_norm': - R[i]=R[i+1] - else: - with tf.variable_scope(layer_list[i][0], reuse=tf.AUTO_REUSE): - print("Layer",i,":",layer_list[i][4]) - args = layer_list[i][2] - kwargs = layer_list[i][3] - - # octree_avg_pool - if layer_list[i][4][:9]=='conv_pool': - layer_list[i][1]=octree_avg_pool - if layer_list[i][4][:4]=='conv': - kwargs['octree']=octree - if layer_list[i][0][:5] == 'depth': - with tf.variable_scope('conv_bn_relu'): - z = layer_list[i][1](A[i], *args, **kwargs) - else: - z = layer_list[i][1](A[i], *args, **kwargs) - - # Define LRP rule layer by layer - if i<=13: - rho = lambda x: x + 0.25*tf.nn.relu(x) - else: - rho = lambda x: x - incr = lambda z: z+1e-9 - - z = incr(rho(z)) #LRP Step 1 - s = R[i+1]/z #LRP Step 2 - print('Differentiating with respect to layer',i) - c = tf.gradients(tf.math.reduce_sum(z*s), A[i], stop_gradients=[s, R[i+1]]) #LRP Step 3 - Cs[i]=c - R[i] = A[i]*c #LRP Step 4 - for i in range(1, L): - layer_label = [ord(c) for c in "layer %d: %s"%(i, layer_list[i][4])] - result_table.append([tf.constant(layer_label), R[i]]) - result_table.append([tf.constant([ord(c) for c in 'output']), R[-1]]) - return [octree, R[1], A[-1], result_table] - -network_ocnn=network_ocnn_class() - -def run_lrp(octree, label): - # logit = network_ocnn.call(octree) - outputs = network_ocnn.LRP(octree) - tf_saver = tf.train.Saver(max_to_keep=10) - - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - with tf.Session(config=config) as sess: - #Load Data - sess.run(tf.global_variables_initializer()) - path=tf.train.latest_checkpoint('logs/pdbbind/ocnn_points_20200704_class_6/model/') - tf_saver.restore(sess, path) - print('Test Restored') - for i in range(1): - # tmp = sess.run((logit)) - results, label = sess.run((outputs, label)) - print(results) - - octree, heatmap, output, result_table = results - - print("== Summed Relevance By Layer ==") - for result in result_table: - result[0] = "Relevance in " + ''.join(chr(c) for c in result[0]) - result[1] = round(tf.math.reduce_sum(result[1]).eval(), 3) - for r in result: - print(str(r).ljust(45), end='') - print('') - print("===============================") - - heatmap = tf.math.reduce_sum(heatmap, 1) - heatmap=tf.keras.backend.repeat_elements(heatmap, channel, axis=0) - - print("Actual Label:", label[0], "| Predicted Label:", np.argmax(output)) - - heatmap_octree = octree_set_property(in_octree=octree, property_name='feature', in_property=heatmap, depth=depth) - - with open('./output.octree', "wb") as f: - f.write(heatmap_octree.eval().tobytes()) - -octree, label = PointDataset(ParseExample(), - TransformPoints(distort=False, depth=depth), - Points2Octree(depth=depth))(record_names='/media/data/data_share/pdbbind/tfrecords/test_class_points_den3.tfrecords', batch_size=1, shuffle_size=1) - -# if use this, also enable_eager_execution at the top of file. -# with open('./input.octree', "wb") as f: -# f.write(octree.numpy().tobytes()) - -run_lrp(octree, label) diff --git a/tensorflow/script/tensorflow_lrp_bbgv2.py b/tensorflow/script/tensorflow_lrp_bbgv2.py deleted file mode 100644 index e798ca5..0000000 --- a/tensorflow/script/tensorflow_lrp_bbgv2.py +++ /dev/null @@ -1,488 +0,0 @@ -#Blake Gaines - -import os -import sys -import numpy as np -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' -import tensorflow as tf -tf.autograph.set_verbosity(0) -from dataset import * -from ocnn import * -from tqdm import tqdm -import subprocess - -depth=7 -# channel=3 -# nout=40 -resblock_num=3 - - - -channel=24 -nout=2 - -model_path = 'logs/pdbbind/ocnn_points_20200704_class_7/model/' -record_name = '/media/data/data_share/pdbbind/tfrecords/test_class_points_den4.tfrecords' - -# flags = parse_args() -# depth, channel, nout = FLAGS.MODEL.depth, FLAGS.MODEL.channel, FLAGS.MODEL.nout - -#Original network for comparison, first defined in network_cls.py -def network_ocnn_original(octree, training=False, reuse=False): - channels = [2048, 1024, 512, 256, 128, 64, 32, 32, 32] - with tf.variable_scope("ocnn", reuse=reuse): - data = octree_property(octree, property_name="feature", dtype=tf.float32, - depth=depth, channel=channel) - data = tf.reshape(data, [1, channel, -1, 1]) - for d in range(depth, 2, -1): - with tf.variable_scope('depth_%d' % d): - data = octree_conv_bn_relu(data, octree, d, channels[d], training) - data, _ = octree_max_pool(data, octree, d) - - with tf.variable_scope("full_voxel"): - data = octree_full_voxel(data, depth=2) - data = tf.layers.dropout(data, rate=0.5, training=training) - - with tf.variable_scope("fc1"): - data = fc_bn_relu(data, channels[2], training=training) - data = tf.layers.dropout(data, rate=0.5, training=training) - - with tf.variable_scope("fc2"): - logit = dense(data, nout, use_bias=True) - return logit - -def network_resnet_original(octree, training=False, reuse=None): - channels = [2048, 1024, 512, 256, 128, 64, 32, 32, 32] - with tf.variable_scope("ocnn_resnet", reuse=reuse): - data = octree_property(octree, property_name="feature", dtype=tf.float32, - depth=depth, channel=channel) - data = tf.reshape(data, [1, channel, -1, 1]) - - with tf.variable_scope("conv1"): - data = octree_conv_bn_relu(data, octree, depth, channels[depth], training) - - for d in range(depth, 2, -1): - for i in range(0, resblock_num): - with tf.variable_scope('resblock_%d_%d' % (d, i)): - data = octree_resblock(data, octree, d, channels[d], 1, training) - with tf.variable_scope('max_pool_%d' % d): - data, _ = octree_max_pool(data, octree, d) - - with tf.variable_scope("global_average"): - data = octree_full_voxel(data, depth=2) - data = tf.reduce_mean(data, 2) - - data = tf.layers.dropout(data, rate=0.5, training=training) - - with tf.variable_scope("fc2"): - logit = dense(data, nout, use_bias=True) - - return logit - -def octree_conv1x1_bn_LRP(inputs, nout, training): - conv = octree_conv1x1(inputs, nout, use_bias=False) - return conv - -def octree_conv1x1_bn_relu_LRP(inputs, nout, training): - conv = octree_conv1x1_bn_LRP(inputs, nout, training) - return conv - -def octree_conv_bn_relu_LRP(data, octree, depth, channel, training, kernel_size=[3], - stride=1, fast_mode=False): - with tf.variable_scope('conv_bn_relu'): - data = octree_conv_memory(data, octree, depth, channel, kernel_size, stride) - return data - -def fc_bn_relu_LRP(inputs, nout, training): - fc = dense(inputs, nout, use_bias=False) - return fc - -def octree_resblock_1(data, octree, depth, num_out, stride, training, bottleneck=4): - if stride == 2: - data, mask = octree_max_pool(data, octree, depth=depth) - return data - -def octree_resblock_2(data, octree, depth, num_out, stride, training, bottleneck=4): - channelb = int(num_out / bottleneck) - with tf.variable_scope("1x1x1_a"): - block1 = octree_conv1x1_bn_relu(data, channelb, training=training) - return block1, data - -def octree_resblock_3(block1_data, octree, depth, num_out, stride, training, bottleneck=4): - channelb = int(num_out / bottleneck) - block1, data = block1_data - if stride == 2: - depth -= 1 - with tf.variable_scope("3x3x3"): - block2 = octree_conv_bn_relu(block1, octree, depth, channelb, training) - return block2, data - -def octree_resblock_4(block2_data, octree, depth, num_out, stride, training, bottleneck=4): - block2, data = block2_data - with tf.variable_scope("1x1x1_b"): - block3 = octree_conv1x1_bn(block2, num_out, training=training) - return block3, data - -def octree_resblock_5(block3_data, octree, depth, num_out, stride, training, bottleneck=4): - block3, data = block3_data - num_in = int(data.shape[1]) - block4 = data - if num_in != num_out: - with tf.variable_scope("1x1x1_c"): - block4 = octree_conv1x1_bn(data, num_out, training=training) - return tf.nn.relu(block3 + block4) - -def octree_resblock_1_LRP(data, octree, depth, num_out, stride, training, bottleneck=4): - if stride == 2: - data = octree_avg_pool(data, octree, depth=depth) - return data - -def octree_resblock_2_LRP(data, octree, depth, num_out, stride, training, bottleneck=4): - channelb = int(num_out / bottleneck) - with tf.variable_scope("1x1x1_a"): - block1 = octree_conv1x1_bn_relu_LRP(data, channelb, training=training) - return block1, data - -def octree_resblock_3_LRP(block1_data, octree, depth, num_out, stride, training, bottleneck=4): - channelb = int(num_out / bottleneck) - block1, data = block1_data - if stride == 2: - depth -= 1 - with tf.variable_scope("3x3x3"): - block2 = octree_conv_bn_relu_LRP(block1, octree, depth, channelb, training) - return block2, data - -def octree_resblock_4_LRP(block2_data, octree, depth, num_out, stride, training, bottleneck=4): - block2, data = block2_data - with tf.variable_scope("1x1x1_b"): - block3 = octree_conv1x1_bn_LRP(block2, num_out, training=training) - return block3, data - -def octree_resblock_5_LRP(block3_data, octree, depth, num_out, stride, training, bottleneck=4): - block3, data = block3_data - num_in = int(data.shape[1]) - block4 = data - if num_in != num_out: - with tf.variable_scope("1x1x1_c"): - block4 = octree_conv1x1_bn_LRP(data, num_out, training=training) - return block3 + block4 - -#Function to call octree_max_pool and only return the first value instead of a tuple -octree_max_pool_first = lambda x, *args, **kwargs: octree_max_pool(x, *args, **kwargs)[0] - -replace_functions = { #Functions to replace during backpropagation - octree_max_pool_first: octree_avg_pool, - octree_conv_bn_relu: octree_conv_bn_relu_LRP, - fc_bn_relu: fc_bn_relu_LRP, - octree_resblock_1: octree_resblock_1_LRP, - octree_resblock_2: octree_resblock_2_LRP, - octree_resblock_3: octree_resblock_3_LRP, - octree_resblock_4: octree_resblock_4_LRP, - octree_resblock_5: octree_resblock_5_LRP, -} - -class network_ocnn_class(): - def __init__(self, training=False): - self.channels = [2048, 1024, 512, 256, 128, 64, 32, 32, 32] - - #Creating Layer List: ("Variable Scope", Layer Function, [Args], {Kwargs}, "Layer cName") - self.layer_list = [ - ('ocnn', octree_property, [], {'property_name': 'feature', 'dtype': tf.float32, 'depth': depth, 'channel': channel}, 'octree_property'), - ('ocnn', tf.reshape, [[1, channel, -1, 1]], {}, 'reshape'), - ] - - for d in range(depth, 2, -1): - # self.layer_list.append(('depth_%d'%d, octree_conv_memory, [], {'depth':d, 'channel':self.channels[d]}, 'conv_depth_%d'%d)) - # self.layer_list.append(('depth_%d'%d, tf.layers.batch_normalization, [], {'axis':1, 'training':training}, 'batch_norm_depth_%d'%d)) - # self.layer_list.append(('depth_%d'%d, tf.nn.relu, [], {}, 'relu_depth_%d'%d)) - self.layer_list.append(['depth_%d'%d, octree_conv_bn_relu, [], {'depth':d, 'channel':self.channels[d], 'training':training}, 'conv %d'%d]) - - self.layer_list.append(['depth_%d'%d, octree_max_pool_first, [], {'depth': d}, 'conv pooling depth %d'%d]) - - self.layer_list.extend([ - ('full_voxel', octree_full_voxel, [], {'depth': 2}, 'full voxel'), - ('full_voxel', tf.layers.dropout, [], {'rate': 0.5, 'training': training}, 'dropout 1'), - - # ('fc1', dense, [self.channels[2]], {}, 'dense'), - # ('fc1', batch_norm, [], {'training': training}, 'batch_norm'), - # ('fc1', tf.nn.relu, [], {}, 'relu'), - ['fc1', fc_bn_relu, [self.channels[2]], {'training': training}, 'dense'], - - ('fc1', tf.layers.dropout, [], {'rate': 0.5, 'training': training} , 'dropout 2'), - - ('fc2', dense, [nout], {'use_bias': True}, 'fully connected'), - ]) - - def call(self, octree, reuse=tf.AUTO_REUSE, overall_scope="ocnn"): - data = octree - with tf.variable_scope(overall_scope, reuse=reuse): - for layer in self.layer_list: - with tf.variable_scope(layer[0], reuse=reuse): - args = layer[2] - kwargs = layer[3] - if 'conv' in layer[4]: - kwargs['octree']=octree - data = layer[1](data, *args, **kwargs) - return data - - def LRP(self, octree, label=None, overall_scope="ocnn"): - layer_list = self.layer_list - L=len(layer_list) - A=[0]*(L+1) - - A[0] = octree - with tf.variable_scope(overall_scope): - #Forward Pass - for i in range(0,L): - with tf.variable_scope(layer_list[i][0], reuse=tf.AUTO_REUSE): - args = layer_list[i][2] - kwargs = layer_list[i][3] - if 'conv' in layer_list[i][4]: - kwargs['octree']=octree - - A[i+1] = layer_list[i][1](A[i], *args, **kwargs) - - #Create a mask for the outputs - if label is None: - T = A[-1]/tf.reduce_max(A[-1]) - T = tf.floor(tf.nn.relu(T)) - else: - T = tf.eye(nout)[label[0]] - - #Create a list to store relevance values, append output layer with mask - R = [[None]]*L + [(A[-1]*T)] - - result_table=[] - #Backward Pass - for i in range(1,L)[::-1]: - if layer_list[i][4][:10] == 'batch_norm': - R[i]=R[i+1] - else: - with tf.variable_scope(layer_list[i][0], reuse=tf.AUTO_REUSE): - print("Layer",i,":",layer_list[i][4]) - args = layer_list[i][2] - kwargs = layer_list[i][3] - #Replace max_pool layers with avg_pool layers - if layer_list[i][1] in replace_functions: - layer_list[i][1]=replace_functions[layer_list[i][1]] - #Add octree argument if needed - if 'conv' in layer_list[i][4]: - kwargs['octree']=octree - - - # Define LRP rule layer by layer - # if i<=7: - # rho = lambda x: x + 0.25*tf.nn.relu(x) #LRP-gamma Rule - # else: - # rho = lambda x: x #LRP-o Rule - rho = lambda x: tf.nn.relu(x) - incr = lambda z: z+1e-9 +0.5*(tf.math.reduce_mean(z**2)**.5) - - - print("name scope:",tf.get_default_graph().get_name_scope()) - weights = tf.trainable_variables(scope=tf.get_default_graph().get_name_scope()[:-2]) - print("weights:", weights) - for w in weights: - w.assign(rho(w))#rho(w)) - #return weights - - # z = layer_list[i][1](A[i], *args, **kwargs) - # for inp in z.op.inputs: - # if inp.shape.is_fully_defined(): - # inp = np.ones(inp.shape) - - # kernel = tf.get_variable("ocnn/fc2/dense_1/kernel", shape=weights[0].shape, initializer=tf.contrib.layers.xavier_initializer()) - - - z = layer_list[i][1](A[i], *args, **kwargs) - - #z = rho(z) - - z = incr(z) #LRP Step 1 - s = R[i+1]/z #LRP Step 2 - c = tf.gradients(tf.math.reduce_sum(z*s), A[i], stop_gradients=[s, R[i+1]]) #LRP Step 3 - R[i] = A[i]*c #LRP Step 4 - #Create result_table - for i in range(1, L): - layer_label = [ord(c) for c in "layer %d: %s"%(i, layer_list[i][4])] - result_table.append([tf.constant(layer_label), R[i]]) - result_table.append([tf.constant([ord(c) for c in 'output']), tf.nn.relu(R[-1])]) - return [octree, R[1], A[-1], result_table] - -network_ocnn=network_ocnn_class() - -def return_resnet_layers(d, i, training): - channels = [2048, 1024, 512, 256, 128, 64, 32, 32, 32] - layer_list=[] - resblock_functions = [ - octree_resblock_1, - octree_resblock_2, - octree_resblock_3, - octree_resblock_4, - octree_resblock_5, - ] - for layer in range(len(resblock_functions)): - layer_list.append(['resblock_%d_%d'%(d, i), resblock_functions[layer], [], {'depth':d, 'num_out':channels[d],'stride':1,'training':training}, 'resblock %i conv %d (%i)'%(layer+1, d, i)]) - return layer_list - -class network_resnet_class(network_ocnn_class): - def __init__(self, training=False): - self.channels = [2048, 1024, 512, 256, 128, 64, 32, 32, 32] - - #Creating Layer List: ("Variable Scope", Layer Function, [Args], {Kwargs}, "Layer cName") - self.layer_list = [ - ('ocnn_resnet', octree_property, [], {'property_name': 'feature', 'dtype': tf.float32, 'depth': depth, 'channel': channel}, 'octree_property'), - ('ocnn_resnet', tf.reshape, [[1, channel, -1, 1]], {}, 'reshape'), - ['conv1',octree_conv_bn_relu, [], {'depth':depth, 'channel':self.channels[depth], 'training':training}, 'conv 1'] - ] - - for d in range(depth, 2, -1): - for i in range(0, resblock_num): - self.layer_list.extend(return_resnet_layers(d,i, training)) - self.layer_list.append(['max_pool_%d'%d, octree_max_pool_first, [], {'depth': d}, 'conv pooling depth %d'%d]) - - self.layer_list.extend([ - ('global_average', octree_full_voxel, [], {'depth': 2}, 'full voxel'), - ('global_average', tf.reduce_mean, [2], {}, 'reduce mean'), - ('dropout', tf.layers.dropout, [], {'rate': 0.5, 'training': training}, 'dropout 1'), - ('fc2', dense, [nout], {'use_bias': True}, 'fully connected'), - ]) - - def call(self, octree, reuse=tf.AUTO_REUSE): - return super().call(octree, reuse=tf.AUTO_REUSE, overall_scope='ocnn_resnet') - - def LRP(self, octree, label=None): - return super().LRP(octree, label, overall_scope='ocnn_resnet') - -network_resnet = network_resnet_class() - -label_list = sorted([x[1] for x in os.walk('dataset/ModelNet40/ModelNet40.points')][0]) - -#checkpoint_dir = 'logs/m40/0608_resnet_points/model' -# checkpoint_dir = 'logs/m40/0322_ocnn_octree/model/' -#checkpoint_dir = 'QQ/ocnn_points_20200704_class_6/model/' - -octree2points_path = './../../octree/build/octree2points' -output_path = './ocnn_lrp_results/' -octree_files_path = 'octree_heatmaps/' - -print("Checkpoint Directory:", model_path) - -def run_lrp(octree, label, save=False): - with open('./output_octree.txt', 'a+') as f: - f.seek(0) - f.truncate() - outputs = network_ocnn.LRP(octree) - tf_saver = tf.train.Saver(max_to_keep=10) - - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - with tf.Session(config=config) as sess: - #Load Model - sess.run(tf.global_variables_initializer()) - path=tf.train.latest_checkpoint(model_path) - #print(tf.train.list_variables(path)) - tf_saver.restore(sess, path) - - results, category = sess.run((outputs, label)) - - octree, heatmap, logit, result_table = results - print(result_table) - - # Format and print result_table - print("== Summed Relevance By Layer ==") - for result in result_table: - result[0] = "Relevance in " + ''.join(chr(c) for c in result[0]) - result[1] = round(tf.math.reduce_sum(result[1]).eval(), 3) - for r in result: - print(str(r).ljust(45), end='') - print('') - print("===============================") - - heatmap = tf.math.reduce_sum(heatmap, 1) - heatmap=tf.keras.backend.repeat_elements(heatmap, channel, axis=0) - - print("Actual Label:", category[0], "| Predicted Label:", np.argmax(logit)) - - heatmap_octree = octree_set_property(in_octree=octree, property_name='feature', in_property=heatmap, depth=depth) - - filename = octree_files_path + 'heatmap_%d.octree'%(category[0]) - - var = tf.trainable_variables() - var_vals = sess.run(var) - for v, val in zip(var, var_vals): - print("var: {}, value: {}".format(v.name, val.shape)) - conv_filter = [v for v in tf.trainable_variables() if v.name == "ocnn/depth_5/conv_bn_relu/octree_conv/weights:0"][0].eval() - # print(filter, filter.shape) - - with open('./output_octree.txt', 'a+') as f: - f.write(filename+'\n') - - with open(filename, "wb+") as f: - f.write(heatmap_octree.eval().tobytes()) - - if save: - subprocess.call([octree2points_path, '--filenames', './output_octree.txt', '--suffix', 'ply', '--output_path', output_path]) - print("Saved output to %sheatmap_%d.ply"%(output_path, category[0])) - - - -import pandas as pd -from scipy.special import softmax - -def test(octree,label,iters=925): - - columns=['label', 'accuracy','sensitivity','specificity','total'] - stats = np.zeros((nout,len(columns))) - stats = pd.DataFrame(data=stats, columns=columns) - stats['label'] = label_list - - outputs = network_ocnn.call(octree) - tf_saver = tf.train.Saver(max_to_keep=10) - - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - - with tf.Session(config=config) as sess: - #Load Model - sess.run(tf.global_variables_initializer()) - path=tf.train.latest_checkpoint(checkpoint_dir) - tf_saver.restore(sess, path) - - for i in tqdm(range(iters)): - logits, labels = sess.run((outputs, label)) - softmax_array = softmax(logits) - labels = np.eye(nout)[labels] - predictions = np.eye(nout)[np.argmax(softmax_array, axis=1)] - batch_correct = np.where((predictions==labels),1,0) - stats['accuracy'] += np.sum(batch_correct, axis=0) - stats['specificity'] += np.sum(np.where(predictions-batch_correct==-1, 1, 0), axis=0) - stats['sensitivity'] += np.sum(np.where(predictions+batch_correct==2, 1, 0), axis=0) - stats['total']+=np.sum(labels, axis=0) - - total = np.sum(stats['total']) - overall_sensitivity = np.sum(stats['sensitivity']) / total - stats['sensitivity'] /= stats['total'] - stats['specificity'] /= (total-stats['total']) #Uses dataframe __sub__ method - stats['accuracy'] /= total - - print(stats.sort_values(by='sensitivity', ascending=False)[['label','sensitivity','specificity','total']]) - print("\nrecords tested:", total) - print("overall sensitivity:", overall_sensitivity, '\n') - - return stats - -# octree, label=OctreeDataset(ParseExample())( -# record_names='dataset/ModelNet40/m40_5_2_12_test_octree.tfrecords', batch_size=32, -# shuffle_size=0, return_iter=False) -# test(octree, label) - -# octree, label=OctreeDataset(ParseExample())( -# record_names='dataset/ModelNet40/m40_5_2_12_test_octree.tfrecords', batch_size=1, -# shuffle_size=0, return_iter=False) - -octree, label = PointDataset(ParseExample(), - TransformPoints(distort=False, depth=depth), - Points2Octree(depth=depth))(record_names=record_name, batch_size=1, shuffle_size=0) - -run_lrp(octree, label, save=False) diff --git a/tensorflow/script/tensorflow_lrp_bg.py b/tensorflow/script/tensorflow_lrp_bg.py deleted file mode 100644 index 00b33ae..0000000 --- a/tensorflow/script/tensorflow_lrp_bg.py +++ /dev/null @@ -1,407 +0,0 @@ -# Blake Gaines - -import os -import sys -import numpy as np -import tensorflow as tf -from dataset import * -from ocnn import * -from tqdm import tqdm -import subprocess -import seaborn as sns - -# tf.enable_eager_execution() # for save input.octree - -depth = 5 -channel = 24 -nout = 1 -y_type = 'float' - -model_path = 'logs/pdbbind/ocnn_points_20200716_reg_5/model/' -record_name = '/media/data/data_share/pdbbind/tfrecords/test_reg_points_den3.tfrecords' -# record_name = '../util/2xnb.tfrecords' -# record_name = '/media/data/data_share/pdbbind/tfrecords/pitt_example_den3.tfrecords' - -# Original network for comparison, first defined in network_cls.py -def network_ocnn_original(octree, training=False, reuse=False): - channels = [2048, 1024, 512, 256, 128, 64, 32, 32, 32] - with tf.variable_scope("ocnn", reuse=reuse): - data = octree_property(octree, property_name="feature", dtype=tf.float32, - depth=depth, channel=channel) - data = tf.reshape(data, [1, channel, -1, 1]) - for d in range(depth, 2, -1): - with tf.variable_scope('depth_%d' % d): - data = octree_conv_bn_relu(data, octree, d, channels[d], training) - data, _ = octree_max_pool(data, octree, d) - - with tf.variable_scope("full_voxel"): - data = octree_full_voxel(data, depth=2) - data = tf.layers.dropout(data, rate=0.5, training=training) - - with tf.variable_scope("fc1"): - data = fc_bn_relu(data, channels[2], training=training) - data = tf.layers.dropout(data, rate=0.5, training=training) - - with tf.variable_scope("fc2"): - logit = dense(data, nout, use_bias=True) - return logit - - -def octree_conv_bn_relu_LRP(data, octree, depth, channel, training, kernel_size=[3], - stride=1, fast_mode=False): - with tf.variable_scope('conv_bn_relu'): - data = octree_conv_memory(data, octree, depth, channel, kernel_size, stride) - return data - - -def fc_bn_relu_LRP(inputs, nout, training): - fc = dense(inputs, nout, use_bias=False) # QQ: why use false? - return fc - - -# Function to call octree_max_pool and only return the first value instead of a tuple -octree_max_pool_first = lambda x, *args, **kwargs: octree_max_pool(x, *args, **kwargs)[0] - -replace_functions = { # Functions to replace during backpropagation - octree_max_pool_first: octree_avg_pool, - octree_conv_bn_relu: octree_conv_bn_relu_LRP, - fc_bn_relu: fc_bn_relu_LRP, -} - - -class network_ocnn_class(): - def __init__(self, training=False): - self.channels = [2048, 1024, 512, 256, 128, 64, 32, 32, 32] - - # Creating Layer List: ("Variable Scope", Layer Function, [Args], {Kwargs}, "Layer cName") - self.layer_list = [ - ('ocnn', octree_property, [], - {'property_name': 'feature', 'dtype': tf.float32, 'depth': depth, 'channel': channel}, 'octree_property'), - ('ocnn', tf.reshape, [[1, channel, -1, 1]], {}, 'reshape'), - ] - - for d in range(depth, 2, -1): - # self.layer_list.append(('depth_%d'%d, octree_conv_memory, [], {'depth':d, 'channel':self.channels[d]}, 'conv_depth_%d'%d)) - # self.layer_list.append(('depth_%d'%d, tf.layers.batch_normalization, [], {'axis':1, 'training':training}, 'batch_norm_depth_%d'%d)) - # self.layer_list.append(('depth_%d'%d, tf.nn.relu, [], {}, 'relu_depth_%d'%d)) - self.layer_list.append(['depth_%d' % d, octree_conv_bn_relu, [], - {'depth': d, 'channel': self.channels[d], 'training': training}, 'conv %d' % d]) - - self.layer_list.append( - ['depth_%d' % d, octree_max_pool_first, [], {'depth': d}, 'conv pooling depth %d' % d]) - - self.layer_list.extend([ - ('full_voxel', octree_full_voxel, [], {'depth': 2}, 'full voxel'), - ('full_voxel', tf.layers.dropout, [], {'rate': 0.5, 'training': training}, 'dropout 1'), - - # ('fc1', dense, [self.channels[2]], {}, 'dense'), - # ('fc1', batch_norm, [], {'training': training}, 'batch_norm'), - # ('fc1', tf.nn.relu, [], {}, 'relu'), - ['fc1', fc_bn_relu, [self.channels[2]], {'training': training}, 'dense'], - - ('fc1', tf.layers.dropout, [], {'rate': 0.5, 'training': training}, 'dropout 2'), - - ('fc2', dense, [nout], {'use_bias': True}, 'fully connected'), # QQ: set use_bias to True when use regression - ]) - - def call(self, octree, reuse=tf.AUTO_REUSE): - data = octree - with tf.variable_scope("ocnn", reuse=reuse): - for layer in self.layer_list: - with tf.variable_scope(layer[0], reuse=reuse): - args = layer[2] - kwargs = layer[3] - if layer[4][:4] == 'conv': - kwargs['octree'] = octree - # if 'depth' in layer[4]: - # with tf.variable_scope('conv_bn_relu'): - # data = layer[1](data, *args, **kwargs) - # else: - data = layer[1](data, *args, **kwargs) - return data - - def LRP(self, octree, label=None): - layer_list = self.layer_list - L = len(layer_list) - print('Total {} layers'.format(L)) - A = [0] * (L + 1) - - A[0] = octree - with tf.variable_scope("ocnn"): - # Forward Pass - for i in range(0, L): - with tf.variable_scope(layer_list[i][0], reuse=tf.AUTO_REUSE): - args = layer_list[i][2] - kwargs = layer_list[i][3] - if layer_list[i][4][:4] == 'conv': - kwargs['octree'] = octree - # if 'depth' in layer_list[i][4]: - # with tf.variable_scope('conv_bn_relu'): - # A[i+1] = layer_list[i][1](A[i], *args, **kwargs) - # else: - A[i + 1] = layer_list[i][1](A[i], *args, **kwargs) - - # Create a mask for the outputs - if label is None: - T = A[-1] / tf.reduce_max(A[-1]) - T = tf.floor(tf.nn.relu(T)) - else: - T = tf.eye(nout)[label[0]] - - # Create a list to store relevance values, append output layer with mask - R = [None] * L + [(A[-1] * T)] - - result_table = [] - # Backward Pass - for i in range(1, L)[::-1]: - if layer_list[i][4][:10] == 'batch_norm': - R[i] = R[i + 1] - print('batch norm, relevance in layer {} equal to layer {}'.format(i, i+1)) - else: - with tf.variable_scope(layer_list[i][0], reuse=tf.AUTO_REUSE): - print("Layer", i, ":", layer_list[i][4]) - args = layer_list[i][2] - kwargs = layer_list[i][3] - - # Replace max_pool layers with avg_pool layers - if layer_list[i][1] in replace_functions: - print("Replacing Layer:", layer_list[i][4]) - layer_list[i][1] = replace_functions[layer_list[i][1]] - # Add octree argument if needed - if layer_list[i][4][:4] == 'conv': - kwargs['octree'] = octree - # Add another variable_scope if needed - # if 'depth' in layer_list[i][4]: - # with tf.variable_scope('conv_bn_relu'): - # z = layer_list[i][1](A[i], *args, **kwargs) - # else: - z = layer_list[i][1](A[i], *args, **kwargs) - - # Define LRP rule layer by layer - # if i <= 13: - # rho = lambda x: x + 0.25 * tf.nn.relu(x) # LRP-gamma Rule - # else: - # rho = lambda x: x # LRP-o Rule - rho = lambda x: x # LRP-o Rule - incr = lambda z: z+1e-9+0.25*(tf.math.reduce_mean(z**2)**.5) - - z = incr(rho(z)) # LRP Step 1 - s = R[i + 1] / z # LRP Step 2 - c = tf.gradients(tf.math.reduce_sum(z * s), A[i], stop_gradients=[s, R[i + 1]]) # LRP Step 3 - R[i] = A[i] * c # LRP Step 4 - pass - # Create result_table - for i in range(1, L): - layer_label = [ord(c) for c in "layer %d: %s" % (i, layer_list[i][4])] - result_table.append([tf.constant(layer_label), R[i]]) - result_table.append([tf.constant([ord(c) for c in 'output']), R[-1]]) - return [octree, R[1], A[-1], result_table] - - -network_ocnn = network_ocnn_class() - - -def run_lrp(octree, label, index): - with open('./output_octree.txt', 'a') as f: - f.seek(0) - f.truncate() - outputs = network_ocnn.LRP(octree, label) - tf_saver = tf.train.Saver(max_to_keep=10) - - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - with tf.Session(config=config) as sess: - # Load Model - sess.run(tf.global_variables_initializer()) - path = tf.train.latest_checkpoint(model_path) - tf_saver.restore(sess, path) - - num=0 - while num <= index: - results, category = sess.run((outputs, label)) - num += 1 - octree, heatmap, logit, result_table = results - - # Format and print result_table - print("== Summed Relevance By Layer ==") - for result in result_table: - result[0] = "Relevance in " + ''.join(chr(c) for c in result[0]) - result[1] = round(tf.math.reduce_sum(result[1]).eval(), 8) - for r in result: - print(str(r).ljust(45), end='') - print('') - print("===============================") - - heatmap = tf.math.reduce_sum(heatmap, 1) - heatmap = tf.keras.backend.repeat_elements(heatmap, channel, axis=0) - - print("Actual Label:", category[0], "| Predicted Label:", np.argmax(logit)) - - heatmap_octree = octree_set_property(in_octree=octree, property_name='feature', in_property=heatmap, - depth=depth) - - filename = 'octree_heatmaps/heatmap_%d.octree' % (category[0]) - print(filename) - - with open('./output_octree.txt', 'a+') as f: - f.write(filename + '\n') - - with open(filename, "wb") as f: - f.write(heatmap_octree.eval().tobytes()) - - subprocess.call(['./../../octree/build/octree2points', '--filenames', './output_octree.txt', '--suffix', 'ply', - '--output_path', './ocnn_lrp_results']) - - -import pandas as pd -from scipy.special import softmax -from scipy.stats import pearsonr - -from sklearn import metrics -from sklearn.metrics import roc_curve, auc, mean_squared_error, mean_absolute_error -import matplotlib.pyplot as plt - -def report_auc(all_labels, all_probs): - fpr, tpr, thresholds = metrics.roc_curve(all_labels, all_probs, pos_label=1) - roc_auc = auc(fpr, tpr) - print('AUC: {}'.format(metrics.auc(fpr, tpr))) - - plt.figure() - lw = 2 - plt.plot(fpr, tpr, color='darkorange', - lw=lw, label='ROC curve (area = %0.2f)' % roc_auc) - plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--') - plt.xlim([0.0, 1.0]) - plt.ylim([0.0, 1.05]) - plt.xlabel('False Positive Rate') - plt.ylabel('True Positive Rate') - plt.title('Receiver operating characteristic example') - plt.legend(loc="lower right") - plt.show() - pass - -def report_reg_metrics(all_labels, all_preds): - rmse = np.sqrt(mean_squared_error(all_labels, all_preds)) - mae = mean_absolute_error(all_labels, all_preds) - r = pearsonr(all_labels, all_preds) - print('rmse: {}, mae: {}, r: {}'.format(rmse, mae, r)) - pass - -def report_scatter(all_labels, all_probs): - plt.figure() - sns.scatterplot(all_labels, all_probs) - plt.show() - - -def test_reg(octree, label, iters=925, test_size = 285): - columns = ['label', 'accuracy', 'sensitivity', 'specificity', 'total'] - stats = np.zeros((nout, len(columns))) - stats = pd.DataFrame(data=stats, columns=columns) - # stats['label'] = sorted([x[1] for x in os.walk('/home/bbg16101/OCNN_LRP/O-CNN/tensorflow/script/dataset/ModelNet40/ModelNet40.points')][0]) - # stats['label'] = pd.Series(['0', '1']) - - outputs = network_ocnn.call(octree) - tf_saver = tf.train.Saver(max_to_keep=10) - - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - - all_labels = [] - all_preds = [] - - with tf.Session(config=config) as sess: - # Load Model - sess.run(tf.global_variables_initializer()) - path = tf.train.latest_checkpoint(model_path) - print('Load model') - tf_saver.restore(sess, path) - for i in tqdm(range(iters)): - logits, labels = sess.run((outputs, label)) - all_labels += labels.tolist() - all_preds += np.array(logits).flatten().tolist() - - all_labels = all_labels.reshape(285,-1) - all_preds = all_preds.reshape(285,-1) - - all_labels_mean = all_labels.mean(axis=1) - all_preds_mean = all_preds.mean(axis=1) - - if all_labels.std(axis=1).sum() !=0: - print(all_labels) - - report_reg_metrics(all_labels_mean, all_preds_mean) - report_scatter(all_labels_mean, all_preds_mean) - return stats - -def test(octree, label, iters=925): - columns = ['label', 'accuracy', 'sensitivity', 'specificity', 'total'] - stats = np.zeros((nout, len(columns))) - stats = pd.DataFrame(data=stats, columns=columns) - # stats['label'] = sorted([x[1] for x in os.walk('/home/bbg16101/OCNN_LRP/O-CNN/tensorflow/script/dataset/ModelNet40/ModelNet40.points')][0]) - stats['label'] = pd.Series(['0', '1']) - - outputs = network_ocnn.call(octree) - tf_saver = tf.train.Saver(max_to_keep=10) - - config = tf.ConfigProto() - config.gpu_options.allow_growth = True - - all_labels = [] - all_probs = [] - - with tf.Session(config=config) as sess: - # Load Model - sess.run(tf.global_variables_initializer()) - path = tf.train.latest_checkpoint('logs/pdbbind/ocnn_points_20200704_class_6/model/') - print('Load model') - tf_saver.restore(sess, path) - for i in tqdm(range(iters)): - logits, labels = sess.run((outputs, label)) - softmax_array = softmax(logits, axis=1) - labels = np.eye(nout)[labels] - predictions = np.eye(nout)[np.argmax(softmax_array, axis=1)] - batch_correct = np.where((predictions == labels), 1, 0) - stats['accuracy'] += np.sum(batch_correct, axis=0) - stats['specificity'] += np.sum(np.where(predictions - batch_correct == -1, 1, 0), axis=0) - stats['sensitivity'] += np.sum(np.where(predictions + batch_correct == 2, 1, 0), axis=0) - stats['total'] += np.sum(labels, axis=0) - - all_labels += labels[:,1].tolist() - all_probs += softmax_array[:,1].tolist() - - total = np.sum(stats['total']) - overall_sensitivity = np.sum(stats['sensitivity']) / total - stats['sensitivity'] /= stats['total'] - stats['specificity'] /= (-stats['total'] + total) - stats['accuracy'] /= total - print(stats.sort_values(by='sensitivity', ascending=False)[ - ['label', 'sensitivity', 'specificity', 'total']]) # accuracy, sensitivity, specificity, total - print("\nrecords tested:", total) - print("overall accuracy:", stats['accuracy'][0], '\n') - # print("overall sensitivity:", overall_sensitivity, '\n') - - report_auc(all_labels, all_probs) - report_scatter(all_labels, all_probs) - return stats - - -octree, label = PointDataset(ParseExample(y_type = y_type), - TransformPoints(distort=False, depth=depth), - Points2Octree(depth=depth))(record_names=record_name, batch_size=5, - shuffle_size=1) - -# grids, label = Point2GridDataset(ParseExample(y_type = y_type, **flags), -# TransformPoints(**flags, bounding_sphere=bounding_sphere), -# MakeGrids(out_size, feature_num)) - -# octree, label=OctreeDataset(ParseExample())( -# record_names='dataset/ModelNet40/m40_5_2_12_test_octree.tfrecords', batch_size=1, -# shuffle_size=0, return_iter=False) - -# if use this, also enable_eager_execution at the top of file. -# with open('./input.octree', "wb") as f: -# f.write(octree.numpy().tobytes()) - -# run_lrp(octree, label, 0) -# test(octree=octree, label=label, iters=17) -test_reg(octree=octree, label=label, iters=57) \ No newline at end of file diff --git a/tensorflow/script/tfsolver.py b/tensorflow/script/tfsolver.py index 7305020..6c293be 100644 --- a/tensorflow/script/tfsolver.py +++ b/tensorflow/script/tfsolver.py @@ -10,17 +10,33 @@ class TFSolver: def __init__(self, flags, compute_graph=None, build_solver=build_solver): - self.flags = flags + self.flags = flags.SOLVER self.graph = compute_graph self.build_solver = build_solver def build_train_graph(self): - self.train_tensors, train_names = self.graph('train', training=True, reuse=False, task = self.flags['task']) - self.test_tensors, self.test_names = self.graph('test', training=False, reuse=True, task = self.flags['task']) - total_loss = self.train_tensors[train_names.index('total_loss')] # TODO: use dict - self.train_op, lr = self.build_solver(total_loss, LRFactory(self.flags)) - self.summaries(train_names + ['lr'], self.train_tensors + [lr,], self.test_names) + gpu_num = len(self.flags.gpu) + train_params = {'dataset': 'train', 'training': True, 'reuse': False} + test_params = {'dataset': 'test', 'training': False, 'reuse': True} + if gpu_num > 1: + train_params['gpu_num'] = gpu_num + test_params['gpu_num'] = gpu_num + + self.train_tensors, train_names = self.graph(**train_params) + self.test_tensors, self.test_names = self.graph(**test_params) + self.total_loss = self.train_tensors[train_names.index('total_loss')] + solver_param = [self.total_loss, LRFactory(self.flags)] + if gpu_num > 1: + solver_param.append(gpu_num) + self.train_op, lr = self.build_solver(*solver_param) # qq: self.train_op is the optimizer. + + if gpu_num > 1: # average the tensors from different gpus for summaries + with tf.device('/cpu:0'): + self.train_tensors = average_tensors(self.train_tensors) + self.test_tensors = average_tensors(self.test_tensors) + self.summaries(train_names + ['lr'], self.train_tensors + [lr,], self.test_names) + def summaries(self, train_names, train_tensors, test_names): self.summ_train = summary_train(train_names, train_tensors) self.summ_test, self.summ_holder = summary_test(test_names) @@ -35,7 +51,13 @@ def summ2txt(self, values, step, flag='a'): fid.write(msg + '\n') def build_test_graph(self): - self.test_tensors, self.test_names = self.graph('test', training=False, reuse=False, task = self.flags['task']) + gpu_num = len(self.flags.gpu) + test_params = {'dataset': 'test', 'training': False, 'reuse': False} + if gpu_num > 1: test_params['gpu_num'] = gpu_num + self.test_tensors, self.test_names = self.graph(**test_params) + if gpu_num > 1: # average the tensors from different gpus + with tf.device('/cpu:0'): + self.test_tensors = average_tensors(self.test_tensors) def restore(self, sess, ckpt): print('Load checkpoint: ' + ckpt) @@ -54,12 +76,28 @@ def run_k_iterations(self, sess, k, tensors): for j in range(num): avg_results[j] /= k + avg_results = self.result_callback(avg_results) return avg_results + def result_callback(self, avg_results): + return avg_results # calc some metrics, such as IoU, based on the graph output + + def qq_set_update_after_k_round(self): + self.opt = tf.train.AdamOptimizer(learning_rate=0.1) + tvs = tf.trainable_variables() + accum_vars = [tf.Variable(tf.zeros_like(tv.initialized_value()), trainable=False) for tv in tvs] + self.zero_ops = [tv.assign(tf.zeros_like(tv)) for tv in accum_vars] + gvs = self.opt.compute_gradients(self.total_loss, tvs) + self.accum_ops = [accum_vars[i].assign_add(gv[0]) for i, gv in enumerate(gvs)] + self.train_step = self.opt.apply_gradients([(accum_vars[i], gv[1]) for i, gv in enumerate(gvs)]) + def train(self): # build the computation graph self.build_train_graph() + # qq: add + self.qq_set_update_after_k_round() + # checkpoint start_iter = 1 self.tf_saver = tf.train.Saver(max_to_keep=self.flags.ckpt_num) @@ -70,15 +108,10 @@ def train(self): ckpt = tf.train.latest_checkpoint(ckpt_path) if ckpt: start_iter = int(ckpt[ckpt.find("iter")+5:-5]) + 1 - # timeline_skip, timeline_iter = 50, 2 - # session config = tf.ConfigProto(allow_soft_placement=True) config.gpu_options.allow_growth = True - # options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) - # run_metadata = tf.RunMetadata() - with tf.Session(config=config) as sess: summary_writer = tf.summary.FileWriter(self.flags.logdir, sess.graph) @@ -89,34 +122,19 @@ def train(self): print('Start training ...') for i in tqdm(range(start_iter, self.flags.max_iter + 1), ncols=80): # training - # https://stackoverflow.com/questions/34293714/can-i-measure-the-execution-time-of-individual-operations-with-tensorflow# - # run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) - # run_metadata = tf.RunMetadata() - # summary, _ = sess.run([self.summ_train, self.train_op], options=run_options, run_metadata=run_metadata) + # qq: revise the training, to update gradients after multiple iterations + # first 2 lines are original code. # summary, _ = sess.run([self.summ_train, self.train_op]) - # if i < timeline_skip: - summary, _ = sess.run([self.summ_train, self.train_op]) - # iter_test_result = sess.run(self.test_tensors) # qq add for for test - # else: # add for time - # summary, _ = sess.run([self.summ_train, self.train_op], - # options=options, run_metadata=run_metadata) - - # if (i == timeline_skip + timeline_iter - 1): - # # summary_writer.add_run_metadata(run_metadata, 'step_%d'%i, i) - # # write timeline to a json file - # fetched_timeline = timeline.Timeline(run_metadata.step_stats) - # chrome_trace = fetched_timeline.generate_chrome_trace_format() - # with open(os.path.join(self.flags.logdir, 'timeline.json'), 'w') as f: - # f.write(chrome_trace) - - summary_writer.add_summary(summary, i) - # summary_writer.add_run_metadata(run_metadata, 'step%d' % i, i) - - # Create the Timeline object, and write it to a json - # tl = timeline.Timeline(run_metadata.step_stats) - # ctf = tl.generate_chrome_trace_format() - # with open(os.path.join(self.flags.logdir, 'timeline.json'), 'w') as f: - # f.write(ctf) + # summary_writer.add_summary(summary, i) + if i == 0: + sess.run(self.zero_ops) + if i % 10 !=0 or i ==0: + sess.run(self.accum_ops) + else: + sess.run(self.accum_ops) + sess.run(self.train_step) + sess.run(self.zero_ops) + # qq: end revise # testing if i % self.flags.test_every_iter == 0: @@ -137,7 +155,6 @@ def train(self): def timeline(self): # build the computation graph - self.build_train_graph() # session @@ -145,18 +162,17 @@ def timeline(self): config.gpu_options.allow_growth = True options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) run_metadata = tf.RunMetadata() - timeline_skip, timeline_iter = 50, 1 + timeline_skip, timeline_iter = 100, 2 with tf.Session(config=config) as sess: summary_writer = tf.summary.FileWriter(self.flags.logdir, sess.graph) print('Initialize ...') self.initialize(sess) print('Start profiling ...') - for i in tqdm(range(0, timeline_skip + timeline_iter)): - if i < timeline_skip or i > (timeline_skip + timeline_iter): + for i in tqdm(range(0, timeline_skip + timeline_iter), ncols=80): + if i < timeline_skip: summary, _ = sess.run([self.summ_train, self.train_op]) else: - print('i: ', i) summary, _ = sess.run([self.summ_train, self.train_op], options=options, run_metadata=run_metadata) if (i == timeline_skip + timeline_iter - 1): @@ -166,10 +182,8 @@ def timeline(self): chrome_trace = fetched_timeline.generate_chrome_trace_format() with open(os.path.join(self.flags.logdir, 'timeline.json'), 'w') as f: f.write(chrome_trace) - print('Profiling done!') - - summary_writer.add_summary(summary, i) - + summary_writer.add_summary(summary, i) + print('Profiling done!') def param_stats(self): # build the computation graph @@ -191,15 +205,16 @@ def param_stats(self): def test(self): # build graph self.build_test_graph() + self.qq_set_update_after_k_round() # checkpoint assert(self.flags.ckpt) # the self.flags.ckpt should be provided tf_saver = tf.train.Saver(max_to_keep=10) # start - num_tensors = 4 # len(self.test_tensors) # qq: original has 4, revise for test, since we add logit and input in loss. + num_tensors = len(self.test_tensors) avg_test = [0] * num_tensors - config = tf.ConfigProto() + config = tf.ConfigProto(allow_soft_placement=True) config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: summary_writer = tf.summary.FileWriter(self.flags.logdir, sess.graph) @@ -207,12 +222,13 @@ def test(self): # restore and initialize self.initialize(sess) + print('Restore from checkpoint: %s' % self.flags.ckpt) tf_saver.restore(sess, self.flags.ckpt) print('Start testing ...') - test_logits = [] # qq add to record all logits for i in range(0, self.flags.test_iter): iter_test_result = sess.run(self.test_tensors) + iter_test_result = self.result_callback(iter_test_result) # run testing average for j in range(num_tensors): avg_test[j] += iter_test_result[j] @@ -223,11 +239,14 @@ def test(self): print(reports) self.summ2txt(iter_test_result, i) - # summary - print('Testing done!\n') - reports = 'batch: %04d; ' % self.flags.test_iter + # Final testing results for j in range(num_tensors): avg_test[j] /= self.flags.test_iter + avg_test = self.result_callback(avg_test) + # print the results + print('Testing done!\n') + reports = 'ALL: %04d; ' % self.flags.test_iter + for j in range(num_tensors): reports += '%s: %0.4f; ' % (self.test_names[j], avg_test[j]) print(reports) self.summ2txt(avg_test, 'ALL') @@ -262,7 +281,6 @@ def test_ave(self, test_size = 285): all_labels = np.array(test_labels).reshape(test_size, -1) all_labels = all_labels.reshape(-1, test_size) - print(all_labels) all_preds = all_preds.reshape(-1, test_size) all_labels_mean = all_labels.mean(axis=0) @@ -271,7 +289,6 @@ def test_ave(self, test_size = 285): # if abs(all_labels.std(axis=0).sum()) < 1e-4: # print(all_labels.std(axis=0)) # print(all_labels) - np.savetxt('pred.csv', np.array([all_labels_mean, all_preds_mean]).swapaxes(0,1)) def report_reg_metrics(all_labels, all_preds): from scipy.stats import pearsonr, spearmanr, kendalltau diff --git a/tensorflow/test/test_all.py b/tensorflow/test/test_all.py index cb5f0ed..e23c68d 100644 --- a/tensorflow/test/test_all.py +++ b/tensorflow/test/test_all.py @@ -11,6 +11,8 @@ from test_octree_nearest import OctreeNearestTest from test_octree_gather import OctreeGatherTest from test_octree_align import OctreeAlignTest +from test_octree2points import Octree2PointsTest +from test_points_property import PointsPropertyTest if __name__ == "__main__": diff --git a/tensorflow/test/test_octree2col.py b/tensorflow/test/test_octree2col.py index 66a9016..76376f2 100644 --- a/tensorflow/test/test_octree2col.py +++ b/tensorflow/test/test_octree2col.py @@ -23,7 +23,7 @@ def initialize(self): [9, 10, 11, 12, 13, 14, 15, 16, 17]] def forward(self, kernel_size, stride, idx_map): - kernel = kernel_size[0] * kernel_size[1] * kernel_size[2]; + kernel = kernel_size[0] * kernel_size[1] * kernel_size[2] # kernel = 27 btm_h = 8 top_h = 8 if stride == 1 else 1 diff --git a/tensorflow/test/test_octree2points.py b/tensorflow/test/test_octree2points.py new file mode 100644 index 0000000..ea71f77 --- /dev/null +++ b/tensorflow/test/test_octree2points.py @@ -0,0 +1,49 @@ +import os +import sys +import numpy as np +import tensorflow as tf +sys.path.append("..") +sys.path.append("../script") +from libs import * +from ocnn import octree2points + +class Octree2PointsTest(tf.test.TestCase): + + def test_forward1(self): + octree = octree_batch(octree_samples(['octree_3'])) + pts = octree2points(octree, depth=5, pts_channel=3) + + gt = np.array([[18.5, 16.605, 16.605], [18.5, 16.48, 17.5], + [18.5, 17.5, 16.48], [18.5, 16.48, 18.5], + [18.5, 16.48, 19.5], [18.5, 18.5, 16.48], + [18.5, 19.5, 16.48], [16.48, 18.5, 20.5], + [18.5, 16.48, 20.5], [18.5, 16.48, 21.5], + [16.48, 20.5, 18.5], [18.5, 20.5, 16.48], + [18.5, 21.5, 16.48]]) + + with self.cached_session() as sess: + pt = sess.run(pts) + self.assertAllClose(pt, gt) + + def test_forward2(self): + octree = octree_batch(octree_samples(['octree_3', 'octree_3'])) + pts = octree2points(octree, depth=5, pts_channel=4) + + gt = np.array([[18.5, 16.605, 16.605], [18.5, 16.48, 17.5], + [18.5, 17.5, 16.48], [18.5, 16.48, 18.5], + [18.5, 16.48, 19.5], [18.5, 18.5, 16.48], + [18.5, 19.5, 16.48], [16.48, 18.5, 20.5], + [18.5, 16.48, 20.5], [18.5, 16.48, 21.5], + [16.48, 20.5, 18.5], [18.5, 20.5, 16.48], + [18.5, 21.5, 16.48]]) + idx = np.concatenate([np.zeros([13, 1]), np.ones([13, 1])], axis=0) + gt = np.concatenate([np.concatenate([gt, gt], axis=0), idx], axis=1) + + with self.cached_session() as sess: + pt = sess.run(pts) + self.assertAllClose(pt, gt) + + +if __name__ == "__main__": + os.environ['CUDA_VISIBLE_DEVICES'] = '0' + tf.test.main() diff --git a/tensorflow/test/test_octree_grow.py b/tensorflow/test/test_octree_grow.py index 0e9a718..04a2d7a 100644 --- a/tensorflow/test/test_octree_grow.py +++ b/tensorflow/test/test_octree_grow.py @@ -1,101 +1,40 @@ import os import sys import numpy as np -# from tqdm import tqdm import tensorflow as tf sys.path.append("..") -from config import * -from ocnn import * +from libs import * -# # ===================== -# tf.enable_eager_execution() +tf.compat.v1.enable_eager_execution() -# octree = octree_new(batch_size=1, channel=4, has_displace=True) -# octree = octree_grow(octree, target_depth=1, full_octree=True) -# print('octree_1.octree') -# with open('octree_1.octree', 'wb') as f: -# f.write(octree.numpy().tobytes()) - -# octree = octree_grow(octree, target_depth=2, full_octree=True) -# print('octree_2.octree') -# with open('octree_2.octree', 'wb') as f: -# f.write(octree.numpy().tobytes()) - -# # load test octree -# depth = 6 -# with open('octree_test.octree', 'rb') as f: -# bytes_in = f.read() -# octree_gt = tf.decode_raw(bytes=bytes_in, out_type=tf.int8) -# label_gt = [None]*10 -# for d in range(2, depth + 1): -# label = octree_property(octree_gt, property_name="split", dtype=tf.float32, -# depth=d, channel=1) -# label_gt[d] = tf.reshape(tf.cast(label, dtype=tf.int32), [-1]) - -# for d in range(2, depth + 1): -# octree = octree_update(octree, label_gt[d], depth=d, mask=1) -# if d < depth: -# octree = octree_grow(octree, target_depth=d+1, full_octree=False) - -# print('octree_6.octree') -# with open('octree_6.octree', 'wb') as f: -# f.write(octree.numpy().tobytes()) - - -# ==================== -# tf.enable_eager_execution() - - -depth = 6 -octree = octree_new(batch_size=1, channel=4, has_displace=True) +depth, channel = 5, 3 +octree = octree_new(batch_size=1, channel=channel, has_displace=False) octree = octree_grow(octree, target_depth=1, full_octree=True) octree = octree_grow(octree, target_depth=2, full_octree=True) -# load test octree -test_set = '/home/penwan/workspace/ps/Dataset/completion_test_octree_6_lmdb_new.tfrecord' -octree_gt, _ = dataset(test_set, 1) -label_gt = [None]*10 -for d in range(2, depth + 1): - label = octree_property(octree_gt, property_name="split", dtype=tf.float32, - depth=d, channel=1) - label_gt[d] = tf.reshape(tf.cast(label, dtype=tf.int32), [-1]) - +octree_gt = tf.decode_raw(octree_samples('octree_2'), out_type=tf.int8) for d in range(2, depth + 1): - octree = octree_update(octree, label_gt[d], depth=d, mask=1) + child = octree_child(octree_gt, depth=d) + label = tf.cast(child > -1, tf.int32) + octree = octree_update(octree, label, depth=d, mask=1) if d < depth: - octree = octree_grow(octree, target_depth=d+1, full_octree=False) - -# d = 2 -# octree = octree_update(octree, label_gt[d], depth=d, mask=1) -# octree = octree_grow(octree, target_depth=d+1, full_octree=False) - -# d = 3 -# octree = octree_update(octree, label_gt[d], depth=d, mask=1) -# octree = octree_grow(octree, target_depth=d+1, full_octree=False) - -# d = 4 -# octree = octree_update(octree, label_gt[d], depth=d, mask=1) -# octree = octree_grow(octree, target_depth=d+1, full_octree=False) - -# d = 5 -# octree = octree_update(octree, label_gt[d], depth=d, mask=1) -# octree = octree_grow(octree, target_depth=d+1, full_octree=False) + octree = octree_grow(octree, target_depth=d + 1, full_octree=False) -# d = 6 -# octree = octree_update(octree, label_gt[d], depth=d, mask=1) +signal = octree_signal(octree_gt, depth, channel) +octree = octree_set_property(octree, signal, property_name="feature", depth=depth) -config = tf.ConfigProto() -config.gpu_options.allow_growth = True -with tf.Session(config=config) as sess: - tf.summary.FileWriter('./logs', sess.graph) - o1, o2 = sess.run([octree_gt, octree]) +# config = tf.ConfigProto() +# config.gpu_options.allow_growth = True +# with tf.Session(config=config) as sess: +# tf.summary.FileWriter('./logs', sess.graph) +# o1, o2 = sess.run([octree_gt, octree]) -# o1 = octree_gt.numpy() -# o2 = octree.numpy() +o1 = octree_gt.numpy() +o2 = octree.numpy() -print('output') +print('Please check the output files: `octree_1.octree` and `octree_2.octree`') with open('octree_1.octree', 'wb') as f: f.write(o1.tobytes()) with open('octree_2.octree', 'wb') as f: diff --git a/tensorflow/test/test_octree_key.py b/tensorflow/test/test_octree_key.py new file mode 100644 index 0000000..57eedd2 --- /dev/null +++ b/tensorflow/test/test_octree_key.py @@ -0,0 +1,58 @@ +import tensorflow as tf +import numpy as np +import os +import sys +sys.path.append("..") +from libs import * + +tf.enable_eager_execution() + + +class OctreeKeyTest(tf.test.TestCase): + + def test_decode_encode_key(self): + octree = octree_batch(octree_samples(['octree_1', 'octree_1'])) + xyz = octree_property(octree, property_name='xyz', depth=5, channel=1, dtype=tf.uint64) + xyz = tf.reshape(xyz, [-1]) + pts = octree_decode_key(xyz) + xyz_encode = octree_encode_key(pts) + + gt = tf.constant([ + [16, 16, 16, 0], [16, 16, 17, 0], [16, 17, 16, 0], [16, 17, 17, 0], + [17, 16, 16, 0], [17, 16, 17, 0], [17, 17, 16, 0], [17, 17, 17, 0], + [16, 16, 16, 1], [16, 16, 17, 1], [16, 17, 16, 1], [16, 17, 17, 1], + [17, 16, 16, 1], [17, 16, 17, 1], [17, 17, 16, 1], [17, 17, 17, 1]], + dtype=tf.uint16) + + # test + with self.cached_session() as sess: + self.assertAllEqual(gt, pts) + self.assertAllEqual(xyz_encode, xyz) + + def test_xyz_key(self): + octree = octree_batch(octree_samples(['octree_1', 'octree_1'])) + xyz = octree_property(octree, property_name='xyz', depth=5, channel=1, dtype=tf.uint64) + xyz = tf.reshape(xyz, [-1]) + + key = octree_xyz2key(xyz) + xyz_out = octree_key2xyz(key) + + # test + with self.cached_session() as sess: + self.assertAllEqual(xyz_out, xyz) + + def test_search_key(self): + octree = octree_batch(octree_samples(['octree_1', 'octree_1'])) + key = tf.constant([28673, 281474976739335, 10], dtype=tf.uint64) + idx_gt = tf.constant([1, 15, -1], dtype=tf.int32) + idx = octree_search_key(key, octree, depth=5, is_xyz=False) + + # test + with self.cached_session() as sess: + self.assertAllEqual(idx_gt, idx) + + + +if __name__ == "__main__": + os.environ['CUDA_VISIBLE_DEVICES'] = '0' + tf.test.main() diff --git a/tensorflow/test/test_octree_linear.py b/tensorflow/test/test_octree_linear.py index 4d48afc..81631e5 100644 --- a/tensorflow/test/test_octree_linear.py +++ b/tensorflow/test/test_octree_linear.py @@ -42,5 +42,5 @@ def test_forward_backward(self): if __name__ == "__main__": - os.environ['CUDA_VISIBLE_DEVICES'] = '1' + os.environ['CUDA_VISIBLE_DEVICES'] = '0' tf.test.main() \ No newline at end of file diff --git a/tensorflow/test/test_octree_nearest.py b/tensorflow/test/test_octree_nearest.py index eb6d1b8..efdcbf9 100644 --- a/tensorflow/test/test_octree_nearest.py +++ b/tensorflow/test/test_octree_nearest.py @@ -16,9 +16,7 @@ def test_forward_backward(self): data = tf.constant(data_np) nearest1 = octree_tile(data, octree, depth) - xyz = octree_property( - octree, property_name='xyz', depth=depth+1, channel=1, dtype=tf.uint32) - xyz = tf.reshape(xyz, [-1]) + xyz = octree_xyz(octree, depth=depth+1, decode=False) xyz = tf.cast(octree_decode_key(xyz), dtype=tf.float32) xyz += tf.constant([0.5, 0.5, 0.5, 0.0], dtype=tf.float32) xyz *= tf.constant([0.5, 0.5, 0.5, 1.0], dtype=tf.float32) diff --git a/tensorflow/test/test_octree_search.py b/tensorflow/test/test_octree_search.py index 56eaabe..855da9c 100644 --- a/tensorflow/test/test_octree_search.py +++ b/tensorflow/test/test_octree_search.py @@ -27,7 +27,7 @@ def test_forward1(self): def test_forward2(self): octree, data, idx_gt = self.init_data() - data = tf.cast(data.T, tf.uint8) + data = tf.cast(data.T, tf_uints) data = octree_encode_key(data) idx = octree_search_key(data, octree, depth=5, is_xyz=True) with self.cached_session() as sess: @@ -36,7 +36,7 @@ def test_forward2(self): def test_forward3(self): octree, data, idx_gt = self.init_data() - data = tf.cast(data.T, tf.uint8) + data = tf.cast(data.T, tf_uints) data = octree_encode_key(data) key = octree_xyz2key(data) idx = octree_search_key(key, octree, depth=5, is_xyz=False) diff --git a/tensorflow/test/test_points_property.py b/tensorflow/test/test_points_property.py index 2d310b5..7614edc 100644 --- a/tensorflow/test/test_points_property.py +++ b/tensorflow/test/test_points_property.py @@ -1,44 +1,55 @@ +from libs import * import os import sys import numpy as np import tensorflow as tf sys.path.append('..') -from libs import * -import pyoctree - -tf.enable_eager_execution() - -filename1 = '/home/ps/workspace/ocnn-tf/script/logs/seg/0430_run_dataset/00.points' -filename2 = '/home/ps/workspace/ocnn-tf/script/logs/seg/0430_run_dataset/01.points' - -with open(filename1, 'rb') as fid: - points_raw1 = fid.read() -with open(filename2, 'rb') as fid: - points_raw2 = fid.read() -points_raw = [points_raw1, points_raw2] - -tf_xyz = points_property(points_raw, property_name='xyz', channel=4) -tf_label = points_property(points_raw, property_name='label', channel=1) - -points1 = pyoctree.Points() -succ = points1.read_points(filename1) -points2 = pyoctree.Points() -succ = points2.read_points(filename2) - -np_xyz1 = np.array(points1.points()).reshape((-1, 3)) -np_xyz2 = np.array(points2.points()).reshape((-1, 3)) -np_label1 = np.array(points1.labels()).reshape((-1, 1)) -np_label2 = np.array(points2.labels()).reshape((-1, 1)) -np_id1 = np.zeros(np_label1.shape) -np_id2 = np.ones(np_label2.shape) - -np_xyz = np.concatenate( - (np.concatenate((np_xyz1, np_id1), axis=1), - np.concatenate((np_xyz2, np_id2), axis=1)), axis=0).astype(np.float32) -np_label = np.concatenate((np_label1, np_label2), axis=0).astype(np.float32) -eq1 = np.array_equal(np_xyz, tf_xyz.numpy()) -eq2 = np.array_equal(np_label, tf_label.numpy()) -print('Pass xyz test: ', eq1) -print('Pass label test: ', eq2) +class PointsPropertyTest(tf.test.TestCase): + + def test_points_property(self): + points = np.arange(15, dtype=np.float32).reshape([-1, 3]) + index = np.array([[0.0], [0.0], [0.0], [1.0], [1.0]], dtype=np.float32) + points4 = np.concatenate([points, index], axis=1) + normals = np.arange(0, 1.5, 0.1, dtype=np.float32).reshape([-1, 3]) + labels = np.arange(5, dtype=np.float32) + feats = np.arange(10, dtype=np.float32).reshape([-1, 2]) + + # creat points + pts1 = points_new(points[:3, :], normals[:3, :], feats[:3, :], labels[:3]) + pts2 = points_new(points[3:, :], normals[3:, :], feats[3:, :], labels[3:]) + + # # save points + # with open('points1.points', 'wb') as fid: + # fid.write(pts1.numpy()[0]) + # with open('points2.points', 'wb') as fid: + # fid.write(pts2.numpy()[0]) + + # get property + tf_labels = points_property([pts1, pts2], property_name='label', channel=1) + tf_labels = tf.reshape(tf_labels, [-1]) + tf_normals = points_property([pts1, pts2], property_name='normal', channel=3) + tf_pts3 = points_property([pts1, pts2], property_name='xyz', channel=3) + tf_pts4 = points_property([pts1, pts2], property_name='xyz', channel=4) + tf_feats = points_property([pts1, pts2], property_name='feature', channel=2) + + # set property + points_set = -np.arange(9, dtype=np.float32).reshape([-1, 3]) + pts3 = points_set_property(pts1, points_set, property_name='xyz') + tf_points_set = points_property(pts3, property_name='xyz', channel=3) + feats_set = -np.arange(6, dtype=np.float32).reshape([-1, 2]) + tf_feats_set = points_property(pts3, property_name='feature', channel=2) + + with self.cached_session() as sess: + self.assertAllEqual(tf_labels, labels) + self.assertAllEqual(tf_normals, normals) + self.assertAllEqual(tf_pts3, points) + self.assertAllEqual(tf_pts4, points4) + self.assertAllEqual(tf_feats, feats) + self.assertAllEqual(tf_points_set, points_set) + + +if __name__ == "__main__": + os.environ['CUDA_VISIBLE_DEVICES'] = '0' + tf.test.main() diff --git a/tensorflow/util/.DS_Store b/tensorflow/util/.DS_Store deleted file mode 100644 index 8f70269..0000000 Binary files a/tensorflow/util/.DS_Store and /dev/null differ diff --git a/tensorflow/util/convert_grids_tfrecords.py b/tensorflow/util/convert_grids_tfrecords.py deleted file mode 100644 index 9969b10..0000000 --- a/tensorflow/util/convert_grids_tfrecords.py +++ /dev/null @@ -1,103 +0,0 @@ -import os -import argparse -import tensorflow as tf -from random import shuffle - -from make_voxel import read_points_file, make_grid - -parser = argparse.ArgumentParser() -parser.add_argument('--file_dir', type=str, required=True, - help='Base folder containing the data') -parser.add_argument('--list_file', type=str, required=True, - help='File containing the list of data') -parser.add_argument('--records_name', type=str, required=True, - help='Name of tfrecords') -parser.add_argument('--file_type', type=str, required=False, default='data', - help='File type') -parser.add_argument('--shuffle', type=str, required=False, default='', - help='Whether to shuffle the data order') -parser.add_argument('--label_type', type=str, required=False, default='int', - help='If label is int for classification or float for regression') -parser.add_argument('--resolution', type=float, required=False, default=1.0, - help='If label is int for classification or float for regression') -shuffle_data = False - - -def _int64_feature(value): - return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) - - -def _bytes_feature(value): - return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) - - -# QQ add -def _float_feature(value): - return tf.train.Feature(float_list=tf.train.FloatList(value=[value])) - - -def load_octree(file): - with open(file, 'rb') as f: - octree_bytes = f.read() - return octree_bytes - - -def write_data_to_tfrecords(file_dir, list_file, records_name, file_type, label_type='int', resolution = 1.0): - [data, label, index] = get_data_label_pair(list_file, label_type) - - writer = tf.python_io.TFRecordWriter(records_name) - for i in range(len(data)): - if not i % 1000: - print('data loaded: {}/{}'.format(i, len(data))) - - # octree_file = load_octree(os.path.join(file_dir, data[i])) - coords, features = read_points_file(os.path.join(file_dir, data[i])) - grid_string = make_grid(coords, features, grid_resolution=resolution, radius=32.0) - - if label_type == 'int': - feature = {file_type: _bytes_feature(grid_string), - 'label': _int64_feature(label[i]), - 'index': _int64_feature(index[i]), - 'filename': _bytes_feature(('%06d_%s' % (i, data[i])).encode('utf8'))} - else: - feature = {file_type: _bytes_feature(grid_string), - 'label': _float_feature(label[i]), # QQ: add float option - 'index': _int64_feature(index[i]), - 'filename': _bytes_feature(('%06d_%s' % (i, data[i])).encode('utf8'))} - example = tf.train.Example(features=tf.train.Features(feature=feature)) - writer.write(example.SerializeToString()) - writer.close() - - -def get_data_label_pair(list_file, label_tye='int'): - file_list = [] - label_list = [] - with open(list_file) as f: - for line in f: - file, label = line.split() - file_list.append(file) - if label_tye == 'int': # QQ: add float option - label_list.append(int(label)) - else: - label_list.append(float(label)) - index_list = list(range(len(label_list))) - - if shuffle_data: - c = list(zip(file_list, label_list, index_list)) - shuffle(c) - file_list, label_list, index_list = zip(*c) - with open(list_file + '.shuffle.txt', 'w') as f: - for item in c: - f.write('{} {}\n'.format(item[0], item[1])) - return file_list, label_list, index_list - - -if __name__ == '__main__': - args = parser.parse_args() - shuffle_data = args.shuffle - write_data_to_tfrecords(args.file_dir, - args.list_file, - args.records_name, - args.file_type, - args.label_type, # QQ: add float option - args.resolution) diff --git a/tensorflow/util/convert_tfrecords.py b/tensorflow/util/convert_tfrecords.py index ed20cf3..d7b99c6 100644 --- a/tensorflow/util/convert_tfrecords.py +++ b/tensorflow/util/convert_tfrecords.py @@ -22,6 +22,7 @@ def _int64_feature(value): return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) + def _bytes_feature(value): return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) @@ -70,7 +71,7 @@ def get_data_label_pair(list_file, label_tye = 'int'): if label_tye == 'int': # QQ: add float option label_list.append(int(label)) else: - label_list.append(float(label)) + label_list.append(float(label)) index_list = list(range(len(label_list))) if shuffle_data: diff --git a/tensorflow/util/parse_ckpt.py b/tensorflow/util/parse_ckpt.py index b42cc9d..bf4c30f 100644 --- a/tensorflow/util/parse_ckpt.py +++ b/tensorflow/util/parse_ckpt.py @@ -4,30 +4,44 @@ parser = argparse.ArgumentParser() +parser.add_argument('--run', type=str, required=False, default='list_var', + help='The command to run') parser.add_argument('--ckpt', type=str, required=True, help='The path of the ckpt file') parser.add_argument('--skips', type=str, required=False, nargs='*', help="Skip specific variables") +parser.add_argument('--vars', type=str, required=False, nargs='*', + help="The variable names") args = parser.parse_args() -ckpt, skips = args.ckpt, args.skips +ckpt, skips, varis = args.ckpt, args.skips, args.vars if not skips: skips = [] +if not varis: varis = [] -reader = tf.contrib.framework.load_checkpoint(ckpt) -all_vars = tf.contrib.framework.list_variables(ckpt) +def print_var(): + for v in varis: + t = tf.train.load_variable(ckpt, v) + print(v, '\n', t) -total_num = 0 -for idx, var in enumerate(all_vars): - name, shape = var[0], var[1] +def list_var(): + all_vars = tf.train.list_variables(ckpt) + total_num = 0 + for idx, var in enumerate(all_vars): + name, shape = var[0], var[1] - exclude = False - for s in skips: - exclude = s in name - if exclude: break - if exclude: continue + exclude = False + for s in skips: + exclude = s in name + if exclude: + break + if exclude: + continue - shape_str = '; '.join([str(s) for s in shape]) - shape_num = prod(shape) - print("{:3}, {}, [{}], {}".format(idx, name, shape_str, shape_num)) - total_num += shape_num + shape_str = '; '.join([str(s) for s in shape]) + shape_num = prod(shape) + print("{:3}, {}, [{}], {}".format(idx, name, shape_str, shape_num)) + total_num += shape_num -print('Total parameters: {}'.format(total_num)) + print('Total parameters: {}'.format(total_num)) + +if __name__ == '__main__': + eval(args.run + '()') \ No newline at end of file diff --git a/tensorflow/util/prepare_full_voxel_model.py b/tensorflow/util/prepare_full_voxel_model.py deleted file mode 100644 index 9832e61..0000000 --- a/tensorflow/util/prepare_full_voxel_model.py +++ /dev/null @@ -1,23 +0,0 @@ -from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.keras.preprocessing import dataset_utils - -def path_to_tensor(): - pass - -# refer to https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/preprocessing/image_dataset.py -def paths_and_labels_to_dataset(image_paths, - image_size, - num_channels, - labels, - label_mode, - num_classes, - interpolation): - """Constructs a dataset of images and labels.""" - # TODO(fchollet): consider making num_parallel_calls settable - path_ds = dataset_ops.Dataset.from_tensor_slices(image_paths) - img_ds = path_ds.map( - lambda x: path_to_image(x, image_size, num_channels, interpolation)) - if label_mode: - label_ds = dataset_utils.labels_to_dataset(labels, label_mode, num_classes) - img_ds = dataset_ops.Dataset.zip((img_ds, label_ds)) - return img_ds \ No newline at end of file diff --git a/tensorflow/util/revert_tfrecords.py b/tensorflow/util/revert_tfrecords.py index 6129dbe..f164c7b 100644 --- a/tensorflow/util/revert_tfrecords.py +++ b/tensorflow/util/revert_tfrecords.py @@ -13,10 +13,9 @@ help='Specify the count of the data to extract') parser.add_argument('--file_type', type=str, required=False, default='data', help='File type') -parser.add_argument('--label_type', type=str, required=False, default='float', - help='If label is int for classification or float for regression') -def read_data_from_tfrecords(records_name, output_path, list_file, file_type, count, label_type = 'int'): + +def read_data_from_tfrecords(records_name, output_path, list_file, file_type, count): records_iterator = tf.python_io.tf_record_iterator(records_name) count = count if count != 0 else float('Inf') @@ -27,10 +26,8 @@ def read_data_from_tfrecords(records_name, output_path, list_file, file_type, co example = tf.train.Example() example.ParseFromString(string_record) - if label_type == 'int': - label = int(example.features.feature['label'].int64_list.value[0]) - else: - label = float(example.features.feature['label'].float_list.value[0]) + label = int(example.features.feature['label'].int64_list.value[0]) + # index = int(example.features.feature['index'].int64_list.value[0]) octree = example.features.feature[file_type].bytes_list.value[0] if 'filename' in example.features.feature: filename = example.features.feature['filename'].bytes_list.value[0] \ @@ -55,5 +52,4 @@ def read_data_from_tfrecords(records_name, output_path, list_file, file_type, co args.output_path, args.list_file, args.file_type, - args.count, - args.label_type) + args.count) diff --git a/visualize/.DS_Store b/visualize/.DS_Store deleted file mode 100644 index 4f11359..0000000 Binary files a/visualize/.DS_Store and /dev/null differ diff --git a/visualize/adaptive_octree_opt.py b/visualize/adaptive_octree_opt.py deleted file mode 100644 index a8fc6f2..0000000 --- a/visualize/adaptive_octree_opt.py +++ /dev/null @@ -1,205 +0,0 @@ -# An optimized version of adaptive_octree.py -# reduce the number of points (and lines for output) -# accelerate for loop by multiprocessing. (Can not make it.) - -import numpy as np -import os -import vtk -import operator -from utils import * -import time -import math - - -by_folder = False # if True, all complexes in complex_path will be treated. o.w only single complex with complex_id wull be treated. -in_folder = False # for shell script. assume working directory is already the folder for complex - -complex_path = '/media/data/data_share/pdbbind/refined_set_v2018/' #'../test_set/' #'/media/data/data_share/pdbbind/refined_set_v2018/' #'../test_set/' ## -complex_id = '1a1e' #'1a1e' # when by_folder is False, will handle this single complex -plot_octree = True # if True, we will create non-adaptive version - -plot_adaptive = False # if True, we will create the adaptive version. -light = False # light will associate with plot_adaptive, if true, no vtk files will generated. -protein_flag = False # if True, protein points will be included instead of pocket. - -adaptive_line = True # Now need always be True. -center_by_ligand = True # if True, the ligand will be in center of Cubic. -shift_to_ori = False # if True, the visualization will have same coordinate system with original pdb - -total_depth = 10 # depth of octree -# area1, area2, area3, area4 = 12, 20, 36, 68 #16, 32, 64, 64 # Use for adaptive resolution -target_range = 32 # The area that around the center of ligand -line_depth = 5 # Only plot first line_depth octree division segment lines. -save_folder ='./results' # '/media/data/data_share/OctSurf/vtk_result/' # # The folder to save generated vtk file -differ = False # if True, octree segment line will have different color. ow the same color 0 will be used. -# max_amount = 300 - -# sometimes we want to skip some cases. -skip_list = [ '1a4r', "2y80", '2pjt', '4g17'] # '1a1e', '2pjt', '4g17' finished - -def vtk_from_cubic_dic(cubic_dic, total_depth, complex_id, differ=False, - shift = [0,0,0], save_folder = './results'): - - vtk_line_dic = {} - for i in range(total_depth + 1): - current_depth = total_depth - i - base = target_range*2 / 2 ** current_depth - vtk_dic = {} - color = get_color_for_dep(current_depth + 1, differ) - for key, value in cubic_dic.items(): - [depth, x, y, z] = [int(item) for item in key.split('_')] - coord_x = x * base - target_range - coord_y = y * base - target_range - coord_z = z * base - target_range - min_coord = [coord_x, coord_y, coord_z] - max_coord = [coord_x + base, coord_y + base, coord_z + base] - cubic = Cubic([min_coord, max_coord], key) - cubic.set_color_new(value) - vtk_dic = cubic.load_to_vtk_opt(vtk_dic) - if current_depth < line_depth: # current_depth < depth for all. - vtk_line_dic = cubic.write_oct_line_opt(vtk_line_dic, color) - - if not os.path.isdir('{}/{}/'.format(save_folder, complex_id)): - os.makedirs('{}/{}/'.format(save_folder, complex_id)) - file_name = '{}/{}/{}_Octree_{}_{}'.format(save_folder, complex_id, complex_id, target_range, current_depth) - vtk_writer(vtk_dic=vtk_dic, file_name = file_name, shift = shift) - - if current_depth > 0: - upper_layer_dic = get_upper_layer_dic(cubic_dic) - cubic_dic = upper_layer_dic - - # dept = 0 - color = get_color_for_dep(0, differ) - vtk_line_dic = cubic.write_edge_line_opt(vtk_line_dic, color) - - pts, lines, colors = fill_vtk_lines(vtk_line_dic, shift) - - linesPolyData = vtk.vtkPolyData() - linesPolyData.SetPoints(pts) - linesPolyData.SetLines(lines) - linesPolyData.GetCellData().SetScalars(colors) - - # add for clean data - # cleanFilter = vtk.vtkCleanPolyData() - # cleanFilter.SetInputConnection(linesPolyData) - # cleanFilter.Update() - # mapper = vtk.vtkPolyDataMapper() - # mapper.SetInputConnection(cleanFilter.GetOutputPort()) - - ShellVolmapper = vtk.vtkDataSetMapper() - ShellVolmapper.SetScalarModeToUseCellData() - ShellVolmapper.UseLookupTableScalarRangeOn() - ShellVolmapper.SetInputData(linesPolyData) - ShellVolactor = vtk.vtkActor() - ShellVolactor.SetMapper(ShellVolmapper) - - # modelwriter = vtk.vtkUnstructuredGridWriter() - modelwriter = vtk.vtkDataSetWriter() - modelwriter.SetFileName('{}/{}/{}_segline_{}_{}.vtk'.format(save_folder, complex_id, complex_id, target_range, line_depth)) - # modelwriter.SetInputData(grid) - modelwriter.SetInputData(ShellVolmapper.GetInput()) - modelwriter.Write() - print('Save {}/{}/{}_segline_{}_{}.vtk'.format(save_folder, complex_id, complex_id, target_range, line_depth)) - - -def points_to_vtk_by_id(complex_id, path = '../test_set/', protein_flag = False, - total_depth = 10, line_depth = 10, save_folder = './results'): - # path = path + complex_id +'/' - path = path #+ '/' - #file_names = [path + complex_id + '_protein_pc.ply', path + complex_id + '_ligand_pc.ply'] - file_names = [path + complex_id + '_cdk_pocket.xyz', - path + complex_id + '_cdk_protein.xyz', - path + complex_id + '_cdk_ligand.xyz'] - print(file_names) - - if protein_flag: - x_protein, y_protein, z_protein = read_xyz_file(file_names[1]) - else: - x_protein, y_protein, z_protein = read_xyz_file(file_names[0]) - x_ligand, y_ligand, z_ligand = read_xyz_file(file_names[2]) - - protein_xyz, ligand_xyz, _, center, ligand_center = centerize([x_protein, y_protein, z_protein], - [x_ligand, y_ligand, z_ligand], - by_ligand = center_by_ligand) - if not shift_to_ori: - center = [0,0,0] - [x_protein, y_protein, z_protein], [x_ligand, y_ligand, z_ligand] = protein_xyz, ligand_xyz - del protein_xyz, ligand_xyz - - x_protein, y_protein, z_protein = filter_by_center_range(x_protein, y_protein, z_protein, target_range) - x_ligand, y_ligand, z_ligand = filter_by_center_range(x_ligand, y_ligand, z_ligand, target_range) - - ligand_area = max(max(x_ligand), max(y_ligand), max(z_ligand)) + 7.5 + 2 - ligand_area = math.ceil(ligand_area) - # area1, area2, area3, area4 = ligand_area, ligand_area * 2, min(ligand_area * 4, target_range), min(ligand_area * 8, target_range) - area1, area2, area3, area4 = ligand_area, ligand_area * 2, min(ligand_area * 3, target_range), min(ligand_area * 5, target_range) - - - print('Area: {}, {}, {}, {}'.format(area1, area2, area3, area4)) - - x_protein, y_protein, z_protein = get_vol_index(x_protein, y_protein, z_protein, total_depth, target_range) - x_ligand, y_ligand, z_ligand = get_vol_index(x_ligand, y_ligand, z_ligand, total_depth, target_range) - - time1 = time.time() - cubic_dic = create_cubic_dic([x_protein, y_protein, z_protein], - [x_ligand, y_ligand, z_ligand], total_depth) - del x_protein, y_protein, z_protein, x_ligand, y_ligand, z_ligand - time2 = time.time() - print('Finished create_cubic_dic, time {}s'.format(time2-time1)) - - if not os.path.isdir('{}/{}/'.format(save_folder, complex_id)): - os.makedirs('{}/{}/'.format(save_folder, complex_id)) - time3 = None - if plot_adaptive: - adap_cubics = Adaptive_Cubics(cubic_dic, complex_id, ligand_center, center, adaptive_line, differ) - adap_cubics.get_adaptive_cubic(area1, area2, area3, area4, target_range, line_depth, - total_depth, save_folder, light = light) - del adap_cubics - time3 = time.time() - print('Finished get_adaptive_cubic, time {}s'.format(time3 - time2)) - if plot_octree: - vtk_from_cubic_dic(cubic_dic, total_depth, complex_id, differ, center, save_folder) - time4 = time.time() - if time3 is not None: - diff = time4 - time3 - else: - diff = time4 - time2 - print('Finished vtk_from_cubic_dic, time {}s'.format(diff)) - - -def points_to_vtk_by_folder(path = '../test_set', protein_flag=False, - skip_list = [], save_folder = './results'): - i = 0 - for dir in os.listdir(path): - if dir in skip_list: - continue - if not os.path.isdir(os.path.join(path, dir)): - continue - try: - path = path + dir + '/' - points_to_vtk_by_id(dir, path, protein_flag, save_folder= save_folder) - i += 1 - except FileNotFoundError: - print('not find {}'.format(dir)) - else: - pass - # if i > max_amount: - # break - -if __name__ == "__main__": - if by_folder: - # points_to_vtk_by_folder(skip_list = ["1a1e", "4g17", "3v2w", "2y80", "4prg", "1a4r"]) - points_to_vtk_by_folder(complex_path, protein_flag=protein_flag, skip_list = skip_list) - if in_folder: - path = os.getcwd() - complex_id = path.split('/')[-1] - save_folder = '~/2019_Spring/OctSurf/python/results' - for depth in range(12,13): - print('{}, {}'.format(complex_id, depth)) - points_to_vtk_by_id(complex_id, path= path, protein_flag=protein_flag, total_depth = depth, - line_depth = depth, save_folder = save_folder) - if not by_folder and not in_folder: - path = complex_path + complex_id + '/' - points_to_vtk_by_id(complex_id, path=path, protein_flag=protein_flag, total_depth = total_depth, - line_depth=line_depth, save_folder=save_folder) - diff --git a/visualize/octree_parse.py b/visualize/octree_parse.py deleted file mode 100644 index 8f053a6..0000000 --- a/visualize/octree_parse.py +++ /dev/null @@ -1,80 +0,0 @@ -import sys -import numpy as np -sys.path.insert(0, '/home/qil15006/2020Summer/OCNN_Jun26_2020/octree/build/python') -import pyoctree - -from adaptive_octree_opt import vtk_from_cubic_dic - -pro_lig_flag_pos = 18 -kLeaf_flag = -1 - -def convert_key_to_xyz(key, depth): - xyz = [0,0,0] - for i in range(depth): - for j in range(3): - mask = 1 << (3 * i + 2 - j) - xyz[j] |= (key & mask) >> (2 * i + 2 - j) - return xyz - -def octree_info_to_cubic_dic(depth, node_number, keys, children, features): - cubic_dic = {} - for node_idx_d in range(node_number): - child = children[node_idx_d] - if child == kLeaf_flag: - continue - key = keys[node_idx_d] - xyz = convert_key_to_xyz(key, depth) - [x,y,z] = xyz - key_for_cubic_dic = '{}_{}_{}_{}'.format(depth, x,y,z) - if features[node_idx_d][pro_lig_flag_pos] == 1: - cubic_dic[key_for_cubic_dic] = 'Protein' - elif features[node_idx_d][pro_lig_flag_pos] == 0: - cubic_dic[key_for_cubic_dic] = 'Ligand' - else: - cubic_dic[key_for_cubic_dic] = 'Mixture' - return cubic_dic - - -oct = pyoctree.Octree() -# oct.read_octree("/home/qil15006/2020Summer/OCNN_Jun26_2020/tensorflow/script/input.octree") -# oct.read_octree("/home/qil15006/2020Summer/OCNN_Jun26_2020/octree/build/test_folder_qq/1a1e_points_6_2_001.octree") -# oct.read_octree("/home/qil15006/2020Summer/OCNN_Jun26_2020/octree/build/test_folder_qq/2xnb_points_6_2_000.octree") -# oct.read_octree("/media/data/data_share/pdbbind/CASF-2016/coreset/1z95/octree_folder/1z95_points_5_2_000.octree") -# oct.read_octree("/media/data/data_share/pdbbind/refined_set_v2018/1a1e/octree_folder/1a1e_points_8_2_000.octree") -# oct.read_octree("/home/qil15006/2020Summer/OCNN_Jun26_2020/tensorflow/script/octree_heatmaps/heatmap_1.octree") -oct.read_octree("/media/data/data_share/pdbbind/v2018-other-PL/4ii9/4ii9_points_7_2_000.octree") - - -num_channel = oct.num_channel() - -depth = oct.depth() -features = oct.features(depth) -node_number = oct.num_nodes(depth) -children = oct.children(depth) -keys = oct.keys(depth) - -# non_empty_idx = [] -# coords = [] -# for i in range(len(children)): -# if children[i] >= 0: -# non_empty_idx.append(i) -# x,y,z = convert_key_to_xyz(keys[i], 6) -# coords.append((x,y,z)) -# # coords = np.array(coords) -# -# -# grids_in_oc_count = 0 -# for i in range(len(grids_idx[0])): -# grid_coord = (grids_idx[0][i], grids_idx[1][i], grids_idx[2][i]) -# if grid_coord in coords: -# grids_in_oc_count += 1 -# else: -# print(grid_coord) - - - -features = np.array(features).reshape((num_channel, node_number)) -features = np.swapaxes(features, 0, 1) -cubic_dic = octree_info_to_cubic_dic(depth, node_number, keys, children, features) -vtk_from_cubic_dic(cubic_dic, depth, 'input', differ = False, save_folder = './') - diff --git a/visualize/points_filter.py b/visualize/points_filter.py deleted file mode 100644 index 47cac5e..0000000 --- a/visualize/points_filter.py +++ /dev/null @@ -1,132 +0,0 @@ -import numpy as np -import seaborn as sns -import matplotlib.pyplot as plt - - -def ply_filter(filename, center = None, threshold = None, normalize = False, cut_negative = False, log_norm = False): - points = [] - rewrite_lines = [] - prefix = filename.split('.ply')[0] - with open(filename, 'r') as f: - lines = f.readlines() - check_line = False - for line in lines: - if not check_line: - if 'end_header' not in line: - rewrite_lines.append(line) - continue - else: - check_line = True - rewrite_lines.append(line) - elif len(line) <3: - break - else: - line = line.replace('\n', '') - contents = line.split(' ') - if len(contents) == 7: - contents = contents[:-1] - contents = [float(item) for item in contents] - point = contents[:6] - points.append(point) - - points = np.array(points) - if center is not None: - points[:, :3] *= 32 - points[:, :3] += center - - - plt.figure() - sns.distplot(points[:,3]) - plt.show() - print(points.shape) - - if threshold is not None: - min_thres = np.percentile(points[:, 3], threshold[0]) - max_thres = np.percentile(points[:, 3], threshold[1]) - print(min_thres, max_thres) - flag = np.logical_or(points[:, 3]max_thres) - points = points[flag,:] - - print(points[:, 3].argmax(), points[:, 3].max()) - print(points[:, 3].argmin(), points[:, 3].min()) - print(points.shape) - - if normalize: - # tmp = points[:,3] - # tmp = (tmp - tmp.mean())/tmp.std() - # points[:,3] = tmp - tmp = points[:, 3] - tmp_min = tmp.min() - tmp_max = tmp.max() - print("max {}, min {}".format(tmp_max, tmp_min)) - scale = tmp_max - tmp_min - tmp = (tmp - tmp_min)/scale - points[:, 3:] = tmp.reshape(-1,1) - # points[:, 4] = tmp - # points[:, 5] = tmp - - plt.figure() - sns.distplot(points[:, 3]) - plt.show() - - if cut_negative: - points[points[:,3]<0, 3:] = 0 - if log_norm: - tmp = points[:,3] - top_n_value = np.sort(tmp)[-2] - tmp += 1 - tmp = np.log10(tmp)/np.log10(top_n_value) - tmp[tmp > 1] = 1 - tmp = tmp.reshape((-1,1)) - points[:,3:] = tmp - plt.figure() - sns.distplot(points[:, 3]) - plt.show() - return points, prefix, rewrite_lines - -def write_ply(points, prefix, rewrite_lines): - write_filename = prefix + '_filt.ply' - print(write_filename) - with open(write_filename, 'w') as f: - for line in rewrite_lines: - if 'element vertex'not in line: - f.write(line) - else: - f.write('element vertex {}\n'.format(points.shape[0])) - for i in range(points.shape[0]): - string_list = [str(item) for item in points[i,:]] - f.write(' '.join(string_list) + '\n') - return points - -def ply_to_xyz(points, prefix, threshold=None): - sorted_points = points[points[:, 3].argsort()] - size = sorted_points.shape[0] - write_filename = prefix + '_filt.xyz' - with open(write_filename, 'w') as f: - f.write(str(size) + '\n') - for i in range(size): - string_list = [str(item) for item in points[i, :3]] - if i < size/4: - string_list = ['H'] + string_list - elif i < size/2: - string_list = ['C'] + string_list - elif i< size/4 * 3: - string_list = ['N'] + string_list - else: - string_list = ['O'] + string_list - f.write(' '.join(string_list) + '\n') - pass - -points, prefix, rewrite_lines = ply_filter('../tensorflow/script/ocnn_lrp_results/molecule_heatmap-1.ply', - center = np.array([24.589, 26.7851, 27.4071]), - threshold = None, normalize=False, - cut_negative = False, log_norm=False) -write_ply(points, prefix, rewrite_lines) -# ply_to_xyz(points, prefix, threshold = None) - - -# center -# 2xnb: [24.589, 26.7851, 27.4071] -# 1w40: [37.2144, -0.397999, 12.6513] -# 4djv: [24.5628, 10.4633, 21.5968] -# 3udh: [23.6291, 36.6386, 41.1921] \ No newline at end of file diff --git a/visualize/utils.py b/visualize/utils.py deleted file mode 100644 index 8925f5c..0000000 --- a/visualize/utils.py +++ /dev/null @@ -1,1036 +0,0 @@ -# utils for adaptive_octree_opt.py. - - -import numpy as np -import vtk -import time - -def read_xyz_file(file_name): - with open('{}'.format(file_name), 'r') as data_fid: - points, x, y, z = [], [], [], [] - for row in data_fid.readlines(): - row = row.replace('\n', '') - numbers = row.split(' ') - try: - point_coord = [float(item) for item in numbers[0:3]] - point_x = point_coord[0] - point_y = point_coord[1] - point_z = point_coord[2] - points.append(point_coord) - x.append(point_x) - y.append(point_y) - z.append(point_z) - except ValueError: - print('ValueError: {}, row {}'.format(file_name, row)) - except IndexError: - print('IndexError: {}, row {}'.format(file_name, row)) - else: - pass - return np.array(x), np.array(y), np.array(z) - -def read_feature_file(file_name): - with open('{}'.format(file_name), 'r') as data_fid: - features = [] - for row in data_fid.readlines(): - row = row.replace('\n', '') - row_features = row.split(' ') - try: - row_features = [float(item) for item in row_features] - features.append(row_features) - except ValueError: - print('ValueError: {}, row {}'.format(file_name, row)) - except IndexError: - print('IndexError: {}, row {}'.format(file_name, row)) - else: - pass - return features - -class Point(object): - def __init__(self, coord): - self.coord = coord - self.string = '{}_{}_{}'.format(coord[0], coord[1], coord[2]) - self.order = None - - def get_coord(self): - return self.coord - -class Cubic(object): - """ - A class that describe a cubic in 3D space. - Can detect if this cubic contain points, then spilt into 8 child cubics and plot. - """ - def __init__(self, coord, id): - self.id = id - min_coord = coord[0] - max_coord = coord[1] - self.min_coord = min_coord - self.max_coord = max_coord - self.min_x = min_coord[0] - self.min_y = min_coord[1] - self.min_z = min_coord[2] - self.max_x = max_coord[0] - self.max_y = max_coord[1] - self.max_z = max_coord[2] - - self.dot1 = [self.min_x, self.min_y, self.min_z] - self.dot2 = [self.min_x, self.min_y, self.max_z] - self.dot3 = [self.min_x, self.max_y, self.min_z] - self.dot4 = [self.min_x, self.max_y, self.max_z] - self.dot5 = [self.max_x, self.min_y, self.min_z] - self.dot6 = [self.max_x, self.min_y, self.max_z] - self.dot7 = [self.max_x, self.max_y, self.min_z] - self.dot8 = [self.max_x, self.max_y, self.max_z] - - self.resolution = self.max_z - self.min_z - self.mid_point = [(self.min_x + self.max_x)/2, (self.min_y + self.max_y)/2, (self.min_z + self.max_z)/2] - - def set_color_new(self, value): - if value == 'Protein': - self.color = [.4, .4, 1, 1] - self.color_scale = 11 - elif value == 'Ligand': - self.color = [.4, 1, .4, 1] - self.color_scale = 12 - elif value == 'Mixture': - self.color = [.4, 1, 1, 1] - self.color_scale = 13 - elif value == 'Empty': - self.color = [1, 1, 1, 1] - self.color_scale = 14 - else: - print('error in assign color') - - def member_test(self, points): - if points[0] >= self.min_x and points[0] <= self.max_x and \ - points[1] >= self.min_y and points[1] <= self.max_y and \ - points[2] >= self.min_z and points[2] <= self.max_z: - return True - else: - return False - - def load_to_vtk(self, vtk_dic): - if 'points' not in vtk_dic: - vtk_dic['points'] = [] - vtk_dic['num_points'] = 0 - vtk_dic['cells'] = [] - vtk_dic['num_cells'] = 0 - vtk_dic['cell_color_scale'] = [] - - vtk_dic['Ligand'] = 0 - vtk_dic['Protein'] = 0 - vtk_dic['Mixture'] = 0 - vtk_dic['Empty'] = 0 - - vtk_dic['points'] += [self.dot1, self.dot2, self.dot3, self.dot4, self.dot5, self.dot6, self.dot7, self.dot8] - index_start = vtk_dic['num_points'] - - # use all 6 surfaces as cells. - new_cells = [[4, 0 + index_start, 1 + index_start, 3 + index_start, 2 + index_start], - [4, 4 + index_start, 5 + index_start, 7 + index_start, 6 + index_start], - [4, 0 + index_start, 4 + index_start, 6 + index_start, 2 + index_start], - [4, 2 + index_start, 6 + index_start, 7 + index_start, 3 + index_start], - [4, 3 + index_start, 7 + index_start, 5 + index_start, 1 + index_start], - [4, 0 + index_start, 4 + index_start, 5 + index_start, 1 + index_start]] - vtk_dic['cells'] += new_cells - vtk_dic['num_cells'] += 6 - for i in range(6): - vtk_dic['cell_color_scale'].append(self.color_scale) - - if self.color_scale == 11: - vtk_dic['Protein'] += 1 - elif self.color_scale == 12: - vtk_dic['Ligand'] += 1 - elif self.color_scale == 13: - vtk_dic['Mixture'] += 1 - elif self.color_scale == 14: - vtk_dic['Empty'] += 1 - - vtk_dic['num_points'] += 8 - if len(vtk_dic['points']) != vtk_dic['num_points']: - print('points error') - print(len(vtk_dic['points']), vtk_dic['num_points']) - if len(vtk_dic['cells']) != vtk_dic['num_cells']: - print('cell error') - return vtk_dic - - # print('number of ligand cells: {}'.format(vtk_dic['ligand'])) - # print('number of pocket cells: {}'.format(vtk_dic['pocket'])) - # print('number of mix cells: {}'.format(vtk_dic['mix'])) - # print('number of empty cells: {}'.format(vtk_dic['empty'])) - # print('number of total cells: {}'.format(vtk_dic['ligand'] + vtk_dic['pocket'] + vtk_dic['mix'] + vtk_dic['empty'])) - - def load_to_vtk_opt(self, vtk_dic_opt): - if 'points' not in vtk_dic_opt: - vtk_dic_opt['points'] = {} - vtk_dic_opt['num_points'] = 0 - vtk_dic_opt['cells'] = [] - vtk_dic_opt['num_cells'] = 0 - vtk_dic_opt['cell_color_scale'] = [] - - vtk_dic_opt['Ligand'] = 0 - vtk_dic_opt['Protein'] = 0 - vtk_dic_opt['Mixture'] = 0 - vtk_dic_opt['Empty'] = 0 - - # current_cubic_points = [self.dot1, self.dot2, self.dot3, self.dot4, self.dot5, self.dot6, self.dot7, self.dot8] - current_cubic_cells = [[self.dot1, self.dot2, self.dot4, self.dot3], - [self.dot5, self.dot6, self.dot8, self.dot7], - [self.dot1, self.dot5, self.dot7, self.dot3], - [self.dot3, self.dot7, self.dot8, self.dot4], - [self.dot4, self.dot8, self.dot6, self.dot2], - [self.dot1, self.dot5, self.dot6, self.dot2]] - - for cell in current_cubic_cells: - cell_order = [] - for point in cell: - point = Point(point) - if point.string in vtk_dic_opt['points']: - point_order = vtk_dic_opt['points'][point.string] - cell_order.append(point_order) - else: - point_order = len(vtk_dic_opt['points']) - vtk_dic_opt['points'][point.string] = point_order - cell_order.append(point_order) - vtk_dic_opt['cells'].append(cell_order) - vtk_dic_opt['cell_color_scale'].append(self.color_scale) - - vtk_dic_opt['num_cells'] += 6 - if self.color_scale == 11: - vtk_dic_opt['Protein'] += 1 - elif self.color_scale == 12: - vtk_dic_opt['Ligand'] += 1 - elif self.color_scale == 13: - vtk_dic_opt['Mixture'] += 1 - elif self.color_scale == 14: - vtk_dic_opt['Empty'] += 1 - else: - print('Cubic {} have no color scale settled.'.format(self.id)) - - if len(vtk_dic_opt['cells']) != vtk_dic_opt['num_cells']: - print('cell error') - return vtk_dic_opt - - def write_oct_line_opt(self, vtk_line_dic, color): - """ - :param pts_dic: {key, value}, where key is Point.string, value is order of the point. - :param lines_list: - :param colors_list: - :param color: - :return: - """ - if len(vtk_line_dic) == 0: - vtk_line_dic['pts_dic'] = {} - vtk_line_dic['lines_dic'] = {} - vtk_line_dic['colors_list'] = [] - pts_dic = vtk_line_dic['pts_dic'] - lines_dic = vtk_line_dic['lines_dic'] - colors_list = vtk_line_dic['colors_list'] - - pts_len = len(pts_dic) - pts_inc = 0 - - p0 = [self.min_x, (self.min_y + self.max_y) / 2, (self.min_z + self.max_z) / 2] - p1 = [self.max_x, (self.min_y + self.max_y) / 2, (self.min_z + self.max_z) / 2] - p2 = [(self.min_x + self.max_x) / 2, self.min_y, (self.min_z + self.max_z) / 2] - p3 = [(self.min_x + self.max_x) / 2, self.max_y, (self.min_z + self.max_z) / 2] - p4 = [(self.min_x + self.max_x) / 2, (self.min_y + self.max_y) / 2, self.min_z] - p5 = [(self.min_x + self.max_x) / 2, (self.min_y + self.max_y) / 2, self.max_z] - - p6 = [self.min_x, (self.min_y + self.max_y) / 2, self.min_z] - p7 = [self.min_x, (self.min_y + self.max_y) / 2, self.max_z] - p8 = [self.min_x, self.min_y, (self.min_z + self.max_z) / 2] - p9 = [self.min_x, self.max_y, (self.min_z + self.max_z) / 2] - - p10 = [(self.min_x + self.max_x) / 2, self.min_y, self.min_z] - p11 = [(self.min_x + self.max_x) / 2, self.min_y, self.max_z] - p12 = [(self.min_x + self.max_x) / 2, self.max_y, self.min_z] - p13 = [(self.min_x + self.max_x) / 2, self.max_y, self.max_z] - - p14 = [self.max_x, (self.min_y + self.max_y) / 2, self.min_z] - p15 = [self.max_x, (self.min_y + self.max_y) / 2, self.max_z] - p16 = [self.max_x, self.min_y, (self.min_z + self.max_z) / 2] - p17 = [self.max_x, self.max_y, (self.min_z + self.max_z) / 2] - - line_list = [[p0, p1], [p2,p3], [p4, p5], [p6, p7], [p8, p9], [p14, p15], [p16, p17], - [p8, p16], [p10, p11], [p9, p17], [p12, p13], [p11, p13], [p7, p15], - [p6, p14], [p10, p12]] - - for line in line_list: - start_point = Point(line[0]) - end_point = Point(line[1]) - if start_point.string in pts_dic and end_point.string in pts_dic: - start_point_order = pts_dic[start_point.string] - end_point_order = pts_dic[end_point.string] - elif start_point.string not in pts_dic and end_point.string in pts_dic: - start_point_order = pts_len + pts_inc - pts_dic[start_point.string] = start_point_order - pts_inc += 1 - end_point_order = pts_dic[end_point.string] - elif start_point.string in pts_dic and end_point.string not in pts_dic: - end_point_order = pts_len + pts_inc - pts_dic[end_point.string] = end_point_order - pts_inc += 1 - start_point_order = pts_dic[start_point.string] - else: - start_point_order = pts_len + pts_inc - pts_dic[start_point.string] = start_point_order - pts_inc += 1 - end_point_order = pts_len + pts_inc - pts_dic[end_point.string] = end_point_order - pts_inc += 1 - line_string = '{}_{}'.format(start_point_order, end_point_order) - if line_string not in lines_dic: - lines_dic[line_string] = len(lines_dic) - colors_list.append(color) - - vtk_line_dic['pts_dic'] = pts_dic - vtk_line_dic['lines_dic'], vtk_line_dic['colors_list'] = lines_dic, colors_list - - return vtk_line_dic - - def write_edge_line_opt(self, vtk_line_dic, color): - if 'pts_dic' not in vtk_line_dic: - vtk_line_dic['pts_dic'] = {} - vtk_line_dic['lines_dic'] = {} - vtk_line_dic['colors_list'] = [] - pts_dic = vtk_line_dic['pts_dic'] - lines_dic, colors_list = vtk_line_dic['lines_dic'], vtk_line_dic['colors_list'] - pts_len = len(pts_dic) - pts_inc = 0 - - p0 = self.dot1 #= [self.min_x, self.min_y, self.min_z] - p1 = self.dot2 #= [self.min_x, self.min_y, self.max_z] - p2 = self.dot3 #= [self.min_x, self.max_y, self.min_z] - p3 = self.dot4 #= [self.min_x, self.max_y, self.max_z] - p4 = self.dot5 #= [self.max_x, self.min_y, self.min_z] - p5 = self.dot6 #= [self.max_x, self.min_y, self.max_z] - p6 = self.dot7 #= [self.max_x, self.max_y, self.min_z] - p7 = self.dot8 #= [self.max_x, self.max_y, self.max_z] - - line_list = [[p0,p1], [p1,p3], [p2,p3], [p0,p2], [p4,p5], [p5,p7], [p6,p7], [p4,p6], - [p0,p4], [p2,p6], [p3,p7], [p1,p5]] - - for line in line_list: - start_point = Point(line[0]) - end_point = Point(line[1]) - if start_point.string in pts_dic and end_point.string in pts_dic: - start_point_order = pts_dic[start_point.string] - end_point_order = pts_dic[end_point.string] - elif start_point.string not in pts_dic and end_point.string in pts_dic: - start_point_order = pts_len + pts_inc - pts_dic[start_point.string] = start_point_order - pts_inc += 1 - end_point_order = pts_dic[end_point.string] - elif start_point.string in pts_dic and end_point.string not in pts_dic: - end_point_order = pts_len + pts_inc - pts_dic[end_point.string] = end_point_order - pts_inc += 1 - start_point_order = pts_dic[start_point.string] - else: - start_point_order = pts_len + pts_inc - pts_dic[start_point.string] = start_point_order - pts_inc += 1 - end_point_order = pts_len + pts_inc - pts_dic[end_point.string] = end_point_order - pts_inc += 1 - line_string = '{}_{}'.format(start_point_order, end_point_order) - if line_string not in lines_dic: - lines_dic[line_string] = len(lines_dic) - colors_list.append(color) - vtk_line_dic['pts_dic'] = pts_dic - vtk_line_dic['lines_dic'], vtk_line_dic['colors_list'] = lines_dic, colors_list - return vtk_line_dic - -class Adaptive_Cubics(object): - def __init__(self, cubic_dic, complex_id, focus_center = [0,0,0], center = [0,0,0], - adaptive_line = True, differ = False, - count = False): - self.cubic_dic = cubic_dic - self.complex_id = complex_id - self.differ = differ - self.adaptive_line = adaptive_line - self.focus_center = focus_center - self.center = center - self.count = count - - def get_adaptive_cubic(self, core_area, side_area1, side_area2, side_area3, - target_range, line_depth, total_depth, save_folder, light = False): - """ - :param core_area: the range core area that need the finest resolution - :param side_area1: the range side area that need less resolution - :param side_area2: more range side area that need much less resolution - :return: - """ - self.vtk_dic = {} - self.fine_cubic_id_set = set() - self.parent_cubic_id_set = set() - - self.fine_cubic_count = {} - # self.non_fine_cubic_id_set = set() - - for value in ['Protein', 'Ligand', 'Mixture', 'Empty']: - self.fine_cubic_count[value] = 0 - - side1_layer_dic = self.construct_adaptive_dic_V1(self.cubic_dic, core_area, - target_range, self.focus_center, light) - side2_layer_dic = self.construct_adaptive_dic_V1(side1_layer_dic, side_area1, - target_range, self.focus_center, light) - side3_layer_dic = self.construct_adaptive_dic_V1(side2_layer_dic, side_area2, - target_range, self.focus_center, light) - side4_layer_dic = self.construct_adaptive_dic_V1(side3_layer_dic, side_area3, - target_range, self.focus_center, light) - side5_layer_dic = self.construct_adaptive_dic_V1(side4_layer_dic, target_range, - target_range, self.focus_center, light) - - print('Fine Cubic Dic Length: {}'.format(len(self.fine_cubic_id_set))) - # add for adaptive count: - # count_cubic_id_set = set() - # parent_cubic_id_set = set() - - if self.adaptive_line: - segline_cubic_id_set = set() - vtk_line_dic = {} - for cubic_id in self.fine_cubic_id_set: - # if not light: - # segline_cubic_id_set.add(cubic.id) # why need this? - # pass - # self.get_complement_adaptive_id(cubic_id, count_cubic_id_set, target_range) - parent_cubic_id = get_parent_cubic_id(cubic_id, target_range) - if parent_cubic_id in self.fine_cubic_id_set: - print('Parent and Fine conflict: {}'.format(parent_cubic_id)) - - while int(parent_cubic_id.split('_')[0]) >= 0: - # self.get_complement_adaptive_id(parent_cubic_id, parent_cubic_id_set, target_range) - # parent_cubic_set.add(parent_cubic) - # self.get_complement_adaptive(parent_cubic, count_cubic_set, target_range) - if int(parent_cubic_id.split('_')[0]) < line_depth: - segline_cubic_id_set.add(parent_cubic_id) - parent_cubic_id = get_parent_cubic_id(parent_cubic_id, target_range) - - if not light: - # for parent_cubic_id in parent_cubic_id_set: - # if int(parent_cubic_id.split('_')[0]) < line_depth: - # segline_cubic_id_set.add(parent_cubic_id) - print('parent_cubic_id_set length: {}'.format(len(self.parent_cubic_id_set))) - print('segline_cubic_id_set: {}'.format(len(segline_cubic_id_set))) - for cubic_id in segline_cubic_id_set: - depth = int(cubic_id.split('_')[0]) - color = get_color_for_dep(depth + 1, self.differ) - cubic = cubic_id_to_cubic(cubic_id, target_range) - vtk_line_dic = cubic.write_oct_line_opt(vtk_line_dic, color) - - max_cubic = Cubic( - [[-target_range, -target_range, -target_range], [target_range, target_range, target_range]], - '0_0_0_0') - color = get_color_for_dep(0, self.differ) - vtk_line_dic = max_cubic.write_edge_line_opt(vtk_line_dic, color) - - pts, lines, colors = fill_vtk_lines(vtk_line_dic, self.center) - write_seg_vtk(pts, lines, colors, self.complex_id, target_range, line_depth, save_folder) - - # - # for cubic_id in self.parent_cubic_id_set: - # if cubic_id not in segline_cubic_id_set: - # print('parent cubic {} not in segline_cubic_id_set'.format(cubic_id)) - - print('Final Node: {}'.format(len(self.fine_cubic_id_set))) - print('Mid Node: {}'.format(len(segline_cubic_id_set))) - - if not light: - file_name = '{}/{}/{}_Octree_Adaptive_{}_{}'.format(save_folder, self.complex_id, self.complex_id, target_range, total_depth) - vtk_writer(vtk_dic=self.vtk_dic, file_name=file_name, shift=self.center) - - def get_adaptive_count(self,core_area, side_area1, side_area2, side_area3, - target_range, line_depth, total_depth, save_folder, light = False): - depth_cubic_dic = {} - for depth in range(0, total_depth + 1): - depth_cubic_dic[depth] = set() - for cubic_id, value in self.cubic_dic: - [depth, x, y, z] = [int(item) for item in cubic_id.split('_')] - while depth >= 0: - depth_cubic_dic[depth].add(cubic_id) - depth = depth - 1 - x >> 1 - y >> 1 - z >> 1 - cubic_id = '{}_{}_{}_{}'.format(depth, x, y, z) - - - - - - - - def get_complement_adaptive_id(self, cubic_id, count_cubic_id_set, target_range): - [depth, x, y, z] = [int(item) for item in cubic_id.split('_')] - if depth <= 0: - count_cubic_id_set.add(cubic_id) - return - x = x >> 1 << 1 - y = y >> 1 << 1 - z = z >> 1 << 1 - - for i in range(0, 2): - for j in range(0, 2): - for k in range(0, 2): - string = '{}_{}_{}_{}'.format(depth, x + i, y + j, z + k) - - # base = target_range * 2 / 2 ** depth - # coord_x = x * base - target_range + i - # coord_y = y * base - target_range + j - # coord_z = z * base - target_range + k - # min_coord = [coord_x, coord_y, coord_z] - # max_coord = [coord_x + base, coord_y + base, coord_z + base] - # comp_cubic = Cubic([min_coord, max_coord], string) - # count_cubic_set.add(comp_cubic) - count_cubic_id_set.add(string) - - def construct_adaptive_dic_V1(self, cubic_dic, area, target_range, - focus_center=[0, 0, 0], light=False): - ''' - :param cubic_dic: dictionary, key is cubic.id, value is Protein, Mixture, Ligand. - :param area: within this area, use current depth in cubic_dic, otherwise need to compute upper layer - :param vtk_dic: dictionary that used to plot cubic. - :param seg_dic: dictionary that used to plot segment line of adaptive cibics. - :return: - ''' - - side_dic = {} - for key, value in cubic_dic.items(): - [depth, x, y, z] = [int(item) for item in key.split('_')] - - # if cubic's parent in the core range, the all child of its parent should be in fine_cubic_set. - p_x = x >> 1 - p_y = y >> 1 - p_z = z >> 1 - p_depth = depth - 1 - - p_base = target_range * 2 / 2 ** p_depth - p_coord_x = p_x * p_base - target_range - p_coord_y = p_y * p_base - target_range - p_coord_z = p_z * p_base - target_range - [focus_x, focus_y, focus_z] = focus_center - - # test if this cubic is in core area, if so, dump into adaptive cubic_dic - core_flag_z = min(abs(p_coord_z - focus_z), abs(p_coord_z + p_base - focus_z)) <= area - core_flag_y = min(abs(p_coord_y - focus_y), abs(p_coord_y + p_base - focus_y)) <= area - core_flag_x = min(abs(p_coord_x - focus_x), abs(p_coord_x + p_base - focus_x)) <= area - core_flag = core_flag_x and core_flag_y and core_flag_z - - p_id = '{}_{}_{}_{}'.format(p_depth, p_x, p_y, p_z) - - if not core_flag: - if p_id not in side_dic: - side_dic[p_id] = value - elif value != side_dic[p_id]: - side_dic[p_id] = 'Mixture' - else: - pass - - else: - self.parent_cubic_id_set.add(p_id) - # self.non_fine_cubic_id_set.add(p_id) - b_x = p_x << 1 - b_y = p_y << 1 - b_z = p_z << 1 - for i in range(0, 2): - for j in range(0, 2): - for k in range(0, 2): - n_x = b_x + i - n_y = b_y + j - n_z = b_z + k - n_id = '{}_{}_{}_{}'.format(depth, n_x, n_y, n_z) - - if n_id in self.parent_cubic_id_set: - continue - if n_id in self.fine_cubic_id_set: - continue - self.fine_cubic_id_set.add(n_id) - - if n_id in cubic_dic: - base = target_range * 2 / 2 ** depth - n_coord_x = n_x * base - target_range - n_coord_y = n_y * base - target_range - n_coord_z = n_z * base - target_range - min_coord = [n_coord_x, n_coord_y, n_coord_z] - max_coord = [n_coord_x + base, n_coord_y + base, n_coord_z + base] - n_cubic = Cubic([min_coord, max_coord], n_id) - n_value = cubic_dic[n_cubic.id] - n_cubic.set_color_new(value) - if not light: - self.vtk_dic = n_cubic.load_to_vtk_opt(self.vtk_dic) - self.fine_cubic_count[n_value] += 1 - else: - self.fine_cubic_count['Empty'] += 1 - #cubic.set_color_new('Empty') - pass - return side_dic - -def cubic_id_to_cubic(id, target_range): - ''' - based on id, target range, the cubic is identicle. - :param id: - :param target_range: - :return: - ''' - [depth, x, y, z] = [int(item) for item in id.split('_')] - # (depth, x, y, z) = id - base = target_range * 2 / 2 ** depth - coord_x = x * base - target_range - coord_y = y * base - target_range - coord_z = z * base - target_range - min_coord = [coord_x, coord_y, coord_z] - max_coord = [coord_x + base, coord_y + base, coord_z + base] - return Cubic([min_coord, max_coord], id) - -def point_string_to_coord(string): - coord = string.split('_') - return [float(item) for item in coord] - -def write_seg_vtk(pts, lines, colors, complex_id, target_range, line_depth, save_folder): - linesPolyData = vtk.vtkPolyData() - linesPolyData.SetPoints(pts) - linesPolyData.SetLines(lines) - linesPolyData.GetCellData().SetScalars(colors) - - # clean = vtk.vtkCleanPolyData() - # clean.SetInputConnection(linesPolyData) - # clean.Update() - # pd = clean.GetOutput() - - # print('clean segline_Adaptive data.') - # then = time.time() - # # pd = linesPolyData.getPolyData() - # # clean = vtk.vtkCleanPolyData() - # # clean.SetInput(linesPolyData) - # # clean.Update() - # # pd = clean.GetOutput() - # - # # clean = vtk.vtkCleanPolyData() - # # clean.SetInputConnection(linesPolyData) - # # clean.Update() - # # pd = clean.GetOutput() - # - # now = time.time() - # diff = int(now - then) - # print('clean segline_Adaptive data done. spend {} seconds'.format(diff)) - - ShellVolmapper = vtk.vtkDataSetMapper() - ShellVolmapper.SetScalarModeToUseCellData() - ShellVolmapper.UseLookupTableScalarRangeOn() - # ShellVolmapper.SetLookupTable(lut) - ShellVolmapper.SetInputData(linesPolyData) - ShellVolactor = vtk.vtkActor() - ShellVolactor.SetMapper(ShellVolmapper) - - # modelwriter = vtk.vtkUnstructuredGridWriter() - modelwriter = vtk.vtkDataSetWriter() - modelwriter.SetFileName('{}/{}/{}_segline_Adaptive_{}_{}.vtk'.format(save_folder, complex_id, complex_id, - target_range, line_depth)) - # modelwriter.SetInputData(grid) - modelwriter.SetInputData(ShellVolmapper.GetInput()) - modelwriter.Write() - print('Save {}/{}/{}_segline_Adaptive_{}_{}.vtk'.format(save_folder, complex_id, complex_id, target_range, line_depth)) - -def get_parent_cubic(cubic, target_range): - id = cubic.id - [depth, x, y, z] = [int(item) for item in id.split('_')] - x = x >> 1 - y = y >> 1 - z = z >> 1 - depth -= 1 - base = target_range * 2 / 2 ** depth - coord_x = x * base - target_range - coord_y = y * base - target_range - coord_z = z * base - target_range - min_coord = [coord_x, coord_y, coord_z] - max_coord = [coord_x + base, coord_y + base, coord_z + base] - string = '{}_{}_{}_{}'.format(depth, x, y, z) - parent_cubic = Cubic([min_coord, max_coord], string) - return parent_cubic - -def get_parent_cubic_id(id, target_range): - [depth, x, y, z] = [int(item) for item in id.split('_')] - x = x >> 1 - y = y >> 1 - z = z >> 1 - depth -= 1 - # base = target_range * 2 / 2 ** depth - # coord_x = x * base - target_range - # coord_y = y * base - target_range - # coord_z = z * base - target_range - # min_coord = [coord_x, coord_y, coord_z] - # max_coord = [coord_x + base, coord_y + base, coord_z + base] - parent_id = '{}_{}_{}_{}'.format(depth, x, y, z) - # parent_cubic = Cubic([min_coord, max_coord], string) - return parent_id - -def get_color_for_dep(depth, differ = True): - # need to be different with the volume scaler (0,1,2,3). - if differ == False: - color = 0 - return color - if depth == 0: - color = 0 #[0, 0, 0] # white - elif depth == 1: - color = 1 # [255, 0, 0] # red - elif depth == 2: - color = 2 # [255, 128, 0] - elif depth == 3: - color = 3 #[255,255,0] - elif depth == 4: - color = 4 # [0, 255, 0] - elif depth == 5: - color = 5 #[0, 255, 255] - elif depth == 6: - color = 6 #[0, 0, 255] - elif depth == 7: - color = 7 # [255, 0 , 255] - elif depth == 8 : - color = 8 - else: - color = 9 - return color - -# A better way is use class in vtk package, this solve the color issue to distinguish ligand, pocket. -def vtk_writer(vtk_dic, file_name, shift = [0,0,0]): - points = vtk.vtkPoints() - grid = vtk.vtkUnstructuredGrid() - Color = vtk.vtkFloatArray() - Scale = vtk.vtkFloatArray() - - sorted_dic = sorted(vtk_dic['points'].items(), key=lambda x: x[1]) - pts = vtk.vtkPoints() - for item in sorted_dic: - # pts.InsertNextPoint(point_string_to_coord(item[0])) - coord = point_string_to_coord(item[0]) - coord = [coord[0] + shift[0], coord[1] + shift[1], coord[2] + shift[2]] - pts.InsertNextPoint(coord) - grid.SetPoints(pts) - - # for vtk_point in vtk_dic['points']: - # Coord = vtk_point - # test = points.InsertNextPoint(*Coord) - # #Color.InsertTuple1(test, 0) - # grid.SetPoints(points) - - for i in range(len(vtk_dic['cells'])): - vtk_node = vtk_dic['cells'][i] - elem = vtk.vtkQuad() - elem.GetPointIds().SetId(0, vtk_node[0]) - elem.GetPointIds().SetId(1, vtk_node[1]) - elem.GetPointIds().SetId(2, vtk_node[2]) - elem.GetPointIds().SetId(3, vtk_node[3]) - Quad4cell = grid.InsertNextCell(elem.GetCellType(), elem.GetPointIds()) - color = vtk_dic['cell_color_scale'][i] - Color.InsertTuple1(Quad4cell, color) - - lut = vtk.vtkLookupTable() - lut.SetNumberOfTableValues(5) - lut.SetTableRange(0, 5) - lut.Build() - lut.SetTableValue(0, 0, 0, 0, 1) # Black - lut.SetTableValue(1, 1, 0, 0, 1) # Red - lut.SetTableValue(2, 0, 1, 0, 1) # Green - lut.SetTableValue(3, 0, 0, 1, 1) # Blue - lut.SetTableValue(4, 1, 1, 1, 1) # White - - grid.GetCellData().SetScalars(Color) - ShellVolmapper = vtk.vtkDataSetMapper() - # ShellVolmapper = vtk.vtkUnstructuredGridVolumeRayCastMapper() - ShellVolmapper.SetScalarModeToUseCellData() - ShellVolmapper.UseLookupTableScalarRangeOn() - ShellVolmapper.SetLookupTable(lut) - ShellVolmapper.SetInputData(grid) - ShellVolactor = vtk.vtkActor() - ShellVolactor.SetMapper(ShellVolmapper) - - modelwriter = vtk.vtkUnstructuredGridWriter() - modelwriter.SetFileName('{}.vtk'.format(file_name)) - #modelwriter.SetInputData(grid) - modelwriter.SetInputData(ShellVolmapper.GetInput()) - modelwriter.Write() - print('Save {}.vtk'.format(file_name)) - - -def construct_adaptive_dic(cubic_dic, area, vtk_dic, fine_cubic_set, target_range, - focus_center = [0,0,0], light =False): - ''' - :param cubic_dic: dictionary, key is cubic.id, value is Protein, Mixture, Ligand. - :param area: within this area, use current depth in cubic_dic, otherwise need to compute upper layer - :param vtk_dic: dictionary that used to plot cubic. - :param seg_dic: dictionary that used to plot segment line of adaptive cibics. - :return: - ''' - side_dic = {} - for key, value in cubic_dic.items(): - [depth, x, y, z] = [int(item) for item in key.split('_')] - base = target_range * 2 / 2 ** depth - coord_x = x * base - target_range - coord_y = y * base - target_range - coord_z = z * base - target_range - min_coord = [coord_x, coord_y, coord_z] - max_coord = [coord_x + base, coord_y + base, coord_z + base] - [focus_x, focus_y, focus_z] = focus_center - - # test if this cubic is in core area, if so, dump into adaptive cubic_dic - core_flag_z = min(abs(coord_z - focus_z), abs(coord_z + base - focus_z)) <= area - core_flag_y = min(abs(coord_y - focus_y), abs(coord_y + base - focus_y)) <= area - core_flag_x = min(abs(coord_x - focus_x), abs(coord_x + base - focus_x)) <= area - core_flag = core_flag_x and core_flag_y and core_flag_z - - # find parent cubic or upper layer cubic. - # parent cubic for adaptive segment line, upper layer cubic for cubic plot. - x = x >> 1 - y = y >> 1 - z = z >> 1 - depth -= 1 - string = '{}_{}_{}_{}'.format(depth, x, y, z) - - if core_flag: - cubic = Cubic([min_coord, max_coord], key) - cubic.set_color_new(value) - if not light: - vtk_dic = cubic.load_to_vtk_opt(vtk_dic) - fine_cubic_set.add(cubic) - else: - if string not in side_dic: - side_dic[string] = value - elif value != side_dic[string]: - side_dic[string] = 'Mixture' - else: - pass - return side_dic - - - - -def centerize(pocket_xyz, ligand_xyz, by_ligand = True, protein_xyz = None): - if by_ligand: - [x, y, z] = ligand_xyz - center_x, center_y, center_z = x.mean(), y.mean(), z.mean() - x, y, z = x - center_x, y - center_y, z - center_z - ligand_xyz = [x, y, z] - [x, y, z] = pocket_xyz - x, y, z = x - center_x, y - center_y, z - center_z - pocket_xyz = [x, y, z] - ligand_center = [0, 0, 0] - center = [center_x, center_y, center_z] - - if protein_xyz is not None: - [x, y, z] = protein_xyz - x, y, z = x - center_x, y - center_y, z - center_z - protein_xyz = [x, y, z] - - else: - [x_ligand, y_ligand, z_ligand] = ligand_xyz - x_ligand_max, y_ligand_max, z_ligand_max = x_ligand.max(), y_ligand.max(), z_ligand.max() - x_ligand_min, y_ligand_min, z_ligand_min = x_ligand.min(), y_ligand.min(), z_ligand.min() - if protein_xyz is None: - [x_pocket, y_pocket, z_pocket] = pocket_xyz - x_protein_max, y_protein_max, z_protein_max = x_pocket.max(), y_pocket.max(), z_pocket.max() - x_protein_min, y_protein_min, z_protein_min = x_pocket.min(), y_pocket.min(), z_pocket.min() - else: - [x_protein, y_protein, z_protein] = protein_xyz - x_protein_max, y_protein_max, z_protein_max = x_protein.max(), y_protein.max(), z_protein.max() - x_protein_min, y_protein_min, z_protein_min = x_protein.min(), y_protein.min(), z_protein.min() - x_max, y_max, z_max = max(x_ligand_max, x_protein_max), max(y_ligand_max, y_protein_max), max(z_ligand_max, z_protein_max) - x_min, y_min, z_min = min(x_ligand_min, x_protein_min), min(y_ligand_min, y_protein_min), min(z_ligand_min, z_protein_min) - center = [(x_max+x_min)/2, (y_max+y_min)/2, (z_max+z_min)/2] - ligand_center = [x_ligand.mean() - center[0], y_ligand.mean() - center[1], z_ligand.mean()- center[2]] - ligand_xyz = [ligand_xyz[0] - center[0], ligand_xyz[1] - center[1], ligand_xyz[2] - center[2]] - pocket_xyz = [pocket_xyz[0] - center[0], pocket_xyz[1] - center[1], pocket_xyz[2] - center[2]] - if protein_xyz is not None: - [x, y, z] = protein_xyz - x, y, z = x - center[0], y - center[1], z - center[2] - protein_xyz = [x, y, z] - - return pocket_xyz, ligand_xyz, protein_xyz, center, ligand_center - -def filter_by_center_range(x, y, z, target_range, feature=None): # Here x, y, z is list - x = np.array(x) - y = np.array(y) - z = np.array(z) - - min_x, min_y, min_z = -target_range, -target_range, -target_range - max_x, max_y, max_z = target_range, target_range, target_range - - flags_x = np.logical_and(x > min_x, x < max_x) - flags_y = np.logical_and(y > min_y, y < max_y) - flags_z = np.logical_and(z > min_z, z < max_z) - flag1 = np.logical_and(flags_x, flags_y) - flag2 = np.logical_and(flag1, flags_z) - x = x[(flag2)] - y = y[(flag2)] - z = z[(flag2)] - - if feature is not None: - feature = np.array(feature) - feature = feature[(flag2)] - return x,y,z,feature - return x, y, z - -def get_vol_index(x, y, z, depth, target_range): - min_x, min_y, min_z = -target_range, -target_range, -target_range - x = x - min_x - y = y - min_y - z = z - min_z - resolution = target_range*2 / 2 ** depth - x = (x // resolution).astype(int) - y = (y // resolution).astype(int) - z = (z // resolution).astype(int) - # vol_set = set() - # for i in range(len(x)): - # vol_set.add((x[i], y[i], z[i])) - # print(vol_set) - # print(min(x), max(x)) - return x, y, z - -def get_comp_dic(cubic_dic, comp_layer_dic = {}): - for key, value in cubic_dic.items(): - [depth, x, y, z] = [int(item) for item in key.split('_')] - x = x >> 1 << 1 - y = y >> 1 << 1 - z = z >> 1 << 1 - - for i in range(0,2): - for j in range(0,2): - for k in range(0,2): - string = '{}_{}_{}_{}'.format(depth, x+i, y+j, z+k) - if string not in cubic_dic: - comp_layer_dic[string] = 'Empty' - else: - comp_layer_dic[string] = cubic_dic[string] - pass - - # for key, value in comp_layer_dic.items(): - # if value == 'Ligand': - # print(key) - return comp_layer_dic - - - -def get_upper_layer_dic(cubic_dic): - upper_layer_dic = {} - for key, value in cubic_dic.items(): - [depth, x, y, z] = [int(item) for item in key.split('_')] - x = x >> 1 - y = y >> 1 - z = z >> 1 - depth -= 1 - string = '{}_{}_{}_{}'.format(depth, x,y,z) - if string not in upper_layer_dic: - upper_layer_dic[string] = value - elif value != upper_layer_dic[string]: - upper_layer_dic[string] = 'Mixture' - else: - pass - return upper_layer_dic - - -def create_cubic_dic(protein_xyz, ligand_xyz, depth): - cubic_dic = {} - [x, y, z] = protein_xyz - print('Protein points number: {}'.format(len(x))) - for i in range(len(x)): - string = '{}_{}_{}_{}'.format(depth, x[i], y[i], z[i]) - if string not in cubic_dic: - cubic_dic[string] = 'Protein' - else: - pass - - [x, y, z] = ligand_xyz - print('Ligand points number: {}'.format(len(x))) - for i in range(len(x)): - string = '{}_{}_{}_{}'.format(depth, x[i], y[i], z[i]) - if string not in cubic_dic: - cubic_dic[string] = 'Ligand' - elif cubic_dic[string] == 'Protein': - cubic_dic[string] = 'Mixture' - else: - pass - return cubic_dic - -def create_cubic_dic_with_feature(protein_xyz, ligand_xyz, depth, pocket_feature, ligand_feature): - cubic_dic = {} - [x, y, z] = protein_xyz - for i in range(len(x)): - string = '{}_{}_{}_{}'.format(depth, x[i], y[i], z[i]) - feature = pocket_feature[i] - atom_type = feature[0] - atom_index = 'P' + str(feature[1]) - atom_detail_type = 'P' + str(feature[0]) - if string not in cubic_dic: - cubic_dic[string] = {} - cubic_dic[string]['PLM'] = 'Protein' - cubic_dic[string]['Atoms'] = [atom_type] - cubic_dic[string]['Atom_index'] = [atom_index] - cubic_dic[string]['Atom_Detail_Type'] = [atom_detail_type] - else: - cubic_dic[string]['Atom_Detail_Type'].append(atom_detail_type) - if atom_type not in cubic_dic[string]['Atoms']: - cubic_dic[string]['Atoms'].append(atom_type) - if atom_index not in cubic_dic[string]['Atom_index']: - cubic_dic[string]['Atom_index'].append(atom_index) - - [x, y, z] = ligand_xyz - for i in range(len(x)): - string = '{}_{}_{}_{}'.format(depth, x[i], y[i], z[i]) - feature = ligand_feature[i] - atom_type = feature[0] - atom_index = 'L' + str(feature[1]) - atom_detail_type = 'L' + str(feature[0]) - if string not in cubic_dic: - cubic_dic[string] = {} - cubic_dic[string]['PLM'] = 'Ligand' - cubic_dic[string]['Atoms'] = [atom_type] - cubic_dic[string]['Atom_index'] = [atom_index] - cubic_dic[string]['Atom_Detail_Type'] = [atom_detail_type] - elif cubic_dic[string]['PLM'] == 'Protein': - cubic_dic[string]['PLM'] = 'Mixture' - cubic_dic[string]['Atom_Detail_Type'].append(atom_detail_type) - else: - cubic_dic[string]['Atom_Detail_Type'].append(atom_detail_type) - if atom_type not in cubic_dic[string]['Atoms']: - cubic_dic[string]['Atoms'].append(atom_type) - if atom_index not in cubic_dic[string]['Atom_index']: - cubic_dic[string]['Atom_index'].append(atom_index) - return cubic_dic - -def line_string_to_idx(string): - contents = string.split('_') - contents = [int(item) for item in contents] - return contents - -def fill_vtk_lines(vtk_line_dic, shift = [0,0,0]): - pts_dic = vtk_line_dic['pts_dic'] - lines_dic, colors_list = vtk_line_dic['lines_dic'], vtk_line_dic['colors_list'] - - pts = vtk.vtkPoints() - sorted_dic = sorted(pts_dic.items(), key=lambda x: x[1]) - for item in sorted_dic: - coord = point_string_to_coord(item[0]) - coord = [coord[0] + shift[0], coord[1] + shift[1], coord[2] + shift[2]] - pts.InsertNextPoint(coord) - - - lines = vtk.vtkCellArray() - colors = vtk.vtkFloatArray() - sorted_line_dic = sorted(lines_dic.items(), key=lambda x: x[1]) - for item in sorted_line_dic: - line_idx = line_string_to_idx(item[0]) - line0 = vtk.vtkLine() - line0.GetPointIds().SetId(0, line_idx[0]) - line0.GetPointIds().SetId(1, line_idx[1]) - lines.InsertNextCell(line0) - - # for line in lines_list: - # points = line.split('_') - # points = [int(item) for item in points] - # line0 = vtk.vtkLine() - # line0.GetPointIds().SetId(0, points[0]) - # line0.GetPointIds().SetId(1, points[1]) - # lines.InsertNextCell(line0) - for color in colors_list: - colors.InsertNextTuple1(color) - return pts, lines, colors