Repository: jiachens/ModelNet40-C Branch: master Commit: fad786c430c0 Files: 461 Total size: 12.8 MB Directory structure: gitextract_51znd243/ ├── .gitignore ├── .gitmodules ├── CurveNet/ │ ├── README.md │ └── core/ │ ├── data.py │ ├── main_cls.py │ ├── main_normal.py │ ├── main_partseg.py │ ├── models/ │ │ ├── curvenet_cls.py │ │ ├── curvenet_normal.py │ │ ├── curvenet_seg.py │ │ ├── curvenet_util.py │ │ └── walk.py │ └── util.py ├── GDANet/ │ ├── README.md │ └── model/ │ ├── GDANet_cls.py │ ├── GDANet_ptseg.py │ ├── __init__.py │ └── util/ │ ├── GDANet_util.py │ ├── __init__.py │ ├── data_util.py │ └── util.py ├── LICENSE ├── PCT_Pytorch/ │ ├── LICENSE │ ├── README.md │ ├── checkpoints/ │ │ └── best/ │ │ └── models/ │ │ └── model.t7 │ ├── data.py │ ├── main.py │ ├── model.py │ ├── model_new.py │ ├── pointnet2_ops_lib/ │ │ ├── MANIFEST.in │ │ ├── pointnet2_ops/ │ │ │ ├── __init__.py │ │ │ ├── _ext-src/ │ │ │ │ ├── include/ │ │ │ │ │ ├── ball_query.h │ │ │ │ │ ├── cuda_utils.h │ │ │ │ │ ├── group_points.h │ │ │ │ │ ├── interpolate.h │ │ │ │ │ ├── sampling.h │ │ │ │ │ └── utils.h │ │ │ │ └── src/ │ │ │ │ ├── ball_query.cpp │ │ │ │ ├── ball_query_gpu.cu │ │ │ │ ├── bindings.cpp │ │ │ │ ├── group_points.cpp │ │ │ │ ├── group_points_gpu.cu │ │ │ │ ├── interpolate.cpp │ │ │ │ ├── interpolate_gpu.cu │ │ │ │ ├── sampling.cpp │ │ │ │ └── sampling_gpu.cu │ │ │ ├── _version.py │ │ │ ├── pointnet2_modules.py │ │ │ └── pointnet2_utils.py │ │ └── setup.py │ ├── test.sh │ ├── train.sh │ └── util.py ├── README.md ├── all_utils.py ├── aug_utils.py ├── configs/ │ ├── bn/ │ │ ├── dgcnn.yaml │ │ ├── pct.yaml │ │ ├── pointnet.yaml │ │ ├── pointnet2.yaml │ │ ├── rscnn.yaml │ │ └── simpleview.yaml │ ├── corruption/ │ │ ├── curvenet.yaml │ │ ├── dgcnn.yaml │ │ ├── gdanet.yaml │ │ ├── pct.yaml │ │ ├── pointMLP.yaml │ │ ├── pointMLP2.yaml │ │ ├── pointnet.yaml │ │ ├── pointnet2.yaml │ │ ├── rscnn.yaml │ │ └── simpleview.yaml │ ├── cutmix/ │ │ ├── dgcnn_k.yaml │ │ ├── dgcnn_r.yaml │ │ ├── pct_k.yaml │ │ ├── pct_r.yaml │ │ ├── pointnet2_k.yaml │ │ ├── pointnet2_r.yaml │ │ ├── pointnet_k.yaml │ │ ├── pointnet_r.yaml │ │ ├── rscnn_k.yaml │ │ ├── rscnn_r.yaml │ │ ├── simpleview_k.yaml │ │ └── simpleview_r.yaml │ ├── dgcnn_curvenet_run_1.yaml │ ├── dgcnn_dgcnn_0.25_run_1.yaml │ ├── dgcnn_dgcnn_0.25_valid_run_1.yaml │ ├── dgcnn_dgcnn_0.5_run_1.yaml │ ├── dgcnn_dgcnn_0.5_valid_run_1.yaml │ ├── dgcnn_dgcnn_ce_run_1.yaml │ ├── dgcnn_dgcnn_ce_valid_run_1.yaml │ ├── dgcnn_dgcnn_run_1.yaml │ ├── dgcnn_dgcnn_valid_run_1.yaml │ ├── dgcnn_gdanet_run_1.yaml │ ├── dgcnn_pct_run_1.yaml │ ├── dgcnn_pointMLP2_run_1.yaml │ ├── dgcnn_pointMLP_run_1.yaml │ ├── dgcnn_pointnet2_0.25_run_1.yaml │ ├── dgcnn_pointnet2_0.25_valid_run_1.yaml │ ├── dgcnn_pointnet2_0.5_run_1.yaml │ ├── dgcnn_pointnet2_0.5_valid_run_1.yaml │ ├── dgcnn_pointnet2_ce_run_1.yaml │ ├── dgcnn_pointnet2_ce_valid_run_1.yaml │ ├── dgcnn_pointnet2_run_1.yaml │ ├── dgcnn_pointnet2_valid_run_1.yaml │ ├── dgcnn_pointnet_0.25_run_1.yaml │ ├── dgcnn_pointnet_0.25_valid_run_1.yaml │ ├── dgcnn_pointnet_0.5_run_1.yaml │ ├── dgcnn_pointnet_0.5_valid_run_1.yaml │ ├── dgcnn_pointnet_ce_run_1.yaml │ ├── dgcnn_pointnet_ce_valid_run_1.yaml │ ├── dgcnn_pointnet_run_1.yaml │ ├── dgcnn_pointnet_valid_run_1.yaml │ ├── dgcnn_rscnn_0.25_run_1.yaml │ ├── dgcnn_rscnn_0.25_valid_run_1.yaml │ ├── dgcnn_rscnn_0.5_run_1.yaml │ ├── dgcnn_rscnn_0.5_valid_run_1.yaml │ ├── dgcnn_rscnn_ce_run_1.yaml │ ├── dgcnn_rscnn_ce_valid_run_1.yaml │ ├── dgcnn_rscnn_run_1.yaml │ ├── dgcnn_rscnn_valid_run_1.yaml │ ├── dgcnn_simpleview_0.25_run_1.yaml │ ├── dgcnn_simpleview_0.25_valid_run_1.yaml │ ├── dgcnn_simpleview_0.5_run_1.yaml │ ├── dgcnn_simpleview_0.5_valid_run_1.yaml │ ├── dgcnn_simpleview_ce_run_1.yaml │ ├── dgcnn_simpleview_ce_valid_run_1.yaml │ ├── dgcnn_simpleview_run_1.yaml │ ├── dgcnn_simpleview_valid_run_1.yaml │ ├── mixup/ │ │ ├── dgcnn.yaml │ │ ├── pct.yaml │ │ ├── pointnet.yaml │ │ ├── pointnet2.yaml │ │ ├── rscnn.yaml │ │ └── simpleview.yaml │ ├── pgd/ │ │ ├── dgcnn.yaml │ │ ├── pct.yaml │ │ └── pointnet.yaml │ ├── pointnet2_dgcnn_run_1.yaml │ ├── pointnet2_dgcnn_valid_run_1.yaml │ ├── pointnet2_pointnet2_run_1.yaml │ ├── pointnet2_pointnet2_valid_run_1.yaml │ ├── pointnet2_pointnet_run_1.yaml │ ├── pointnet2_pointnet_valid_run_1.yaml │ ├── pointnet2_rscnn_run_1.yaml │ ├── pointnet2_rscnn_valid_run_1.yaml │ ├── pointnet2_simpleview_run_1.yaml │ ├── pointnet2_simpleview_valid_run_1.yaml │ ├── rscnn_dgcnn_run_1.yaml │ ├── rscnn_pointnet2_run_1.yaml │ ├── rscnn_pointnet_run_1.yaml │ ├── rscnn_rscnn_run_1.yaml │ ├── rscnn_simpleview_run_1.yaml │ ├── rsmix/ │ │ ├── dgcnn.yaml │ │ ├── pct.yaml │ │ ├── pointnet.yaml │ │ ├── pointnet2.yaml │ │ ├── rscnn.yaml │ │ └── simpleview.yaml │ ├── tent/ │ │ ├── dgcnn.yaml │ │ ├── pct.yaml │ │ ├── pointnet.yaml │ │ ├── pointnet2.yaml │ │ ├── rscnn.yaml │ │ └── simpleview.yaml │ └── tent_cutmix/ │ ├── dgcnn.yaml │ ├── pct.yaml │ ├── pointnet.yaml │ ├── pointnet2.yaml │ ├── rscnn.yaml │ └── simpleview.yaml ├── configs.py ├── data/ │ ├── convert.py │ ├── create_modelnet40_small.py │ ├── create_modelnet40_valid.py │ ├── distortion.py │ ├── generate_c.py │ ├── occlusion.py │ ├── process.py │ └── util.py ├── dataloader.py ├── dgcnn/ │ ├── .gitignore │ ├── README.md │ ├── pytorch/ │ │ ├── README.md │ │ ├── data.py │ │ ├── main.py │ │ ├── model.py │ │ └── util.py │ └── tensorflow/ │ ├── README.md │ ├── evaluate.py │ ├── models/ │ │ ├── dgcnn.py │ │ └── transform_nets.py │ ├── part_seg/ │ │ ├── README.md │ │ ├── download_data.sh │ │ ├── part_seg_model.py │ │ ├── test.py │ │ ├── testing_ply_file_list.txt │ │ └── train_multi_gpu.py │ ├── provider.py │ ├── sem_seg/ │ │ ├── README.md │ │ ├── batch_inference.py │ │ ├── collect_indoor3d_data.py │ │ ├── download_data.sh │ │ ├── eval_iou_accuracy.py │ │ ├── indoor3d_util.py │ │ ├── meta/ │ │ │ ├── all_data_label.txt │ │ │ ├── anno_paths.txt │ │ │ ├── area1_data_label.txt │ │ │ ├── area2_data_label.txt │ │ │ ├── area3_data_label.txt │ │ │ ├── area4_data_label.txt │ │ │ ├── area5_data_label.txt │ │ │ ├── area6_data_label.txt │ │ │ └── class_names.txt │ │ ├── model.py │ │ ├── test_job.sh │ │ ├── train.py │ │ └── train_job.sh │ ├── train.py │ └── utils/ │ ├── data_prep_util.py │ ├── eulerangles.py │ ├── pc_util.py │ ├── plyfile.py │ └── tf_util.py ├── download.sh ├── emd/ │ ├── README.md │ ├── emd.cpp │ ├── emd_cuda.cu │ ├── emd_module.py │ └── setup.py ├── eval_cor.sh ├── eval_og.sh ├── eval_tent_cutmix.sh ├── gdrivedl.py ├── main.py ├── models/ │ ├── __init__.py │ ├── curvenet.py │ ├── dgcnn.py │ ├── gdanet.py │ ├── model_utils.py │ ├── mv.py │ ├── mv_utils.py │ ├── pct.py │ ├── pointmlp.py │ ├── pointmlp2.py │ ├── pointnet.py │ ├── pointnet2.py │ ├── resnet.py │ └── rscnn.py ├── pc_utils.py ├── pointMLP/ │ ├── .gitignore │ ├── LICENSE │ ├── README.md │ └── classification_ModelNet40/ │ ├── data.py │ ├── helper.py │ ├── main.py │ ├── models/ │ │ ├── __init__.py │ │ └── pointmlp.py │ ├── utils/ │ │ ├── __init__.py │ │ ├── logger.py │ │ ├── misc.py │ │ └── progress/ │ │ ├── .gitignore │ │ ├── LICENSE │ │ ├── MANIFEST.in │ │ ├── README.rst │ │ ├── progress/ │ │ │ ├── __init__.py │ │ │ ├── bar.py │ │ │ ├── counter.py │ │ │ ├── helpers.py │ │ │ └── spinner.py │ │ ├── setup.py │ │ └── test_progress.py │ └── voting.py ├── pointnet2_pyt/ │ ├── .gitignore │ ├── .pre-commit-config.yaml │ ├── .travis.yml │ ├── MANIFEST.in │ ├── README.rst │ ├── UNLICENSE │ ├── __init__.py │ ├── pointnet2/ │ │ ├── __init__.py │ │ ├── _ext-src/ │ │ │ ├── include/ │ │ │ │ ├── ball_query.h │ │ │ │ ├── cuda_utils.h │ │ │ │ ├── group_points.h │ │ │ │ ├── interpolate.h │ │ │ │ ├── sampling.h │ │ │ │ └── utils.h │ │ │ └── src/ │ │ │ ├── ball_query.cpp │ │ │ ├── ball_query_gpu.cu │ │ │ ├── bindings.cpp │ │ │ ├── group_points.cpp │ │ │ ├── group_points_gpu.cu │ │ │ ├── interpolate.cpp │ │ │ ├── interpolate_gpu.cu │ │ │ ├── sampling.cpp │ │ │ └── sampling_gpu.cu │ │ ├── data/ │ │ │ ├── .gitignore │ │ │ ├── Indoor3DSemSegLoader.py │ │ │ ├── ModelNet40Loader.py │ │ │ ├── __init__.py │ │ │ └── data_utils.py │ │ ├── models/ │ │ │ ├── __init__.py │ │ │ ├── pointnet2_msg_cls.py │ │ │ ├── pointnet2_msg_sem.py │ │ │ ├── pointnet2_ssg_cls.py │ │ │ └── pointnet2_ssg_sem.py │ │ ├── train/ │ │ │ ├── __init__.py │ │ │ ├── train_cls.py │ │ │ └── train_sem_seg.py │ │ └── utils/ │ │ ├── .gitignore │ │ ├── __init__.py │ │ ├── linalg_utils.py │ │ ├── pointnet2_modules.py │ │ └── pointnet2_utils.py │ ├── setup.py │ ├── tests/ │ │ ├── conftest.py │ │ ├── test_cls_msg.py │ │ ├── test_cls_ssg.py │ │ ├── test_semseg_msg.py │ │ └── test_semseg_ssg.py │ └── tox.ini ├── pointnet2_tf/ │ ├── LICENSE │ ├── README.md │ ├── data/ │ │ └── README.md │ ├── evaluate.py │ ├── modelnet_dataset.py │ ├── modelnet_h5_dataset.py │ ├── models/ │ │ ├── pointnet2_cls_msg.py │ │ ├── pointnet2_cls_ssg.py │ │ ├── pointnet2_part_seg.py │ │ ├── pointnet2_part_seg_msg_one_hot.py │ │ ├── pointnet2_sem_seg.py │ │ └── pointnet_cls_basic.py │ ├── part_seg/ │ │ ├── command.sh │ │ ├── command_one_hot.sh │ │ ├── evaluate.py │ │ ├── part_dataset.py │ │ ├── part_dataset_all_normal.py │ │ ├── test.py │ │ ├── train.py │ │ └── train_one_hot.py │ ├── scannet/ │ │ ├── README.md │ │ ├── pc_util.py │ │ ├── preprocessing/ │ │ │ ├── collect_scannet_scenes.py │ │ │ ├── demo.py │ │ │ ├── fetch_label_names.py │ │ │ ├── scannet-labels.combined.tsv │ │ │ └── scannet_util.py │ │ ├── scannet_dataset.py │ │ ├── scene_util.py │ │ └── train.py │ ├── tf_ops/ │ │ ├── 3d_interpolation/ │ │ │ ├── interpolate.cpp │ │ │ ├── tf_interpolate.cpp │ │ │ ├── tf_interpolate.py │ │ │ ├── tf_interpolate_compile.sh │ │ │ ├── tf_interpolate_op_test.py │ │ │ └── visu_interpolation.py │ │ ├── grouping/ │ │ │ ├── .gitignore │ │ │ ├── test/ │ │ │ │ ├── compile.sh │ │ │ │ ├── query_ball_point.cpp │ │ │ │ ├── query_ball_point.cu │ │ │ │ ├── query_ball_point_block.cu │ │ │ │ ├── query_ball_point_grid.cu │ │ │ │ ├── selection_sort.cpp │ │ │ │ ├── selection_sort.cu │ │ │ │ └── selection_sort_const.cu │ │ │ ├── tf_grouping.cpp │ │ │ ├── tf_grouping.py │ │ │ ├── tf_grouping_compile.sh │ │ │ ├── tf_grouping_g.cu │ │ │ └── tf_grouping_op_test.py │ │ └── sampling/ │ │ ├── .gitignore │ │ ├── tf_sampling.cpp │ │ ├── tf_sampling.py │ │ ├── tf_sampling_compile.sh │ │ └── tf_sampling_g.cu │ ├── train.py │ ├── train_multi_gpu.py │ └── utils/ │ ├── README.md │ ├── compile_render_balls_so.sh │ ├── pc_util.py │ ├── pointnet_util.py │ ├── provider.py │ ├── render_balls_so.cpp │ ├── show3d_balls.py │ └── tf_util.py ├── pointnet_pyt/ │ ├── .gitignore │ ├── LICENSE │ ├── README.md │ ├── misc/ │ │ ├── modelnet_id.txt │ │ └── num_seg_classes.txt │ ├── pointnet/ │ │ ├── __init__.py │ │ ├── dataset.py │ │ └── model.py │ ├── scripts/ │ │ ├── build.sh │ │ └── download.sh │ ├── setup.py │ └── utils/ │ ├── render_balls_so.cpp │ ├── show3d_balls.py │ ├── show_cls.py │ ├── show_seg.py │ ├── train_classification.py │ └── train_segmentation.py ├── requirements.txt ├── rs_cnn/ │ ├── .gitignore │ ├── CMakeLists.txt │ ├── LICENSE │ ├── README.md │ ├── cfgs/ │ │ ├── config_msn_partseg.yaml │ │ └── config_ssn_cls.yaml │ ├── data/ │ │ ├── ModelNet40Loader.py │ │ ├── ShapeNetPartLoader.py │ │ ├── __init__.py │ │ └── data_utils.py │ ├── docs/ │ │ ├── _config.yml │ │ └── index.md │ ├── models/ │ │ ├── __init__.py │ │ ├── rscnn_msn_seg.py │ │ └── rscnn_ssn_cls.py │ ├── train_cls.py │ ├── train_cls.sh │ ├── train_partseg.py │ ├── train_partseg.sh │ ├── utils/ │ │ ├── __init__.py │ │ ├── _ext/ │ │ │ ├── __init__.py │ │ │ └── pointnet2/ │ │ │ └── __init__.py │ │ ├── build_ffi.py │ │ ├── cinclude/ │ │ │ ├── ball_query_gpu.h │ │ │ ├── ball_query_wrapper.h │ │ │ ├── cuda_utils.h │ │ │ ├── group_points_gpu.h │ │ │ ├── group_points_wrapper.h │ │ │ ├── interpolate_gpu.h │ │ │ ├── interpolate_wrapper.h │ │ │ ├── sampling_gpu.h │ │ │ └── sampling_wrapper.h │ │ ├── csrc/ │ │ │ ├── ball_query.c │ │ │ ├── ball_query_gpu.cu │ │ │ ├── group_points.c │ │ │ ├── group_points_gpu.cu │ │ │ ├── interpolate.c │ │ │ ├── interpolate_gpu.cu │ │ │ ├── sampling.c │ │ │ └── sampling_gpu.cu │ │ ├── linalg_utils.py │ │ ├── pointnet2_modules.py │ │ ├── pointnet2_modules_updated.py │ │ ├── pointnet2_utils.py │ │ └── pytorch_utils/ │ │ ├── __init__.py │ │ └── pytorch_utils.py │ ├── voting_evaluate_cls.py │ └── voting_evaluate_partseg.py ├── setup.sh ├── third_party/ │ ├── bn_helper.py │ └── tent_helper.py └── visualize/ ├── README.md ├── config.py ├── confusion_matrix.py ├── examples.py ├── main_results.py └── pointflow_fig_colorful.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ *__pycache__/ data/modelnet40_ply_hdf5_2048 data/ModelNet40 data/modelnet40_c runs/ pretrained/ cor_exp/ *.out /output # Created by https://www.toptal.com/developers/gitignore/api/python,cuda,zsh,c++ # Edit at https://www.toptal.com/developers/gitignore?templates=python,cuda,zsh,c++ ### C++ ### # Prerequisites *.d # Compiled Object files *.slo *.lo *.o *.obj # Precompiled Headers *.gch *.pch # Compiled Dynamic libraries *.so *.dylib *.dll # Fortran module files *.mod *.smod # Compiled Static libraries *.lai *.la *.a *.lib # Executables *.exe *.out *.app ### CUDA ### *.i *.ii *.gpu *.ptx *.cubin *.fatbin ### Python ### # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ cover/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder .pybuilder/ target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: # .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # PEP 582; used by e.g. github.com/David-OConnor/pyflow __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # pytype static type analyzer .pytype/ # Cython debug symbols cython_debug/ ### Zsh ### # Zsh compiled script + zrecompile backup *.zwc *.zwc.old # Zsh completion-optimization dumpfile *zcompdump* # Zsh zcalc history .zcalc_history # A popular plugin manager's files ._zinit .zinit_lstupd # zdharma/zshelldoc tool's files zsdoc/data # robbyrussell/oh-my-zsh/plugins/per-directory-history plugin's files # (when set-up to store the history in the local directory) .directory_history # MichaelAquilina/zsh-autoswitch-virtualenv plugin's files # (for Zsh plugins using Python) # Zunit tests' output /tests/_output/* !/tests/_output/.gitkeep # End of https://www.toptal.com/developers/gitignore/api/python,cuda,zsh,c++ {"mode":"full","isActive":false} ================================================ FILE: .gitmodules ================================================ [submodule "PyGeM"] path = PyGeM url = https://github.com/mathLab/PyGeM.git [submodule "visualize/mitsuba2"] path = visualize/mitsuba2 url = https://github.com/mitsuba-renderer/mitsuba2 ================================================ FILE: CurveNet/README.md ================================================ # CurveNet Official implementation of "Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis", ICCV 2021 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/walk-in-the-cloud-learning-curves-for-point/3d-point-cloud-classification-on-modelnet40)](https://paperswithcode.com/sota/3d-point-cloud-classification-on-modelnet40?p=walk-in-the-cloud-learning-curves-for-point) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/walk-in-the-cloud-learning-curves-for-point/3d-part-segmentation-on-shapenet-part)](https://paperswithcode.com/sota/3d-part-segmentation-on-shapenet-part?p=walk-in-the-cloud-learning-curves-for-point) Paper: https://arxiv.org/abs/2105.01288 ![CurveNet](./poster3.png) ## Requirements - Python>=3.7 - PyTorch>=1.2 - Packages: glob, h5py, sklearn ## Contents - [Point Cloud Classification](#point-cloud-classification) - [Point Cloud Part Segmentation](#point-cloud-part-segmentation) - [Point Cloud Normal Estimation](#point-cloud-normal-estimation) **NOTE:** Please change your current directory to ```core/``` first before excuting the following commands. ## Point Cloud Classification ### Data The ModelNet40 dataset is primarily used for the classification experiments. At your first run, the program will automatically download the data if it is not in ```data/```. Or, you can manually download the [offical data](https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip) and unzip to ```data/```. Alternatively, you can place your downloaded data anywhere you like, and link the path to ```DATA_DIR``` in ```core/data.py```. Otherwise, the download will still be automatically triggered. ### Train Train with our default settings (same as in the paper): ``` python3 main_cls.py --exp_name=curvenet_cls_1 ``` Train with customized settings with the flags: ```--lr```, ```--scheduler```, ```--batch_size```. Alternatively, you can directly modify ```core/start_cls.sh``` and simply run: ``` ./start_cls.sh ``` **NOTE:** Our reported model achieves **93.8%/94.2%** accuracy (see sections below). However, due to randomness, the best result might require repeated training processes. Hence, we also provide another benchmark result here (where we repeated 5 runs with different random seeds, and report their average), which is **93.65%** accuracy. ### Evaluation Evaluate without voting: ``` python3 main_cls.py --exp_name=curvenet_cls_1 --eval=True --model_path=PATH_TO_YOUR_MODEL ``` Alternatively, you can directly modify ```core/test_cls.sh``` and simply run: ``` ./test_cls.sh ``` For voting, we used the ```voting_evaluate_cls.py```script provided in [RSCNN](https://github.com/Yochengliu/Relation-Shape-CNN). Please refer to their license for usage. ### Evaluation with our pretrained model: Please download our pretrained model ```cls/``` at [google drive](https://drive.google.com/drive/folders/1kX-zIipyzB0iMaopcijzdTRuHeTzfTSz?usp=sharing). And then run: ``` python3 main_cls.py --exp_name=curvenet_cls_pretrained --eval --model_path=PATH_TO_PRETRAINED/cls/models/model.t7 ```   ## Point Cloud Part Segmentation ### Data The ShapeNet Part dataset is primarily used for the part segmentation experiments. At your first run, the program will automatically download the data if it is not in ```data/```. Or, you can manually download the [offical data](https://shapenet.cs.stanford.edu/media/shapenet_part_seg_hdf5_data.zip) and unzip to ```data/```. Alternatively, you can place your downloaded data anywhere you like, and link the path to ```DATA_DIR``` in ```core/data.py```. Otherwise, the download will still be automatically triggered. ### Train Train with our default settings (same as in the paper): ``` python3 main_partseg.py --exp_name=curvenet_seg_1 ``` Train with customized settings with the flags: ```--lr```, ```--scheduler```, ```--batch_size```. Alternatively, you can directly modify ```core/start_part.sh``` and simply run: ``` ./start_part.sh ``` **NOTE:** Our reported model achieves **86.6%/86.8%** mIoU (see sections below). However, due to randomness, the best result might require repeated training processes. Hence, we also provide another benchmark result here (where we repeated 5 runs with different random seeds, and report their average), which is **86.46** mIoU. ### Evaluation Evaluate without voting: ``` python3 main_partseg.py --exp_name=curvenet_seg_1 --eval=True --model_path=PATH_TO_YOUR_MODEL ``` Alternatively, you can directly modify ```core/test_cls.sh``` and simply run: ``` ./test_cls.sh ``` For voting, we used the ```voting_evaluate_partseg.py```script provided in [RSCNN](https://github.com/Yochengliu/Relation-Shape-CNN). Please refer to their license for usage. ### Evaluation with our pretrained model: Please download our pretrained model ```partseg/``` at [google drive](https://drive.google.com/drive/folders/1kX-zIipyzB0iMaopcijzdTRuHeTzfTSz?usp=sharing). And then run: ``` python3 main_partseg.py --exp_name=curvenet_seg_pretrained --eval=True --model_path=PATH_TO_PRETRAINED/partseg/models/model.t7 ```   ## Point Cloud Normal Estimation ### Data The ModelNet40 dataset is used for the normal estimation experiments. We have preprocessed the raw ModelNet40 dataset into ```.h5``` files. Each point cloud instance contains 2048 randomly sampled points and point-to-point normal ground truths. Please download our processed data [here](https://drive.google.com/file/d/1j6lB3ZOF0_x_l9bqdchAxIYBi7Devie8/view?usp=sharing) and place it to ```data/```, or you need to specify the data root path in ```core/data.py```. ### Train Train with our default settings (same as in the paper): ``` python3 main_normal.py --exp_name=curvenet_normal_1 ``` Train with customized settings with the flags: ```--multiplier```, ```--lr```, ```--scheduler```, ```--batch_size```. Alternatively, you can directly modify ```core/start_normal.sh``` and simply run: ``` ./start_normal.sh ``` ### Evaluation Evaluate without voting: ``` python3 main_normal.py --exp_name=curvenet_normal_1 --eval=True --model_path=PATH_TO_YOUR_MODEL ``` Alternatively, you can directly modify ```core/test_normal.sh``` and simply run: ``` ./test_normal.sh ``` ### Evaluation with our pretrained model: Please download our pretrained model ```normal/``` at [google drive](https://drive.google.com/drive/folders/1kX-zIipyzB0iMaopcijzdTRuHeTzfTSz?usp=sharing). And then run: ``` python3 main_normal.py --exp_name=curvenet_normal_pretrained --eval=True --model_path=PATH_TO_PRETRAINED/normal/models/model.t7 ``` ## Citation If you find this repo useful in your work or research, please cite: ``` @InProceedings{Xiang_2021_ICCV, author = {Xiang, Tiange and Zhang, Chaoyi and Song, Yang and Yu, Jianhui and Cai, Weidong}, title = {Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {915-924} } ``` ## Acknowledgement Our code borrows a lot from: - [DGCNN](https://github.com/WangYueFt/dgcnn) - [DGCNN.pytorch](https://github.com/AnTao97/dgcnn.pytorch) - [CloserLook3D](https://github.com/zeliu98/CloserLook3D) ================================================ FILE: CurveNet/core/data.py ================================================ """ @Author: Yue Wang @Contact: yuewangx@mit.edu @File: data.py @Time: 2018/10/13 6:21 PM Modified by @Author: Tiange Xiang @Contact: txia7609@uni.sydney.edu.au @Time: 2021/1/21 3:10 PM """ import os import sys import glob import h5py import numpy as np import torch from torch.utils.data import Dataset # change this to your data root DATA_DIR = '../data/' def download_modelnet40(): if not os.path.exists(DATA_DIR): os.mkdir(DATA_DIR) if not os.path.exists(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048')): os.mkdir(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048')) www = 'https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip' zipfile = os.path.basename(www) os.system('wget %s --no-check-certificate; unzip %s' % (www, zipfile)) os.system('mv %s %s' % (zipfile[:-4], DATA_DIR)) os.system('rm %s' % (zipfile)) def download_shapenetpart(): if not os.path.exists(DATA_DIR): os.mkdir(DATA_DIR) if not os.path.exists(os.path.join(DATA_DIR, 'shapenet_part_seg_hdf5_data')): os.mkdir(os.path.join(DATA_DIR, 'shapenet_part_seg_hdf5_data')) www = 'https://shapenet.cs.stanford.edu/media/shapenet_part_seg_hdf5_data.zip' zipfile = os.path.basename(www) os.system('wget %s --no-check-certificate; unzip %s' % (www, zipfile)) os.system('mv %s %s' % (zipfile[:-4], os.path.join(DATA_DIR, 'shapenet_part_seg_hdf5_data'))) os.system('rm %s' % (zipfile)) def load_data_normal(partition): f = h5py.File(os.path.join(DATA_DIR, 'modelnet40_normal', 'normal_%s.h5'%partition), 'r+') data = f['xyz'][:].astype('float32') label = f['normal'][:].astype('float32') f.close() return data, label def load_data_cls(partition): download_modelnet40() all_data = [] all_label = [] for h5_name in glob.glob(os.path.join(DATA_DIR, 'modelnet40*hdf5_2048', '*%s*.h5'%partition)): f = h5py.File(h5_name, 'r+') data = f['data'][:].astype('float32') label = f['label'][:].astype('int64') f.close() all_data.append(data) all_label.append(label) all_data = np.concatenate(all_data, axis=0) all_label = np.concatenate(all_label, axis=0) return all_data, all_label def load_data_partseg(partition): download_shapenetpart() all_data = [] all_label = [] all_seg = [] if partition == 'trainval': file = glob.glob(os.path.join(DATA_DIR, 'shapenet_part_seg_hdf5_data', 'hdf5_data', '*train*.h5')) \ + glob.glob(os.path.join(DATA_DIR, 'shapenet_part_seg_hdf5_data', 'hdf5_data', '*val*.h5')) else: file = glob.glob(os.path.join(DATA_DIR, 'shapenet_part_seg_hdf5_data', 'hdf5_data', '*%s*.h5'%partition)) for h5_name in file: f = h5py.File(h5_name, 'r+') data = f['data'][:].astype('float32') label = f['label'][:].astype('int64') seg = f['pid'][:].astype('int64') f.close() all_data.append(data) all_label.append(label) all_seg.append(seg) all_data = np.concatenate(all_data, axis=0) all_label = np.concatenate(all_label, axis=0) all_seg = np.concatenate(all_seg, axis=0) return all_data, all_label, all_seg def translate_pointcloud(pointcloud): xyz1 = np.random.uniform(low=2./3., high=3./2., size=[3]) xyz2 = np.random.uniform(low=-0.2, high=0.2, size=[3]) translated_pointcloud = np.add(np.multiply(pointcloud, xyz1), xyz2).astype('float32') return translated_pointcloud def jitter_pointcloud(pointcloud, sigma=0.01, clip=0.02): N, C = pointcloud.shape pointcloud += np.clip(sigma * np.random.randn(N, C), -1*clip, clip) return pointcloud def rotate_pointcloud(pointcloud): theta = np.pi*2 * np.random.uniform() rotation_matrix = np.array([[np.cos(theta), -np.sin(theta)],[np.sin(theta), np.cos(theta)]]) pointcloud[:,[0,2]] = pointcloud[:,[0,2]].dot(rotation_matrix) # random rotation (x,z) return pointcloud class ModelNet40(Dataset): def __init__(self, num_points, partition='train'): self.data, self.label = load_data_cls(partition) self.num_points = num_points self.partition = partition def __getitem__(self, item): pointcloud = self.data[item][:self.num_points] label = self.label[item] if self.partition == 'train': pointcloud = translate_pointcloud(pointcloud) #pointcloud = rotate_pointcloud(pointcloud) np.random.shuffle(pointcloud) return pointcloud, label def __len__(self): return self.data.shape[0] class ModelNetNormal(Dataset): def __init__(self, num_points, partition='train'): self.data, self.label = load_data_normal(partition) self.num_points = num_points self.partition = partition def __getitem__(self, item): pointcloud = self.data[item][:self.num_points] label = self.label[item][:self.num_points] if self.partition == 'train': #pointcloud = translate_pointcloud(pointcloud) idx = np.arange(0, pointcloud.shape[0], dtype=np.int64) np.random.shuffle(idx) pointcloud = self.data[item][idx] label = self.label[item][idx] return pointcloud, label def __len__(self): return self.data.shape[0] class ShapeNetPart(Dataset): def __init__(self, num_points=2048, partition='train', class_choice=None): self.data, self.label, self.seg = load_data_partseg(partition) self.cat2id = {'airplane': 0, 'bag': 1, 'cap': 2, 'car': 3, 'chair': 4, 'earphone': 5, 'guitar': 6, 'knife': 7, 'lamp': 8, 'laptop': 9, 'motor': 10, 'mug': 11, 'pistol': 12, 'rocket': 13, 'skateboard': 14, 'table': 15} self.seg_num = [4, 2, 2, 4, 4, 3, 3, 2, 4, 2, 6, 2, 3, 3, 3, 3] self.index_start = [0, 4, 6, 8, 12, 16, 19, 22, 24, 28, 30, 36, 38, 41, 44, 47] self.num_points = num_points self.partition = partition self.class_choice = class_choice if self.class_choice != None: id_choice = self.cat2id[self.class_choice] indices = (self.label == id_choice).squeeze() self.data = self.data[indices] self.label = self.label[indices] self.seg = self.seg[indices] self.seg_num_all = self.seg_num[id_choice] self.seg_start_index = self.index_start[id_choice] else: self.seg_num_all = 50 self.seg_start_index = 0 def __getitem__(self, item): pointcloud = self.data[item][:self.num_points] label = self.label[item] seg = self.seg[item][:self.num_points] if self.partition == 'trainval': pointcloud = translate_pointcloud(pointcloud) indices = list(range(pointcloud.shape[0])) np.random.shuffle(indices) pointcloud = pointcloud[indices] seg = seg[indices] return pointcloud, label, seg def __len__(self): return self.data.shape[0] ================================================ FILE: CurveNet/core/main_cls.py ================================================ """ @Author: Yue Wang @Contact: yuewangx@mit.edu @File: main_cls.py @Time: 2018/10/13 10:39 PM Modified by @Author: Tiange Xiang @Contact: txia7609@uni.sydney.edu.au @Time: 2021/01/21 3:10 PM """ from __future__ import print_function import os import argparse import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.optim.lr_scheduler import CosineAnnealingLR, MultiStepLR from data import ModelNet40 from models.curvenet_cls import CurveNet import numpy as np from torch.utils.data import DataLoader from util import cal_loss, IOStream import sklearn.metrics as metrics def _init_(): # fix random seed torch.manual_seed(seed) np.random.seed(seed) torch.cuda.manual_seed_all(seed) torch.cuda.manual_seed(seed) torch.set_printoptions(10) torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True os.environ['PYTHONHASHSEED'] = str(seed) # prepare file structures if not os.path.exists('../checkpoints'): os.makedirs('../checkpoints') if not os.path.exists('../checkpoints/'+args.exp_name): os.makedirs('../checkpoints/'+args.exp_name) if not os.path.exists('../checkpoints/'+args.exp_name+'/'+'models'): os.makedirs('../checkpoints/'+args.exp_name+'/'+'models') os.system('cp main_cls.py ../checkpoints/'+args.exp_name+'/main_cls.py.backup') os.system('cp models/curvenet_cls.py ../checkpoints/'+args.exp_name+'/curvenet_cls.py.backup') def train(args, io): train_loader = DataLoader(ModelNet40(partition='train', num_points=args.num_points), num_workers=8, batch_size=args.batch_size, shuffle=True, drop_last=True) test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), num_workers=8, batch_size=args.test_batch_size, shuffle=False, drop_last=False) device = torch.device("cuda" if args.cuda else "cpu") io.cprint("Let's use" + str(torch.cuda.device_count()) + "GPUs!") # create model model = CurveNet().to(device) model = nn.DataParallel(model) if args.use_sgd: io.cprint("Use SGD") opt = optim.SGD(model.parameters(), lr=args.lr*100, momentum=args.momentum, weight_decay=1e-4) else: io.cprint("Use Adam") opt = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4) if args.scheduler == 'cos': scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=1e-3) elif args.scheduler == 'step': scheduler = MultiStepLR(opt, [120, 160], gamma=0.1) criterion = cal_loss best_test_acc = 0 for epoch in range(args.epochs): #################### # Train #################### train_loss = 0.0 count = 0.0 model.train() train_pred = [] train_true = [] for data, label in train_loader: data, label = data.to(device), label.to(device).squeeze() data = data.permute(0, 2, 1) batch_size = data.size()[0] opt.zero_grad() logits = model(data) loss = criterion(logits, label) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), 1) opt.step() preds = logits.max(dim=1)[1] count += batch_size train_loss += loss.item() * batch_size train_true.append(label.cpu().numpy()) train_pred.append(preds.detach().cpu().numpy()) if args.scheduler == 'cos': scheduler.step() elif args.scheduler == 'step': if opt.param_groups[0]['lr'] > 1e-5: scheduler.step() if opt.param_groups[0]['lr'] < 1e-5: for param_group in opt.param_groups: param_group['lr'] = 1e-5 train_true = np.concatenate(train_true) train_pred = np.concatenate(train_pred) outstr = 'Train %d, loss: %.6f, train acc: %.6f' % (epoch, train_loss*1.0/count, metrics.accuracy_score( train_true, train_pred)) io.cprint(outstr) #################### # Test #################### test_loss = 0.0 count = 0.0 model.eval() test_pred = [] test_true = [] for data, label in test_loader: data, label = data.to(device), label.to(device).squeeze() data = data.permute(0, 2, 1) batch_size = data.size()[0] logits = model(data) loss = criterion(logits, label) preds = logits.max(dim=1)[1] count += batch_size test_loss += loss.item() * batch_size test_true.append(label.cpu().numpy()) test_pred.append(preds.detach().cpu().numpy()) test_true = np.concatenate(test_true) test_pred = np.concatenate(test_pred) test_acc = metrics.accuracy_score(test_true, test_pred) outstr = 'Test %d, loss: %.6f, test acc: %.6f' % (epoch, test_loss*1.0/count, test_acc) io.cprint(outstr) if test_acc >= best_test_acc: best_test_acc = test_acc torch.save(model.state_dict(), '../checkpoints/%s/models/model.t7' % args.exp_name) io.cprint('best: %.3f' % best_test_acc) def test(args, io): test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), batch_size=args.test_batch_size, shuffle=False, drop_last=False) device = torch.device("cuda" if args.cuda else "cpu") #Try to load models model = CurveNet().to(device) model = nn.DataParallel(model) model.load_state_dict(torch.load(args.model_path)) model = model.eval() test_acc = 0.0 count = 0.0 test_true = [] test_pred = [] for data, label in test_loader: data, label = data.to(device), label.to(device).squeeze() data = data.permute(0, 2, 1) batch_size = data.size()[0] logits = model(data) preds = logits.max(dim=1)[1] test_true.append(label.cpu().numpy()) test_pred.append(preds.detach().cpu().numpy()) test_true = np.concatenate(test_true) test_pred = np.concatenate(test_pred) test_acc = metrics.accuracy_score(test_true, test_pred) outstr = 'Test :: test acc: %.6f'%(test_acc) io.cprint(outstr) if __name__ == "__main__": # Training settings parser = argparse.ArgumentParser(description='Point Cloud Recognition') parser.add_argument('--exp_name', type=str, default='exp', metavar='N', help='Name of the experiment') parser.add_argument('--dataset', type=str, default='modelnet40', metavar='N', choices=['modelnet40']) parser.add_argument('--batch_size', type=int, default=32, metavar='batch_size', help='Size of batch)') parser.add_argument('--test_batch_size', type=int, default=16, metavar='batch_size', help='Size of batch)') parser.add_argument('--epochs', type=int, default=200, metavar='N', help='number of episode to train ') parser.add_argument('--use_sgd', type=bool, default=True, help='Use SGD') parser.add_argument('--lr', type=float, default=0.001, metavar='LR', help='learning rate (default: 0.001, 0.1 if using sgd)') parser.add_argument('--momentum', type=float, default=0.9, metavar='M', help='SGD momentum (default: 0.9)') parser.add_argument('--scheduler', type=str, default='cos', metavar='N', choices=['cos', 'step'], help='Scheduler to use, [cos, step]') parser.add_argument('--no_cuda', type=bool, default=False, help='enables CUDA training') parser.add_argument('--eval', type=bool, default=False, help='evaluate the model') parser.add_argument('--num_points', type=int, default=1024, help='num of points to use') parser.add_argument('--model_path', type=str, default='', metavar='N', help='Pretrained model path') args = parser.parse_args() seed = np.random.randint(1, 10000) _init_() if args.eval: io = IOStream('../checkpoints/' + args.exp_name + '/eval.log') else: io = IOStream('../checkpoints/' + args.exp_name + '/run.log') io.cprint(str(args)) io.cprint('random seed is: ' + str(seed)) args.cuda = not args.no_cuda and torch.cuda.is_available() if args.cuda: io.cprint( 'Using GPU : ' + str(torch.cuda.current_device()) + ' from ' + str(torch.cuda.device_count()) + ' devices') else: io.cprint('Using CPU') if not args.eval: train(args, io) else: with torch.no_grad(): test(args, io) ================================================ FILE: CurveNet/core/main_normal.py ================================================ """ @Author: Tiange Xiang @Contact: txia7609@uni.sydney.edu.au @File: main_normal.py @Time: 2021/01/21 3:10 PM """ from __future__ import print_function import os import argparse import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.optim.lr_scheduler import CosineAnnealingLR, MultiStepLR from data import ModelNetNormal from models.curvenet_normal import CurveNet import numpy as np from torch.utils.data import DataLoader from util import IOStream def _init_(): # fix random seed torch.manual_seed(seed) np.random.seed(seed) torch.cuda.manual_seed_all(seed) torch.cuda.manual_seed(seed) torch.set_printoptions(10) torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True os.environ['PYTHONHASHSEED'] = str(seed) # prepare file structures if not os.path.exists('../checkpoints'): os.makedirs('../checkpoints') if not os.path.exists('../checkpoints/'+args.exp_name): os.makedirs('../checkpoints/'+args.exp_name) if not os.path.exists('../checkpoints/'+args.exp_name+'/'+'models'): os.makedirs('../checkpoints/'+args.exp_name+'/'+'models') os.system('cp main_normal.py ../checkpoints/'+args.exp_name+'/main_normal.py.backup') os.system('cp models/curvenet_normal.py ../checkpoints/'+args.exp_name+'/curvenet_normal.py.backup') def train(args, io): train_loader = DataLoader(ModelNetNormal(args.num_points, partition='train'), num_workers=8, batch_size=args.batch_size, shuffle=True, drop_last=True) test_loader = DataLoader(ModelNetNormal(args.num_points, partition='test'), num_workers=8, batch_size=args.test_batch_size, shuffle=False, drop_last=False) device = torch.device("cuda" if args.cuda else "cpu") # create model model = CurveNet(args.multiplier).to(device) model = nn.DataParallel(model) io.cprint("Let's use" + str(torch.cuda.device_count()) + "GPUs!") if args.use_sgd: io.cprint("Use SGD") opt = optim.SGD(model.parameters(), lr=args.lr*100, momentum=args.momentum, weight_decay=1e-4) else: io.cprint("Use Adam") opt = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4) if args.scheduler == 'cos': scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=1e-3) elif args.scheduler == 'step': scheduler = MultiStepLR(opt, [140, 180], gamma=0.1) criterion = torch.nn.CosineEmbeddingLoss() best_test_loss = 99 for epoch in range(args.epochs): #################### # Train #################### train_loss = 0.0 count = 0.0 model.train() for data, seg in train_loader: data, seg = data.to(device), seg.to(device) data = data.permute(0, 2, 1) batch_size = data.size()[0] opt.zero_grad() seg_pred = model(data) seg_pred = seg_pred.permute(0, 2, 1).contiguous() #print(seg_pred.shape, seg.shape) loss = criterion(seg_pred.view(-1, 3), seg.view(-1,3).squeeze(), torch.tensor(1).cuda()) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), 1) opt.step() count += batch_size train_loss += loss.item() * batch_size if args.scheduler == 'cos': scheduler.step() elif args.scheduler == 'step': if opt.param_groups[0]['lr'] > 1e-5: scheduler.step() if opt.param_groups[0]['lr'] < 1e-5: for param_group in opt.param_groups: param_group['lr'] = 1e-5 outstr = 'Train %d, loss: %.6f' % (epoch, train_loss/count) io.cprint(outstr) #################### # Test #################### test_loss = 0.0 count = 0.0 model.eval() for data, seg in test_loader: data, seg = data.to(device), seg.to(device) data = data.permute(0, 2, 1) batch_size = data.size()[0] seg_pred = model(data) seg_pred = seg_pred.permute(0, 2, 1).contiguous() loss = criterion(seg_pred.view(-1, 3), seg.view(-1,3).squeeze(), torch.tensor(1).cuda()) count += batch_size test_loss += loss.item() * batch_size if test_loss*1.0/count <= best_test_loss: best_test_loss = test_loss*1.0/count torch.save(model.state_dict(), '../checkpoints/%s/models/model.t7' % args.exp_name) outstr = 'Test %d, loss: %.6f, best loss %.6f' % (epoch, test_loss/count, best_test_loss) io.cprint(outstr) def test(args, io): test_loader = DataLoader(ModelNetNormal(args.num_points, partition='test'), batch_size=args.test_batch_size, shuffle=False, drop_last=False) device = torch.device("cuda" if args.cuda else "cpu") #Try to load models model = CurveNet(args.multiplier).to(device) model = nn.DataParallel(model) model.load_state_dict(torch.load(args.model_path)) criterion = torch.nn.CosineEmbeddingLoss() model = model.eval() test_loss = 0.0 count = 0 for data, seg in test_loader: data, seg = data.to(device), seg.to(device) #print(data.shape, seg.shape) data = data.permute(0, 2, 1) batch_size = data.size()[0] seg_pred = model(data) seg_pred = seg_pred.permute(0, 2, 1).contiguous() loss = criterion(seg_pred.view(-1, 3), seg.view(-1,3).squeeze(), torch.tensor(1).cuda()) count += batch_size test_loss += loss.item() * batch_size outstr = 'Test :: test loss: %.6f' % (test_loss*1.0/count) io.cprint(outstr) if __name__ == "__main__": # Training settings parser = argparse.ArgumentParser(description='Point Cloud Part Segmentation') parser.add_argument('--exp_name', type=str, default='exp', metavar='N', help='Name of the experiment') parser.add_argument('--batch_size', type=int, default=32, metavar='batch_size', help='Size of batch)') parser.add_argument('--test_batch_size', type=int, default=16, metavar='batch_size', help='Size of batch)') parser.add_argument('--epochs', type=int, default=200, metavar='N', help='number of episode to train ') parser.add_argument('--use_sgd', type=bool, default=True, help='Use SGD') parser.add_argument('--lr', type=float, default=0.0005, metavar='LR', help='learning rate') parser.add_argument('--multiplier', type=float, default=2.0, metavar='MP', help='network expansion multiplier') parser.add_argument('--momentum', type=float, default=0.9, metavar='M', help='SGD momentum (default: 0.9)') parser.add_argument('--scheduler', type=str, default='cos', metavar='N', choices=['cos', 'step'], help='Scheduler to use, [cos, step]') parser.add_argument('--no_cuda', type=bool, default=False, help='enables CUDA training') parser.add_argument('--eval', type=bool, default=False, help='evaluate the model') parser.add_argument('--num_points', type=int, default=1024, help='num of points to use') parser.add_argument('--model_path', type=str, default='', metavar='N', help='Pretrained model path') args = parser.parse_args() seed = np.random.randint(1, 10000) _init_() io = IOStream('../checkpoints/' + args.exp_name + '/run.log') io.cprint(str(args)) io.cprint('random seed is: ' + str(seed)) args.cuda = not args.no_cuda and torch.cuda.is_available() if args.cuda: io.cprint( 'Using GPU : ' + str(torch.cuda.current_device()) + ' from ' + str(torch.cuda.device_count()) + ' devices') else: io.cprint('Using CPU') if not args.eval: train(args, io) else: with torch.no_grad(): test(args, io) ================================================ FILE: CurveNet/core/main_partseg.py ================================================ """ @Author: An Tao @Contact: ta19@mails.tsinghua.edu.cn @File: main_partseg.py @Time: 2019/12/31 11:17 AM Modified by @Author: Tiange Xiang @Contact: txia7609@uni.sydney.edu.au @Time: 2021/01/21 3:10 PM """ from __future__ import print_function import os import argparse import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.optim.lr_scheduler import CosineAnnealingLR, StepLR, MultiStepLR from data import ShapeNetPart from models.curvenet_seg import CurveNet import numpy as np from torch.utils.data import DataLoader from util import cal_loss, IOStream import sklearn.metrics as metrics seg_num = [4, 2, 2, 4, 4, 3, 3, 2, 4, 2, 6, 2, 3, 3, 3, 3] index_start = [0, 4, 6, 8, 12, 16, 19, 22, 24, 28, 30, 36, 38, 41, 44, 47] def _init_(): # fix random seed torch.manual_seed(seed) np.random.seed(seed) torch.cuda.manual_seed_all(seed) torch.cuda.manual_seed(seed) torch.set_printoptions(10) torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True os.environ['PYTHONHASHSEED'] = str(seed) # prepare file structures if not os.path.exists('../checkpoints'): os.makedirs('../checkpoints') if not os.path.exists('../checkpoints/'+args.exp_name): os.makedirs('../checkpoints/'+args.exp_name) if not os.path.exists('../checkpoints/'+args.exp_name+'/'+'models'): os.makedirs('../checkpoints/'+args.exp_name+'/'+'models') os.system('cp main_partseg.py ../checkpoints/'+args.exp_name+'/main_partseg.py.backup') os.system('cp models/curvenet_seg.py ../checkpoints/'+args.exp_name+'/curvenet_seg.py.backup') def calculate_shape_IoU(pred_np, seg_np, label, class_choice, eva=False): label = label.squeeze() shape_ious = [] category = {} for shape_idx in range(seg_np.shape[0]): if not class_choice: start_index = index_start[label[shape_idx]] num = seg_num[label[shape_idx]] parts = range(start_index, start_index + num) else: parts = range(seg_num[label[0]]) part_ious = [] for part in parts: I = np.sum(np.logical_and(pred_np[shape_idx] == part, seg_np[shape_idx] == part)) U = np.sum(np.logical_or(pred_np[shape_idx] == part, seg_np[shape_idx] == part)) if U == 0: iou = 1 # If the union of groundtruth and prediction points is empty, then count part IoU as 1 else: iou = I / float(U) part_ious.append(iou) shape_ious.append(np.mean(part_ious)) if label[shape_idx] not in category: category[label[shape_idx]] = [shape_ious[-1]] else: category[label[shape_idx]].append(shape_ious[-1]) if eva: return shape_ious, category else: return shape_ious def train(args, io): train_dataset = ShapeNetPart(partition='trainval', num_points=args.num_points, class_choice=args.class_choice) if (len(train_dataset) < 100): drop_last = False else: drop_last = True train_loader = DataLoader(train_dataset, num_workers=8, batch_size=args.batch_size, shuffle=True, drop_last=drop_last) test_loader = DataLoader(ShapeNetPart(partition='test', num_points=args.num_points, class_choice=args.class_choice), num_workers=8, batch_size=args.test_batch_size, shuffle=False, drop_last=False) device = torch.device("cuda" if args.cuda else "cpu") io.cprint("Let's use" + str(torch.cuda.device_count()) + "GPUs!") seg_num_all = train_loader.dataset.seg_num_all seg_start_index = train_loader.dataset.seg_start_index # create model model = CurveNet().to(device) model = nn.DataParallel(model) if args.use_sgd: print("Use SGD") opt = optim.SGD(model.parameters(), lr=args.lr*100, momentum=args.momentum, weight_decay=1e-4) else: print("Use Adam") opt = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4) if args.scheduler == 'cos': scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=1e-3) elif args.scheduler == 'step': scheduler = MultiStepLR(opt, [140, 180], gamma=0.1) criterion = cal_loss best_test_iou = 0 for epoch in range(args.epochs): #################### # Train #################### train_loss = 0.0 count = 0.0 model.train() train_true_cls = [] train_pred_cls = [] train_true_seg = [] train_pred_seg = [] train_label_seg = [] for data, label, seg in train_loader: seg = seg - seg_start_index label_one_hot = np.zeros((label.shape[0], 16)) for idx in range(label.shape[0]): label_one_hot[idx, label[idx]] = 1 label_one_hot = torch.from_numpy(label_one_hot.astype(np.float32)) data, label_one_hot, seg = data.to(device), label_one_hot.to(device), seg.to(device) data = data.permute(0, 2, 1) batch_size = data.size()[0] opt.zero_grad() seg_pred = model(data, label_one_hot) seg_pred = seg_pred.permute(0, 2, 1).contiguous() loss = criterion(seg_pred.view(-1, seg_num_all), seg.view(-1,1).squeeze()) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), 1) opt.step() pred = seg_pred.max(dim=2)[1] # (batch_size, num_points) count += batch_size train_loss += loss.item() * batch_size seg_np = seg.cpu().numpy() # (batch_size, num_points) pred_np = pred.detach().cpu().numpy() # (batch_size, num_points) train_true_cls.append(seg_np.reshape(-1)) # (batch_size * num_points) train_pred_cls.append(pred_np.reshape(-1)) # (batch_size * num_points) train_true_seg.append(seg_np) train_pred_seg.append(pred_np) train_label_seg.append(label.reshape(-1)) if args.scheduler == 'cos': scheduler.step() elif args.scheduler == 'step': if opt.param_groups[0]['lr'] > 1e-5: scheduler.step() if opt.param_groups[0]['lr'] < 1e-5: for param_group in opt.param_groups: param_group['lr'] = 1e-5 train_true_cls = np.concatenate(train_true_cls) train_pred_cls = np.concatenate(train_pred_cls) train_acc = metrics.accuracy_score(train_true_cls, train_pred_cls) avg_per_class_acc = metrics.balanced_accuracy_score(train_true_cls, train_pred_cls) train_true_seg = np.concatenate(train_true_seg, axis=0) train_pred_seg = np.concatenate(train_pred_seg, axis=0) train_label_seg = np.concatenate(train_label_seg) train_ious = calculate_shape_IoU(train_pred_seg, train_true_seg, train_label_seg, args.class_choice) outstr = 'Train %d, loss: %.6f, train acc: %.6f, train avg acc: %.6f, train iou: %.6f' % (epoch, train_loss*1.0/count, train_acc, avg_per_class_acc, np.mean(train_ious)) io.cprint(outstr) #################### # Test #################### test_loss = 0.0 count = 0.0 model.eval() test_true_cls = [] test_pred_cls = [] test_true_seg = [] test_pred_seg = [] test_label_seg = [] for data, label, seg in test_loader: seg = seg - seg_start_index label_one_hot = np.zeros((label.shape[0], 16)) for idx in range(label.shape[0]): label_one_hot[idx, label[idx]] = 1 label_one_hot = torch.from_numpy(label_one_hot.astype(np.float32)) data, label_one_hot, seg = data.to(device), label_one_hot.to(device), seg.to(device) data = data.permute(0, 2, 1) batch_size = data.size()[0] seg_pred = model(data, label_one_hot) seg_pred = seg_pred.permute(0, 2, 1).contiguous() loss = criterion(seg_pred.view(-1, seg_num_all), seg.view(-1,1).squeeze()) pred = seg_pred.max(dim=2)[1] count += batch_size test_loss += loss.item() * batch_size seg_np = seg.cpu().numpy() pred_np = pred.detach().cpu().numpy() test_true_cls.append(seg_np.reshape(-1)) test_pred_cls.append(pred_np.reshape(-1)) test_true_seg.append(seg_np) test_pred_seg.append(pred_np) test_label_seg.append(label.reshape(-1)) test_true_cls = np.concatenate(test_true_cls) test_pred_cls = np.concatenate(test_pred_cls) test_acc = metrics.accuracy_score(test_true_cls, test_pred_cls) avg_per_class_acc = metrics.balanced_accuracy_score(test_true_cls, test_pred_cls) test_true_seg = np.concatenate(test_true_seg, axis=0) test_pred_seg = np.concatenate(test_pred_seg, axis=0) test_label_seg = np.concatenate(test_label_seg) test_ious = calculate_shape_IoU(test_pred_seg, test_true_seg, test_label_seg, args.class_choice) outstr = 'Test %d, loss: %.6f, test acc: %.6f, test avg acc: %.6f, test iou: %.6f, best iou %.6f' % (epoch, test_loss*1.0/count, test_acc, avg_per_class_acc, np.mean(test_ious), best_test_iou) io.cprint(outstr) if np.mean(test_ious) >= best_test_iou: best_test_iou = np.mean(test_ious) torch.save(model.state_dict(), '../checkpoints/%s/models/model.t7' % args.exp_name) def test(args, io): test_loader = DataLoader(ShapeNetPart(partition='test', num_points=args.num_points, class_choice=args.class_choice), batch_size=args.test_batch_size, shuffle=True, drop_last=False) device = torch.device("cuda" if args.cuda else "cpu") #Try to load models seg_start_index = test_loader.dataset.seg_start_index model = CurveNet().to(device) model = nn.DataParallel(model) model.load_state_dict(torch.load(args.model_path)) model = model.eval() test_acc = 0.0 test_true_cls = [] test_pred_cls = [] test_true_seg = [] test_pred_seg = [] test_label_seg = [] category = {} for data, label, seg in test_loader: seg = seg - seg_start_index label_one_hot = np.zeros((label.shape[0], 16)) for idx in range(label.shape[0]): label_one_hot[idx, label[idx]] = 1 label_one_hot = torch.from_numpy(label_one_hot.astype(np.float32)) data, label_one_hot, seg = data.to(device), label_one_hot.to(device), seg.to(device) data = data.permute(0, 2, 1) seg_pred = model(data, label_one_hot) seg_pred = seg_pred.permute(0, 2, 1).contiguous() pred = seg_pred.max(dim=2)[1] seg_np = seg.cpu().numpy() pred_np = pred.detach().cpu().numpy() test_true_cls.append(seg_np.reshape(-1)) test_pred_cls.append(pred_np.reshape(-1)) test_true_seg.append(seg_np) test_pred_seg.append(pred_np) test_label_seg.append(label.reshape(-1)) test_true_cls = np.concatenate(test_true_cls) test_pred_cls = np.concatenate(test_pred_cls) test_acc = metrics.accuracy_score(test_true_cls, test_pred_cls) avg_per_class_acc = metrics.balanced_accuracy_score(test_true_cls, test_pred_cls) test_true_seg = np.concatenate(test_true_seg, axis=0) test_pred_seg = np.concatenate(test_pred_seg, axis=0) test_label_seg = np.concatenate(test_label_seg) test_ious,category = calculate_shape_IoU(test_pred_seg, test_true_seg, test_label_seg, args.class_choice, eva=True) outstr = 'Test :: test acc: %.6f, test avg acc: %.6f, test iou: %.6f' % (test_acc, avg_per_class_acc, np.mean(test_ious)) io.cprint(outstr) results = [] for key in category.keys(): results.append((int(key), np.mean(category[key]), len(category[key]))) results.sort(key=lambda x:x[0]) for re in results: io.cprint('idx: %d mIoU: %.3f num: %d' % (re[0], re[1], re[2])) if __name__ == "__main__": # Training settings parser = argparse.ArgumentParser(description='Point Cloud Part Segmentation') parser.add_argument('--exp_name', type=str, default='exp', metavar='N', help='Name of the experiment') parser.add_argument('--dataset', type=str, default='shapenetpart', metavar='N', choices=['shapenetpart']) parser.add_argument('--class_choice', type=str, default=None, metavar='N', choices=['airplane', 'bag', 'cap', 'car', 'chair', 'earphone', 'guitar', 'knife', 'lamp', 'laptop', 'motor', 'mug', 'pistol', 'rocket', 'skateboard', 'table']) parser.add_argument('--batch_size', type=int, default=32, metavar='batch_size', help='Size of batch)') parser.add_argument('--test_batch_size', type=int, default=16, metavar='batch_size', help='Size of batch)') parser.add_argument('--epochs', type=int, default=200, metavar='N', help='number of episode to train ') parser.add_argument('--use_sgd', type=bool, default=True, help='Use SGD') parser.add_argument('--lr', type=float, default=0.0005, metavar='LR', help='learning rate (default: 0.001, 0.1 if using sgd)') parser.add_argument('--momentum', type=float, default=0.9, metavar='M', help='SGD momentum (default: 0.9)') parser.add_argument('--scheduler', type=str, default='step', metavar='N', choices=['cos', 'step'], help='Scheduler to use, [cos, step]') parser.add_argument('--no_cuda', type=bool, default=False, help='enables CUDA training') parser.add_argument('--eval', type=bool, default=False, help='evaluate the model') parser.add_argument('--num_points', type=int, default=2048, help='num of points to use') parser.add_argument('--model_path', type=str, default='', metavar='N', help='Pretrained model path') args = parser.parse_args() seed = np.random.randint(1, 10000) _init_() if args.eval: io = IOStream('../checkpoints/' + args.exp_name + '/eval.log') else: io = IOStream('../checkpoints/' + args.exp_name + '/run.log') io.cprint(str(args)) io.cprint('random seed is: ' + str(seed)) args.cuda = not args.no_cuda and torch.cuda.is_available() if args.cuda: io.cprint( 'Using GPU : ' + str(torch.cuda.current_device()) + ' from ' + str(torch.cuda.device_count()) + ' devices') else: io.cprint('Using CPU') if not args.eval: train(args, io) else: with torch.no_grad(): test(args, io) ================================================ FILE: CurveNet/core/models/curvenet_cls.py ================================================ """ @Author: Tiange Xiang @Contact: txia7609@uni.sydney.edu.au @File: curvenet_cls.py @Time: 2021/01/21 3:10 PM """ import torch.nn as nn import torch.nn.functional as F from .curvenet_util import * curve_config = { 'default': [[100, 5], [100, 5], None, None], 'long': [[10, 30], None, None, None] } class CurveNet(nn.Module): def __init__(self, num_classes=40, k=20, setting='default'): super(CurveNet, self).__init__() assert setting in curve_config additional_channel = 32 self.lpfa = LPFA(9, additional_channel, k=k, mlp_num=1, initial=True) # encoder self.cic11 = CIC(npoint=1024, radius=0.05, k=k, in_channels=additional_channel, output_channels=64, bottleneck_ratio=2, mlp_num=1, curve_config=curve_config[setting][0]) self.cic12 = CIC(npoint=1024, radius=0.05, k=k, in_channels=64, output_channels=64, bottleneck_ratio=4, mlp_num=1, curve_config=curve_config[setting][0]) self.cic21 = CIC(npoint=1024, radius=0.05, k=k, in_channels=64, output_channels=128, bottleneck_ratio=2, mlp_num=1, curve_config=curve_config[setting][1]) self.cic22 = CIC(npoint=1024, radius=0.1, k=k, in_channels=128, output_channels=128, bottleneck_ratio=4, mlp_num=1, curve_config=curve_config[setting][1]) self.cic31 = CIC(npoint=256, radius=0.1, k=k, in_channels=128, output_channels=256, bottleneck_ratio=2, mlp_num=1, curve_config=curve_config[setting][2]) self.cic32 = CIC(npoint=256, radius=0.2, k=k, in_channels=256, output_channels=256, bottleneck_ratio=4, mlp_num=1, curve_config=curve_config[setting][2]) self.cic41 = CIC(npoint=64, radius=0.2, k=k, in_channels=256, output_channels=512, bottleneck_ratio=2, mlp_num=1, curve_config=curve_config[setting][3]) self.cic42 = CIC(npoint=64, radius=0.4, k=k, in_channels=512, output_channels=512, bottleneck_ratio=4, mlp_num=1, curve_config=curve_config[setting][3]) self.conv0 = nn.Sequential( nn.Conv1d(512, 1024, kernel_size=1, bias=False), nn.BatchNorm1d(1024), nn.ReLU(inplace=True)) self.conv1 = nn.Linear(1024 * 2, 512, bias=False) self.conv2 = nn.Linear(512, num_classes) self.bn1 = nn.BatchNorm1d(512) self.dp1 = nn.Dropout(p=0.5) def forward(self, xyz): l0_points = self.lpfa(xyz, xyz) l1_xyz, l1_points = self.cic11(xyz, l0_points) l1_xyz, l1_points = self.cic12(l1_xyz, l1_points) l2_xyz, l2_points = self.cic21(l1_xyz, l1_points) l2_xyz, l2_points = self.cic22(l2_xyz, l2_points) l3_xyz, l3_points = self.cic31(l2_xyz, l2_points) l3_xyz, l3_points = self.cic32(l3_xyz, l3_points) l4_xyz, l4_points = self.cic41(l3_xyz, l3_points) l4_xyz, l4_points = self.cic42(l4_xyz, l4_points) x = self.conv0(l4_points) x_max = F.adaptive_max_pool1d(x, 1) x_avg = F.adaptive_avg_pool1d(x, 1) x = torch.cat((x_max, x_avg), dim=1).squeeze(-1) x = F.relu(self.bn1(self.conv1(x).unsqueeze(-1)), inplace=True).squeeze(-1) x = self.dp1(x) x = self.conv2(x) return x ================================================ FILE: CurveNet/core/models/curvenet_normal.py ================================================ """ @Author: Tiange Xiang @Contact: txia7609@uni.sydney.edu.au @File: curvenet_normal.py @Time: 2021/01/21 3:10 PM """ import torch.nn as nn import torch.nn.functional as F from .curvenet_util import * curve_config = { 'default': [[100, 5], [100, 5], None, None] } class CurveNet(nn.Module): def __init__(self, num_classes=3, k=20, multiplier=1.0, setting='default'): super(CurveNet, self).__init__() assert setting in curve_config additional_channel = 64 channels = [128, 256, 512, 1024] channels = [int(c * multiplier) for c in channels] self.lpfa = LPFA(9, additional_channel, k=k, mlp_num=1, initial=True) # encoder self.cic11 = CIC(npoint=1024, radius=0.1, k=k, in_channels=additional_channel, output_channels=channels[0], bottleneck_ratio=2, curve_config=curve_config[setting][0]) self.cic12 = CIC(npoint=1024, radius=0.1, k=k, in_channels=channels[0], output_channels=channels[0], bottleneck_ratio=4, curve_config=curve_config[setting][0]) self.cic21 = CIC(npoint=256, radius=0.2, k=k, in_channels=channels[0], output_channels=channels[1], bottleneck_ratio=2, curve_config=curve_config[setting][1]) self.cic22 = CIC(npoint=256, radius=0.2, k=k, in_channels=channels[1], output_channels=channels[1], bottleneck_ratio=4, curve_config=curve_config[setting][1]) self.cic31 = CIC(npoint=64, radius=0.4, k=k, in_channels=channels[1], output_channels=channels[2], bottleneck_ratio=2, curve_config=curve_config[setting][2]) self.cic32 = CIC(npoint=64, radius=0.4, k=k, in_channels=channels[2], output_channels=channels[2], bottleneck_ratio=4, curve_config=curve_config[setting][2]) self.cic41 = CIC(npoint=16, radius=0.8, k=15, in_channels=channels[2], output_channels=channels[3], bottleneck_ratio=2, curve_config=curve_config[setting][3]) self.cic42 = CIC(npoint=16, radius=0.8, k=15, in_channels=channels[3], output_channels=channels[3], bottleneck_ratio=4, curve_config=curve_config[setting][3]) #self.cic43 = CIC(npoint=16, radius=0.8, k=15, in_channels=2048, output_channels=2048, bottleneck_ratio=4, curve_config=curve_config[setting][3]) # decoder self.fp3 = PointNetFeaturePropagation(in_channel=channels[3] + channels[2], mlp=[channels[2], channels[2]], att=[channels[3], channels[3]//2, channels[3]//8]) self.up_cic4 = CIC(npoint=64, radius=0.8, k=k, in_channels=channels[2], output_channels=channels[2], bottleneck_ratio=4) self.fp2 = PointNetFeaturePropagation(in_channel=channels[2] + channels[1], mlp=[channels[1], channels[1]], att=[channels[2], channels[2]//2, channels[2]//8]) self.up_cic3 = CIC(npoint=256, radius=0.4, k=k, in_channels=channels[1], output_channels=channels[1], bottleneck_ratio=4) self.fp1 = PointNetFeaturePropagation(in_channel=channels[1] + channels[0], mlp=[channels[0], channels[0]], att=[channels[1], channels[1]//2, channels[1]//8]) self.up_cic2 = CIC(npoint=1024, radius=0.1, k=k, in_channels=channels[0]+3, output_channels=channels[0], bottleneck_ratio=4) self.up_cic1 = CIC(npoint=1024, radius=0.1, k=k, in_channels=channels[0], output_channels=channels[0], bottleneck_ratio=4) self.point_conv = nn.Sequential( nn.Conv2d(9, additional_channel, kernel_size=1, bias=False), nn.BatchNorm2d(additional_channel), nn.LeakyReLU(negative_slope=0.2, inplace=True)) self.conv1 = nn.Conv1d(channels[0], num_classes, 1) def forward(self, xyz): l0_points = self.lpfa(xyz, xyz) l1_xyz, l1_points = self.cic11(xyz, l0_points) l1_xyz, l1_points = self.cic12(l1_xyz, l1_points) l2_xyz, l2_points = self.cic21(l1_xyz, l1_points) l2_xyz, l2_points = self.cic22(l2_xyz, l2_points) l3_xyz, l3_points = self.cic31(l2_xyz, l2_points) l3_xyz, l3_points = self.cic32(l3_xyz, l3_points) l4_xyz, l4_points = self.cic41(l3_xyz, l3_points) l4_xyz, l4_points = self.cic42(l4_xyz, l4_points) #l4_xyz, l4_points = self.cic43(l4_xyz, l4_points) l3_points = self.fp3(l3_xyz, l4_xyz, l3_points, l4_points) l3_xyz, l3_points = self.up_cic4(l3_xyz, l3_points) l2_points = self.fp2(l2_xyz, l3_xyz, l2_points, l3_points) l2_xyz, l2_points = self.up_cic3(l2_xyz, l2_points) l1_points = self.fp1(l1_xyz, l2_xyz, l1_points, l2_points) x = torch.cat((l1_xyz, l1_points), dim=1) xyz, x = self.up_cic2(l1_xyz, x) xyz, x = self.up_cic1(xyz, x) x = self.conv1(x) return x ================================================ FILE: CurveNet/core/models/curvenet_seg.py ================================================ """ @Author: Tiange Xiang @Contact: txia7609@uni.sydney.edu.au @File: curvenet_seg.py @Time: 2021/01/21 3:10 PM """ import torch.nn as nn import torch.nn.functional as F from .curvenet_util import * curve_config = { 'default': [[100, 5], [100, 5], None, None, None] } class CurveNet(nn.Module): def __init__(self, num_classes=50, category=16, k=32, setting='default'): super(CurveNet, self).__init__() assert setting in curve_config additional_channel = 32 self.lpfa = LPFA(9, additional_channel, k=k, mlp_num=1, initial=True) # encoder self.cic11 = CIC(npoint=2048, radius=0.2, k=k, in_channels=additional_channel, output_channels=64, bottleneck_ratio=2, curve_config=curve_config[setting][0]) self.cic12 = CIC(npoint=2048, radius=0.2, k=k, in_channels=64, output_channels=64, bottleneck_ratio=4, curve_config=curve_config[setting][0]) self.cic21 = CIC(npoint=512, radius=0.4, k=k, in_channels=64, output_channels=128, bottleneck_ratio=2, curve_config=curve_config[setting][1]) self.cic22 = CIC(npoint=512, radius=0.4, k=k, in_channels=128, output_channels=128, bottleneck_ratio=4, curve_config=curve_config[setting][1]) self.cic31 = CIC(npoint=128, radius=0.8, k=k, in_channels=128, output_channels=256, bottleneck_ratio=2, curve_config=curve_config[setting][2]) self.cic32 = CIC(npoint=128, radius=0.8, k=k, in_channels=256, output_channels=256, bottleneck_ratio=4, curve_config=curve_config[setting][2]) self.cic41 = CIC(npoint=32, radius=1.2, k=31, in_channels=256, output_channels=512, bottleneck_ratio=2, curve_config=curve_config[setting][3]) self.cic42 = CIC(npoint=32, radius=1.2, k=31, in_channels=512, output_channels=512, bottleneck_ratio=4, curve_config=curve_config[setting][3]) self.cic51 = CIC(npoint=8, radius=2.0, k=7, in_channels=512, output_channels=1024, bottleneck_ratio=2, curve_config=curve_config[setting][4]) self.cic52 = CIC(npoint=8, radius=2.0, k=7, in_channels=1024, output_channels=1024, bottleneck_ratio=4, curve_config=curve_config[setting][4]) self.cic53 = CIC(npoint=8, radius=2.0, k=7, in_channels=1024, output_channels=1024, bottleneck_ratio=4, curve_config=curve_config[setting][4]) # decoder self.fp4 = PointNetFeaturePropagation(in_channel=1024 + 512, mlp=[512, 512], att=[1024, 512, 256]) self.up_cic5 = CIC(npoint=32, radius=1.2, k=31, in_channels=512, output_channels=512, bottleneck_ratio=4) self.fp3 = PointNetFeaturePropagation(in_channel=512 + 256, mlp=[256, 256], att=[512, 256, 128]) self.up_cic4 = CIC(npoint=128, radius=0.8, k=k, in_channels=256, output_channels=256, bottleneck_ratio=4) self.fp2 = PointNetFeaturePropagation(in_channel=256 + 128, mlp=[128, 128], att=[256, 128, 64]) self.up_cic3 = CIC(npoint=512, radius=0.4, k=k, in_channels=128, output_channels=128, bottleneck_ratio=4) self.fp1 = PointNetFeaturePropagation(in_channel=128 + 64, mlp=[64, 64], att=[128, 64, 32]) self.up_cic2 = CIC(npoint=2048, radius=0.2, k=k, in_channels=128+64+64+category+3, output_channels=256, bottleneck_ratio=4) self.up_cic1 = CIC(npoint=2048, radius=0.2, k=k, in_channels=256, output_channels=256, bottleneck_ratio=4) self.global_conv2 = nn.Sequential( nn.Conv1d(1024, 128, kernel_size=1, bias=False), nn.BatchNorm1d(128), nn.LeakyReLU(negative_slope=0.2)) self.global_conv1 = nn.Sequential( nn.Conv1d(512, 64, kernel_size=1, bias=False), nn.BatchNorm1d(64), nn.LeakyReLU(negative_slope=0.2)) self.conv1 = nn.Conv1d(256, 256, 1, bias=False) self.bn1 = nn.BatchNorm1d(256) self.drop1 = nn.Dropout(0.5) self.conv2 = nn.Conv1d(256, num_classes, 1) self.se = nn.Sequential(nn.AdaptiveAvgPool1d(1), nn.Conv1d(256, 256//8, 1, bias=False), nn.BatchNorm1d(256//8), nn.LeakyReLU(negative_slope=0.2), nn.Conv1d(256//8, 256, 1, bias=False), nn.Sigmoid()) def forward(self, xyz, l=None): batch_size = xyz.size(0) l0_points = self.lpfa(xyz, xyz) l1_xyz, l1_points = self.cic11(xyz, l0_points) l1_xyz, l1_points = self.cic12(l1_xyz, l1_points) l2_xyz, l2_points = self.cic21(l1_xyz, l1_points) l2_xyz, l2_points = self.cic22(l2_xyz, l2_points) l3_xyz, l3_points = self.cic31(l2_xyz, l2_points) l3_xyz, l3_points = self.cic32(l3_xyz, l3_points) l4_xyz, l4_points = self.cic41(l3_xyz, l3_points) l4_xyz, l4_points = self.cic42(l4_xyz, l4_points) l5_xyz, l5_points = self.cic51(l4_xyz, l4_points) l5_xyz, l5_points = self.cic52(l5_xyz, l5_points) l5_xyz, l5_points = self.cic53(l5_xyz, l5_points) # global features emb1 = self.global_conv1(l4_points) emb1 = emb1.max(dim=-1, keepdim=True)[0] # bs, 64, 1 emb2 = self.global_conv2(l5_points) emb2 = emb2.max(dim=-1, keepdim=True)[0] # bs, 128, 1 # Feature Propagation layers l4_points = self.fp4(l4_xyz, l5_xyz, l4_points, l5_points) l4_xyz, l4_points = self.up_cic5(l4_xyz, l4_points) l3_points = self.fp3(l3_xyz, l4_xyz, l3_points, l4_points) l3_xyz, l3_points = self.up_cic4(l3_xyz, l3_points) l2_points = self.fp2(l2_xyz, l3_xyz, l2_points, l3_points) l2_xyz, l2_points = self.up_cic3(l2_xyz, l2_points) l1_points = self.fp1(l1_xyz, l2_xyz, l1_points, l2_points) if l is not None: l = l.view(batch_size, -1, 1) emb = torch.cat((emb1, emb2, l), dim=1) # bs, 128 + 16, 1 l = emb.expand(-1,-1, xyz.size(-1)) x = torch.cat((l1_xyz, l1_points, l), dim=1) xyz, x = self.up_cic2(l1_xyz, x) xyz, x = self.up_cic1(xyz, x) x = F.leaky_relu(self.bn1(self.conv1(x)), 0.2, inplace=True) se = self.se(x) x = x * se x = self.drop1(x) x = self.conv2(x) return x ================================================ FILE: CurveNet/core/models/curvenet_util.py ================================================ """ @Author: Yue Wang @Contact: yuewangx@mit.edu @File: pointnet_util.py @Time: 2018/10/13 10:39 PM Modified by @Author: Tiange Xiang @Contact: txia7609@uni.sydney.edu.au @Time: 2021/01/21 3:10 PM """ import torch import torch.nn as nn import torch.nn.functional as F from time import time import numpy as np from .walk import Walk def knn(x, k): k = k + 1 inner = -2 * torch.matmul(x.transpose(2, 1), x) xx = torch.sum(x**2, dim=1, keepdim=True) pairwise_distance = -xx - inner - xx.transpose(2, 1) idx = pairwise_distance.topk(k=k, dim=-1)[1] # (batch_size, num_points, k) return idx def normal_knn(x, k): inner = -2 * torch.matmul(x.transpose(2, 1), x) xx = torch.sum(x**2, dim=1, keepdim=True) pairwise_distance = -xx - inner - xx.transpose(2, 1) idx = pairwise_distance.topk(k=k, dim=-1)[1] # (batch_size, num_points, k) return idx def pc_normalize(pc): l = pc.shape[0] centroid = np.mean(pc, axis=0) pc = pc - centroid m = np.max(np.sqrt(np.sum(pc**2, axis=1))) pc = pc / m return pc def square_distance(src, dst): """ Calculate Euclid distance between each two points. """ B, N, _ = src.shape _, M, _ = dst.shape dist = -2 * torch.matmul(src, dst.permute(0, 2, 1)) dist += torch.sum(src ** 2, -1).view(B, N, 1) dist += torch.sum(dst ** 2, -1).view(B, 1, M) return dist def index_points(points, idx): """ Input: points: input points data, [B, N, C] idx: sample index data, [B, S] Return: new_points:, indexed points data, [B, S, C] """ device = points.device B = points.shape[0] view_shape = list(idx.shape) view_shape[1:] = [1] * (len(view_shape) - 1) repeat_shape = list(idx.shape) repeat_shape[0] = 1 batch_indices = torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape) new_points = points[batch_indices, idx, :] return new_points def farthest_point_sample(xyz, npoint): """ Input: xyz: pointcloud data, [B, N, 3] npoint: number of samples Return: centroids: sampled pointcloud index, [B, npoint] """ device = xyz.device B, N, C = xyz.shape centroids = torch.zeros(B, npoint, dtype=torch.long).to(device) distance = torch.ones(B, N).to(device) * 1e10 farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device) * 0 batch_indices = torch.arange(B, dtype=torch.long).to(device) for i in range(npoint): centroids[:, i] = farthest centroid = xyz[batch_indices, farthest, :].view(B, 1, 3) dist = torch.sum((xyz - centroid) ** 2, -1) mask = dist < distance distance[mask] = dist[mask] farthest = torch.max(distance, -1)[1] return centroids def query_ball_point(radius, nsample, xyz, new_xyz): """ Input: radius: local region radius nsample: max sample number in local region xyz: all points, [B, N, 3] new_xyz: query points, [B, S, 3] Return: group_idx: grouped points index, [B, S, nsample] """ device = xyz.device B, N, C = xyz.shape _, S, _ = new_xyz.shape group_idx = torch.arange(N, dtype=torch.long).to(device).view(1, 1, N).repeat([B, S, 1]) sqrdists = square_distance(new_xyz, xyz) group_idx[sqrdists > radius ** 2] = N group_idx = group_idx.sort(dim=-1)[0][:, :, :nsample] group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, nsample]) mask = group_idx == N group_idx[mask] = group_first[mask] return group_idx def sample_and_group(npoint, radius, nsample, xyz, points, returnfps=False): """ Input: npoint: radius: nsample: xyz: input points position data, [B, N, 3] points: input points data, [B, N, D] Return: new_xyz: sampled points position data, [B, npoint, nsample, 3] new_points: sampled points data, [B, npoint, nsample, 3+D] """ new_xyz = index_points(xyz, farthest_point_sample(xyz, npoint)) torch.cuda.empty_cache() idx = query_ball_point(radius, nsample, xyz, new_xyz) torch.cuda.empty_cache() new_points = index_points(points, idx) torch.cuda.empty_cache() if returnfps: return new_xyz, new_points, idx else: return new_xyz, new_points class Attention_block(nn.Module): ''' Used in attention U-Net. ''' def __init__(self,F_g,F_l,F_int): super(Attention_block,self).__init__() self.W_g = nn.Sequential( nn.Conv1d(F_g, F_int, kernel_size=1,stride=1,padding=0,bias=True), nn.BatchNorm1d(F_int) ) self.W_x = nn.Sequential( nn.Conv1d(F_l, F_int, kernel_size=1,stride=1,padding=0,bias=True), nn.BatchNorm1d(F_int) ) self.psi = nn.Sequential( nn.Conv1d(F_int, 1, kernel_size=1,stride=1,padding=0,bias=True), nn.BatchNorm1d(1), nn.Sigmoid() ) def forward(self,g,x): g1 = self.W_g(g) x1 = self.W_x(x) psi = F.leaky_relu(g1+x1, negative_slope=0.2) psi = self.psi(psi) return psi, 1. - psi class LPFA(nn.Module): def __init__(self, in_channel, out_channel, k, mlp_num=2, initial=False): super(LPFA, self).__init__() self.k = k self.device = torch.device('cuda') self.initial = initial if not initial: self.xyz2feature = nn.Sequential( nn.Conv2d(9, in_channel, kernel_size=1, bias=False), nn.BatchNorm2d(in_channel)) self.mlp = [] for _ in range(mlp_num): self.mlp.append(nn.Sequential(nn.Conv2d(in_channel, out_channel, 1, bias=False), nn.BatchNorm2d(out_channel), nn.LeakyReLU(0.2))) in_channel = out_channel self.mlp = nn.Sequential(*self.mlp) def forward(self, x, xyz, idx=None): x = self.group_feature(x, xyz, idx) x = self.mlp(x) if self.initial: x = x.max(dim=-1, keepdim=False)[0] else: x = x.mean(dim=-1, keepdim=False) return x def group_feature(self, x, xyz, idx): batch_size, num_dims, num_points = x.size() if idx is None: idx = knn(xyz, k=self.k)[:,:,:self.k] # (batch_size, num_points, k) idx_base = torch.arange(0, batch_size, device=self.device).view(-1, 1, 1) * num_points idx = idx + idx_base idx = idx.view(-1) xyz = xyz.transpose(2, 1).contiguous() # bs, n, 3 point_feature = xyz.view(batch_size * num_points, -1)[idx, :] point_feature = point_feature.view(batch_size, num_points, self.k, -1) # bs, n, k, 3 points = xyz.view(batch_size, num_points, 1, 3).expand(-1, -1, self.k, -1) # bs, n, k, 3 point_feature = torch.cat((points, point_feature, point_feature - points), dim=3).permute(0, 3, 1, 2).contiguous() if self.initial: return point_feature x = x.transpose(2, 1).contiguous() # bs, n, c feature = x.view(batch_size * num_points, -1)[idx, :] feature = feature.view(batch_size, num_points, self.k, num_dims) #bs, n, k, c x = x.view(batch_size, num_points, 1, num_dims) feature = feature - x feature = feature.permute(0, 3, 1, 2).contiguous() point_feature = self.xyz2feature(point_feature) #bs, c, n, k feature = F.leaky_relu(feature + point_feature, 0.2) return feature #bs, c, n, k class PointNetFeaturePropagation(nn.Module): def __init__(self, in_channel, mlp, att=None): super(PointNetFeaturePropagation, self).__init__() self.mlp_convs = nn.ModuleList() self.mlp_bns = nn.ModuleList() last_channel = in_channel self.att = None if att is not None: self.att = Attention_block(F_g=att[0],F_l=att[1],F_int=att[2]) for out_channel in mlp: self.mlp_convs.append(nn.Conv1d(last_channel, out_channel, 1)) self.mlp_bns.append(nn.BatchNorm1d(out_channel)) last_channel = out_channel def forward(self, xyz1, xyz2, points1, points2): """ Input: xyz1: input points position data, [B, C, N] xyz2: sampled input points position data, [B, C, S], skipped xyz points1: input points data, [B, D, N] points2: input points data, [B, D, S], skipped features Return: new_points: upsampled points data, [B, D', N] """ xyz1 = xyz1.permute(0, 2, 1) xyz2 = xyz2.permute(0, 2, 1) points2 = points2.permute(0, 2, 1) B, N, C = xyz1.shape _, S, _ = xyz2.shape if S == 1: interpolated_points = points2.repeat(1, N, 1) else: dists = square_distance(xyz1, xyz2) dists, idx = dists.sort(dim=-1) dists, idx = dists[:, :, :3], idx[:, :, :3] # [B, N, 3] dist_recip = 1.0 / (dists + 1e-8) norm = torch.sum(dist_recip, dim=2, keepdim=True) weight = dist_recip / norm interpolated_points = torch.sum(index_points(points2, idx) * weight.view(B, N, 3, 1), dim=2) # skip attention if self.att is not None: psix, psig = self.att(interpolated_points.permute(0, 2, 1), points1) points1 = points1 * psix if points1 is not None: points1 = points1.permute(0, 2, 1) new_points = torch.cat([points1, interpolated_points], dim=-1) else: new_points = interpolated_points new_points = new_points.permute(0, 2, 1) for i, conv in enumerate(self.mlp_convs): bn = self.mlp_bns[i] new_points = F.leaky_relu(bn(conv(new_points)), 0.2) return new_points class CIC(nn.Module): def __init__(self, npoint, radius, k, in_channels, output_channels, bottleneck_ratio=2, mlp_num=2, curve_config=None): super(CIC, self).__init__() self.in_channels = in_channels self.output_channels = output_channels self.bottleneck_ratio = bottleneck_ratio self.radius = radius self.k = k self.npoint = npoint planes = in_channels // bottleneck_ratio self.use_curve = curve_config is not None if self.use_curve: self.curveaggregation = CurveAggregation(planes) self.curvegrouping = CurveGrouping(planes, k, curve_config[0], curve_config[1]) self.conv1 = nn.Sequential( nn.Conv1d(in_channels, planes, kernel_size=1, bias=False), nn.BatchNorm1d(in_channels // bottleneck_ratio), nn.LeakyReLU(negative_slope=0.2, inplace=True)) self.conv2 = nn.Sequential( nn.Conv1d(planes, output_channels, kernel_size=1, bias=False), nn.BatchNorm1d(output_channels)) if in_channels != output_channels: self.shortcut = nn.Sequential( nn.Conv1d(in_channels, output_channels, kernel_size=1, bias=False), nn.BatchNorm1d(output_channels)) self.relu = nn.LeakyReLU(negative_slope=0.2, inplace=True) self.maxpool = MaskedMaxPool(npoint, radius, k) self.lpfa = LPFA(planes, planes, k, mlp_num=mlp_num, initial=False) def forward(self, xyz, x): # max pool if xyz.size(-1) != self.npoint: xyz, x = self.maxpool( xyz.transpose(1, 2).contiguous(), x) xyz = xyz.transpose(1, 2) shortcut = x x = self.conv1(x) # bs, c', n idx = knn(xyz, self.k) if self.use_curve: # curve grouping curves = self.curvegrouping(x, xyz, idx[:,:,1:]) # avoid self-loop # curve aggregation x = self.curveaggregation(x, curves) x = self.lpfa(x, xyz, idx=idx[:,:,:self.k]) #bs, c', n, k x = self.conv2(x) # bs, c, n if self.in_channels != self.output_channels: shortcut = self.shortcut(shortcut) x = self.relu(x + shortcut) return xyz, x class CurveAggregation(nn.Module): def __init__(self, in_channel): super(CurveAggregation, self).__init__() self.in_channel = in_channel mid_feature = in_channel // 2 self.conva = nn.Conv1d(in_channel, mid_feature, kernel_size=1, bias=False) self.convb = nn.Conv1d(in_channel, mid_feature, kernel_size=1, bias=False) self.convc = nn.Conv1d(in_channel, mid_feature, kernel_size=1, bias=False) self.convn = nn.Conv1d(mid_feature, mid_feature, kernel_size=1, bias=False) self.convl = nn.Conv1d(mid_feature, mid_feature, kernel_size=1, bias=False) self.convd = nn.Sequential( nn.Conv1d(mid_feature * 2, in_channel, kernel_size=1, bias=False), nn.BatchNorm1d(in_channel)) self.line_conv_att = nn.Conv2d(in_channel, 1, kernel_size=1, bias=False) def forward(self, x, curves): curves_att = self.line_conv_att(curves) # bs, 1, c_n, c_l curver_inter = torch.sum(curves * F.softmax(curves_att, dim=-1), dim=-1) #bs, c, c_n curves_intra = torch.sum(curves * F.softmax(curves_att, dim=-2), dim=-2) #bs, c, c_l curver_inter = self.conva(curver_inter) # bs, mid, n curves_intra = self.convb(curves_intra) # bs, mid ,n x_logits = self.convc(x).transpose(1, 2).contiguous() x_inter = F.softmax(torch.bmm(x_logits, curver_inter), dim=-1) # bs, n, c_n x_intra = F.softmax(torch.bmm(x_logits, curves_intra), dim=-1) # bs, l, c_l curver_inter = self.convn(curver_inter).transpose(1, 2).contiguous() curves_intra = self.convl(curves_intra).transpose(1, 2).contiguous() x_inter = torch.bmm(x_inter, curver_inter) x_intra = torch.bmm(x_intra, curves_intra) curve_features = torch.cat((x_inter, x_intra),dim=-1).transpose(1, 2).contiguous() x = x + self.convd(curve_features) return F.leaky_relu(x, negative_slope=0.2) class CurveGrouping(nn.Module): def __init__(self, in_channel, k, curve_num, curve_length): super(CurveGrouping, self).__init__() self.curve_num = curve_num self.curve_length = curve_length self.in_channel = in_channel self.k = k self.att = nn.Conv1d(in_channel, 1, kernel_size=1, bias=False) self.walk = Walk(in_channel, k, curve_num, curve_length) def forward(self, x, xyz, idx): # starting point selection in self attention style x_att = torch.sigmoid(self.att(x)) x = x * x_att _, start_index = torch.topk(x_att, self.curve_num, dim=2, sorted=False) start_index = start_index.squeeze().unsqueeze(2) curves = self.walk(xyz, x, idx, start_index) #bs, c, c_n, c_l return curves class MaskedMaxPool(nn.Module): def __init__(self, npoint, radius, k): super(MaskedMaxPool, self).__init__() self.npoint = npoint self.radius = radius self.k = k def forward(self, xyz, features): sub_xyz, neighborhood_features = sample_and_group(self.npoint, self.radius, self.k, xyz, features.transpose(1,2)) neighborhood_features = neighborhood_features.permute(0, 3, 1, 2).contiguous() sub_features = F.max_pool2d( neighborhood_features, kernel_size=[1, neighborhood_features.shape[3]] ) # bs, c, n, 1 sub_features = torch.squeeze(sub_features, -1) # bs, c, n return sub_xyz, sub_features ================================================ FILE: CurveNet/core/models/walk.py ================================================ """ @Author: Tiange Xiang @Contact: txia7609@uni.sydney.edu.au @File: walk.py @Time: 2021/01/21 3:10 PM """ import numpy as np import torch import torch.nn as nn import torch.nn.functional as F def batched_index_select(input, dim, index): views = [input.shape[0]] + \ [1 if i != dim else -1 for i in range(1, len(input.shape))] expanse = list(input.shape) expanse[0] = -1 expanse[dim] = -1 index = index.view(views).expand(expanse) return torch.gather(input, dim, index) def gumbel_softmax(logits, dim, temperature=1): """ ST-gumple-softmax w/o random gumbel samplings input: [*, n_class] return: flatten --> [*, n_class] an one-hot vector """ y = F.softmax(logits / temperature, dim=dim) shape = y.size() _, ind = y.max(dim=-1) y_hard = torch.zeros_like(y).view(-1, shape[-1]) y_hard.scatter_(1, ind.view(-1, 1), 1) y_hard = y_hard.view(*shape) y_hard = (y_hard - y).detach() + y return y_hard class Walk(nn.Module): ''' Walk in the cloud ''' def __init__(self, in_channel, k, curve_num, curve_length): super(Walk, self).__init__() self.curve_num = curve_num self.curve_length = curve_length self.k = k self.agent_mlp = nn.Sequential( nn.Conv2d(in_channel * 2, 1, kernel_size=1, bias=False), nn.BatchNorm2d(1)) self.momentum_mlp = nn.Sequential( nn.Conv1d(in_channel * 2, 2, kernel_size=1, bias=False), nn.BatchNorm1d(2)) def crossover_suppression(self, cur, neighbor, bn, n, k): # cur: bs*n, 3 # neighbor: bs*n, 3, k neighbor = neighbor.detach() cur = cur.unsqueeze(-1).detach() dot = torch.bmm(cur.transpose(1,2), neighbor) # bs*n, 1, k norm1 = torch.norm(cur, dim=1, keepdim=True) norm2 = torch.norm(neighbor, dim=1, keepdim=True) divider = torch.clamp(norm1 * norm2, min=1e-8) ans = torch.div(dot, divider).squeeze() # bs*n, k # normalize to [0, 1] ans = 1. + ans ans = torch.clamp(ans, 0., 1.0) return ans.detach() def forward(self, xyz, x, adj, cur): bn, c, tot_points = x.size() # raw point coordinates xyz = xyz.transpose(1,2).contiguous # bs, n, 3 # point features x = x.transpose(1,2).contiguous() # bs, n, c flatten_x = x.view(bn * tot_points, -1) batch_offset = torch.arange(0, bn, device=torch.device('cuda')).detach() * tot_points # indices of neighbors for the starting points tmp_adj = (adj + batch_offset.view(-1,1,1)).view(adj.size(0)*adj.size(1),-1) #bs, n, k # batch flattened indices for teh starting points flatten_cur = (cur + batch_offset.view(-1,1,1)).view(-1) curves = [] # one step at a time for step in range(self.curve_length): if step == 0: # get starting point features using flattend indices starting_points = flatten_x[flatten_cur, :].contiguous() pre_feature = starting_points.view(bn, self.curve_num, -1, 1).transpose(1,2) # bs * n, c else: # dynamic momentum cat_feature = torch.cat((cur_feature.squeeze(), pre_feature.squeeze()),dim=1) att_feature = F.softmax(self.momentum_mlp(cat_feature),dim=1).view(bn, 1, self.curve_num, 2) # bs, 1, n, 2 cat_feature = torch.cat((cur_feature, pre_feature),dim=-1) # bs, c, n, 2 # update curve descriptor pre_feature = torch.sum(cat_feature * att_feature, dim=-1, keepdim=True) # bs, c, n pre_feature_cos = pre_feature.transpose(1,2).contiguous().view(bn * self.curve_num, -1) pick_idx = tmp_adj[flatten_cur] # bs*n, k # get the neighbors of current points pick_values = flatten_x[pick_idx.view(-1),:] # reshape to fit crossover suppresion below pick_values_cos = pick_values.view(bn * self.curve_num, self.k, c) pick_values = pick_values_cos.view(bn, self.curve_num, self.k, c) pick_values_cos = pick_values_cos.transpose(1,2).contiguous() pick_values = pick_values.permute(0,3,1,2) # bs, c, n, k pre_feature_expand = pre_feature.expand_as(pick_values) # concat current point features with curve descriptors pre_feature_expand = torch.cat((pick_values, pre_feature_expand),dim=1) # which node to pick next? pre_feature_expand = self.agent_mlp(pre_feature_expand) # bs, 1, n, k if step !=0: # cross over supression d = self.crossover_suppression(cur_feature_cos - pre_feature_cos, pick_values_cos - cur_feature_cos.unsqueeze(-1), bn, self.curve_num, self.k) d = d.view(bn, self.curve_num, self.k).unsqueeze(1) # bs, 1, n, k pre_feature_expand = torch.mul(pre_feature_expand, d) pre_feature_expand = gumbel_softmax(pre_feature_expand, -1) #bs, 1, n, k cur_feature = torch.sum(pick_values * pre_feature_expand, dim=-1, keepdim=True) # bs, c, n, 1 cur_feature_cos = cur_feature.transpose(1,2).contiguous().view(bn * self.curve_num, c) cur = torch.argmax(pre_feature_expand, dim=-1).view(-1, 1) # bs * n, 1 flatten_cur = batched_index_select(pick_idx, 1, cur).squeeze() # bs * n # collect curve progress curves.append(cur_feature) return torch.cat(curves,dim=-1) ================================================ FILE: CurveNet/core/util.py ================================================ """ @Author: Yue Wang @Contact: yuewangx@mit.edu @File: util @Time: 4/5/19 3:47 PM """ import numpy as np import torch import torch.nn.functional as F def cal_loss(pred, gold, smoothing=True): ''' Calculate cross entropy loss, apply label smoothing if needed. ''' gold = gold.contiguous().view(-1) if smoothing: eps = 0.2 n_class = pred.size(1) one_hot = torch.zeros_like(pred).scatter(1, gold.view(-1, 1), 1) one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1) log_prb = F.log_softmax(pred, dim=1) loss = -(one_hot * log_prb).sum(dim=1).mean() else: loss = F.cross_entropy(pred, gold, reduction='mean') return loss class IOStream(): def __init__(self, path): self.f = open(path, 'a') def cprint(self, text): print(text) self.f.write(text+'\n') self.f.flush() def close(self): self.f.close() ================================================ FILE: GDANet/README.md ================================================ # Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud. This repository is built for the paper: __Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud (_AAAI2021_)__ [[arXiv](https://arxiv.org/abs/2012.10921)]
by [Mutian Xu*](https://mutianxu.github.io/), [Junhao Zhang*](https://junhaozhang98.github.io/), Zhipeng Zhou, Mingye Xu, Xiaojuan Qi and Yu Qiao. ## Overview Geometry-Disentangled Attention Network for 3D object point cloud classification and segmentation (GDANet): ## Citation If you find the code or trained models useful, please consider citing: @misc{xu2021learning, title={Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud}, author={Mutian Xu and Junhao Zhang and Zhipeng Zhou and Mingye Xu and Xiaojuan Qi and Yu Qiao}, year={2021}, eprint={2012.10921}, archivePrefix={arXiv}, primaryClass={cs.CV} ## Installation ### Requirements * Linux (tested on Ubuntu 14.04/16.04) * Python 3.5+ * PyTorch 1.0+ ### Dataset * Create the folder to symlink the data later: `mkdir -p data` * __Object Classification__: Download and unzip [ModelNet40](https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip) (415M), then symlink the path to it as follows (you can alternatively modify the path [here](https://github.com/mutianxu/GDANet/blob/main/util/data_util.py#L12)) : `ln -s /path to modelnet40/modelnet40_ply_hdf5_2048 data` * __Shape Part Segmentation__: Download and unzip [ShapeNet Part](https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip) (674M), then symlink the path to it as follows (you can alternatively modify the path [here](https://github.com/mutianxu/GDANet/blob/main/util/data_util.py#L70)) : `ln -s /path to shapenet part/shapenetcore_partanno_segmentation_benchmark_v0_normal data` ## Usage ### Object Classification on ModelNet40 * Train: `python main_cls.py` * Test: * Run the voting evaluation script, after this voting you will get an accuracy of 93.8% if all things go right: `python voting_eval_modelnet.py --model_path 'pretrained/GDANet_ModelNet40_93.4.t7'` * You can also directly evaluate our pretrained model without voting to get an accuracy of 93.4%: `python main.py --eval True --model_path 'pretrained/GDANet_ModelNet40_93.4.t7'` ### Shape Part Segmentation on ShapeNet Part * Train: * Training from scratch: `python main_ptseg.py` * If you want resume training from checkpoints, specify `resume` in the args: `python main_ptseg.py --resume True` * Test: You can choose to test the model with the best instance mIoU, class mIoU or accuracy, by specifying `model_type` in the args: * `python main_ptseg.py --model_type 'ins_iou'` (best instance mIoU, default) * `python main_ptseg.py --model_type 'cls_iou'` (best class mIoU) * `python main_ptseg.py --model_type 'acc'` (best accuracy) ## Other information Please contact Mutian Xu (mino1018@outlook.com) or Junhao Zhang (junhaozhang98@gmail.com) for further discussion. ## Acknowledgement This code is is partially borrowed from [DGCNN](https://github.com/WangYueFt/dgcnn) and [PointNet++](https://github.com/charlesq34/pointnet2). ================================================ FILE: GDANet/model/GDANet_cls.py ================================================ import torch.nn as nn import torch import torch.nn.functional as F from .util.GDANet_util import local_operator, GDM, SGCAM class GDANET(nn.Module): def __init__(self, number_class=40): super(GDANET, self).__init__() self.bn1 = nn.BatchNorm2d(64, momentum=0.1) self.bn11 = nn.BatchNorm2d(64, momentum=0.1) self.bn12 = nn.BatchNorm1d(64, momentum=0.1) self.bn2 = nn.BatchNorm2d(64, momentum=0.1) self.bn21 = nn.BatchNorm2d(64, momentum=0.1) self.bn22 = nn.BatchNorm1d(64, momentum=0.1) self.bn3 = nn.BatchNorm2d(128, momentum=0.1) self.bn31 = nn.BatchNorm2d(128, momentum=0.1) self.bn32 = nn.BatchNorm1d(128, momentum=0.1) self.bn4 = nn.BatchNorm1d(512, momentum=0.1) self.conv1 = nn.Sequential(nn.Conv2d(6, 64, kernel_size=1, bias=True), self.bn1) self.conv11 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1, bias=True), self.bn11) self.conv12 = nn.Sequential(nn.Conv1d(64 * 2, 64, kernel_size=1, bias=True), self.bn12) self.conv2 = nn.Sequential(nn.Conv2d(67 * 2, 64, kernel_size=1, bias=True), self.bn2) self.conv21 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1, bias=True), self.bn21) self.conv22 = nn.Sequential(nn.Conv1d(64 * 2, 64, kernel_size=1, bias=True), self.bn22) self.conv3 = nn.Sequential(nn.Conv2d(131 * 2, 128, kernel_size=1, bias=True), self.bn3) self.conv31 = nn.Sequential(nn.Conv2d(128, 128, kernel_size=1, bias=True), self.bn31) self.conv32 = nn.Sequential(nn.Conv1d(128, 128, kernel_size=1, bias=True), self.bn32) self.conv4 = nn.Sequential(nn.Conv1d(256, 512, kernel_size=1, bias=True), self.bn4) self.SGCAM_1s = SGCAM(64) self.SGCAM_1g = SGCAM(64) self.SGCAM_2s = SGCAM(64) self.SGCAM_2g = SGCAM(64) self.linear1 = nn.Linear(1024, 512, bias=True) self.bn6 = nn.BatchNorm1d(512) self.dp1 = nn.Dropout(p=0.4) self.linear2 = nn.Linear(512, 256, bias=True) self.bn7 = nn.BatchNorm1d(256) self.dp2 = nn.Dropout(p=0.4) self.linear3 = nn.Linear(256, number_class, bias=True) def forward(self, x): B, C, N = x.size() ############### """block 1""" # Local operator: x1 = local_operator(x, k=30) x1 = F.relu(self.conv1(x1)) x1 = F.relu(self.conv11(x1)) x1 = x1.max(dim=-1, keepdim=False)[0] # Geometry-Disentangle Module: x1s, x1g = GDM(x1, M=256) # Sharp-Gentle Complementary Attention Module: y1s = self.SGCAM_1s(x1, x1s.transpose(2, 1)) y1g = self.SGCAM_1g(x1, x1g.transpose(2, 1)) z1 = torch.cat([y1s, y1g], 1) z1 = F.relu(self.conv12(z1)) ############### """block 2""" x1t = torch.cat((x, z1), dim=1) x2 = local_operator(x1t, k=30) x2 = F.relu(self.conv2(x2)) x2 = F.relu(self.conv21(x2)) x2 = x2.max(dim=-1, keepdim=False)[0] x2s, x2g = GDM(x2, M=256) y2s = self.SGCAM_2s(x2, x2s.transpose(2, 1)) y2g = self.SGCAM_2g(x2, x2g.transpose(2, 1)) z2 = torch.cat([y2s, y2g], 1) z2 = F.relu(self.conv22(z2)) ############### x2t = torch.cat((x1t, z2), dim=1) x3 = local_operator(x2t, k=30) x3 = F.relu(self.conv3(x3)) x3 = F.relu(self.conv31(x3)) x3 = x3.max(dim=-1, keepdim=False)[0] z3 = F.relu(self.conv32(x3)) ############### x = torch.cat((z1, z2, z3), dim=1) x = F.relu(self.conv4(x)) x11 = F.adaptive_max_pool1d(x, 1).view(B, -1) x22 = F.adaptive_avg_pool1d(x, 1).view(B, -1) x = torch.cat((x11, x22), 1) x = F.relu(self.bn6(self.linear1(x))) x = self.dp1(x) x = F.relu(self.bn7(self.linear2(x))) x = self.dp2(x) x = self.linear3(x) return x ================================================ FILE: GDANet/model/GDANet_ptseg.py ================================================ import torch.nn as nn import torch import torch.nn.functional as F from util.GDANet_util import local_operator_withnorm, local_operator, GDM, SGCAM class GDANet(nn.Module): def __init__(self, num_classes): super(GDANet, self).__init__() self.bn1 = nn.BatchNorm2d(64, momentum=0.1) self.bn11 = nn.BatchNorm2d(64, momentum=0.1) self.bn12 = nn.BatchNorm1d(64, momentum=0.1) self.bn2 = nn.BatchNorm2d(64, momentum=0.1) self.bn21 = nn.BatchNorm2d(64, momentum=0.1) self.bn22 = nn.BatchNorm1d(64, momentum=0.1) self.bn3 = nn.BatchNorm2d(128, momentum=0.1) self.bn31 = nn.BatchNorm2d(128, momentum=0.1) self.bn32 = nn.BatchNorm1d(128, momentum=0.1) self.bn4 = nn.BatchNorm1d(512, momentum=0.1) self.bnc = nn.BatchNorm1d(64, momentum=0.1) self.bn5 = nn.BatchNorm1d(256, momentum=0.1) self.bn6 = nn.BatchNorm1d(256, momentum=0.1) self.bn7 = nn.BatchNorm1d(128, momentum=0.1) self.conv1 = nn.Sequential(nn.Conv2d(9, 64, kernel_size=1, bias=True), self.bn1) self.conv11 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1, bias=True), self.bn11) self.conv12 = nn.Sequential(nn.Conv1d(64*2, 64, kernel_size=1, bias=True), self.bn12) self.conv2 = nn.Sequential(nn.Conv2d(67 * 2, 64, kernel_size=1, bias=True), self.bn2) self.conv21 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1, bias=True), self.bn21) self.conv22 = nn.Sequential(nn.Conv1d(64*2, 64, kernel_size=1, bias=True), self.bn22) self.conv3 = nn.Sequential(nn.Conv2d(131 * 2, 128, kernel_size=1, bias=True), self.bn3) self.conv31 = nn.Sequential(nn.Conv2d(128, 128, kernel_size=1, bias=True), self.bn31) self.conv32 = nn.Sequential(nn.Conv1d(128, 128, kernel_size=1, bias=True), self.bn32) self.conv4 = nn.Sequential(nn.Conv1d(256, 512, kernel_size=1, bias=True), self.bn4) self.convc = nn.Sequential(nn.Conv1d(16, 64, kernel_size=1, bias=True), self.bnc) self.conv5 = nn.Sequential(nn.Conv1d(256 + 512 + 64, 256, kernel_size=1, bias=True), self.bn5) self.dp1 = nn.Dropout(0.4) self.conv6 = nn.Sequential(nn.Conv1d(256, 256, kernel_size=1, bias=True), self.bn6) self.dp2 = nn.Dropout(0.4) self.conv7 = nn.Sequential(nn.Conv1d(256, 128, kernel_size=1, bias=True), self.bn7) self.conv8 = nn.Conv1d(128, num_classes, kernel_size=1, bias=True) self.SGCAM_1s = SGCAM(64) self.SGCAM_1g = SGCAM(64) self.SGCAM_2s = SGCAM(64) self.SGCAM_2g = SGCAM(64) def forward(self, x, norm_plt, cls_label): B, C, N = x.size() ############### """block 1""" x1 = local_operator_withnorm(x, norm_plt, k=30) x1 = F.relu(self.conv1(x1)) x1 = F.relu(self.conv11(x1)) x1 = x1.max(dim=-1, keepdim=False)[0] x1h, x1l = GDM(x1, M=512) x1h = self.SGCAM_1s(x1, x1h.transpose(2, 1)) x1l = self.SGCAM_1g(x1, x1l.transpose(2, 1)) x1 = torch.cat([x1h, x1l], 1) x1 = F.relu(self.conv12(x1)) ############### """block 1""" x1t = torch.cat((x, x1), dim=1) x2 = local_operator(x1t, k=30) x2 = F.relu(self.conv2(x2)) x2 = F.relu(self.conv21(x2)) x2 = x2.max(dim=-1, keepdim=False)[0] x2h, x2l = GDM(x2, M=512) x2h = self.SGCAM_2s(x2, x2h.transpose(2, 1)) x2l = self.SGCAM_2g(x2, x2l.transpose(2, 1)) x2 = torch.cat([x2h, x2l], 1) x2 = F.relu(self.conv22(x2)) ############### x2t = torch.cat((x1t, x2), dim=1) x3 = local_operator(x2t, k=30) x3 = F.relu(self.conv3(x3)) x3 = F.relu(self.conv31(x3)) x3 = x3.max(dim=-1, keepdim=False)[0] x3 = F.relu(self.conv32(x3)) ############### xx = torch.cat((x1, x2, x3), dim=1) xc = F.relu(self.conv4(xx)) xc = F.adaptive_max_pool1d(xc, 1).view(B, -1) cls_label = cls_label.view(B, 16, 1) cls_label = F.relu(self.convc(cls_label)) cls = torch.cat((xc.view(B, 512, 1), cls_label), dim=1) cls = cls.repeat(1, 1, N) x = torch.cat((xx, cls), dim=1) x = F.relu(self.conv5(x)) x = self.dp1(x) x = F.relu(self.conv6(x)) x = self.dp2(x) x = F.relu(self.conv7(x)) x = self.conv8(x) x = F.log_softmax(x, dim=1) x = x.permute(0, 2, 1) # b,n,50 return x ================================================ FILE: GDANet/model/__init__.py ================================================ ================================================ FILE: GDANet/model/util/GDANet_util.py ================================================ import torch from torch import nn def knn(x, k): inner = -2*torch.matmul(x.transpose(2, 1), x) xx = torch.sum(x**2, dim=1, keepdim=True) pairwise_distance = -xx - inner - xx.transpose(2, 1) idx = pairwise_distance.topk(k=k, dim=-1)[1] # (batch_size, num_points, k) return idx, pairwise_distance def local_operator(x, k): batch_size = x.size(0) num_points = x.size(2) x = x.view(batch_size, -1, num_points) idx, _ = knn(x, k=k) device = torch.device('cuda') idx_base = torch.arange(0, batch_size, device=device).view(-1, 1, 1) * num_points idx = idx + idx_base idx = idx.view(-1) _, num_dims, _ = x.size() x = x.transpose(2, 1).contiguous() neighbor = x.view(batch_size * num_points, -1).contiguous()[idx, :] neighbor = neighbor.view(batch_size, num_points, k, num_dims).contiguous() x = x.view(batch_size, num_points, 1, num_dims).contiguous().repeat(1, 1, k, 1) feature = torch.cat((neighbor-x, neighbor), dim=3).permute(0, 3, 1, 2).contiguous() # local and global all in return feature def local_operator_withnorm(x, norm_plt, k): batch_size = x.size(0) num_points = x.size(2) x = x.view(batch_size, -1, num_points) norm_plt = norm_plt.view(batch_size, -1, num_points) idx, _ = knn(x, k=k) # (batch_size, num_points, k) device = torch.device('cuda') idx_base = torch.arange(0, batch_size, device=device).view(-1, 1, 1) * num_points idx = idx + idx_base idx = idx.view(-1) _, num_dims, _ = x.size() x = x.transpose(2, 1).contiguous() norm_plt = norm_plt.transpose(2, 1).contiguous() neighbor = x.view(batch_size * num_points, -1)[idx, :] neighbor_norm = norm_plt.view(batch_size * num_points, -1)[idx, :] neighbor = neighbor.view(batch_size, num_points, k, num_dims) neighbor_norm = neighbor_norm.view(batch_size, num_points, k, num_dims) x = x.view(batch_size, num_points, 1, num_dims).repeat(1, 1, k, 1) feature = torch.cat((neighbor-x, neighbor, neighbor_norm), dim=3).permute(0, 3, 1, 2) # 3c return feature def GDM(x, M): """ Geometry-Disentangle Module M: number of disentangled points in both sharp and gentle variation components """ k = 64 # number of neighbors to decide the range of j in Eq.(5) tau = 0.2 # threshold in Eq.(2) sigma = 2 # parameters of f (Gaussian function in Eq.(2)) ############### """Graph Construction:""" device = torch.device('cuda') batch_size = x.size(0) num_points = x.size(2) x = x.view(batch_size, -1, num_points) idx, p = knn(x, k=k) # p: -[(x1-x2)^2+...] # here we add a tau p1 = torch.abs(p) p1 = torch.sqrt(p1) mask = p1 < tau # here we add a sigma p = p / (sigma * sigma) w = torch.exp(p) # b,n,n w = torch.mul(mask.float(), w) b = 1/torch.sum(w, dim=1) b = b.reshape(batch_size, num_points, 1).repeat(1, 1, num_points) c = torch.eye(num_points, num_points, device=device) c = c.expand(batch_size, num_points, num_points) D = b * c # b,n,n A = torch.matmul(D, w) # normalized adjacency matrix A_hat # Get Aij in a local area: idx2 = idx.view(batch_size * num_points, -1) idx_base2 = torch.arange(0, batch_size * num_points, device=device).view(-1, 1) * num_points idx2 = idx2 + idx_base2 idx2 = idx2.reshape(batch_size * num_points, k)[:, 1:k] idx2 = idx2.reshape(batch_size * num_points * (k - 1)) idx2 = idx2.view(-1) A = A.view(-1).contiguous() A = A[idx2].reshape(batch_size, num_points, k - 1).contiguous() # Aij: b,n,k ############### """Disentangling Point Clouds into Sharp(xs) and Gentle(xg) Variation Components:""" idx_base = torch.arange(0, batch_size, device=device).view(-1, 1, 1) * num_points idx = idx + idx_base idx = idx.reshape(batch_size * num_points, k)[:, 1:k] idx = idx.reshape(batch_size * num_points * (k - 1)) _, num_dims, _ = x.size() x = x.transpose(2, 1).contiguous() # b,n,c neighbor = x.view(batch_size * num_points, -1).contiguous()[idx, :] neighbor = neighbor.view(batch_size, num_points, k - 1, num_dims).contiguous() # b,n,k,c A = A.reshape(batch_size, num_points, k - 1, 1).contiguous() # b,n,k,1 n = A.mul(neighbor) # b,n,k,c n = torch.sum(n, dim=2) # b,n,c pai = torch.norm(x - n, dim=-1).pow(2) # Eq.(5) pais = pai.topk(k=M, dim=-1)[1] # first M points as the sharp variation component paig = (-pai).topk(k=M, dim=-1)[1] # last M points as the gentle variation component pai_base = torch.arange(0, batch_size, device=device).view(-1, 1) * num_points indices = (pais + pai_base).view(-1) indiceg = (paig + pai_base).view(-1) xs = x.view(batch_size * num_points, -1).contiguous()[indices, :] xg = x.view(batch_size * num_points, -1).contiguous()[indiceg, :] xs = xs.view(batch_size, M, -1).contiguous() # b,M,c xg = xg.view(batch_size, M, -1).contiguous() # b,M,c return xs, xg class SGCAM(nn.Module): """Sharp-Gentle Complementary Attention Module:""" def __init__(self, in_channels, inter_channels=None, bn_layer=True): super(SGCAM, self).__init__() self.in_channels = in_channels self.inter_channels = inter_channels if self.inter_channels is None: self.inter_channels = in_channels // 2 if self.inter_channels == 0: self.inter_channels = 1 conv_nd = nn.Conv1d bn = nn.BatchNorm1d self.g = conv_nd(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0) if bn_layer: self.W = nn.Sequential( conv_nd(in_channels=self.inter_channels, out_channels=self.in_channels, kernel_size=1, stride=1, padding=0), bn(self.in_channels) ) nn.init.constant(self.W[1].weight, 0) nn.init.constant(self.W[1].bias, 0) else: self.W = conv_nd(in_channels=self.inter_channels, out_channels=self.in_channels, kernel_size=1, stride=1, padding=0) nn.init.constant(self.W.weight, 0) nn.init.constant(self.W.bias, 0) self.theta = conv_nd(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0) self.phi = conv_nd(in_channels=self.in_channels, out_channels=self.inter_channels, kernel_size=1, stride=1, padding=0) def forward(self, x, x_2): batch_size = x.size(0) g_x = self.g(x_2).view(batch_size, self.inter_channels, -1).contiguous() g_x = g_x.permute(0, 2, 1).contiguous() theta_x = self.theta(x).view(batch_size, self.inter_channels, -1).contiguous() theta_x = theta_x.permute(0, 2, 1).contiguous() phi_x = self.phi(x_2).view(batch_size, self.inter_channels, -1).contiguous() W = torch.matmul(theta_x, phi_x) # Attention Matrix N = W.size(-1) W_div_C = W / N y = torch.matmul(W_div_C, g_x) y = y.permute(0, 2, 1).contiguous() y = y.view(batch_size, self.inter_channels, *x.size()[2:]).contiguous() W_y = self.W(y) y = W_y + x return y ================================================ FILE: GDANet/model/util/__init__.py ================================================ ================================================ FILE: GDANet/model/util/data_util.py ================================================ import glob import h5py import numpy as np from torch.utils.data import Dataset import os import json def load_data(partition): all_data = [] all_label = [] for h5_name in glob.glob('./data/modelnet40_ply_hdf5_2048/ply_data_%s*.h5' % partition): f = h5py.File(h5_name) data = f['data'][:].astype('float32') label = f['label'][:].astype('int64') f.close() all_data.append(data) all_label.append(label) all_data = np.concatenate(all_data, axis=0) all_label = np.concatenate(all_label, axis=0) return all_data, all_label def pc_normalize(pc): centroid = np.mean(pc, axis=0) pc = pc - centroid m = np.max(np.sqrt(np.sum(pc ** 2, axis=1))) pc = pc / m return pc def translate_pointcloud(pointcloud): xyz1 = np.random.uniform(low=2./3., high=3./2., size=[3]) xyz2 = np.random.uniform(low=-0.2, high=0.2, size=[3]) translated_pointcloud = np.add(np.multiply(pointcloud, xyz1), xyz2).astype('float32') return translated_pointcloud def jitter_pointcloud(pointcloud, sigma=0.01, clip=0.02): N, C = pointcloud.shape pointcloud += np.clip(sigma * np.random.randn(N, C), -1*clip, clip) return pointcloud # =========== ModelNet40 ================= class ModelNet40(Dataset): def __init__(self, num_points, partition='train'): self.data, self.label = load_data(partition) self.num_points = num_points self.partition = partition # Here the new given partition will cover the 'train' def __getitem__(self, item): # indice of the pts or label pointcloud = self.data[item][:self.num_points] label = self.label[item] if self.partition == 'train': # pointcloud = pc_normalize(pointcloud) # you can try to add it or not to train our model pointcloud = translate_pointcloud(pointcloud) np.random.shuffle(pointcloud) # shuffle the order of pts return pointcloud, label def __len__(self): return self.data.shape[0] # =========== ShapeNet Part ================= class PartNormalDataset(Dataset): def __init__(self, npoints=2500, split='train', normalize=False): self.npoints = npoints self.root = './data/shapenetcore_partanno_segmentation_benchmark_v0_normal' self.catfile = os.path.join(self.root, 'synsetoffset2category.txt') self.cat = {} self.normalize = normalize with open(self.catfile, 'r') as f: for line in f: ls = line.strip().split() self.cat[ls[0]] = ls[1] self.cat = {k: v for k, v in self.cat.items()} self.meta = {} with open(os.path.join(self.root, 'train_test_split', 'shuffled_train_file_list.json'), 'r') as f: train_ids = set([str(d.split('/')[2]) for d in json.load(f)]) with open(os.path.join(self.root, 'train_test_split', 'shuffled_val_file_list.json'), 'r') as f: val_ids = set([str(d.split('/')[2]) for d in json.load(f)]) with open(os.path.join(self.root, 'train_test_split', 'shuffled_test_file_list.json'), 'r') as f: test_ids = set([str(d.split('/')[2]) for d in json.load(f)]) for item in self.cat: self.meta[item] = [] dir_point = os.path.join(self.root, self.cat[item]) fns = sorted(os.listdir(dir_point)) if split == 'trainval': fns = [fn for fn in fns if ((fn[0:-4] in train_ids) or (fn[0:-4] in val_ids))] elif split == 'train': fns = [fn for fn in fns if fn[0:-4] in train_ids] elif split == 'val': fns = [fn for fn in fns if fn[0:-4] in val_ids] elif split == 'test': fns = [fn for fn in fns if fn[0:-4] in test_ids] else: print('Unknown split: %s. Exiting..' % (split)) exit(-1) for fn in fns: token = (os.path.splitext(os.path.basename(fn))[0]) self.meta[item].append(os.path.join(dir_point, token + '.txt')) self.datapath = [] for item in self.cat: for fn in self.meta[item]: self.datapath.append((item, fn)) self.classes = dict(zip(self.cat, range(len(self.cat)))) # Mapping from category ('Chair') to a list of int [10,11,12,13] as segmentation labels self.seg_classes = {'Earphone': [16, 17, 18], 'Motorbike': [30, 31, 32, 33, 34, 35], 'Rocket': [41, 42, 43], 'Car': [8, 9, 10, 11], 'Laptop': [28, 29], 'Cap': [6, 7], 'Skateboard': [44, 45, 46], 'Mug': [36, 37], 'Guitar': [19, 20, 21], 'Bag': [4, 5], 'Lamp': [24, 25, 26, 27], 'Table': [47, 48, 49], 'Airplane': [0, 1, 2, 3], 'Pistol': [38, 39, 40], 'Chair': [12, 13, 14, 15], 'Knife': [22, 23]} self.cache = {} # from index to (point_set, cls, seg) tuple self.cache_size = 20000 def __getitem__(self, index): if index in self.cache: point_set, normal, seg, cls = self.cache[index] else: fn = self.datapath[index] cat = self.datapath[index][0] cls = self.classes[cat] cls = np.array([cls]).astype(np.int32) data = np.loadtxt(fn[1]).astype(np.float32) point_set = data[:, 0:3] normal = data[:, 3:6] seg = data[:, -1].astype(np.int32) if len(self.cache) < self.cache_size: self.cache[index] = (point_set, normal, seg, cls) if self.normalize: point_set = pc_normalize(point_set) choice = np.random.choice(len(seg), self.npoints, replace=True) # resample # note that the number of points in some points clouds is less than 2048, thus use random.choice # remember to use the same seed during train and test for a getting stable result point_set = point_set[choice, :] seg = seg[choice] normal = normal[choice, :] return point_set, cls, seg, normal def __len__(self): return len(self.datapath) if __name__ == '__main__': train = ModelNet40(1024) test = ModelNet40(1024, 'test') for data, label in train: print(data.shape) print(label.shape) ================================================ FILE: GDANet/model/util/util.py ================================================ import numpy as np import torch import torch.nn.functional as F def cal_loss(pred, gold, smoothing=True): ''' Calculate cross entropy loss, apply label smoothing if needed. ''' gold = gold.contiguous().view(-1) # gold is the groudtruth label in the dataloader if smoothing: eps = 0.2 n_class = pred.size(1) # the number of feature_dim of the ouput, which is output channels one_hot = torch.zeros_like(pred).scatter(1, gold.view(-1, 1), 1) one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1) log_prb = F.log_softmax(pred, dim=1) loss = -(one_hot * log_prb).sum(dim=1).mean() else: loss = F.cross_entropy(pred, gold, reduction='mean') return loss # create a file and write the text into it: class IOStream(): def __init__(self, path): self.f = open(path, 'a') def cprint(self, text): print(text) self.f.write(text+'\n') self.f.flush() def close(self): self.f.close() def to_categorical(y, num_classes): """ 1-hot encodes a tensor """ new_y = torch.eye(num_classes)[y.cpu().data.numpy(),] if (y.is_cuda): return new_y.cuda(non_blocking=True) return new_y def compute_overall_iou(pred, target, num_classes): shape_ious = [] pred = pred.max(dim=2)[1] # (batch_size, num_points) the pred_class_idx of each point in each sample pred_np = pred.cpu().data.numpy() target_np = target.cpu().data.numpy() for shape_idx in range(pred.size(0)): # sample_idx part_ious = [] for part in range(num_classes): # class_idx! no matter which category, only consider all part_classes of all categories, check all 50 classes # for target, each point has a class no matter which category owns this point! also 50 classes!!! # only return 1 when both belongs to this class, which means correct: I = np.sum(np.logical_and(pred_np[shape_idx] == part, target_np[shape_idx] == part)) # always return 1 when either is belongs to this class: U = np.sum(np.logical_or(pred_np[shape_idx] == part, target_np[shape_idx] == part)) F = np.sum(target_np[shape_idx] == part) if F != 0: iou = I / float(U) # iou across all points for this class part_ious.append(iou) # append the iou of this class shape_ious.append(np.mean(part_ious)) # each time append an average iou across all classes of this sample (sample_level!) return shape_ious # [batch_size] ================================================ FILE: LICENSE ================================================ BSD 3-Clause License Copyright (c) 2021, University of Michigan All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ================================================ FILE: PCT_Pytorch/LICENSE ================================================ MIT License Copyright (c) 2021 Strawberry-Eat-Mango Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: PCT_Pytorch/README.md ================================================ ## PCT: Point Cloud Transformer This is a Pytorch implementation of PCT: Point Cloud Transformer. Paper link: https://arxiv.org/pdf/2012.09688.pdf ### Requirements python >= 3.7 pytorch >= 1.6 h5py scikit-learn and ```shell script pip install pointnet2_ops_lib/. ``` The code is from https://github.com/erikwijmans/Pointnet2_PyTorch https://github.com/WangYueFt/dgcnn and https://github.com/MenghaoGuo/PCT ### Models We get an accuracy of 93.2% on the ModelNet40(http://modelnet.cs.princeton.edu/) validation dataset The path of the model is in ./checkpoints/best/models/model.t7 ### Example training and testing ```shell script # train python main.py --exp_name=train --num_points=1024 --use_sgd=True --batch_size 32 --epochs 250 --lr 0.0001 # test python main.py --exp_name=test --num_points=1024 --use_sgd=True --eval=True --model_path=checkpoints/best/models/model.t7 --test_batch_size 8 ``` ### Citation If it is helpful for your work, please cite this paper: ```latex @misc{guo2020pct, title={PCT: Point Cloud Transformer}, author={Meng-Hao Guo and Jun-Xiong Cai and Zheng-Ning Liu and Tai-Jiang Mu and Ralph R. Martin and Shi-Min Hu}, year={2020}, eprint={2012.09688}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ================================================ FILE: PCT_Pytorch/checkpoints/best/models/model.t7 ================================================ [File too large to display: 11.0 MB] ================================================ FILE: PCT_Pytorch/data.py ================================================ import os import glob import h5py import numpy as np from torch.utils.data import Dataset def download(): BASE_DIR = os.path.dirname(os.path.abspath(__file__)) DATA_DIR = os.path.join(BASE_DIR, 'data') if not os.path.exists(DATA_DIR): os.mkdir(DATA_DIR) if not os.path.exists(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048')): www = 'https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip' zipfile = os.path.basename(www) os.system('wget %s; unzip %s' % (www, zipfile)) os.system('mv %s %s' % (zipfile[:-4], DATA_DIR)) os.system('rm %s' % (zipfile)) def load_data(partition): download() BASE_DIR = os.path.dirname(os.path.abspath(__file__)) DATA_DIR = os.path.join(BASE_DIR, 'data') all_data = [] all_label = [] for h5_name in glob.glob(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048', 'ply_data_%s*.h5'%partition)): f = h5py.File(h5_name) data = f['data'][:].astype('float32') label = f['label'][:].astype('int64') f.close() all_data.append(data) all_label.append(label) all_data = np.concatenate(all_data, axis=0) all_label = np.concatenate(all_label, axis=0) return all_data, all_label def random_point_dropout(pc, max_dropout_ratio=0.875): ''' batch_pc: BxNx3 ''' # for b in range(batch_pc.shape[0]): dropout_ratio = np.random.random()*max_dropout_ratio # 0~0.875 drop_idx = np.where(np.random.random((pc.shape[0]))<=dropout_ratio)[0] # print ('use random drop', len(drop_idx)) if len(drop_idx)>0: pc[drop_idx,:] = pc[0,:] # set to the first point return pc def translate_pointcloud(pointcloud): xyz1 = np.random.uniform(low=2./3., high=3./2., size=[3]) xyz2 = np.random.uniform(low=-0.2, high=0.2, size=[3]) translated_pointcloud = np.add(np.multiply(pointcloud, xyz1), xyz2).astype('float32') return translated_pointcloud def jitter_pointcloud(pointcloud, sigma=0.01, clip=0.02): N, C = pointcloud.shape pointcloud += np.clip(sigma * np.random.randn(N, C), -1*clip, clip) return pointcloud class ModelNet40(Dataset): def __init__(self, num_points, partition='train'): self.data, self.label = load_data(partition) self.num_points = num_points self.partition = partition def __getitem__(self, item): pointcloud = self.data[item][:self.num_points] label = self.label[item] if self.partition == 'train': pointcloud = random_point_dropout(pointcloud) # open for dgcnn not for our idea for all pointcloud = translate_pointcloud(pointcloud) np.random.shuffle(pointcloud) return pointcloud, label def __len__(self): return self.data.shape[0] if __name__ == '__main__': train = ModelNet40(1024) test = ModelNet40(1024, 'test') for data, label in train: print(data.shape) print(label.shape) ================================================ FILE: PCT_Pytorch/main.py ================================================ from __future__ import print_function import os import argparse import torch import torch.nn as nn import torch.optim as optim from torch.optim.lr_scheduler import CosineAnnealingLR from data import ModelNet40 from .model import Pct import numpy as np from torch.utils.data import DataLoader from .util import cal_loss, IOStream import sklearn.metrics as metrics import time def _init_(): if not os.path.exists('checkpoints'): os.makedirs('checkpoints') if not os.path.exists('checkpoints/'+args.exp_name): os.makedirs('checkpoints/'+args.exp_name) if not os.path.exists('checkpoints/'+args.exp_name+'/'+'models'): os.makedirs('checkpoints/'+args.exp_name+'/'+'models') os.system('cp main.py checkpoints'+'/'+args.exp_name+'/'+'main.py.backup') os.system('cp model.py checkpoints' + '/' + args.exp_name + '/' + 'model.py.backup') os.system('cp util.py checkpoints' + '/' + args.exp_name + '/' + 'util.py.backup') os.system('cp data.py checkpoints' + '/' + args.exp_name + '/' + 'data.py.backup') def train(args, io): train_loader = DataLoader(ModelNet40(partition='train', num_points=args.num_points), num_workers=8, batch_size=args.batch_size, shuffle=True, drop_last=True) test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), num_workers=8, batch_size=args.test_batch_size, shuffle=True, drop_last=False) device = torch.device("cuda" if args.cuda else "cpu") model = Pct(args).to(device) print(str(model)) model = nn.DataParallel(model) if args.use_sgd: print("Use SGD") opt = optim.SGD(model.parameters(), lr=args.lr*100, momentum=args.momentum, weight_decay=5e-4) else: print("Use Adam") opt = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4) scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=args.lr) criterion = cal_loss best_test_acc = 0 for epoch in range(args.epochs): scheduler.step() train_loss = 0.0 count = 0.0 model.train() train_pred = [] train_true = [] idx = 0 total_time = 0.0 for data, label in (train_loader): data, label = data.to(device), label.to(device).squeeze() data = data.permute(0, 2, 1) batch_size = data.size()[0] opt.zero_grad() start_time = time.time() logits = model(data) loss = criterion(logits, label) loss.backward() opt.step() end_time = time.time() total_time += (end_time - start_time) preds = logits.max(dim=1)[1] count += batch_size train_loss += loss.item() * batch_size train_true.append(label.cpu().numpy()) train_pred.append(preds.detach().cpu().numpy()) idx += 1 print ('train total time is',total_time) train_true = np.concatenate(train_true) train_pred = np.concatenate(train_pred) outstr = 'Train %d, loss: %.6f, train acc: %.6f, train avg acc: %.6f' % (epoch, train_loss*1.0/count, metrics.accuracy_score( train_true, train_pred), metrics.balanced_accuracy_score( train_true, train_pred)) io.cprint(outstr) #################### # Test #################### test_loss = 0.0 count = 0.0 model.eval() test_pred = [] test_true = [] total_time = 0.0 for data, label in test_loader: data, label = data.to(device), label.to(device).squeeze() data = data.permute(0, 2, 1) batch_size = data.size()[0] start_time = time.time() logits = model(data) end_time = time.time() total_time += (end_time - start_time) loss = criterion(logits, label) preds = logits.max(dim=1)[1] count += batch_size test_loss += loss.item() * batch_size test_true.append(label.cpu().numpy()) test_pred.append(preds.detach().cpu().numpy()) print ('test total time is', total_time) test_true = np.concatenate(test_true) test_pred = np.concatenate(test_pred) test_acc = metrics.accuracy_score(test_true, test_pred) avg_per_class_acc = metrics.balanced_accuracy_score(test_true, test_pred) outstr = 'Test %d, loss: %.6f, test acc: %.6f, test avg acc: %.6f' % (epoch, test_loss*1.0/count, test_acc, avg_per_class_acc) io.cprint(outstr) if test_acc >= best_test_acc: best_test_acc = test_acc torch.save(model.state_dict(), 'checkpoints/%s/models/model.t7' % args.exp_name) def test(args, io): test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), batch_size=args.test_batch_size, shuffle=True, drop_last=False) device = torch.device("cuda" if args.cuda else "cpu") model = Pct(args).to(device) model = nn.DataParallel(model) model.load_state_dict(torch.load(args.model_path)) model = model.eval() test_true = [] test_pred = [] for data, label in test_loader: data, label = data.to(device), label.to(device).squeeze() data = data.permute(0, 2, 1) logits = model(data) preds = logits.max(dim=1)[1] if args.test_batch_size == 1: test_true.append([label.cpu().numpy()]) test_pred.append([preds.detach().cpu().numpy()]) else: test_true.append(label.cpu().numpy()) test_pred.append(preds.detach().cpu().numpy()) test_true = np.concatenate(test_true) test_pred = np.concatenate(test_pred) test_acc = metrics.accuracy_score(test_true, test_pred) avg_per_class_acc = metrics.balanced_accuracy_score(test_true, test_pred) outstr = 'Test :: test acc: %.6f, test avg acc: %.6f'%(test_acc, avg_per_class_acc) io.cprint(outstr) if __name__ == "__main__": # Training settings parser = argparse.ArgumentParser(description='Point Cloud Recognition') parser.add_argument('--exp_name', type=str, default='exp', metavar='N', help='Name of the experiment') parser.add_argument('--dataset', type=str, default='modelnet40', metavar='N', choices=['modelnet40']) parser.add_argument('--batch_size', type=int, default=32, metavar='batch_size', help='Size of batch)') parser.add_argument('--test_batch_size', type=int, default=16, metavar='batch_size', help='Size of batch)') parser.add_argument('--epochs', type=int, default=250, metavar='N', help='number of episode to train ') parser.add_argument('--use_sgd', type=bool, default=True, help='Use SGD') parser.add_argument('--lr', type=float, default=0.0001, metavar='LR', help='learning rate (default: 0.001, 0.1 if using sgd)') parser.add_argument('--momentum', type=float, default=0.9, metavar='M', help='SGD momentum (default: 0.9)') parser.add_argument('--no_cuda', type=bool, default=False, help='enables CUDA training') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--eval', type=bool, default=False, help='evaluate the model') parser.add_argument('--num_points', type=int, default=1024, help='num of points to use') parser.add_argument('--dropout', type=float, default=0.5, help='dropout rate') parser.add_argument('--model_path', type=str, default='', metavar='N', help='Pretrained model path') args = parser.parse_args() _init_() io = IOStream('checkpoints/' + args.exp_name + '/run.log') io.cprint(str(args)) args.cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) if args.cuda: io.cprint( 'Using GPU : ' + str(torch.cuda.current_device()) + ' from ' + str(torch.cuda.device_count()) + ' devices') torch.cuda.manual_seed(args.seed) else: io.cprint('Using CPU') if not args.eval: train(args, io) else: test(args, io) ================================================ FILE: PCT_Pytorch/model.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from .util import sample_and_group class Local_op(nn.Module): def __init__(self, in_channels, out_channels): super(Local_op, self).__init__() self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=1, bias=False) self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm1d(out_channels) self.bn2 = nn.BatchNorm1d(out_channels) def forward(self, x): b, n, s, d = x.size() # torch.Size([32, 512, 32, 6]) x = x.permute(0, 1, 3, 2) x = x.reshape(-1, d, s) batch_size, _, N = x.size() x = F.relu(self.bn1(self.conv1(x))) # B, D, N x = F.relu(self.bn2(self.conv2(x))) # B, D, N x = F.adaptive_max_pool1d(x, 1).view(batch_size, -1) x = x.reshape(b, n, -1).permute(0, 2, 1) return x class Pct(nn.Module): def __init__(self, args, output_channels=40): super(Pct, self).__init__() self.args = args self.conv1 = nn.Conv1d(3, 64, kernel_size=1, bias=False) self.conv2 = nn.Conv1d(64, 64, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm1d(64) self.bn2 = nn.BatchNorm1d(64) self.gather_local_0 = Local_op(in_channels=128, out_channels=128) self.gather_local_1 = Local_op(in_channels=256, out_channels=256) self.pt_last = Point_Transformer_Last(args) self.conv_fuse = nn.Sequential(nn.Conv1d(1280, 1024, kernel_size=1, bias=False), nn.BatchNorm1d(1024), nn.LeakyReLU(negative_slope=0.2)) self.linear1 = nn.Linear(1024, 512, bias=False) self.bn6 = nn.BatchNorm1d(512) self.dp1 = nn.Dropout(p=args.dropout) self.linear2 = nn.Linear(512, 256) self.bn7 = nn.BatchNorm1d(256) self.dp2 = nn.Dropout(p=args.dropout) self.linear3 = nn.Linear(256, output_channels) def forward(self, x): xyz = x.permute(0, 2, 1) batch_size, _, _ = x.size() # B, D, N x = F.relu(self.bn1(self.conv1(x))) # B, D, N x = F.relu(self.bn2(self.conv2(x))) x = x.permute(0, 2, 1) new_xyz, new_feature = sample_and_group(npoint=512, radius=0.15, nsample=32, xyz=xyz, points=x) feature_0 = self.gather_local_0(new_feature) feature = feature_0.permute(0, 2, 1) new_xyz, new_feature = sample_and_group(npoint=256, radius=0.2, nsample=32, xyz=new_xyz, points=feature) feature_1 = self.gather_local_1(new_feature) x = self.pt_last(feature_1) x = torch.cat([x, feature_1], dim=1) x = self.conv_fuse(x) x = F.adaptive_max_pool1d(x, 1).view(batch_size, -1) x = F.leaky_relu(self.bn6(self.linear1(x)), negative_slope=0.2) x = self.dp1(x) x = F.leaky_relu(self.bn7(self.linear2(x)), negative_slope=0.2) x = self.dp2(x) x = self.linear3(x) return x class Point_Transformer_Last(nn.Module): def __init__(self, args, channels=256): super(Point_Transformer_Last, self).__init__() self.args = args self.conv1 = nn.Conv1d(channels, channels, kernel_size=1, bias=False) self.conv2 = nn.Conv1d(channels, channels, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm1d(channels) self.bn2 = nn.BatchNorm1d(channels) self.sa1 = SA_Layer(channels) self.sa2 = SA_Layer(channels) self.sa3 = SA_Layer(channels) self.sa4 = SA_Layer(channels) def forward(self, x): # # b, 3, npoint, nsample # conv2d 3 -> 128 channels 1, 1 # b * npoint, c, nsample # permute reshape batch_size, _, N = x.size() # B, D, N x = F.relu(self.bn1(self.conv1(x))) x = F.relu(self.bn2(self.conv2(x))) x1 = self.sa1(x) x2 = self.sa2(x1) x3 = self.sa3(x2) x4 = self.sa4(x3) x = torch.cat((x1, x2, x3, x4), dim=1) return x class SA_Layer(nn.Module): def __init__(self, channels): super(SA_Layer, self).__init__() self.q_conv = nn.Conv1d(channels, channels // 4, 1, bias=False) self.k_conv = nn.Conv1d(channels, channels // 4, 1, bias=False) self.q_conv.weight = self.k_conv.weight self.q_conv.bias = self.k_conv.bias self.v_conv = nn.Conv1d(channels, channels, 1) self.trans_conv = nn.Conv1d(channels, channels, 1) self.after_norm = nn.BatchNorm1d(channels) self.act = nn.ReLU() self.softmax = nn.Softmax(dim=-1) def forward(self, x): # b, n, c x_q = self.q_conv(x).permute(0, 2, 1) # b, c, n x_k = self.k_conv(x) x_v = self.v_conv(x) # b, n, n energy = torch.bmm(x_q, x_k) attention = self.softmax(energy) attention = attention / (1e-9 + attention.sum(dim=1, keepdim=True)) # b, c, n x_r = torch.bmm(x_v, attention) x_r = self.act(self.after_norm(self.trans_conv(x - x_r))) x = x + x_r return x ================================================ FILE: PCT_Pytorch/model_new.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from util import sample_and_group class Local_op(nn.Module): def __init__(self, in_channels, out_channels): super(Local_op, self).__init__() self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=1, bias=False) self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm1d(out_channels) self.bn2 = nn.BatchNorm1d(out_channels) def forward(self, x): b, n, s, d = x.size() # torch.Size([32, 512, 32, 6]) x = x.permute(0, 1, 3, 2) x = x.reshape(-1, d, s) batch_size, _, N = x.size() x = F.relu(self.bn1(self.conv1(x))) # B, D, N x = F.relu(self.bn2(self.conv2(x))) # B, D, N x = F.adaptive_max_pool1d(x, 1).view(batch_size, -1) x = x.reshape(b, n, -1).permute(0, 2, 1) return x class Pct(nn.Module): def __init__(self, args, output_channels=40): super(Pct, self).__init__() self.args = args self.conv1 = nn.Conv1d(3, 64, kernel_size=1, bias=False) self.conv2 = nn.Conv1d(64, 64, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm1d(64) self.bn2 = nn.BatchNorm1d(64) self.gather_local_0 = Local_op(in_channels=128, out_channels=128) self.gather_local_1 = Local_op(in_channels=256, out_channels=256) self.pt_last = Point_Transformer_Last(args) self.conv_fuse = nn.Sequential(nn.Conv1d(1280, 1024, kernel_size=1, bias=False), nn.BatchNorm1d(1024), nn.LeakyReLU(negative_slope=0.2)) self.linear1 = nn.Linear(1024, 512, bias=False) self.bn6 = nn.BatchNorm1d(512) self.dp1 = nn.Dropout(p=args.dropout) self.linear2 = nn.Linear(512, 256) self.bn7 = nn.BatchNorm1d(256) self.dp2 = nn.Dropout(p=args.dropout) self.linear3 = nn.Linear(256, output_channels) def forward(self, x): xyz = x.permute(0, 2, 1) batch_size, _, _ = x.size() # B, D, N x = F.relu(self.bn1(self.conv1(x))) # B, D, N x = F.relu(self.bn2(self.conv2(x))) x = x.permute(0, 2, 1) new_xyz, new_feature = sample_and_group(npoint=512, radius=0.15, nsample=32, xyz=xyz, points=x) feature_0 = self.gather_local_0(new_feature) feature = feature_0.permute(0, 2, 1) new_xyz, new_feature = sample_and_group(npoint=256, radius=0.2, nsample=32, xyz=new_xyz, points=feature) feature_1 = self.gather_local_1(new_feature) x = self.pt_last(feature_1, new_xyz) x = torch.cat([x, feature_1], dim=1) x = self.conv_fuse(x) x = F.adaptive_max_pool1d(x, 1).view(batch_size, -1) x = F.leaky_relu(self.bn6(self.linear1(x)), negative_slope=0.2) x = self.dp1(x) x = F.leaky_relu(self.bn7(self.linear2(x)), negative_slope=0.2) x = self.dp2(x) x = self.linear3(x) return x class Point_Transformer_Last(nn.Module): def __init__(self, args, channels=256): super(Point_Transformer_Last, self).__init__() self.args = args self.conv1 = nn.Conv1d(channels, channels, kernel_size=1, bias=False) self.pos_xyz = nn.Conv1d(3, channels, 1) self.bn1 = nn.BatchNorm1d(channels) self.sa1 = SA_Layer(channels) self.sa2 = SA_Layer(channels) self.sa3 = SA_Layer(channels) self.sa4 = SA_Layer(channels) def forward(self, x, xyz): # # b, 3, npoint, nsample # conv2d 3 -> 128 channels 1, 1 # b * npoint, c, nsample # permute reshape batch_size, _, N = x.size() xyz = xyz.permute(0, 2, 1) xyz = self.pos_xyz(xyz) # B, D, N x = F.relu(self.bn1(self.conv1(x))) x1 = self.sa1(x, xyz) x2 = self.sa2(x1, xyz) x3 = self.sa3(x2, xyz) x4 = self.sa4(x3, xyz) x = torch.cat((x1, x2, x3, x4), dim=1) return x class SA_Layer(nn.Module): def __init__(self, channels): super(SA_Layer, self).__init__() self.q_conv = nn.Conv1d(channels, channels // 4, 1, bias=False) self.k_conv = nn.Conv1d(channels, channels // 4, 1, bias=False) self.q_conv.weight = self.k_conv.weight self.q_conv.bias = self.k_conv.bias self.v_conv = nn.Conv1d(channels, channels, 1) self.trans_conv = nn.Conv1d(channels, channels, 1) self.after_norm = nn.BatchNorm1d(channels) self.act = nn.ReLU() self.softmax = nn.Softmax(dim=-1) def forward(self, x, xyz): # b, n, c x = x + xyz x_q = self.q_conv(x).permute(0, 2, 1) # b, c, n x_k = self.k_conv(x) x_v = self.v_conv(x) # b, n, n energy = torch.bmm(x_q, x_k) attention = self.softmax(energy) attention = attention / (1e-9 + attention.sum(dim=1, keepdim=True)) # b, c, n x_r = torch.bmm(x_v, attention) x_r = self.act(self.after_norm(self.trans_conv(x - x_r))) x = x + x_r return x ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/MANIFEST.in ================================================ graft pointnet2_ops/_ext-src ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/__init__.py ================================================ import pointnet2_ops.pointnet2_modules import pointnet2_ops.pointnet2_utils from pointnet2_ops._version import __version__ ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/ball_query.h ================================================ #pragma once #include at::Tensor ball_query(at::Tensor new_xyz, at::Tensor xyz, const float radius, const int nsample); ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/cuda_utils.h ================================================ #ifndef _CUDA_UTILS_H #define _CUDA_UTILS_H #include #include #include #include #include #include #define TOTAL_THREADS 512 inline int opt_n_threads(int work_size) { const int pow_2 = std::log(static_cast(work_size)) / std::log(2.0); return max(min(1 << pow_2, TOTAL_THREADS), 1); } inline dim3 opt_block_config(int x, int y) { const int x_threads = opt_n_threads(x); const int y_threads = max(min(opt_n_threads(y), TOTAL_THREADS / x_threads), 1); dim3 block_config(x_threads, y_threads, 1); return block_config; } #define CUDA_CHECK_ERRORS() \ do { \ cudaError_t err = cudaGetLastError(); \ if (cudaSuccess != err) { \ fprintf(stderr, "CUDA kernel failed : %s\n%s at L:%d in %s\n", \ cudaGetErrorString(err), __PRETTY_FUNCTION__, __LINE__, \ __FILE__); \ exit(-1); \ } \ } while (0) #endif ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/group_points.h ================================================ #pragma once #include at::Tensor group_points(at::Tensor points, at::Tensor idx); at::Tensor group_points_grad(at::Tensor grad_out, at::Tensor idx, const int n); ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/interpolate.h ================================================ #pragma once #include #include std::vector three_nn(at::Tensor unknowns, at::Tensor knows); at::Tensor three_interpolate(at::Tensor points, at::Tensor idx, at::Tensor weight); at::Tensor three_interpolate_grad(at::Tensor grad_out, at::Tensor idx, at::Tensor weight, const int m); ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/sampling.h ================================================ #pragma once #include at::Tensor gather_points(at::Tensor points, at::Tensor idx); at::Tensor gather_points_grad(at::Tensor grad_out, at::Tensor idx, const int n); at::Tensor furthest_point_sampling(at::Tensor points, const int nsamples); ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/_ext-src/include/utils.h ================================================ #pragma once #include #include #define CHECK_CUDA(x) \ do { \ AT_ASSERT(x.is_cuda(), #x " must be a CUDA tensor"); \ } while (0) #define CHECK_CONTIGUOUS(x) \ do { \ AT_ASSERT(x.is_contiguous(), #x " must be a contiguous tensor"); \ } while (0) #define CHECK_IS_INT(x) \ do { \ AT_ASSERT(x.scalar_type() == at::ScalarType::Int, \ #x " must be an int tensor"); \ } while (0) #define CHECK_IS_FLOAT(x) \ do { \ AT_ASSERT(x.scalar_type() == at::ScalarType::Float, \ #x " must be a float tensor"); \ } while (0) ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/ball_query.cpp ================================================ #include "ball_query.h" #include "utils.h" void query_ball_point_kernel_wrapper(int b, int n, int m, float radius, int nsample, const float *new_xyz, const float *xyz, int *idx); at::Tensor ball_query(at::Tensor new_xyz, at::Tensor xyz, const float radius, const int nsample) { CHECK_CONTIGUOUS(new_xyz); CHECK_CONTIGUOUS(xyz); CHECK_IS_FLOAT(new_xyz); CHECK_IS_FLOAT(xyz); if (new_xyz.is_cuda()) { CHECK_CUDA(xyz); } at::Tensor idx = torch::zeros({new_xyz.size(0), new_xyz.size(1), nsample}, at::device(new_xyz.device()).dtype(at::ScalarType::Int)); if (new_xyz.is_cuda()) { query_ball_point_kernel_wrapper(xyz.size(0), xyz.size(1), new_xyz.size(1), radius, nsample, new_xyz.data_ptr(), xyz.data_ptr(), idx.data_ptr()); } else { AT_ASSERT(false, "CPU not supported"); } return idx; } ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/ball_query_gpu.cu ================================================ #include #include #include #include "cuda_utils.h" // input: new_xyz(b, m, 3) xyz(b, n, 3) // output: idx(b, m, nsample) __global__ void query_ball_point_kernel(int b, int n, int m, float radius, int nsample, const float *__restrict__ new_xyz, const float *__restrict__ xyz, int *__restrict__ idx) { int batch_index = blockIdx.x; xyz += batch_index * n * 3; new_xyz += batch_index * m * 3; idx += m * nsample * batch_index; int index = threadIdx.x; int stride = blockDim.x; float radius2 = radius * radius; for (int j = index; j < m; j += stride) { float new_x = new_xyz[j * 3 + 0]; float new_y = new_xyz[j * 3 + 1]; float new_z = new_xyz[j * 3 + 2]; for (int k = 0, cnt = 0; k < n && cnt < nsample; ++k) { float x = xyz[k * 3 + 0]; float y = xyz[k * 3 + 1]; float z = xyz[k * 3 + 2]; float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z); if (d2 < radius2) { if (cnt == 0) { for (int l = 0; l < nsample; ++l) { idx[j * nsample + l] = k; } } idx[j * nsample + cnt] = k; ++cnt; } } } } void query_ball_point_kernel_wrapper(int b, int n, int m, float radius, int nsample, const float *new_xyz, const float *xyz, int *idx) { cudaStream_t stream = at::cuda::getCurrentCUDAStream(); query_ball_point_kernel<<>>( b, n, m, radius, nsample, new_xyz, xyz, idx); CUDA_CHECK_ERRORS(); } ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/bindings.cpp ================================================ #include "ball_query.h" #include "group_points.h" #include "interpolate.h" #include "sampling.h" PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("gather_points", &gather_points); m.def("gather_points_grad", &gather_points_grad); m.def("furthest_point_sampling", &furthest_point_sampling); m.def("three_nn", &three_nn); m.def("three_interpolate", &three_interpolate); m.def("three_interpolate_grad", &three_interpolate_grad); m.def("ball_query", &ball_query); m.def("group_points", &group_points); m.def("group_points_grad", &group_points_grad); } ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/group_points.cpp ================================================ #include "group_points.h" #include "utils.h" void group_points_kernel_wrapper(int b, int c, int n, int npoints, int nsample, const float *points, const int *idx, float *out); void group_points_grad_kernel_wrapper(int b, int c, int n, int npoints, int nsample, const float *grad_out, const int *idx, float *grad_points); at::Tensor group_points(at::Tensor points, at::Tensor idx) { CHECK_CONTIGUOUS(points); CHECK_CONTIGUOUS(idx); CHECK_IS_FLOAT(points); CHECK_IS_INT(idx); if (points.is_cuda()) { CHECK_CUDA(idx); } at::Tensor output = torch::zeros({points.size(0), points.size(1), idx.size(1), idx.size(2)}, at::device(points.device()).dtype(at::ScalarType::Float)); if (points.is_cuda()) { group_points_kernel_wrapper(points.size(0), points.size(1), points.size(2), idx.size(1), idx.size(2), points.data_ptr(), idx.data_ptr(), output.data_ptr()); } else { AT_ASSERT(false, "CPU not supported"); } return output; } at::Tensor group_points_grad(at::Tensor grad_out, at::Tensor idx, const int n) { CHECK_CONTIGUOUS(grad_out); CHECK_CONTIGUOUS(idx); CHECK_IS_FLOAT(grad_out); CHECK_IS_INT(idx); if (grad_out.is_cuda()) { CHECK_CUDA(idx); } at::Tensor output = torch::zeros({grad_out.size(0), grad_out.size(1), n}, at::device(grad_out.device()).dtype(at::ScalarType::Float)); if (grad_out.is_cuda()) { group_points_grad_kernel_wrapper( grad_out.size(0), grad_out.size(1), n, idx.size(1), idx.size(2), grad_out.data_ptr(), idx.data_ptr(), output.data_ptr()); } else { AT_ASSERT(false, "CPU not supported"); } return output; } ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/group_points_gpu.cu ================================================ #include #include #include "cuda_utils.h" // input: points(b, c, n) idx(b, npoints, nsample) // output: out(b, c, npoints, nsample) __global__ void group_points_kernel(int b, int c, int n, int npoints, int nsample, const float *__restrict__ points, const int *__restrict__ idx, float *__restrict__ out) { int batch_index = blockIdx.x; points += batch_index * n * c; idx += batch_index * npoints * nsample; out += batch_index * npoints * nsample * c; const int index = threadIdx.y * blockDim.x + threadIdx.x; const int stride = blockDim.y * blockDim.x; for (int i = index; i < c * npoints; i += stride) { const int l = i / npoints; const int j = i % npoints; for (int k = 0; k < nsample; ++k) { int ii = idx[j * nsample + k]; out[(l * npoints + j) * nsample + k] = points[l * n + ii]; } } } void group_points_kernel_wrapper(int b, int c, int n, int npoints, int nsample, const float *points, const int *idx, float *out) { cudaStream_t stream = at::cuda::getCurrentCUDAStream(); group_points_kernel<<>>( b, c, n, npoints, nsample, points, idx, out); CUDA_CHECK_ERRORS(); } // input: grad_out(b, c, npoints, nsample), idx(b, npoints, nsample) // output: grad_points(b, c, n) __global__ void group_points_grad_kernel(int b, int c, int n, int npoints, int nsample, const float *__restrict__ grad_out, const int *__restrict__ idx, float *__restrict__ grad_points) { int batch_index = blockIdx.x; grad_out += batch_index * npoints * nsample * c; idx += batch_index * npoints * nsample; grad_points += batch_index * n * c; const int index = threadIdx.y * blockDim.x + threadIdx.x; const int stride = blockDim.y * blockDim.x; for (int i = index; i < c * npoints; i += stride) { const int l = i / npoints; const int j = i % npoints; for (int k = 0; k < nsample; ++k) { int ii = idx[j * nsample + k]; atomicAdd(grad_points + l * n + ii, grad_out[(l * npoints + j) * nsample + k]); } } } void group_points_grad_kernel_wrapper(int b, int c, int n, int npoints, int nsample, const float *grad_out, const int *idx, float *grad_points) { cudaStream_t stream = at::cuda::getCurrentCUDAStream(); group_points_grad_kernel<<>>( b, c, n, npoints, nsample, grad_out, idx, grad_points); CUDA_CHECK_ERRORS(); } ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/interpolate.cpp ================================================ #include "interpolate.h" #include "utils.h" void three_nn_kernel_wrapper(int b, int n, int m, const float *unknown, const float *known, float *dist2, int *idx); void three_interpolate_kernel_wrapper(int b, int c, int m, int n, const float *points, const int *idx, const float *weight, float *out); void three_interpolate_grad_kernel_wrapper(int b, int c, int n, int m, const float *grad_out, const int *idx, const float *weight, float *grad_points); std::vector three_nn(at::Tensor unknowns, at::Tensor knows) { CHECK_CONTIGUOUS(unknowns); CHECK_CONTIGUOUS(knows); CHECK_IS_FLOAT(unknowns); CHECK_IS_FLOAT(knows); if (unknowns.is_cuda()) { CHECK_CUDA(knows); } at::Tensor idx = torch::zeros({unknowns.size(0), unknowns.size(1), 3}, at::device(unknowns.device()).dtype(at::ScalarType::Int)); at::Tensor dist2 = torch::zeros({unknowns.size(0), unknowns.size(1), 3}, at::device(unknowns.device()).dtype(at::ScalarType::Float)); if (unknowns.is_cuda()) { three_nn_kernel_wrapper(unknowns.size(0), unknowns.size(1), knows.size(1), unknowns.data_ptr(), knows.data_ptr(), dist2.data_ptr(), idx.data_ptr()); } else { AT_ASSERT(false, "CPU not supported"); } return {dist2, idx}; } at::Tensor three_interpolate(at::Tensor points, at::Tensor idx, at::Tensor weight) { CHECK_CONTIGUOUS(points); CHECK_CONTIGUOUS(idx); CHECK_CONTIGUOUS(weight); CHECK_IS_FLOAT(points); CHECK_IS_INT(idx); CHECK_IS_FLOAT(weight); if (points.is_cuda()) { CHECK_CUDA(idx); CHECK_CUDA(weight); } at::Tensor output = torch::zeros({points.size(0), points.size(1), idx.size(1)}, at::device(points.device()).dtype(at::ScalarType::Float)); if (points.is_cuda()) { three_interpolate_kernel_wrapper( points.size(0), points.size(1), points.size(2), idx.size(1), points.data_ptr(), idx.data_ptr(), weight.data_ptr(), output.data_ptr()); } else { AT_ASSERT(false, "CPU not supported"); } return output; } at::Tensor three_interpolate_grad(at::Tensor grad_out, at::Tensor idx, at::Tensor weight, const int m) { CHECK_CONTIGUOUS(grad_out); CHECK_CONTIGUOUS(idx); CHECK_CONTIGUOUS(weight); CHECK_IS_FLOAT(grad_out); CHECK_IS_INT(idx); CHECK_IS_FLOAT(weight); if (grad_out.is_cuda()) { CHECK_CUDA(idx); CHECK_CUDA(weight); } at::Tensor output = torch::zeros({grad_out.size(0), grad_out.size(1), m}, at::device(grad_out.device()).dtype(at::ScalarType::Float)); if (grad_out.is_cuda()) { three_interpolate_grad_kernel_wrapper( grad_out.size(0), grad_out.size(1), grad_out.size(2), m, grad_out.data_ptr(), idx.data_ptr(), weight.data_ptr(), output.data_ptr()); } else { AT_ASSERT(false, "CPU not supported"); } return output; } ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/interpolate_gpu.cu ================================================ #include #include #include #include "cuda_utils.h" // input: unknown(b, n, 3) known(b, m, 3) // output: dist2(b, n, 3), idx(b, n, 3) __global__ void three_nn_kernel(int b, int n, int m, const float *__restrict__ unknown, const float *__restrict__ known, float *__restrict__ dist2, int *__restrict__ idx) { int batch_index = blockIdx.x; unknown += batch_index * n * 3; known += batch_index * m * 3; dist2 += batch_index * n * 3; idx += batch_index * n * 3; int index = threadIdx.x; int stride = blockDim.x; for (int j = index; j < n; j += stride) { float ux = unknown[j * 3 + 0]; float uy = unknown[j * 3 + 1]; float uz = unknown[j * 3 + 2]; double best1 = 1e40, best2 = 1e40, best3 = 1e40; int besti1 = 0, besti2 = 0, besti3 = 0; for (int k = 0; k < m; ++k) { float x = known[k * 3 + 0]; float y = known[k * 3 + 1]; float z = known[k * 3 + 2]; float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z); if (d < best1) { best3 = best2; besti3 = besti2; best2 = best1; besti2 = besti1; best1 = d; besti1 = k; } else if (d < best2) { best3 = best2; besti3 = besti2; best2 = d; besti2 = k; } else if (d < best3) { best3 = d; besti3 = k; } } dist2[j * 3 + 0] = best1; dist2[j * 3 + 1] = best2; dist2[j * 3 + 2] = best3; idx[j * 3 + 0] = besti1; idx[j * 3 + 1] = besti2; idx[j * 3 + 2] = besti3; } } void three_nn_kernel_wrapper(int b, int n, int m, const float *unknown, const float *known, float *dist2, int *idx) { cudaStream_t stream = at::cuda::getCurrentCUDAStream(); three_nn_kernel<<>>(b, n, m, unknown, known, dist2, idx); CUDA_CHECK_ERRORS(); } // input: points(b, c, m), idx(b, n, 3), weight(b, n, 3) // output: out(b, c, n) __global__ void three_interpolate_kernel(int b, int c, int m, int n, const float *__restrict__ points, const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ out) { int batch_index = blockIdx.x; points += batch_index * m * c; idx += batch_index * n * 3; weight += batch_index * n * 3; out += batch_index * n * c; const int index = threadIdx.y * blockDim.x + threadIdx.x; const int stride = blockDim.y * blockDim.x; for (int i = index; i < c * n; i += stride) { const int l = i / n; const int j = i % n; float w1 = weight[j * 3 + 0]; float w2 = weight[j * 3 + 1]; float w3 = weight[j * 3 + 2]; int i1 = idx[j * 3 + 0]; int i2 = idx[j * 3 + 1]; int i3 = idx[j * 3 + 2]; out[i] = points[l * m + i1] * w1 + points[l * m + i2] * w2 + points[l * m + i3] * w3; } } void three_interpolate_kernel_wrapper(int b, int c, int m, int n, const float *points, const int *idx, const float *weight, float *out) { cudaStream_t stream = at::cuda::getCurrentCUDAStream(); three_interpolate_kernel<<>>( b, c, m, n, points, idx, weight, out); CUDA_CHECK_ERRORS(); } // input: grad_out(b, c, n), idx(b, n, 3), weight(b, n, 3) // output: grad_points(b, c, m) __global__ void three_interpolate_grad_kernel( int b, int c, int n, int m, const float *__restrict__ grad_out, const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ grad_points) { int batch_index = blockIdx.x; grad_out += batch_index * n * c; idx += batch_index * n * 3; weight += batch_index * n * 3; grad_points += batch_index * m * c; const int index = threadIdx.y * blockDim.x + threadIdx.x; const int stride = blockDim.y * blockDim.x; for (int i = index; i < c * n; i += stride) { const int l = i / n; const int j = i % n; float w1 = weight[j * 3 + 0]; float w2 = weight[j * 3 + 1]; float w3 = weight[j * 3 + 2]; int i1 = idx[j * 3 + 0]; int i2 = idx[j * 3 + 1]; int i3 = idx[j * 3 + 2]; atomicAdd(grad_points + l * m + i1, grad_out[i] * w1); atomicAdd(grad_points + l * m + i2, grad_out[i] * w2); atomicAdd(grad_points + l * m + i3, grad_out[i] * w3); } } void three_interpolate_grad_kernel_wrapper(int b, int c, int n, int m, const float *grad_out, const int *idx, const float *weight, float *grad_points) { cudaStream_t stream = at::cuda::getCurrentCUDAStream(); three_interpolate_grad_kernel<<>>( b, c, n, m, grad_out, idx, weight, grad_points); CUDA_CHECK_ERRORS(); } ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/sampling.cpp ================================================ #include "sampling.h" #include "utils.h" void gather_points_kernel_wrapper(int b, int c, int n, int npoints, const float *points, const int *idx, float *out); void gather_points_grad_kernel_wrapper(int b, int c, int n, int npoints, const float *grad_out, const int *idx, float *grad_points); void furthest_point_sampling_kernel_wrapper(int b, int n, int m, const float *dataset, float *temp, int *idxs); at::Tensor gather_points(at::Tensor points, at::Tensor idx) { CHECK_CONTIGUOUS(points); CHECK_CONTIGUOUS(idx); CHECK_IS_FLOAT(points); CHECK_IS_INT(idx); if (points.is_cuda()) { CHECK_CUDA(idx); } at::Tensor output = torch::zeros({points.size(0), points.size(1), idx.size(1)}, at::device(points.device()).dtype(at::ScalarType::Float)); if (points.is_cuda()) { gather_points_kernel_wrapper(points.size(0), points.size(1), points.size(2), idx.size(1), points.data_ptr(), idx.data_ptr(), output.data_ptr()); } else { AT_ASSERT(false, "CPU not supported"); } return output; } at::Tensor gather_points_grad(at::Tensor grad_out, at::Tensor idx, const int n) { CHECK_CONTIGUOUS(grad_out); CHECK_CONTIGUOUS(idx); CHECK_IS_FLOAT(grad_out); CHECK_IS_INT(idx); if (grad_out.is_cuda()) { CHECK_CUDA(idx); } at::Tensor output = torch::zeros({grad_out.size(0), grad_out.size(1), n}, at::device(grad_out.device()).dtype(at::ScalarType::Float)); if (grad_out.is_cuda()) { gather_points_grad_kernel_wrapper(grad_out.size(0), grad_out.size(1), n, idx.size(1), grad_out.data_ptr(), idx.data_ptr(), output.data_ptr()); } else { AT_ASSERT(false, "CPU not supported"); } return output; } at::Tensor furthest_point_sampling(at::Tensor points, const int nsamples) { CHECK_CONTIGUOUS(points); CHECK_IS_FLOAT(points); at::Tensor output = torch::zeros({points.size(0), nsamples}, at::device(points.device()).dtype(at::ScalarType::Int)); at::Tensor tmp = torch::full({points.size(0), points.size(1)}, 1e10, at::device(points.device()).dtype(at::ScalarType::Float)); if (points.is_cuda()) { furthest_point_sampling_kernel_wrapper( points.size(0), points.size(1), nsamples, points.data_ptr(), tmp.data_ptr(), output.data_ptr()); } else { AT_ASSERT(false, "CPU not supported"); } return output; } ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/_ext-src/src/sampling_gpu.cu ================================================ #include #include #include "cuda_utils.h" // input: points(b, c, n) idx(b, m) // output: out(b, c, m) __global__ void gather_points_kernel(int b, int c, int n, int m, const float *__restrict__ points, const int *__restrict__ idx, float *__restrict__ out) { for (int i = blockIdx.x; i < b; i += gridDim.x) { for (int l = blockIdx.y; l < c; l += gridDim.y) { for (int j = threadIdx.x; j < m; j += blockDim.x) { int a = idx[i * m + j]; out[(i * c + l) * m + j] = points[(i * c + l) * n + a]; } } } } void gather_points_kernel_wrapper(int b, int c, int n, int npoints, const float *points, const int *idx, float *out) { gather_points_kernel<<>>(b, c, n, npoints, points, idx, out); CUDA_CHECK_ERRORS(); } // input: grad_out(b, c, m) idx(b, m) // output: grad_points(b, c, n) __global__ void gather_points_grad_kernel(int b, int c, int n, int m, const float *__restrict__ grad_out, const int *__restrict__ idx, float *__restrict__ grad_points) { for (int i = blockIdx.x; i < b; i += gridDim.x) { for (int l = blockIdx.y; l < c; l += gridDim.y) { for (int j = threadIdx.x; j < m; j += blockDim.x) { int a = idx[i * m + j]; atomicAdd(grad_points + (i * c + l) * n + a, grad_out[(i * c + l) * m + j]); } } } } void gather_points_grad_kernel_wrapper(int b, int c, int n, int npoints, const float *grad_out, const int *idx, float *grad_points) { gather_points_grad_kernel<<>>( b, c, n, npoints, grad_out, idx, grad_points); CUDA_CHECK_ERRORS(); } __device__ void __update(float *__restrict__ dists, int *__restrict__ dists_i, int idx1, int idx2) { const float v1 = dists[idx1], v2 = dists[idx2]; const int i1 = dists_i[idx1], i2 = dists_i[idx2]; dists[idx1] = max(v1, v2); dists_i[idx1] = v2 > v1 ? i2 : i1; } // Input dataset: (b, n, 3), tmp: (b, n) // Ouput idxs (b, m) template __global__ void furthest_point_sampling_kernel( int b, int n, int m, const float *__restrict__ dataset, float *__restrict__ temp, int *__restrict__ idxs) { if (m <= 0) return; __shared__ float dists[block_size]; __shared__ int dists_i[block_size]; int batch_index = blockIdx.x; dataset += batch_index * n * 3; temp += batch_index * n; idxs += batch_index * m; int tid = threadIdx.x; const int stride = block_size; int old = 0; if (threadIdx.x == 0) idxs[0] = old; __syncthreads(); for (int j = 1; j < m; j++) { int besti = 0; float best = -1; float x1 = dataset[old * 3 + 0]; float y1 = dataset[old * 3 + 1]; float z1 = dataset[old * 3 + 2]; for (int k = tid; k < n; k += stride) { float x2, y2, z2; x2 = dataset[k * 3 + 0]; y2 = dataset[k * 3 + 1]; z2 = dataset[k * 3 + 2]; float mag = (x2 * x2) + (y2 * y2) + (z2 * z2); if (mag <= 1e-3) continue; float d = (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1); float d2 = min(d, temp[k]); temp[k] = d2; besti = d2 > best ? k : besti; best = d2 > best ? d2 : best; } dists[tid] = best; dists_i[tid] = besti; __syncthreads(); if (block_size >= 512) { if (tid < 256) { __update(dists, dists_i, tid, tid + 256); } __syncthreads(); } if (block_size >= 256) { if (tid < 128) { __update(dists, dists_i, tid, tid + 128); } __syncthreads(); } if (block_size >= 128) { if (tid < 64) { __update(dists, dists_i, tid, tid + 64); } __syncthreads(); } if (block_size >= 64) { if (tid < 32) { __update(dists, dists_i, tid, tid + 32); } __syncthreads(); } if (block_size >= 32) { if (tid < 16) { __update(dists, dists_i, tid, tid + 16); } __syncthreads(); } if (block_size >= 16) { if (tid < 8) { __update(dists, dists_i, tid, tid + 8); } __syncthreads(); } if (block_size >= 8) { if (tid < 4) { __update(dists, dists_i, tid, tid + 4); } __syncthreads(); } if (block_size >= 4) { if (tid < 2) { __update(dists, dists_i, tid, tid + 2); } __syncthreads(); } if (block_size >= 2) { if (tid < 1) { __update(dists, dists_i, tid, tid + 1); } __syncthreads(); } old = dists_i[0]; if (tid == 0) idxs[j] = old; } } void furthest_point_sampling_kernel_wrapper(int b, int n, int m, const float *dataset, float *temp, int *idxs) { unsigned int n_threads = opt_n_threads(n); cudaStream_t stream = at::cuda::getCurrentCUDAStream(); switch (n_threads) { case 512: furthest_point_sampling_kernel<512> <<>>(b, n, m, dataset, temp, idxs); break; case 256: furthest_point_sampling_kernel<256> <<>>(b, n, m, dataset, temp, idxs); break; case 128: furthest_point_sampling_kernel<128> <<>>(b, n, m, dataset, temp, idxs); break; case 64: furthest_point_sampling_kernel<64> <<>>(b, n, m, dataset, temp, idxs); break; case 32: furthest_point_sampling_kernel<32> <<>>(b, n, m, dataset, temp, idxs); break; case 16: furthest_point_sampling_kernel<16> <<>>(b, n, m, dataset, temp, idxs); break; case 8: furthest_point_sampling_kernel<8> <<>>(b, n, m, dataset, temp, idxs); break; case 4: furthest_point_sampling_kernel<4> <<>>(b, n, m, dataset, temp, idxs); break; case 2: furthest_point_sampling_kernel<2> <<>>(b, n, m, dataset, temp, idxs); break; case 1: furthest_point_sampling_kernel<1> <<>>(b, n, m, dataset, temp, idxs); break; default: furthest_point_sampling_kernel<512> <<>>(b, n, m, dataset, temp, idxs); } CUDA_CHECK_ERRORS(); } ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/_version.py ================================================ __version__ = "3.0.0" ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/pointnet2_modules.py ================================================ from typing import List, Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F from pointnet2_ops import pointnet2_utils def build_shared_mlp(mlp_spec: List[int], bn: bool = True): layers = [] for i in range(1, len(mlp_spec)): layers.append( nn.Conv2d(mlp_spec[i - 1], mlp_spec[i], kernel_size=1, bias=not bn) ) if bn: layers.append(nn.BatchNorm2d(mlp_spec[i])) layers.append(nn.ReLU(True)) return nn.Sequential(*layers) class _PointnetSAModuleBase(nn.Module): def __init__(self): super(_PointnetSAModuleBase, self).__init__() self.npoint = None self.groupers = None self.mlps = None def forward( self, xyz: torch.Tensor, features: Optional[torch.Tensor] ) -> Tuple[torch.Tensor, torch.Tensor]: r""" Parameters ---------- xyz : torch.Tensor (B, N, 3) tensor of the xyz coordinates of the features features : torch.Tensor (B, C, N) tensor of the descriptors of the the features Returns ------- new_xyz : torch.Tensor (B, npoint, 3) tensor of the new features' xyz new_features : torch.Tensor (B, \sum_k(mlps[k][-1]), npoint) tensor of the new_features descriptors """ new_features_list = [] xyz_flipped = xyz.transpose(1, 2).contiguous() new_xyz = ( pointnet2_utils.gather_operation( xyz_flipped, pointnet2_utils.furthest_point_sample(xyz, self.npoint) ) .transpose(1, 2) .contiguous() if self.npoint is not None else None ) for i in range(len(self.groupers)): new_features = self.groupers[i]( xyz, new_xyz, features ) # (B, C, npoint, nsample) new_features = self.mlps[i](new_features) # (B, mlp[-1], npoint, nsample) new_features = F.max_pool2d( new_features, kernel_size=[1, new_features.size(3)] ) # (B, mlp[-1], npoint, 1) new_features = new_features.squeeze(-1) # (B, mlp[-1], npoint) new_features_list.append(new_features) return new_xyz, torch.cat(new_features_list, dim=1) class PointnetSAModuleMSG(_PointnetSAModuleBase): r"""Pointnet set abstrction layer with multiscale grouping Parameters ---------- npoint : int Number of features radii : list of float32 list of radii to group with nsamples : list of int32 Number of samples in each ball query mlps : list of list of int32 Spec of the pointnet before the global max_pool for each scale bn : bool Use batchnorm """ def __init__(self, npoint, radii, nsamples, mlps, bn=True, use_xyz=True): # type: (PointnetSAModuleMSG, int, List[float], List[int], List[List[int]], bool, bool) -> None super(PointnetSAModuleMSG, self).__init__() assert len(radii) == len(nsamples) == len(mlps) self.npoint = npoint self.groupers = nn.ModuleList() self.mlps = nn.ModuleList() for i in range(len(radii)): radius = radii[i] nsample = nsamples[i] self.groupers.append( pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz) if npoint is not None else pointnet2_utils.GroupAll(use_xyz) ) mlp_spec = mlps[i] if use_xyz: mlp_spec[0] += 3 self.mlps.append(build_shared_mlp(mlp_spec, bn)) class PointnetSAModule(PointnetSAModuleMSG): r"""Pointnet set abstrction layer Parameters ---------- npoint : int Number of features radius : float Radius of ball nsample : int Number of samples in the ball query mlp : list Spec of the pointnet before the global max_pool bn : bool Use batchnorm """ def __init__( self, mlp, npoint=None, radius=None, nsample=None, bn=True, use_xyz=True ): # type: (PointnetSAModule, List[int], int, float, int, bool, bool) -> None super(PointnetSAModule, self).__init__( mlps=[mlp], npoint=npoint, radii=[radius], nsamples=[nsample], bn=bn, use_xyz=use_xyz, ) class PointnetFPModule(nn.Module): r"""Propigates the features of one set to another Parameters ---------- mlp : list Pointnet module parameters bn : bool Use batchnorm """ def __init__(self, mlp, bn=True): # type: (PointnetFPModule, List[int], bool) -> None super(PointnetFPModule, self).__init__() self.mlp = build_shared_mlp(mlp, bn=bn) def forward(self, unknown, known, unknow_feats, known_feats): # type: (PointnetFPModule, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor) -> torch.Tensor r""" Parameters ---------- unknown : torch.Tensor (B, n, 3) tensor of the xyz positions of the unknown features known : torch.Tensor (B, m, 3) tensor of the xyz positions of the known features unknow_feats : torch.Tensor (B, C1, n) tensor of the features to be propigated to known_feats : torch.Tensor (B, C2, m) tensor of features to be propigated Returns ------- new_features : torch.Tensor (B, mlp[-1], n) tensor of the features of the unknown features """ if known is not None: dist, idx = pointnet2_utils.three_nn(unknown, known) dist_recip = 1.0 / (dist + 1e-8) norm = torch.sum(dist_recip, dim=2, keepdim=True) weight = dist_recip / norm interpolated_feats = pointnet2_utils.three_interpolate( known_feats, idx, weight ) else: interpolated_feats = known_feats.expand( *(known_feats.size()[0:2] + [unknown.size(1)]) ) if unknow_feats is not None: new_features = torch.cat( [interpolated_feats, unknow_feats], dim=1 ) # (B, C2 + C1, n) else: new_features = interpolated_feats new_features = new_features.unsqueeze(-1) new_features = self.mlp(new_features) return new_features.squeeze(-1) ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/pointnet2_ops/pointnet2_utils.py ================================================ import torch import torch.nn as nn import warnings from torch.autograd import Function from typing import * try: import pointnet2_ops._ext as _ext except ImportError: from torch.utils.cpp_extension import load import glob import os.path as osp import os warnings.warn("Unable to load pointnet2_ops cpp extension. JIT Compiling.") _ext_src_root = osp.join(osp.dirname(__file__), "_ext-src") _ext_sources = glob.glob(osp.join(_ext_src_root, "src", "*.cpp")) + glob.glob( osp.join(_ext_src_root, "src", "*.cu") ) _ext_headers = glob.glob(osp.join(_ext_src_root, "include", "*")) os.environ["TORCH_CUDA_ARCH_LIST"] = "3.7+PTX;5.0;6.0;6.1;6.2;7.0;7.5" _ext = load( "_ext", sources=_ext_sources, extra_include_paths=[osp.join(_ext_src_root, "include")], extra_cflags=["-O3"], extra_cuda_cflags=["-O3", "-Xfatbin", "-compress-all"], with_cuda=True, ) class FurthestPointSampling(Function): @staticmethod def forward(ctx, xyz, npoint): # type: (Any, torch.Tensor, int) -> torch.Tensor r""" Uses iterative furthest point sampling to select a set of npoint features that have the largest minimum distance Parameters ---------- xyz : torch.Tensor (B, N, 3) tensor where N > npoint npoint : int32 number of features in the sampled set Returns ------- torch.Tensor (B, npoint) tensor containing the set """ out = _ext.furthest_point_sampling(xyz, npoint) ctx.mark_non_differentiable(out) return out @staticmethod def backward(ctx, grad_out): return () furthest_point_sample = FurthestPointSampling.apply class GatherOperation(Function): @staticmethod def forward(ctx, features, idx): # type: (Any, torch.Tensor, torch.Tensor) -> torch.Tensor r""" Parameters ---------- features : torch.Tensor (B, C, N) tensor idx : torch.Tensor (B, npoint) tensor of the features to gather Returns ------- torch.Tensor (B, C, npoint) tensor """ ctx.save_for_backward(idx, features) return _ext.gather_points(features, idx) @staticmethod def backward(ctx, grad_out): idx, features = ctx.saved_tensors N = features.size(2) grad_features = _ext.gather_points_grad(grad_out.contiguous(), idx, N) return grad_features, None gather_operation = GatherOperation.apply class ThreeNN(Function): @staticmethod def forward(ctx, unknown, known): # type: (Any, torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor] r""" Find the three nearest neighbors of unknown in known Parameters ---------- unknown : torch.Tensor (B, n, 3) tensor of known features known : torch.Tensor (B, m, 3) tensor of unknown features Returns ------- dist : torch.Tensor (B, n, 3) l2 distance to the three nearest neighbors idx : torch.Tensor (B, n, 3) index of 3 nearest neighbors """ dist2, idx = _ext.three_nn(unknown, known) dist = torch.sqrt(dist2) ctx.mark_non_differentiable(dist, idx) return dist, idx @staticmethod def backward(ctx, grad_dist, grad_idx): return () three_nn = ThreeNN.apply class ThreeInterpolate(Function): @staticmethod def forward(ctx, features, idx, weight): # type(Any, torch.Tensor, torch.Tensor, torch.Tensor) -> Torch.Tensor r""" Performs weight linear interpolation on 3 features Parameters ---------- features : torch.Tensor (B, c, m) Features descriptors to be interpolated from idx : torch.Tensor (B, n, 3) three nearest neighbors of the target features in features weight : torch.Tensor (B, n, 3) weights Returns ------- torch.Tensor (B, c, n) tensor of the interpolated features """ ctx.save_for_backward(idx, weight, features) return _ext.three_interpolate(features, idx, weight) @staticmethod def backward(ctx, grad_out): # type: (Any, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor] r""" Parameters ---------- grad_out : torch.Tensor (B, c, n) tensor with gradients of ouputs Returns ------- grad_features : torch.Tensor (B, c, m) tensor with gradients of features None None """ idx, weight, features = ctx.saved_tensors m = features.size(2) grad_features = _ext.three_interpolate_grad( grad_out.contiguous(), idx, weight, m ) return grad_features, torch.zeros_like(idx), torch.zeros_like(weight) three_interpolate = ThreeInterpolate.apply class GroupingOperation(Function): @staticmethod def forward(ctx, features, idx): # type: (Any, torch.Tensor, torch.Tensor) -> torch.Tensor r""" Parameters ---------- features : torch.Tensor (B, C, N) tensor of features to group idx : torch.Tensor (B, npoint, nsample) tensor containing the indicies of features to group with Returns ------- torch.Tensor (B, C, npoint, nsample) tensor """ ctx.save_for_backward(idx, features) return _ext.group_points(features, idx) @staticmethod def backward(ctx, grad_out): # type: (Any, torch.tensor) -> Tuple[torch.Tensor, torch.Tensor] r""" Parameters ---------- grad_out : torch.Tensor (B, C, npoint, nsample) tensor of the gradients of the output from forward Returns ------- torch.Tensor (B, C, N) gradient of the features None """ idx, features = ctx.saved_tensors N = features.size(2) grad_features = _ext.group_points_grad(grad_out.contiguous(), idx, N) return grad_features, torch.zeros_like(idx) grouping_operation = GroupingOperation.apply class BallQuery(Function): @staticmethod def forward(ctx, radius, nsample, xyz, new_xyz): # type: (Any, float, int, torch.Tensor, torch.Tensor) -> torch.Tensor r""" Parameters ---------- radius : float radius of the balls nsample : int maximum number of features in the balls xyz : torch.Tensor (B, N, 3) xyz coordinates of the features new_xyz : torch.Tensor (B, npoint, 3) centers of the ball query Returns ------- torch.Tensor (B, npoint, nsample) tensor with the indicies of the features that form the query balls """ output = _ext.ball_query(new_xyz, xyz, radius, nsample) ctx.mark_non_differentiable(output) return output @staticmethod def backward(ctx, grad_out): return () ball_query = BallQuery.apply class QueryAndGroup(nn.Module): r""" Groups with a ball query of radius Parameters --------- radius : float32 Radius of ball nsample : int32 Maximum number of features to gather in the ball """ def __init__(self, radius, nsample, use_xyz=True): # type: (QueryAndGroup, float, int, bool) -> None super(QueryAndGroup, self).__init__() self.radius, self.nsample, self.use_xyz = radius, nsample, use_xyz def forward(self, xyz, new_xyz, features=None): # type: (QueryAndGroup, torch.Tensor. torch.Tensor, torch.Tensor) -> Tuple[Torch.Tensor] r""" Parameters ---------- xyz : torch.Tensor xyz coordinates of the features (B, N, 3) new_xyz : torch.Tensor centriods (B, npoint, 3) features : torch.Tensor Descriptors of the features (B, C, N) Returns ------- new_features : torch.Tensor (B, 3 + C, npoint, nsample) tensor """ idx = ball_query(self.radius, self.nsample, xyz, new_xyz) xyz_trans = xyz.transpose(1, 2).contiguous() grouped_xyz = grouping_operation(xyz_trans, idx) # (B, 3, npoint, nsample) grouped_xyz -= new_xyz.transpose(1, 2).unsqueeze(-1) if features is not None: grouped_features = grouping_operation(features, idx) if self.use_xyz: new_features = torch.cat( [grouped_xyz, grouped_features], dim=1 ) # (B, C + 3, npoint, nsample) else: new_features = grouped_features else: assert ( self.use_xyz ), "Cannot have not features and not use xyz as a feature!" new_features = grouped_xyz return new_features class GroupAll(nn.Module): r""" Groups all features Parameters --------- """ def __init__(self, use_xyz=True): # type: (GroupAll, bool) -> None super(GroupAll, self).__init__() self.use_xyz = use_xyz def forward(self, xyz, new_xyz, features=None): # type: (GroupAll, torch.Tensor, torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor] r""" Parameters ---------- xyz : torch.Tensor xyz coordinates of the features (B, N, 3) new_xyz : torch.Tensor Ignored features : torch.Tensor Descriptors of the features (B, C, N) Returns ------- new_features : torch.Tensor (B, C + 3, 1, N) tensor """ grouped_xyz = xyz.transpose(1, 2).unsqueeze(2) if features is not None: grouped_features = features.unsqueeze(2) if self.use_xyz: new_features = torch.cat( [grouped_xyz, grouped_features], dim=1 ) # (B, 3 + C, 1, N) else: new_features = grouped_features else: new_features = grouped_xyz return new_features ================================================ FILE: PCT_Pytorch/pointnet2_ops_lib/setup.py ================================================ import glob import os import os.path as osp from setuptools import find_packages, setup from torch.utils.cpp_extension import BuildExtension, CUDAExtension this_dir = osp.dirname(osp.abspath(__file__)) _ext_src_root = osp.join("pointnet2_ops", "_ext-src") _ext_sources = glob.glob(osp.join(_ext_src_root, "src", "*.cpp")) + glob.glob( osp.join(_ext_src_root, "src", "*.cu") ) _ext_headers = glob.glob(osp.join(_ext_src_root, "include", "*")) requirements = ["torch>=1.4"] exec(open(osp.join("pointnet2_ops", "_version.py")).read()) os.environ["TORCH_CUDA_ARCH_LIST"] = "3.7+PTX;5.0;6.0;6.1;6.2;7.0;7.5" setup( name="pointnet2_ops", version=__version__, author="Erik Wijmans", packages=find_packages(), install_requires=requirements, ext_modules=[ CUDAExtension( name="pointnet2_ops._ext", sources=_ext_sources, extra_compile_args={ "cxx": ["-O3"], "nvcc": ["-O3", "-Xfatbin", "-compress-all"], }, include_dirs=[osp.join(this_dir, _ext_src_root, "include")], ) ], cmdclass={"build_ext": BuildExtension}, include_package_data=True, ) ================================================ FILE: PCT_Pytorch/test.sh ================================================ python main.py --exp_name=test --num_points=1024 --use_sgd=True --eval=True --model_path=checkpoints/best/models/model.t7 --test_batch_size 8 ================================================ FILE: PCT_Pytorch/train.sh ================================================ CUDA_VISIBLE_DEVICES=0 python3.7 main.py --exp_name=train --num_points=1024 --use_sgd=True --batch_size 32 --epochs 250 --lr 0.0001 ================================================ FILE: PCT_Pytorch/util.py ================================================ import torch import torch.nn.functional as F from pointnet2_ops import pointnet2_utils def cal_loss(pred, gold, smoothing=True): ''' Calculate cross entropy loss, apply label smoothing if needed. ''' gold = gold.contiguous().view(-1) if smoothing: eps = 0.2 n_class = pred.size(1) one_hot = torch.zeros_like(pred).scatter(1, gold.view(-1, 1), 1) one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1) log_prb = F.log_softmax(pred, dim=1) loss = -(one_hot * log_prb).sum(dim=1).mean() else: loss = F.cross_entropy(pred, gold, reduction='mean') return loss class IOStream(): def __init__(self, path): self.f = open(path, 'a') def cprint(self, text): print(text) self.f.write(text+'\n') self.f.flush() def close(self): self.f.close() def square_distance(src, dst): """ Calculate Euclid distance between each two points. src^T * dst = xn * xm + yn * ym + zn * zm； sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn; sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm; dist = (xn - xm)^2 + (yn - ym)^2 + (zn - zm)^2 = sum(src**2,dim=-1)+sum(dst**2,dim=-1)-2*src^T*dst Input: src: source points, [B, N, C] dst: target points, [B, M, C] Output: dist: per-point square distance, [B, N, M] """ B, N, _ = src.shape _, M, _ = dst.shape dist = -2 * torch.matmul(src, dst.permute(0, 2, 1)) dist += torch.sum(src ** 2, -1).view(B, N, 1) dist += torch.sum(dst ** 2, -1).view(B, 1, M) return dist def index_points(points, idx): """ Input: points: input points data, [B, N, C] idx: sample index data, [B, S] Return: new_points:, indexed points data, [B, S, C] """ device = points.device B = points.shape[0] view_shape = list(idx.shape) view_shape[1:] = [1] * (len(view_shape) - 1) repeat_shape = list(idx.shape) repeat_shape[0] = 1 batch_indices = torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape) new_points = points[batch_indices, idx, :] return new_points def query_ball_point(radius, nsample, xyz, new_xyz): """ Input: radius: local region radius nsample: max sample number in local region xyz: all points, [B, N, 3] new_xyz: query points, [B, S, 3] Return: group_idx: grouped points index, [B, S, nsample] """ device = xyz.device B, N, C = xyz.shape _, S, _ = new_xyz.shape group_idx = torch.arange(N, dtype=torch.long).to(device).view(1, 1, N).repeat([B, S, 1]) sqrdists = square_distance(new_xyz, xyz) group_idx[sqrdists > radius ** 2] = N group_idx = group_idx.sort(dim=-1)[0][:, :, :nsample] group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, nsample]) mask = group_idx == N group_idx[mask] = group_first[mask] return group_idx def knn_point(nsample, xyz, new_xyz): """ Input: nsample: max sample number in local region xyz: all points, [B, N, C] new_xyz: query points, [B, S, C] Return: group_idx: grouped points index, [B, S, nsample] """ sqrdists = square_distance(new_xyz, xyz) _, group_idx = torch.topk(sqrdists, nsample, dim = -1, largest=False, sorted=False) return group_idx def sample_and_group(npoint, radius, nsample, xyz, points): """ Input: npoint: radius: nsample: xyz: input points position data, [B, N, 3] points: input points data, [B, N, D] Return: new_xyz: sampled points position data, [B, npoint, nsample, 3] new_points: sampled points data, [B, npoint, nsample, 3+D] """ B, N, C = xyz.shape S = npoint xyz = xyz.contiguous() fps_idx = pointnet2_utils.furthest_point_sample(xyz, npoint).long() # [B, npoint] new_xyz = index_points(xyz, fps_idx) new_points = index_points(points, fps_idx) # new_xyz = xyz[:] # new_points = points[:] idx = knn_point(nsample, xyz, new_xyz) #idx = query_ball_point(radius, nsample, xyz, new_xyz) grouped_xyz = index_points(xyz, idx) # [B, npoint, nsample, C] grouped_xyz_norm = grouped_xyz - new_xyz.view(B, S, 1, C) grouped_points = index_points(points, idx) grouped_points_norm = grouped_points - new_points.view(B, S, 1, -1) new_points = torch.cat([grouped_points_norm, new_points.view(B, S, 1, -1).repeat(1, 1, nsample, 1)], dim=-1) return new_xyz, new_points ================================================ FILE: README.md ================================================ # Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/benchmarking-robustness-of-3d-point-cloud/3d-point-cloud-classification-on-modelnet40-c)](https://paperswithcode.com/sota/3d-point-cloud-classification-on-modelnet40-c?p=benchmarking-robustness-of-3d-point-cloud) This repo contains the dataset and code for the paper [Benchmarking Robustness of 3D Point Cloud Recognition against Common Corruptions](https://arxiv.org/abs/2201.12296) by Jiachen Sun et al. This codebase is based on [SimpleView](https://github.com/princeton-vl/SimpleView), and we thank the authors for their great contributions. ## ModelNet40-C ![image](https://github.com/jiachens/ModelNet40-C/blob/master/img/1.png) ![image](https://github.com/jiachens/ModelNet40-C/blob/master/img/54.png) More visualizations can be found [here](https://github.com/jiachens/ModelNet40-C/blob/master/img). [Download ModelNet40-C from Google Drive.](https://drive.google.com/drive/folders/10YeQRh92r_WdL-Dnog2zQfFr03UW4qXX?usp=sharing) [Download ModelNet40-C using our provided script.](#download-datasets-including-modelnet40-c-and-pre-trained-models) [Download ModelNet40-C from Zenodo.](https://zenodo.org/record/6017834#.YgNeKu7MK3J) ## ModelNet40-C Leaderboard **Architecture+Data Augmentation Leaderboard**
| **Architecture** | **Data Augmentation** | **Corruption Error Rate (%)** | **Clean Error Rate (%)** | **Checkpoint** | |------------------|---------------|:-------------------------:|:--------------------:|--------------------------------------------------------------------------------------------------| | PCT | PointCutMix-R | 16.3 | 7.2 | [checkpoint](https://drive.google.com/file/d/1OcH0o7V_RhAOj9pSuS39G43VrBWS-1v3/view?usp=sharing) | | PCT | PointCutMix-K | 16.5 | 6.9 | [checkpoint](https://drive.google.com/file/d/1T4KwMkgAqAItHZc-Q96H1qGMPNoObJkJ/view?usp=sharing) | | DGCNN | PointCutMix-R | 17.3 | 6.8 | [checkpoint](https://drive.google.com/file/d/1Z_6D_MmjecDHhY2q-I-aok9nlD9RkAS1/view?usp=sharing) | | PCT | RSMix | 17.3 | 6.9 | [checkpoint](https://drive.google.com/file/d/18BqbMCpdbEGdyQVdMwYPDrff5bmgeF9B/view?usp=sharing) | | DGCNN | PointCutMix-K | 17.3 | 7.4 | [checkpoint](https://drive.google.com/file/d/1rUQApmyEJUpv7JzkhJuwEeZOmmZ9vEDU/view?usp=sharing) | | RSCNN | PointCutMix-R | 17.9 | 7.6 | [checkpoint](https://drive.google.com/file/d/1EggUiFcCgpHOwjgQKRgBxST1utfOAryc/view?usp=sharing) | | DGCNN | RSMix | 18.1 | 7.1 | [checkpoint](https://drive.google.com/file/d/11tNaF-YsJ6hZNm2pY6LX6Ny-ceGkI0Cr/view?usp=sharing) | | PCT | PGD Adv Train | 18.4 | 8.9 | [checkpoint](https://drive.google.com/file/d/1Y7JaW-CLPCcqQQGiuL9BfVKfAkm6MxEA/view?usp=sharing) | | PointNet++ | PointCutMix-R | 19.1 | 7.1 | [checkpoint](https://drive.google.com/file/d/1un_H1oq18MrN604mbR3htBNqdOgnXqwQ/view?usp=sharing) | | PointNet++ | PointMixup | 19.3 | 7.1 | [checkpoint](https://drive.google.com/file/d/1fzFOeJcenn7a4glsfs7IEcSTjovZoAkB/view?usp=sharing) | | PCT | PointMixup | 19.5 | 7.4 | [checkpoint](https://drive.google.com/file/d/1OcBm-PCImcW8h1mb9ZY4CcX2nDN_rB8b/view?usp=sharing) | | SimpleView | PointCutMix-R | 19.7 | 7.9 | [checkpoint](https://drive.google.com/file/d/178LQKtmCeNIbdPXYZXZHRmAQt-YCY_eL/view?usp=sharing) | | RSCNN | PointMixup | 19.8 | 7.2 | [checkpoint](https://drive.google.com/file/d/1FRPU_QTR3vda1CqPWKkREprIZshv4cYk/view?usp=sharing) | | PointNet++ | PointCutMix-K | 20.2 | 6.7 | [checkpoint](https://drive.google.com/file/d/1JLL7ym-fMUS4VFisf-AENB5trYJb_0-J/view?usp=sharing) | We allow users to directly download all pre-trained models with every data augmentation method [here](#download-datasets-including-modelnet40-c-and-pre-trained-models). **Architecture Leaderboard**
| **Architecture** | **Corruption Error Rate (%)** | **Clean Error Rate (%)** | **Checkpoint** | |------------------|:-------------------------:|:--------------------:|--------------------------------------------------------------------------------------------------| | CurveNet | 22.7 | 6.6 | checkpoint | | PointNet++ | 23.6 | 7.0 | [checkpoint](https://drive.google.com/file/d/18_297KJ8slsJq1rGDsvuQ29VICs-EJTa/view?usp=sharing) | | PCT | 25.5 | 7.1 | [checkpoint](https://drive.google.com/file/d/1NFAhupQKn-sBLYW1YpUAf4jdqMpFcV7Z/view?usp=sharing) | | GDANet | 25.6 | 7.5 | checkpoint | | DGCNN | 25.9 | 7.4 | [checkpoint](https://drive.google.com/file/d/1JMCmujJM4J_OyxuZuDN4befFmtG1_p49/view?usp=sharing) | | RSCNN | 26.2 | 7.7 | [checkpoint](https://drive.google.com/file/d/1RKhXKjZvKvZM2the2qqFhnytAX2H634U/view?usp=sharing) | | SimpleView | 27.2 | 6.1 | [checkpoint](https://drive.google.com/file/d/1jscF5p3Q7DHWl-FgGGemQP3CeXITsTyY/view?usp=sharing) | | PointNet | 28.3 | 9.3 | [checkpoint](https://drive.google.com/file/d/1eW26u0nm6HETwDSiCyCEoLLY3WnOVt73/view?usp=sharing) | | PointMLP | 31.9 | 6.3 | checkpoint | | PointMLP-Elite | 32.4 | 7.2 | checkpoint | More models' results coming soon ...... We allow users to directly download all pre-trained models with standard training [here](#download-datasets-including-modelnet40-c-and-pre-trained-models). ## Getting Started First clone the repository. We would refer to the directory containing the code as `ModelNet40-C`. ``` git clone --recurse-submodules git@github.com:jiachens/ModelNet40-C.git ``` #### Requirements The code is tested on Linux OS with Python version **3.7.5**, CUDA version **10.0**, CuDNN version **7.6** and GCC version **5.4**. We recommend using these versions especially for installing [pointnet++ custom CUDA modules](https://github.com/erikwijmans/Pointnet2_PyTorch/tree/22e8cf527b696b63b66f3873d80ae5f93744bdef). [02-23-2022] The updated codes are tested on Python version **3.7.5**, CUDA version **11.4**, CuDNN version **8.2** and GCC version **7.5** with the latest ```torch``` and ```torchvision``` libs, but we still suggest the original setup in case of any unstable bugs. #### Install Libraries We recommend you first install [Anaconda](https://anaconda.org/) and create a virtual environment. ``` conda create --name modelnetc python=3.7.5 ``` Activate the virtual environment and install the libraries. Make sure you are in `ModelNet40-C`. ``` conda activate modelnetc pip install -r requirements.txt conda install sed # for downloading data and pretrained models ``` For PointNet++, we need to install custom CUDA modules. Make sure you have access to a GPU during this step. You might need to set the appropriate `TORCH_CUDA_ARCH_LIST` environment variable depending on your GPU model. The following command should work for most cases `export TORCH_CUDA_ARCH_LIST="6.0;6.1;6.2;7.0;7.5"`. However, if the install fails, check if `TORCH_CUDA_ARCH_LIST` is correctly set. More details could be found [here](https://en.wikipedia.org/wiki/CUDA#GPUs_supported). Third-party modules `pointnet2_pyt`, `PCT_Pytorch`, `emd`, and `PyGeM` can be installed by the following script. ``` ./setup.sh ``` #### Download Datasets Including ModelNet40-C and Pre-trained Models Make sure you are in `ModelNet40-C`. `download.sh` script can be used for downloading all the data and the pretrained models. It also places them at the correct locations. To download ModelNet40 execute the following command. This will download the ModelNet40 point cloud dataset released with pointnet++ as well as the validation splits used in our work. ``` ./download.sh modelnet40 ``` To generate the ModelNet40-C dataset, please run: ``` python data/process.py python data/generate_c.py ``` NOTE that the generation needs a monitor connected since Open3D library does not support background rendering. We also allow users to download ModelNet40-C directly. Please fill this [Google form](https://docs.google.com/forms/d/e/1FAIpQLSdrzt8EtQdjGMlwIwWAzb39KzzVzijpK6-sPEaps07MjQwGGQ/viewform?usp=sf_link) while downloading our dataset. ``` ./download.sh modelnet40_c ``` To download the pretrained models with standard training recipe, execute the following command. ``` ./download.sh cor_exp ``` To download the pretrained models using different data augmentation strategies, execute the following command. ``` ./download.sh runs ``` #### New Features \[02-23-2022\] - We include PointMLP-Elite and GDANet in our benchmark \[02-18-2022\] - We include CurveNet and PointMLP in our benchmark \[01-28-2022\] - We include Point Cloud Transformer (PCT) in our benchmark - `ModelNet40-C/configs` contains config files to enable different data augmentations and test-time adaptation methods - `ModelNet40-C/aug_utils.py` contains the data augmentation codes in our paper - `ModelNet40-C/third_party` contains the test-time adaptation used in our paper #### Code Organization In Originial SimpleView - `ModelNet40-C/models`: Code for various models in PyTorch. - `ModelNet40-C/configs`: Configuration files for various models. - `ModelNet40-C/main.py`: Training and testing any models. - `ModelNet40-C/configs.py`: Hyperparameters for different models and dataloader. - `ModelNet40-C/dataloader.py`: Code for different variants of the dataloader. - `ModelNet40-C/*_utils.py`: Code for various utility functions. ## Running Experiments #### Training and Config files To train or test any model, we use the `main.py` script. The format for running this script is as follows. ``` python main.py --exp-config ``` The config files are named as `_<_extra>_run_.yaml` (` ∈ [dgcnn, pointnet2, rscnn]`; ` ∈ [dgcnn, pointnet2, rscnn, pointnet, simpleview]` ). For example, the config file to run an experiment for PointNet++ in DGCNN protocol with seed 1 `dgcnn_pointnet2_run_1.yaml`. To run a new experiment with a different seed, you need to change the `SEED` parameter in the config file. All of our experiments are done based on seed 1. We additionally leverage PointCutMix: `configs/cutmix`, PointMixup: `configs/mixup`, RSMix: `configs/rsmix`, and PGD-based adversarial training `configs/pgd` as the training-time config files. For example, to train PCT with PointCutMix-R, please use the following command: ``` python main.py --exp-config configs/cutmix/pct_r.yaml ``` #### Evaluate a pretrained model We provide pretrained models. They can be downloaded using the `./download.sh cor_exp` and `./download.sh runs` commands and are stored in the `ModelNet40-C/runs` (for data augmentation recipes) and `ModelNet40-C/cor_exp` (for standard trained models) folders. To test a pretrained model, the command is of the following format. Additionally, we provide test-time config files in `configs/bn` and `configs/tent` for BN and TENT in our paper with the following commands: ``` python main.py --entry test --model-path //.pth --exp-config configs/.yaml ``` We list all the evaluation commands in the `eval_cor.sh`, `eval_og.sh`, `eval_tent_cutmix.sh` scripts. Note that in `eval_cor.sh` it is expected that pgd with PointNet++, RSCNN, and SimpleView do not have outputs since they do not fit the adversarial training framework. We have mentioned this in our paper. ## Citation Please cite our paper and SimpleView if you use our benchmark and analysis results. Thank you! ``` @article{sun2022benchmarking, title={Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions}, author={Jiachen Sun and Qingzhao Zhang and Bhavya Kailkhura and Zhiding Yu and Chaowei Xiao and Z. Morley Mao}, journal={arXiv preprint arXiv:2201.12296}, year={2022} } ``` ``` @article{goyal2021revisiting, title={Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline}, author={Goyal, Ankit and Law, Hei and Liu, Bowei and Newell, Alejandro and Deng, Jia}, journal={International Conference on Machine Learning}, year={2021} } ``` ## References [1] [Zhang, Jinlai, et al. "PointCutMix: Regularization Strategy for Point Cloud Classification." arXiv preprint arXiv:2101.01461 (2021).](https://arxiv.org/pdf/2101.01461.pdf) [2] [Chen, Yunlu, et al. "Pointmixup: Augmentation for point clouds." Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part III 16. Springer International Publishing, 2020.](https://link.springer.com/content/pdf/10.1007/978-3-030-58580-8_20.pdf) [3] [Lee, Dogyoon, et al. "Regularization Strategy for Point Cloud via Rigidly Mixed Sample." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.](https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_Regularization_Strategy_for_Point_Cloud_via_Rigidly_Mixed_Sample_CVPR_2021_paper.pdf) [4] [Sun, Jiachen, et al. "Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions." Advances in Neural Information Processing Systems 34 (2021).](https://proceedings.neurips.cc/paper/2021/file/82cadb0649a3af4968404c9f6031b233-Paper.pdf) [5] [Schneider, Steffen, et al. "Improving robustness against common corruptions by covariate shift adaptation." arXiv preprint arXiv:2006.16971 (2020).](https://arxiv.org/pdf/2006.16971.pdf) [6] [Wang, Dequan, et al. "Tent: Fully test-time adaptation by entropy minimization." arXiv preprint arXiv:2006.10726 (2020).](https://arxiv.org/pdf/2006.10726.pdf) [7] [Qi, Charles R., et al. "Pointnet: Deep learning on point sets for 3d classification and segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.](https://openaccess.thecvf.com/content_cvpr_2017/papers/Qi_PointNet_Deep_Learning_CVPR_2017_paper.pdf) [8] [Qi, Charles R., et al. "Pointnet++: Deep hierarchical feature learning on point sets in a metric space." arXiv preprint arXiv:1706.02413 (2017).](https://arxiv.org/pdf/1706.02413.pdf) [9] [Liu, Yongcheng, et al. "Relation-shape convolutional neural network for point cloud analysis." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.](https://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Relation-Shape_Convolutional_Neural_Network_for_Point_Cloud_Analysis_CVPR_2019_paper.pdf) [10] [Wang, Yue, et al. "Dynamic graph cnn for learning on point clouds." Acm Transactions On Graphics (tog) 38.5 (2019): 1-12.](https://dl.acm.org/doi/pdf/10.1145/3326362) [11] [Goyal, Ankit, et al. "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline." arXiv preprint arXiv:2106.05304 (2021).](https://arxiv.org/pdf/2106.05304.pdf) [12] [Guo, Meng-Hao, et al. "Pct: Point cloud transformer." Computational Visual Media 7.2 (2021): 187-199.](https://link.springer.com/content/pdf/10.1007/s41095-021-0229-5.pdf) [13] [Xiang, Tiange, et al. "Walk in the cloud: Learning curves for point clouds shape analysis." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021.](https://arxiv.org/pdf/2105.01288.pdf) [14] [Ma, Xu, et al. "Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework." arXiv preprint arXiv:2202.07123 (2022).](https://arxiv.org/pdf/2202.07123.pdf) [15] [Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud.](https://arxiv.org/abs/2012.10921) ================================================ FILE: all_utils.py ================================================ import tensorboardX import pdb import sys from collections import MutableMapping, Hashable import csv import os import torch import torch.nn.functional as F import numpy as np from progressbar import ProgressBar import sys # Additional information that might be necessary to get the model DATASET_NUM_CLASS = { 'modelnet40': 40, 'modelnet40_c': 40, 'modelnet40_rscnn': 40, 'modelnet40_pn2': 40, 'modelnet40_dgcnn': 40, } class TensorboardManager: def __init__(self, path): self.writer = tensorboardX.SummaryWriter(path) def update(self, split, step, vals): for k, v in vals.items(): self.writer.add_scalar('%s_%s' % (split, k), v, step) def close(self): self.writer.flush() self.writer.close() class TrackTrain: def __init__(self, early_stop_patience): self.early_stop_patience = early_stop_patience self.counter = -1 self.best_epoch_val = -1 self.best_epoch_train = -1 self.best_epoch_test = -1 self.best_val = float("-inf") self.best_test = float("-inf") self.best_train = float("-inf") self.test_best_val = float("-inf") def record_epoch(self, epoch_id, train_metric, val_metric, test_metric): assert epoch_id == (self.counter + 1) self.counter += 1 if val_metric >= self.best_val: self.best_val = val_metric self.best_epoch_val = epoch_id self.test_best_val = test_metric if test_metric >= self.best_test: self.best_test = test_metric self.best_epoch_test = epoch_id if train_metric >= self.best_train: self.best_train = train_metric self.best_epoch_train = epoch_id def save_model(self, epoch_id, split): """ Whether to save the current model or not :param epoch_id: :param split: :return: """ assert epoch_id == self.counter if split == 'val': if self.best_epoch_val == epoch_id: _save_model = True else: _save_model = False elif split == 'test': if self.best_epoch_test == epoch_id: _save_model = True else: _save_model = False elif split == 'train': if self.best_epoch_train == epoch_id: _save_model = True else: _save_model = False else: assert False return _save_model def early_stop(self, epoch_id): assert epoch_id == self.counter if (epoch_id - self.best_epoch_val) > self.early_stop_patience: return True else: return False class PerfTrackVal: """ Records epoch wise performance for validation """ def __init__(self, task, extra_param=None): self.task = task if task in ['cls', 'cls_trans']: assert extra_param is None self.all = [] self.class_seen = None self.class_corr = None else: assert False def update(self, data_batch, out): if self.task in ['cls', 'cls_trans']: correct = self.get_correct_list(out['logit'], data_batch['label']) self.all.extend(correct) self.update_class_see_corr(out['logit'], data_batch['label']) else: assert False def agg(self): if self.task in ['cls', 'cls_trans']: perf = { 'acc': self.get_avg_list(self.all), 'class_acc': np.mean(np.array(self.class_corr) / np.array(self.class_seen,dtype=np.float)) } else: assert False return perf def update_class_see_corr(self, logit, label): if self.class_seen is None: num_class = logit.shape[1] self.class_seen = [0] * num_class self.class_corr = [0] * num_class pred_label = logit.argmax(axis=1).to('cpu').tolist() for _pred_label, _label in zip(pred_label, label): self.class_seen[_label] += 1 if _pred_label == _label: self.class_corr[_pred_label] += 1 @staticmethod def get_correct_list(logit, label): label = label.to(logit.device) pred_class = logit.argmax(axis=1) return (label == pred_class).to('cpu').tolist() @staticmethod def get_avg_list(all_list): for x in all_list: assert isinstance(x, bool) return sum(all_list) / len(all_list) class PerfTrackTrain(PerfTrackVal): """ Records epoch wise performance during training """ def __init__(self, task, extra_param=None): super().__init__(task, extra_param) # add a list to track loss self.all_loss = [] def update_loss(self, loss): self.all_loss.append(loss.item()) def agg_loss(self): # print(self.all_loss) return sum(self.all_loss) / len(self.all_loss) def update_all(self, data_batch, out, loss): self.update(data_batch, out) self.update_loss(loss) # source: https://github.com/WangYueFt/dgcnn/blob/master/pytorch/util.py def smooth_loss(pred, gold): eps = 0.2 n_class = pred.size(1) one_hot = torch.zeros_like(pred).scatter(1, gold.view(-1, 1), 1) one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1) log_prb = F.log_softmax(pred, dim=1) loss = -(one_hot * log_prb).sum(dim=1).mean() return loss def rscnn_voting_evaluate_cls(loader, model, data_batch_to_points_target, points_to_inp, out_to_prob, log_file): """ :param loader: :param model: :param data_batch_to_points_target: :param points_to_inp: transform the points to input for the particular model that is evaluated :param out_to_prob: :return: """ import rs_cnn.data.data_utils as d_utils import pointnet2.utils.pointnet2_utils as pointnet2_utils import numpy as np terminal = sys.stdout log = open(log_file, "w") NUM_REPEAT = 300 NUM_VOTE = 10 PointcloudScale = d_utils.PointcloudScale() # initialize random scaling def data_aug(vote_id, pc): # furthest point sampling # (B, npoint) fps_idx = pointnet2_utils.furthest_point_sample(points, 1200) new_fps_idx = fps_idx[:, np.random.choice(1200, num_points, False)] new_points = pointnet2_utils.gather_operation(points.transpose(1, 2).contiguous(), new_fps_idx).transpose(1, 2).contiguous() if vote_id > 0: pc_out = PointcloudScale(new_points) else: pc_out = pc return pc_out print(f"RSCNN EVALUATE, NUM_REPEAT {NUM_REPEAT}, NUM_VOTE {NUM_VOTE}") num_points = loader.dataset.num_points print(f"Number of points {num_points}") # evaluate sys.stdout.flush() PointcloudScale = d_utils.PointcloudScale() # initialize random scaling model.eval() global_acc = 0 with torch.no_grad(): for i in range(NUM_REPEAT): preds = [] labels = [] for j, data in enumerate(loader, 0): points, target = data_batch_to_points_target(data) points, target = points.cuda(), target.cuda() pred = 0 for v in range(NUM_VOTE): new_points = data_aug(v, points) inp = points_to_inp(new_points) out = model(**inp) prob = out_to_prob(out) pred += prob # pred += F.softmax(model(**inp), dim = 1) pred /= NUM_VOTE target = target.view(-1) _, pred_choice = torch.max(pred.data, -1) preds.append(pred_choice) labels.append(target.data) preds = torch.cat(preds, 0) labels = torch.cat(labels, 0) acc = (preds == labels).sum().float() / labels.numel() if acc > global_acc: global_acc = acc message1 = 'Repeat %3d \t Acc: %0.6f' % (i + 1, acc) message2 = '\nBest voting till now, acc: %0.6f' % (global_acc) message = f'{message1} \n {message2}' terminal.write(message) log.write(message) message = '\nBest voting acc: %0.6f' % (global_acc) terminal.write(message) log.write(message) return global_acc # https://github.com/charlesq34/pointnet2/blob/master/evaluate.py # https://github.com/charlesq34/pointnet2/issues/8 # we try to keep the variables names similar to the original implementation def pn2_vote_evaluate_cls(dataloader, model, log_file, num_votes=[12]): from pointnet2_tf.utils import provider model.eval() terminal = sys.stdout log = open(log_file, "w") if isinstance(num_votes, list): pass else: num_votes = [num_votes] for _num_votes in num_votes: print(f"num_votes: {_num_votes}") NUM_CLASSES = DATASET_NUM_CLASS[dataloader.dataset.dataset_name] SHAPE_NAMES = [line.rstrip() for line in open('./data/modelnet40_ply_hdf5_2048/shape_names.txt')] total_correct = 0 total_seen = 0 total_seen_class = [0 for _ in range(NUM_CLASSES)] total_correct_class = [0 for _ in range(NUM_CLASSES)] with torch.no_grad(): for _batch_data in dataloader: # based on https://github.com/charlesq34/pointnet2/blob/master/evaluate.py#L125-L150 batch_data, batch_label = np.array(_batch_data['pc'].cpu()), np.array(_batch_data['label'].cpu()) bsize = batch_data.shape[0] BATCH_SIZE = batch_data.shape[0] NUM_POINT = batch_data.shape[1] batch_pred_sum = np.zeros((BATCH_SIZE, NUM_CLASSES)) # score for classes for vote_idx in range(_num_votes): # Shuffle point order to achieve different farthest samplings shuffled_indices = np.arange(NUM_POINT) np.random.shuffle(shuffled_indices) rotated_data = provider.rotate_point_cloud_by_angle( batch_data[:, shuffled_indices, :], vote_idx/float(_num_votes) * np.pi * 2) inp = {'pc': torch.tensor(rotated_data)} out = model(**inp) pred_val = np.array(out['logit'].cpu()) batch_pred_sum += pred_val pred_val = np.argmax(batch_pred_sum, 1) correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize]) total_correct += correct total_seen += bsize for i in range(bsize): l = batch_label[i] total_seen_class[l] += 1 total_correct_class[l] += (pred_val[i] == l) class_accuracies = np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float) message = "" for i, name in enumerate(SHAPE_NAMES): message += f"\n {'%10s: %0.3f' % (name, class_accuracies[i])}" message += f"\n {'eval accuracy: %f'% (total_correct / float(total_seen))}" message += f"\n {'eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float)))}" terminal.write(message) log.write(message) ================================================ FILE: aug_utils.py ================================================ import numpy as np import torch import sys from main import get_loss sys.path.append("./emd/") import emd_module as emd def cutmix_r(data_batch,cfg): r = np.random.rand(1) if cfg.AUG.BETA > 0 and r < cfg.AUG.PROB: lam = np.random.beta(cfg.AUG.BETA, cfg.AUG.BETA) B = data_batch['pc'].size()[0] rand_index = torch.randperm(B).cuda() target_a = data_batch['label'] target_b = data_batch['label'][rand_index] point_a = torch.zeros(B, 1024, 3) point_b = torch.zeros(B, 1024, 3) point_c = torch.zeros(B, 1024, 3) point_a = data_batch['pc'] point_b = data_batch['pc'][rand_index] point_c = data_batch['pc'][rand_index] # point_a, point_b, point_c = point_a.to(device), point_b.to(device), point_c.to(device) remd = emd.emdModule() remd = remd.cuda() dis, ind = remd(point_a, point_b, 0.005, 300) for ass in range(B): point_c[ass, :, :] = point_c[ass, ind[ass].long(), :] int_lam = int(cfg.DATALOADER.MODELNET40_DGCNN.num_points * lam) int_lam = max(1, int_lam) gamma = np.random.choice(cfg.DATALOADER.MODELNET40_DGCNN.num_points, int_lam, replace=False, p=None) for i2 in range(B): data_batch['pc'][i2, gamma, :] = point_c[i2, gamma, :] # adjust lambda to exactly match point ratio lam = int_lam * 1.0 / cfg.DATALOADER.MODELNET40_DGCNN.num_points # points = data_batch['pc'].transpose(2, 1) data_batch['label_2'] = target_b data_batch['lam'] = lam return data_batch # pred, trans_feat = model(points) # loss = criterion(pred, target_a.long()) * (1. - lam) + criterion(pred, target_b.long()) * lam def cutmix_k(data_batch,cfg): r = np.random.rand(1) if cfg.AUG.BETA > 0 and r < cfg.AUG.PROB: lam = np.random.beta(cfg.AUG.BETA, cfg.AUG.BETA) B = data_batch['pc'].size()[0] rand_index = torch.randperm(B).cuda() target_a = data_batch['label'] target_b = data_batch['label'][rand_index] point_a = torch.zeros(B, 1024, 3) point_b = torch.zeros(B, 1024, 3) point_c = torch.zeros(B, 1024, 3) point_a = data_batch['pc'] point_b = data_batch['pc'][rand_index] point_c = data_batch['pc'][rand_index] remd = emd.emdModule() remd = remd.cuda() dis, ind = remd(point_a, point_b, 0.005, 300) for ass in range(B): point_c[ass, :, :] = point_c[ass, ind[ass].long(), :] int_lam = int(cfg.DATALOADER.MODELNET40_DGCNN.num_points * lam) int_lam = max(1, int_lam) random_point = torch.from_numpy(np.random.choice(1024, B, replace=False, p=None)) # kNN ind1 = torch.tensor(range(B)) query = point_a[ind1, random_point].view(B, 1, 3) dist = torch.sqrt(torch.sum((point_a - query.repeat(1, cfg.DATALOADER.MODELNET40_DGCNN.num_points, 1)) ** 2, 2)) idxs = dist.topk(int_lam, dim=1, largest=False, sorted=True).indices for i2 in range(B): data_batch['pc'][i2, idxs[i2], :] = point_c[i2, idxs[i2], :] # adjust lambda to exactly match point ratio lam = int_lam * 1.0 / cfg.DATALOADER.MODELNET40_DGCNN.num_points # points = points.transpose(2, 1) # pred, trans_feat = model(points) # loss = criterion(pred, target_a.long()) * (1. - lam) + criterion(pred, target_b.long()) * lam data_batch['label_2'] = target_b data_batch['lam'] = lam return data_batch def mixup(data_batch,cfg): batch_size = data_batch['pc'].size()[0] idx_minor = torch.randperm(batch_size) mixrates = (0.5 - np.abs(np.random.beta(cfg.AUG.MIXUPRATE, cfg.AUG.MIXUPRATE, batch_size) - 0.5)) label_main = data_batch['label'] label_minor = data_batch['label'][idx_minor] label_new = torch.zeros(batch_size, 40) for i in range(batch_size): if label_main[i] == label_minor[i]: # same label label_new[i][label_main[i]] = 1.0 else: label_new[i][label_main[i]] = 1 - mixrates[i] label_new[i][label_minor[i]] = mixrates[i] label = label_new data_minor = data_batch['pc'][idx_minor] mix_rate = torch.tensor(mixrates).float() mix_rate = mix_rate.unsqueeze_(1).unsqueeze_(2) mix_rate_expand_xyz = mix_rate.expand(data_batch['pc'].shape) remd = emd.emdModule() remd = remd.cuda() _, ass = remd(data_batch['pc'], data_minor, 0.005, 300) ass = ass.long() for i in range(batch_size): data_minor[i] = data_minor[i][ass[i]] data_batch['pc'] = data_batch['pc'] * (1 - mix_rate_expand_xyz) + data_minor * mix_rate_expand_xyz data_batch['label_2'] = label_minor data_batch['lam'] = torch.tensor(mix_rate).squeeze_() return data_batch def knn_points(k, xyz, query, nsample=512): B, N, C = xyz.shape _, S, _ = query.shape # S=1 tmp_idx = np.arange(N) group_idx = np.repeat(tmp_idx[np.newaxis,np.newaxis,:], B, axis=0) sqrdists = square_distance(query, xyz) # Bx1,N #제곱거리 tmp = np.sort(sqrdists, axis=2) knn_dist = np.zeros((B,1)) for i in range(B): knn_dist[i][0] = tmp[i][0][k] group_idx[i][sqrdists[i]>knn_dist[i][0]]=N # group_idx[sqrdists > radius ** 2] = N # print("group idx : \n",group_idx) # group_idx = group_idx.sort(dim=-1)[0][:, :, :nsample] # for torch.tensor group_idx = np.sort(group_idx, axis=2)[:, :, :nsample] # group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, nsample]) tmp_idx = group_idx[:,:,0] group_first = np.repeat(tmp_idx[:,np.newaxis,:], nsample, axis=2) # repeat the first value of the idx in each batch mask = group_idx == N group_idx[mask] = group_first[mask] return group_idx def cut_points_knn(data_batch, idx, radius, nsample=512, k=512): """ input points : BxNx3(=6 with normal) idx : Bx1 one scalar(int) between 0~len(points) output idx : Bxn_sample """ B, N, C = data_batch.shape B, S = idx.shape query_points = np.zeros((B,1,C)) # print("idx : \n",idx) for i in range(B): query_points[i][0]=data_batch[i][idx[i][0]] # Bx1x3(=6 with normal) # B x n_sample group_idx = knn_points(k=k, xyz=data_batch[:,:,:3], query=query_points[:,:,:3], nsample=nsample) return group_idx, query_points # group_idx: 16x?x6, query_points: 16x1x6 def cut_points(data_batch, idx, radius, nsample=512): """ input points : BxNx3(=6 with normal) idx : Bx1 one scalar(int) between 0~len(points) output idx : Bxn_sample """ B, N, C = data_batch.shape B, S = idx.shape query_points = np.zeros((B,1,C)) # print("idx : \n",idx) for i in range(B): query_points[i][0]=data_batch[i][idx[i][0]] # Bx1x3(=6 with normal) # B x n_sample group_idx = query_ball_point_for_rsmix(radius, nsample, data_batch[:,:,:3], query_points[:,:,:3]) return group_idx, query_points # group_idx: 16x?x6, query_points: 16x1x6 def query_ball_point_for_rsmix(radius, nsample, xyz, new_xyz): """ Input: radius: local region radius nsample: max sample number in local region xyz: all points, [B, N, 3] new_xyz: query points, [B, S, 3] Return: group_idx: grouped points index, [B, S, nsample], S=1 """ # device = xyz.device B, N, C = xyz.shape _, S, _ = new_xyz.shape # group_idx = torch.arange(N, dtype=torch.long).to(device).view(1, 1, N).repeat([B, S, 1]) tmp_idx = np.arange(N) group_idx = np.repeat(tmp_idx[np.newaxis,np.newaxis,:], B, axis=0) sqrdists = square_distance(new_xyz, xyz) group_idx[sqrdists > radius ** 2] = N # group_idx = group_idx.sort(dim=-1)[0][:, :, :nsample] # for torch.tensor group_idx = np.sort(group_idx, axis=2)[:, :, :nsample] # group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, nsample]) tmp_idx = group_idx[:,:,0] group_first = np.repeat(tmp_idx[:,np.newaxis,:], nsample, axis=2) # repeat the first value of the idx in each batch mask = group_idx == N group_idx[mask] = group_first[mask] return group_idx def square_distance(src, dst): """ Calculate Euclid distance between each two points. src^T * dst = xn * xm + yn * ym + zn * zm； sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn; sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm; dist = (xn - xm)^2 + (yn - ym)^2 + (zn - zm)^2 = sum(src**2,dim=-1)+sum(dst**2,dim=-1)-2*src^T*dst Input: src: source points, [B, N, C] dst: target points, [B, M, C] Output: dist: per-point square distance, [B, N, M] """ B, N, _ = src.shape _, M, _ = dst.shape # dist = -2 * torch.matmul(src, dst.permute(0, 2, 1)) # dist += torch.sum(src ** 2, -1).view(B, N, 1) # dist += torch.sum(dst ** 2, -1).view(B, 1, M) dist = -2 * np.matmul(src, dst.transpose(0, 2, 1)) dist += np.sum(src ** 2, -1).reshape(B, N, 1) dist += np.sum(dst ** 2, -1).reshape(B, 1, M) return dist def pts_num_ctrl(pts_erase_idx, pts_add_idx): ''' input : pts - to erase pts - to add output :pts - to add (number controled) ''' if len(pts_erase_idx)>=len(pts_add_idx): num_diff = len(pts_erase_idx)-len(pts_add_idx) if num_diff == 0: pts_add_idx_ctrled = pts_add_idx else: pts_add_idx_ctrled = np.append(pts_add_idx, pts_add_idx[np.random.randint(0, len(pts_add_idx), size=num_diff)]) else: pts_add_idx_ctrled = np.sort(np.random.choice(pts_add_idx, size=len(pts_erase_idx), replace=False)) return pts_add_idx_ctrled def rsmix(data, cfg, n_sample=512, KNN=False): cut_rad = np.random.beta(cfg.AUG.BETA, cfg.AUG.BETA) data_batch = data['pc'].numpy() label_batch = data['label'].numpy() rand_index = np.random.choice(data_batch.shape[0],data_batch.shape[0], replace=False) # label dim : (16,) for model if len(label_batch.shape) is 1: label_batch = np.expand_dims(label_batch, axis=1) label_a = label_batch[:,0] label_b = label_batch[rand_index][:,0] data_batch_rand = data_batch[rand_index] # BxNx3(with normal=6) rand_idx_1 = np.random.randint(0,data_batch.shape[1], (data_batch.shape[0],1)) rand_idx_2 = np.random.randint(0,data_batch.shape[1], (data_batch.shape[0],1)) if KNN: knn_para = min(int(np.ceil(cut_rad*n_sample)),n_sample) pts_erase_idx, query_point_1 = cut_points_knn(data_batch, rand_idx_1, cut_rad, nsample=n_sample, k=knn_para) # B x num_points_in_radius_1 x 3(or 6) pts_add_idx, query_point_2 = cut_points_knn(data_batch_rand, rand_idx_2, cut_rad, nsample=n_sample, k=knn_para) # B x num_points_in_radius_2 x 3(or 6) else: pts_erase_idx, query_point_1 = cut_points(data_batch, rand_idx_1, cut_rad, nsample=n_sample) # B x num_points_in_radius_1 x 3(or 6) pts_add_idx, query_point_2 = cut_points(data_batch_rand, rand_idx_2, cut_rad, nsample=n_sample) # B x num_points_in_radius_2 x 3(or 6) query_dist = query_point_1[:,:,:3] - query_point_2[:,:,:3] pts_replaced = np.zeros((1,data_batch.shape[1],data_batch.shape[2])) lam = np.zeros(data_batch.shape[0],dtype=float) for i in range(data_batch.shape[0]): if pts_erase_idx[i][0][0]==data_batch.shape[1]: tmp_pts_replaced = np.expand_dims(data_batch[i], axis=0) lam_tmp = 0 elif pts_add_idx[i][0][0]==data_batch.shape[1]: pts_erase_idx_tmp = np.unique(pts_erase_idx[i].reshape(n_sample,),axis=0) tmp_pts_erased = np.delete(data_batch[i], pts_erase_idx_tmp, axis=0) # B x N-num_rad_1 x 3(or 6) dup_points_idx = np.random.randint(0,len(tmp_pts_erased), size=len(pts_erase_idx_tmp)) tmp_pts_replaced = np.expand_dims(np.concatenate((tmp_pts_erased, data_batch[i][dup_points_idx]), axis=0), axis=0) lam_tmp = 0 else: pts_erase_idx_tmp = np.unique(pts_erase_idx[i].reshape(n_sample,),axis=0) pts_add_idx_tmp = np.unique(pts_add_idx[i].reshape(n_sample,),axis=0) pts_add_idx_ctrled_tmp = pts_num_ctrl(pts_erase_idx_tmp,pts_add_idx_tmp) tmp_pts_erased = np.delete(data_batch[i], pts_erase_idx_tmp, axis=0) # B x N-num_rad_1 x 3(or 6) # input("INPUT : ") tmp_pts_to_add = np.take(data_batch_rand[i], pts_add_idx_ctrled_tmp, axis=0) tmp_pts_to_add[:,:3] = query_dist[i]+tmp_pts_to_add[:,:3] tmp_pts_replaced = np.expand_dims(np.vstack((tmp_pts_erased,tmp_pts_to_add)), axis=0) lam_tmp = len(pts_add_idx_ctrled_tmp)/(len(pts_add_idx_ctrled_tmp)+len(tmp_pts_erased)) pts_replaced = np.concatenate((pts_replaced, tmp_pts_replaced),axis=0) lam[i] = lam_tmp data_batch_mixed = np.delete(pts_replaced, [0], axis=0) data['pc'] = torch.FloatTensor(data_batch_mixed) data['label'] = torch.tensor(label_a) data['label_2'] = torch.tensor(label_b) data['lam'] = torch.tensor(lam) return data def pgd(data_batch,model, task, loss_name, dataset_name, step= 7, eps=0.05, alpha=0.01): model.eval() data = data_batch['pc'] adv_data=data.clone() adv_data=adv_data+(torch.rand_like(adv_data)*eps*2-eps) adv_data.detach() adv_data_batch = {} for _ in range(step): adv_data.requires_grad=True out = model(**{'pc':adv_data}) adv_data_batch['pc'] = adv_data adv_data_batch['label'] = data_batch['label'] model.zero_grad() loss = get_loss(task, loss_name, adv_data_batch, out, dataset_name) loss.backward() with torch.no_grad(): adv_data = adv_data + alpha * adv_data.grad.sign() delta = adv_data-data # print(delta) delta = torch.clamp(delta,-eps,eps) adv_data = (data+delta).detach_() return adv_data_batch ================================================ FILE: configs/bn/dgcnn.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_dgcnn_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: dgcnn SEED: 1 TASK: cls TRAIN: l2: 1e-4 ADAPT: METHOD: bn ================================================ FILE: configs/bn/pct.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_pct_run_1 LOSS_NAME: smooth MODEL_NAME: pct SEED: 1 TASK: cls OPTIMIZER: pct TRAIN: l2: 1e-4 learning_rate: 0.0001 ADAPT: METHOD: bn ================================================ FILE: configs/bn/pointnet.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_pointnet_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet SEED: 1 TASK: cls_trans TRAIN: l2: 0.0 ADAPT: METHOD: bn ================================================ FILE: configs/bn/pointnet2.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_pointnet2_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet2 SEED: 1 TASK: cls TRAIN: l2: 0.0 ADAPT: METHOD: bn ================================================ FILE: configs/bn/rscnn.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_rscnn_run_1 LOSS_NAME: smooth MODEL_NAME: rscnn SEED: 1 TASK: cls TRAIN: l2: 0.0 ADAPT: METHOD: bn ================================================ FILE: configs/bn/simpleview.yaml ================================================ DATALOADER: batch_size: 18 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_simpleview_run_1 LOSS_NAME: smooth MODEL_NAME: simpleview SEED: 1 TASK: cls ADAPT: METHOD: bn ================================================ FILE: configs/corruption/curvenet.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_curvenet_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: curvenet SEED: 1 TASK: cls TRAIN: l2: 1e-4 ================================================ FILE: configs/corruption/dgcnn.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_dgcnn_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: dgcnn SEED: 1 TASK: cls TRAIN: l2: 1e-4 ================================================ FILE: configs/corruption/gdanet.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_gdanet_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: gdanet SEED: 1 TASK: cls TRAIN: l2: 1e-4 ================================================ FILE: configs/corruption/pct.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_pct_run_1 LOSS_NAME: smooth MODEL_NAME: pct SEED: 1 TASK: cls OPTIMIZER: pct TRAIN: l2: 1e-4 learning_rate: 0.0001 ================================================ FILE: configs/corruption/pointMLP.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_pointMLP_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: pointMLP SEED: 1 TASK: cls TRAIN: l2: 1e-4 ================================================ FILE: configs/corruption/pointMLP2.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_pointMLP2_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: pointMLP2 SEED: 1 TASK: cls TRAIN: l2: 1e-4 ================================================ FILE: configs/corruption/pointnet.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_pointnet_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet SEED: 1 TASK: cls_trans TRAIN: l2: 0.0 ================================================ FILE: configs/corruption/pointnet2.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_pointnet2_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet2 SEED: 1 TASK: cls TRAIN: l2: 0.0 ================================================ FILE: configs/corruption/rscnn.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_rscnn_run_1 LOSS_NAME: smooth MODEL_NAME: rscnn SEED: 1 TASK: cls TRAIN: l2: 0.0 ================================================ FILE: configs/corruption/simpleview.yaml ================================================ DATALOADER: batch_size: 18 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_simpleview_run_1 LOSS_NAME: smooth MODEL_NAME: simpleview SEED: 1 TASK: cls ================================================ FILE: configs/cutmix/dgcnn_k.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: cutmix_k_dgcnn_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: dgcnn SEED: 1 TASK: cls TRAIN: l2: 1e-4 AUG: NAME: cutmix_k BETA: 1. PROB: 0.5 ================================================ FILE: configs/cutmix/dgcnn_r.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: cutmix_r_dgcnn_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: dgcnn SEED: 1 TASK: cls TRAIN: l2: 1e-4 AUG: NAME: cutmix_r BETA: 1. PROB: 0.5 ================================================ FILE: configs/cutmix/pct_k.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: cutmix_k_pct_run_1 LOSS_NAME: smooth MODEL_NAME: pct SEED: 1 TASK: cls OPTIMIZER: pct TRAIN: l2: 1e-4 learning_rate: 0.0001 AUG: NAME: cutmix_k BETA: 1. PROB: 0.5 ================================================ FILE: configs/cutmix/pct_r.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: cutmix_r_pct_run_1 LOSS_NAME: smooth MODEL_NAME: pct SEED: 1 TASK: cls OPTIMIZER: pct TRAIN: l2: 1e-4 learning_rate: 0.0001 AUG: NAME: cutmix_r BETA: 1. PROB: 0.5 ================================================ FILE: configs/cutmix/pointnet2_k.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: cutmix_k_pointnet2_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet2 SEED: 1 TASK: cls TRAIN: l2: 0.0 AUG: NAME: cutmix_k BETA: 1. PROB: 0.5 ================================================ FILE: configs/cutmix/pointnet2_r.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: cutmix_r_pointnet2_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet2 SEED: 1 TASK: cls TRAIN: l2: 0.0 AUG: NAME: cutmix_r BETA: 1. PROB: 0.5 ================================================ FILE: configs/cutmix/pointnet_k.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: cutmix_k_pointnet_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet SEED: 1 TASK: cls_trans TRAIN: l2: 0.0 AUG: NAME: cutmix_k BETA: 1. PROB: 0.5 ================================================ FILE: configs/cutmix/pointnet_r.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: cutmix_r_pointnet_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet SEED: 1 TASK: cls_trans TRAIN: l2: 0.0 AUG: NAME: cutmix_r BETA: 1. PROB: 0.5 ================================================ FILE: configs/cutmix/rscnn_k.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: cutmix_k_rscnn_run_1 LOSS_NAME: smooth MODEL_NAME: rscnn SEED: 1 TASK: cls TRAIN: l2: 0.0 AUG: NAME: cutmix_k BETA: 1. PROB: 0.5 ================================================ FILE: configs/cutmix/rscnn_r.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: cutmix_r_rscnn_run_1 LOSS_NAME: smooth MODEL_NAME: rscnn SEED: 1 TASK: cls TRAIN: l2: 0.0 AUG: NAME: cutmix_r BETA: 1. PROB: 0.5 ================================================ FILE: configs/cutmix/simpleview_k.yaml ================================================ DATALOADER: batch_size: 18 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: cutmix_k_simpleview_run_1 LOSS_NAME: smooth MODEL_NAME: simpleview SEED: 1 TASK: cls AUG: NAME: cutmix_k BETA: 1. PROB: 0.5 ================================================ FILE: configs/cutmix/simpleview_r.yaml ================================================ DATALOADER: batch_size: 18 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: cutmix_r_simpleview_run_1 LOSS_NAME: smooth MODEL_NAME: simpleview SEED: 1 TASK: cls AUG: NAME: cutmix_r BETA: 1. PROB: 0.5 ================================================ FILE: configs/dgcnn_curvenet_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_curvenet_run_1 LOSS_NAME: smooth MODEL_NAME: curvenet SEED: 1 TASK: cls TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_dgcnn_0.25_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.3125_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_dgcnn_0.25_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: dgcnn SEED: 1 TASK: cls TRAIN: l2: 1e-4 ================================================ FILE: configs/dgcnn_dgcnn_0.25_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.3125_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_dgcnn_0.25_valid_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: dgcnn SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 1e-4 ================================================ FILE: configs/dgcnn_dgcnn_0.5_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.625_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_dgcnn_0.5_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: dgcnn SEED: 1 TASK: cls TRAIN: l2: 1e-4 ================================================ FILE: configs/dgcnn_dgcnn_0.5_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.625_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_dgcnn_0.5_valid_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: dgcnn SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 1e-4 ================================================ FILE: configs/dgcnn_dgcnn_ce_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_dgcnn_ce_run_1 LOSS_NAME: cross_entropy METRIC: acc MODEL_NAME: dgcnn SEED: 1 TASK: cls TRAIN: l2: 1e-4 ================================================ FILE: configs/dgcnn_dgcnn_ce_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_dgcnn_ce_valid_run_1 LOSS_NAME: cross_entropy METRIC: acc MODEL_NAME: dgcnn SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 1e-4 ================================================ FILE: configs/dgcnn_dgcnn_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_dgcnn_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: dgcnn SEED: 1 TASK: cls TRAIN: l2: 1e-4 ================================================ FILE: configs/dgcnn_dgcnn_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_dgcnn_valid_run_1 LOSS_NAME: smooth MODEL_NAME: dgcnn SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 0.0001 ================================================ FILE: configs/dgcnn_gdanet_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_gdanet_run_1 LOSS_NAME: smooth MODEL_NAME: gdanet SEED: 1 TASK: cls TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_pct_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pct_run_1 LOSS_NAME: smooth MODEL_NAME: pct SEED: 1 TASK: cls OPTIMIZER: pct TRAIN: l2: 1e-4 learning_rate: 0.0001 ================================================ FILE: configs/dgcnn_pointMLP2_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pointMLP2_run_1 LOSS_NAME: smooth MODEL_NAME: pointMLP2 SEED: 1 TASK: cls TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_pointMLP_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pointMLP_run_1 LOSS_NAME: smooth MODEL_NAME: pointMLP SEED: 1 TASK: cls TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_pointnet2_0.25_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.3125_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pointnet2_0.25_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: pointnet2 SEED: 1 TASK: cls TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_pointnet2_0.25_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.3125_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pointnet2_0.25_valid_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: pointnet2 SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_pointnet2_0.5_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.625_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pointnet2_0.5_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: pointnet2 SEED: 1 TASK: cls TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_pointnet2_0.5_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.625_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pointnet2_0.5_valid_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: pointnet2 SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_pointnet2_ce_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pointnet2_ce_run_1 LOSS_NAME: cross_entropy METRIC: acc MODEL_NAME: pointnet2 SEED: 1 TASK: cls TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_pointnet2_ce_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pointnet2_ce_valid_run_1 LOSS_NAME: cross_entropy METRIC: acc MODEL_NAME: pointnet2 SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_pointnet2_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pointnet2_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet2 SEED: 1 TASK: cls TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_pointnet2_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pointnet2_valid_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet2 SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_pointnet_0.25_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.3125_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pointnet_0.25_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet SEED: 1 TASK: cls_trans TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_pointnet_0.25_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.3125_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pointnet_0.25_valid_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet SEED: 1 TASK: cls_trans EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_pointnet_0.5_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.625_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pointnet_0.5_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet SEED: 1 TASK: cls_trans TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_pointnet_0.5_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.625_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pointnet_0.5_valid_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet SEED: 1 TASK: cls_trans EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_pointnet_ce_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pointnet_ce_run_1 LOSS_NAME: cross_entropy MODEL_NAME: pointnet SEED: 1 TASK: cls_trans TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_pointnet_ce_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pointnet_ce_valid_run_1 LOSS_NAME: cross_entropy MODEL_NAME: pointnet SEED: 1 TASK: cls_trans EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_pointnet_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pointnet_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet SEED: 1 TASK: cls_trans TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_pointnet_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_pointnet_valid_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet SEED: 1 TASK: cls_trans EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_rscnn_0.25_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.3125_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_rscnn_0.25_run_1 LOSS_NAME: smooth MODEL_NAME: rscnn SEED: 1 TASK: cls TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_rscnn_0.25_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.3125_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_rscnn_0.25_valid_run_1 LOSS_NAME: smooth MODEL_NAME: rscnn SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_rscnn_0.5_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.625_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_rscnn_0.5_run_1 LOSS_NAME: smooth MODEL_NAME: rscnn SEED: 1 TASK: cls TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_rscnn_0.5_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.625_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_rscnn_0.5_valid_run_1 LOSS_NAME: smooth MODEL_NAME: rscnn SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_rscnn_ce_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_rscnn_ce_run_1 LOSS_NAME: cross_entropy MODEL_NAME: rscnn SEED: 1 TASK: cls TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_rscnn_ce_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_rscnn_ce_valid_run_1 LOSS_NAME: cross_entropy MODEL_NAME: rscnn SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_rscnn_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_rscnn_run_1 LOSS_NAME: smooth MODEL_NAME: rscnn SEED: 1 TASK: cls TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_rscnn_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_rscnn_valid_run_1 LOSS_NAME: smooth MODEL_NAME: rscnn SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 0.0 ================================================ FILE: configs/dgcnn_simpleview_0.25_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.3125_files.txt batch_size: 18 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_simpleview_0.25_run_1 LOSS_NAME: smooth MODEL_NAME: simpleview SEED: 1 TASK: cls ================================================ FILE: configs/dgcnn_simpleview_0.25_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.3125_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 18 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_simpleview_0.25_valid_run_1 LOSS_NAME: smooth MODEL_NAME: simpleview SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 ================================================ FILE: configs/dgcnn_simpleview_0.5_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.625_files.txt batch_size: 18 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_simpleview_0.5_run_1 LOSS_NAME: smooth MODEL_NAME: simpleview SEED: 1 TASK: cls ================================================ FILE: configs/dgcnn_simpleview_0.5_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_split_0.625_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 18 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_simpleview_0.5_valid_run_1 LOSS_NAME: smooth MODEL_NAME: simpleview SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 ================================================ FILE: configs/dgcnn_simpleview_ce_run_1.yaml ================================================ DATALOADER: batch_size: 18 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_simpleview_ce_run_1 LOSS_NAME: cross_entropy MODEL_NAME: simpleview SEED: 1 TASK: cls ================================================ FILE: configs/dgcnn_simpleview_ce_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 18 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_simpleview_ce_valid_run_1 LOSS_NAME: cross_entropy MODEL_NAME: simpleview SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 ================================================ FILE: configs/dgcnn_simpleview_run_1.yaml ================================================ DATALOADER: batch_size: 18 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_simpleview_run_1 LOSS_NAME: smooth MODEL_NAME: simpleview SEED: 1 TASK: cls ================================================ FILE: configs/dgcnn_simpleview_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_DGCNN: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 18 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: dgcnn_simpleview_valid_run_1 LOSS_NAME: smooth MODEL_NAME: simpleview SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 ================================================ FILE: configs/mixup/dgcnn.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: mixup_dgcnn_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: dgcnn SEED: 1 TASK: cls TRAIN: l2: 1e-4 AUG: NAME: mixup BETA: 1. PROB: 0.5 ================================================ FILE: configs/mixup/pct.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: mixup_pct_run_1 LOSS_NAME: smooth MODEL_NAME: pct SEED: 1 TASK: cls OPTIMIZER: pct TRAIN: l2: 1e-4 learning_rate: 0.0001 AUG: NAME: mixup BETA: 1. PROB: 0.5 ================================================ FILE: configs/mixup/pointnet.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: mixup_pointnet_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet SEED: 1 TASK: cls_trans TRAIN: l2: 0.0 AUG: NAME: mixup BETA: 1. PROB: 0.5 ================================================ FILE: configs/mixup/pointnet2.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: mixup_pointnet2_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet2 SEED: 1 TASK: cls TRAIN: l2: 0.0 AUG: NAME: mixup BETA: 1. PROB: 0.5 ================================================ FILE: configs/mixup/rscnn.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: mixup_rscnn_run_1 LOSS_NAME: smooth MODEL_NAME: rscnn SEED: 1 TASK: cls TRAIN: l2: 0.0 AUG: NAME: mixup BETA: 1. PROB: 0.5 ================================================ FILE: configs/mixup/simpleview.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: mixup_simpleview_run_1 LOSS_NAME: smooth MODEL_NAME: simpleview SEED: 1 METRIC: acc TASK: cls TRAIN: l2: 0.0 AUG: NAME: mixup BETA: 1. PROB: 0.5 ================================================ FILE: configs/pgd/dgcnn.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: pgd_dgcnn_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: dgcnn SEED: 1 TASK: cls TRAIN: l2: 1e-4 AUG: NAME: pgd ================================================ FILE: configs/pgd/pct.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: pgd_pct_run_1 LOSS_NAME: smooth MODEL_NAME: pct SEED: 1 TASK: cls OPTIMIZER: pct TRAIN: l2: 1e-4 learning_rate: 0.0001 AUG: NAME: pgd ================================================ FILE: configs/pgd/pointnet.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: pgd_pointnet_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet SEED: 1 TASK: cls_trans TRAIN: l2: 0.0 AUG: NAME: pgd ================================================ FILE: configs/pointnet2_dgcnn_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_pn2 EXP_ID: pointnet2_dgcnn_run_1 LOSS_NAME: cross_entropy MODEL_NAME: dgcnn SEED: 1 TASK: cls TRAIN: l2: 0.0001 ================================================ FILE: configs/pointnet2_dgcnn_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_PN2: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_pn2 EXP_ID: pointnet2_dgcnn_valid_run_1 LOSS_NAME: cross_entropy MODEL_NAME: dgcnn SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 0.0001 ================================================ FILE: configs/pointnet2_pointnet2_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_pn2 EXP_ID: pointnet2_pointnet2_run_1 LOSS_NAME: cross_entropy MODEL_NAME: pointnet2 SEED: 1 TASK: cls TRAIN: l2: 0.0 ================================================ FILE: configs/pointnet2_pointnet2_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_PN2: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_pn2 EXP_ID: pointnet2_pointnet2_valid_run_1 LOSS_NAME: cross_entropy MODEL_NAME: pointnet2 SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 0.0 ================================================ FILE: configs/pointnet2_pointnet_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_pn2 EXP_ID: pointnet2_pointnet_run_1 LOSS_NAME: cross_entropy MODEL_NAME: pointnet SEED: 1 TASK: cls_trans TRAIN: l2: 0.0 ================================================ FILE: configs/pointnet2_pointnet_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_PN2: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_pn2 EXP_ID: pointnet2_pointnet_valid_run_1 LOSS_NAME: cross_entropy MODEL_NAME: pointnet SEED: 1 TASK: cls_trans EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 0.0 ================================================ FILE: configs/pointnet2_rscnn_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_pn2 EXP_ID: pointnet2_rscnn_run_1 LOSS_NAME: cross_entropy MODEL_NAME: rscnn SEED: 1 TASK: cls TRAIN: l2: 0.0 ================================================ FILE: configs/pointnet2_rscnn_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_PN2: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_pn2 EXP_ID: pointnet2_rscnn_valid_run_1 LOSS_NAME: cross_entropy MODEL_NAME: rscnn SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 TRAIN: l2: 0.0 ================================================ FILE: configs/pointnet2_simpleview_run_1.yaml ================================================ DATALOADER: batch_size: 18 num_workers: 0 EXP: DATASET: modelnet40_pn2 EXP_ID: pointnet2_simpleview_run_1 LOSS_NAME: cross_entropy MODEL_NAME: simpleview SEED: 1 TASK: cls ================================================ FILE: configs/pointnet2_simpleview_valid_run_1.yaml ================================================ DATALOADER: MODELNET40_PN2: train_data_path: ./data/modelnet40_ply_hdf5_2048/train_minus_valid_files.txt valid_data_path: ./data/modelnet40_ply_hdf5_2048/valid_files.txt batch_size: 18 num_workers: 0 EXP: DATASET: modelnet40_pn2 EXP_ID: pointnet2_simpleview_valid_run_1 LOSS_NAME: cross_entropy MODEL_NAME: simpleview SEED: 1 TASK: cls EXP_EXTRA: no_test: true no_val: false val_eval_freq: 25 ================================================ FILE: configs/rscnn_dgcnn_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_rscnn EXP_ID: rscnn_dgcnn_run_1 LOSS_NAME: cross_entropy METRIC: acc MODEL_NAME: dgcnn SEED: 1 TASK: cls TRAIN: l2: 1e-4 ================================================ FILE: configs/rscnn_pointnet2_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_rscnn EXP_ID: rscnn_pointnet2_run_1 LOSS_NAME: cross_entropy METRIC: acc MODEL_NAME: pointnet2 SEED: 1 TASK: cls TRAIN: l2: 0.0 ================================================ FILE: configs/rscnn_pointnet_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_rscnn EXP_ID: rscnn_pointnet_run_1 LOSS_NAME: cross_entropy MODEL_NAME: pointnet SEED: 1 TASK: cls_trans TRAIN: l2: 0.0 ================================================ FILE: configs/rscnn_rscnn_run_1.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_rscnn EXP_ID: rscnn_rscnn_run_1 LOSS_NAME: cross_entropy MODEL_NAME: rscnn SEED: 1 TASK: cls TRAIN: l2: 0.0 ================================================ FILE: configs/rscnn_simpleview_run_1.yaml ================================================ DATALOADER: batch_size: 18 num_workers: 0 EXP: DATASET: modelnet40_rscnn EXP_ID: rscnn_simpleview_run_1 LOSS_NAME: cross_entropy MODEL_NAME: simpleview SEED: 1 TASK: cls ================================================ FILE: configs/rsmix/dgcnn.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: rsmix_dgcnn_run_1 LOSS_NAME: smooth MODEL_NAME: dgcnn SEED: 1 METRIC: acc TASK: cls TRAIN: l2: 0.0 AUG: NAME: rsmix BETA: 1. PROB: 0.5 ================================================ FILE: configs/rsmix/pct.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: rsmix_pct_run_1 LOSS_NAME: smooth MODEL_NAME: pct SEED: 1 TASK: cls OPTIMIZER: pct TRAIN: l2: 1e-4 learning_rate: 0.0001 AUG: NAME: rsmix BETA: 1. PROB: 0.5 ================================================ FILE: configs/rsmix/pointnet.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: rsmix_pointnet_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet SEED: 1 TASK: cls_trans TRAIN: l2: 0.0 AUG: NAME: rsmix BETA: 1. PROB: 0.5 ================================================ FILE: configs/rsmix/pointnet2.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: rsmix_pointnet2_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet2 SEED: 1 TASK: cls TRAIN: l2: 0.0 AUG: NAME: rsmix BETA: 1. PROB: 0.5 ================================================ FILE: configs/rsmix/rscnn.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: rsmix_rscnn_run_1 LOSS_NAME: smooth MODEL_NAME: rscnn SEED: 1 TASK: cls TRAIN: l2: 0.0 AUG: NAME: rsmix BETA: 1. PROB: 0.5 ================================================ FILE: configs/rsmix/simpleview.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_dgcnn EXP_ID: rsmix_simpleview_run_1 LOSS_NAME: smooth MODEL_NAME: simpleview SEED: 1 METRIC: acc TASK: cls TRAIN: l2: 0.0 AUG: NAME: rsmix BETA: 1. PROB: 0.5 ================================================ FILE: configs/tent/dgcnn.yaml ================================================ DATALOADER: batch_size: 16 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_dgcnn_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: dgcnn SEED: 1 TASK: cls TRAIN: l2: 1e-4 ADAPT: METHOD: tent ITER: 10 ================================================ FILE: configs/tent/pct.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_pct_run_1 LOSS_NAME: smooth MODEL_NAME: pct SEED: 1 TASK: cls OPTIMIZER: pct TRAIN: l2: 1e-4 learning_rate: 0.0001 ADAPT: METHOD: tent ITER: 10 ================================================ FILE: configs/tent/pointnet.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_pointnet_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet SEED: 1 TASK: cls_trans TRAIN: l2: 0.0 ADAPT: METHOD: tent ITER: 10 ================================================ FILE: configs/tent/pointnet2.yaml ================================================ DATALOADER: batch_size: 16 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_pointnet2_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet2 SEED: 1 TASK: cls TRAIN: l2: 0.0 ADAPT: METHOD: tent ITER: 10 ================================================ FILE: configs/tent/rscnn.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_rscnn_run_1 LOSS_NAME: smooth MODEL_NAME: rscnn SEED: 1 TASK: cls TRAIN: l2: 0.0 ADAPT: METHOD: tent ITER: 10 ================================================ FILE: configs/tent/simpleview.yaml ================================================ DATALOADER: batch_size: 18 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_simpleview_run_1 LOSS_NAME: smooth MODEL_NAME: simpleview SEED: 1 TASK: cls ADAPT: METHOD: tent ITER: 10 ================================================ FILE: configs/tent_cutmix/dgcnn.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_dgcnn_run_1 LOSS_NAME: smooth METRIC: acc MODEL_NAME: dgcnn SEED: 1 TASK: cls TRAIN: l2: 1e-4 ADAPT: METHOD: tent ITER: 10 ================================================ FILE: configs/tent_cutmix/pct.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_pct_run_1 LOSS_NAME: smooth MODEL_NAME: pct SEED: 1 TASK: cls OPTIMIZER: pct TRAIN: l2: 1e-4 learning_rate: 0.0001 ADAPT: METHOD: tent ITER: 10 ================================================ FILE: configs/tent_cutmix/pointnet.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_pointnet_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet SEED: 1 TASK: cls_trans TRAIN: l2: 0.0 ADAPT: METHOD: tent ITER: 10 ================================================ FILE: configs/tent_cutmix/pointnet2.yaml ================================================ DATALOADER: batch_size: 16 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_pointnet2_run_1 LOSS_NAME: smooth MODEL_NAME: pointnet2 SEED: 1 TASK: cls TRAIN: l2: 0.0 ADAPT: METHOD: tent ITER: 10 ================================================ FILE: configs/tent_cutmix/rscnn.yaml ================================================ DATALOADER: batch_size: 32 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_rscnn_run_1 LOSS_NAME: smooth MODEL_NAME: rscnn SEED: 1 TASK: cls TRAIN: l2: 0.0 ADAPT: METHOD: tent ITER: 10 ================================================ FILE: configs/tent_cutmix/simpleview.yaml ================================================ DATALOADER: batch_size: 18 num_workers: 0 EXP: DATASET: modelnet40_c EXP_ID: c_simpleview_run_1 LOSS_NAME: smooth MODEL_NAME: simpleview SEED: 1 TASK: cls ADAPT: METHOD: tent ITER: 10 ================================================ FILE: configs.py ================================================ from yacs.config import CfgNode as CN _C = CN() # ----------------------------------------------------------------------------- # EXPERIMENT # ----------------------------------------------------------------------------- _C.EXP = CN() _C.EXP.EXP_ID = "" _C.EXP.SEED = 0 _C.EXP.TASK = 'cls' _C.EXP.DATASET = 'modelnet40' _C.EXP.MODEL_NAME = 'mv' _C.EXP.LOSS_NAME = 'cross_entropy' _C.EXP.OPTIMIZER = 'vanilla' _C.EXP.METRIC = 'acc' #------------------------------------------------------------------------------ # Extra Experiment Parameters #------------------------------------------------------------------------------ _C.EXP_EXTRA = CN() _C.EXP_EXTRA.no_val = True _C.EXP_EXTRA.no_test = False _C.EXP_EXTRA.val_eval_freq = 1 _C.EXP_EXTRA.test_eval_freq = 1 _C.EXP_EXTRA.save_ckp = 25 # ----------------------------------------------------------------------------- # DATALOADER (contains things common across the datasets) # ----------------------------------------------------------------------------- _C.DATALOADER = CN() _C.DATALOADER.batch_size = 60 _C.DATALOADER.num_workers = 0 # ----------------------------------------------------------------------------- # TRAINING DETAILS (contains things common across the training) # ----------------------------------------------------------------------------- _C.TRAIN = CN() _C.TRAIN.num_epochs = 300 _C.TRAIN.learning_rate = 1e-3 _C.TRAIN.lr_decay_factor = 0.5 _C.TRAIN.lr_reduce_patience = 10 _C.TRAIN.l2 = 0.0 _C.TRAIN.early_stop = 300 _C.TRAIN.lr_clip = 0.00001 #----------------------------------------------------------------------------- # MODELNET40_RSCNN #----------------------------------------------------------------------------- _C.DATALOADER.MODELNET40_RSCNN = CN() _C.DATALOADER.MODELNET40_RSCNN.data_path = './data/' _C.DATALOADER.MODELNET40_RSCNN.train_data_path = 'train_files.txt' _C.DATALOADER.MODELNET40_RSCNN.valid_data_path = 'train_files.txt' _C.DATALOADER.MODELNET40_RSCNN.test_data_path = 'test_files.txt' _C.DATALOADER.MODELNET40_RSCNN.num_points = 1024 #----------------------------------------------------------------------------- # MODELNET40_PN2 #----------------------------------------------------------------------------- _C.DATALOADER.MODELNET40_PN2 = CN() _C.DATALOADER.MODELNET40_PN2.train_data_path = './data/modelnet40_ply_hdf5_2048/train_files.txt' _C.DATALOADER.MODELNET40_PN2.valid_data_path = './data/modelnet40_ply_hdf5_2048/train_files.txt' _C.DATALOADER.MODELNET40_PN2.test_data_path = './data/modelnet40_ply_hdf5_2048/test_files.txt' _C.DATALOADER.MODELNET40_PN2.num_points = 1024 #----------------------------------------------------------------------------- # MODELNET40_DGCNN #----------------------------------------------------------------------------- _C.DATALOADER.MODELNET40_DGCNN = CN() _C.DATALOADER.MODELNET40_DGCNN.train_data_path = './data/modelnet40_ply_hdf5_2048/train_files.txt' _C.DATALOADER.MODELNET40_DGCNN.valid_data_path = './data/modelnet40_ply_hdf5_2048/train_files.txt' _C.DATALOADER.MODELNET40_DGCNN.test_data_path = './data/modelnet40_ply_hdf5_2048/test_files.txt' _C.DATALOADER.MODELNET40_DGCNN.num_points = 1024 #----------------------------------------------------------------------------- # MODELNET40_C #----------------------------------------------------------------------------- _C.DATALOADER.MODELNET40_C = CN() _C.DATALOADER.MODELNET40_C.test_data_path = './data/modelnet40_c/' _C.DATALOADER.MODELNET40_C.corruption = 'uniform' _C.DATALOADER.MODELNET40_C.severity = 1 # ---------------------------------------------------------------------------- # MODEL # ----------------------------------------------------------------------------- _C.MODEL = CN() # ----------------------------------------------------------------------------- # MV MODEL # ----------------------------------------------------------------------------- _C.MODEL.MV = CN() _C.MODEL.MV.backbone = 'resnet18' _C.MODEL.MV.feat_size = 16 # ----------------------------------------------------------------------------- # RSCNN MODEL # ----------------------------------------------------------------------------- _C.MODEL.RSCNN = CN() _C.MODEL.RSCNN.ssn_or_msn = True # ----------------------------------------------------------------------------- # PN2 MODEL # ----------------------------------------------------------------------------- _C.MODEL.PN2 = CN() _C.MODEL.PN2.version_cls = 1.0 _C.AUG = CN() _C.AUG.NAME = 'none' _C.AUG.BETA = 1. _C.AUG.PROB = 0.5 _C.AUG.MIXUPRATE = 0.4 _C.ADAPT = CN() _C.ADAPT.METHOD = 'none' _C.ADAPT.ITER = 1 def get_cfg_defaults(): """Get a yacs CfgNode object with default values for my_project.""" # Return a clone so that the defaults will not be altered # This is for the "local variable" use pattern return _C.clone() ================================================ FILE: data/convert.py ================================================ import open3d as o3d def load_mesh(filepath): return o3d.io.read_triangle_mesh(filepath) def export_mesh(mesh, filepath): o3d.io.write_triangle_mesh(filepath, mesh) def load_pcd(filepath): return o3d.io.read_point_cloud(filepath) def export_pcd(pcd, filepath): o3d.io.write_point_cloud(filepath, pcd) def mesh_to_pcd(mesh, number_of_points=2048): return mesh.sample_points_uniformly(number_of_points=number_of_points) ================================================ FILE: data/create_modelnet40_small.py ================================================ #!/usr/bin/env python import os import h5py import numpy as np np.random.seed(123) def main(split_size): modelnet40_dir = "./data/modelnet40_ply_hdf5_2048/" modelnet40_train_file = os.path.join( modelnet40_dir, "train_minus_valid_files.txt") modelnet40_train_split_file = os.path.join( modelnet40_dir, f"train_minus_valid_split_{split_size}_files.txt") modelnet40_train_split_path = f"ply_data_trainminusval_split_{split_size}.h5" with open(modelnet40_train_file, "r") as f: modelnet40_train_paths = [l.strip() for l in f.readlines()] data = [] labels = [] for modelnet40_train_path in modelnet40_train_paths: train_h5 = h5py.File(modelnet40_train_path, "r") data.append(train_h5["data"][:]) labels.append(train_h5["label"][:]) data = np.concatenate(data) labels = np.concatenate(labels) train_data = [] train_label = [] for i in range(40): cls_inds = np.where(labels == i)[0] num_objs = len(cls_inds) num_train = int(num_objs * split_size) cls_data = data[cls_inds] np.random.shuffle(cls_data) train_data.append(cls_data[:num_train]) train_label += [i] * num_train train_data = np.concatenate(train_data) train_label = np.array(train_label).reshape(-1, 1) with open(modelnet40_train_split_file, "w") as f: f.write(os.path.join(modelnet40_dir, modelnet40_train_split_path) + "\n") with h5py.File( os.path.join(modelnet40_dir, modelnet40_train_split_path), "w") as f: f.create_dataset("data", data=train_data) f.create_dataset("label", data=train_label) print('data: {}'.format(data.shape)) print('train data: {}'.format(train_data.shape)) print('min_label: {}'.format(labels.min())) print('max_label: {}'.format(labels.max())) if __name__ == "__main__": main(0.5 / 0.8) main(0.25 / 0.8) ================================================ FILE: data/create_modelnet40_valid.py ================================================ #!/usr/bin/env python import os import h5py import numpy as np np.random.seed(123) def main(): modelnet40_dir = "./data/modelnet40_ply_hdf5_2048/" modelnet40_train_minus_valid_path = "ply_data_trainminusval.h5" modelnet40_valid_path = "ply_data_valid.h5" modelnet40_train_minus_valid_file = os.path.join(modelnet40_dir, "train_minus_valid_files.txt") modelnet40_valid_file = os.path.join(modelnet40_dir, "valid_files.txt") modelnet40_train_file = os.path.join(modelnet40_dir, "train_files.txt") with open(modelnet40_train_file, "r") as f: modelnet40_train_paths = [l.strip() for l in f.readlines()] data = [] labels = [] for modelnet40_train_path in modelnet40_train_paths: train_h5 = h5py.File(modelnet40_train_path, "r") data.append(train_h5["data"][:]) labels.append(train_h5["label"][:]) data = np.concatenate(data) labels = np.concatenate(labels) train_data = [] train_label = [] valid_data = [] valid_label = [] for i in range(40): cls_inds = np.where(labels == i)[0] num_objs = len(cls_inds) num_train = int(num_objs * 0.8) num_valid = num_objs - num_train cls_data = data[cls_inds] np.random.shuffle(cls_data) train_data.append(cls_data[:num_train]) valid_data.append(cls_data[num_train:]) train_label += [i] * num_train valid_label += [i] * num_valid train_data = np.concatenate(train_data) valid_data = np.concatenate(valid_data) train_label = np.array(train_label).reshape(-1, 1) valid_label = np.array(valid_label).reshape(-1, 1) with open(modelnet40_train_minus_valid_file, "w") as f: f.write(os.path.join(modelnet40_dir, modelnet40_train_minus_valid_path) + "\n") with open(modelnet40_valid_file, "w") as f: f.write(os.path.join(modelnet40_dir, modelnet40_valid_path) + "\n") with h5py.File(os.path.join(modelnet40_dir, modelnet40_train_minus_valid_path), "w") as f: f.create_dataset("data", data=train_data) f.create_dataset("label", data=train_label) with h5py.File(os.path.join(modelnet40_dir, modelnet40_valid_path), "w") as f: f.create_dataset("data", data=valid_data) f.create_dataset("label", data=valid_label) print('data: {}'.format(data.shape)) print('min_label: {}'.format(labels.min())) print('max_label: {}'.format(labels.max())) if __name__ == "__main__": main() ================================================ FILE: data/distortion.py ================================================ import pygem from pygem import FFD, RBF, IDW import open3d as o3d import copy import numpy as np np.random.seed(2021) def core_distortion(points, n_control_points=[2,2,2], displacement=None): """ Ref: http://mathlab.github.io/PyGeM/tutorial-1-ffd.html """ # the size of displacement matrix: 3 * control_points.shape if displacement is None: displacement = np.zeros((3,*n_control_points)) ffd = FFD(n_control_points=n_control_points) ffd.box_length = [2.,2.,2.] ffd.box_origin = [-1., -1., -1.] ffd.array_mu_x = displacement[0,:,:,:] ffd.array_mu_y = displacement[1,:,:,:] ffd.array_mu_z = displacement[2,:,:,:] new_points = ffd(points) return new_points def distortion(points, direction_mask=np.array([1,1,1]), point_mask=np.ones((5,5,5)), severity=0.5): n_control_points=[5,5,5] # random displacement = np.random.rand(3,*n_control_points) * 2 * severity - np.ones((3,*n_control_points)) * severity displacement *= np.transpose(np.tile(direction_mask, (5, 5, 5, 1)), (3, 0, 1, 2)) displacement *= np.tile(point_mask, (3, 1, 1, 1)) points = core_distortion(points, n_control_points=n_control_points, displacement=displacement) # points = denomalize(points, scale, offset) # set_points(data, points) return points def distortion_2(points, severity=(0.4,3), func = 'gaussian_spline'): rbf = RBF(func=func) xv = np.linspace(-1, 1, severity[1]) yv = np.linspace(-1, 1, severity[1]) zv = np.linspace(-1, 1, severity[1]) z, y, x = np.meshgrid(zv, yv, xv) mesh = np.array([x.ravel(), y.ravel(), z.ravel()]).T rbf.original_control_points = mesh alpha = np.random.uniform(-np.pi,np.pi,mesh.shape[0]) gamma = np.random.uniform(-np.pi,np.pi,mesh.shape[0]) distance = np.ones(mesh.shape[0]) * severity[0] displacement_x = distance * np.cos(alpha) * np.sin(gamma) displacement_y = distance * np.sin(alpha) * np.sin(gamma) displacement_z = distance * np.cos(gamma) displacement = np.array([displacement_x,displacement_y,displacement_z]).T rbf.deformed_control_points = mesh + displacement new_points = rbf(points) return new_points def distortion_3(points, severity=(0.4,3)): idw = IDW() xv = np.linspace(-1, 1, severity[1]) yv = np.linspace(-1, 1, severity[1]) zv = np.linspace(-1, 1, severity[1]) z, y, x = np.meshgrid(zv, yv, xv) mesh = np.array([x.ravel(), y.ravel(), z.ravel()]).T idw.original_control_points = mesh alpha = np.random.uniform(-np.pi,np.pi,mesh.shape[0]) gamma = np.random.uniform(-np.pi,np.pi,mesh.shape[0]) distance = np.ones(mesh.shape[0]) * severity[0] displacement_x = distance * np.cos(alpha) * np.sin(gamma) displacement_y = distance * np.sin(alpha) * np.sin(gamma) displacement_z = distance * np.cos(gamma) displacement = np.array([displacement_x,displacement_y,displacement_z]).T idw.deformed_control_points = mesh + displacement new_points = idw(points) return new_points ================================================ FILE: data/generate_c.py ================================================ ### Generate Various Common Corruptions ### from operator import index import os import h5py import json import numpy as np from numpy import random from convert import * import distortion from occlusion import * from util import * np.random.seed(2021) ### Transformation ### ''' Rotate the point cloud ''' def rotation(pointcloud,severity): N, C = pointcloud.shape c = [2.5, 5, 7.5, 10, 15][severity-1] theta = np.random.uniform(c-2.5,c+2.5) * np.random.choice([-1,1]) * np.pi / 180. gamma = np.random.uniform(c-2.5,c+2.5) * np.random.choice([-1,1]) * np.pi / 180. beta = np.random.uniform(c-2.5,c+2.5) * np.random.choice([-1,1]) * np.pi / 180. matrix_1 = np.array([[1,0,0],[0,np.cos(theta),-np.sin(theta)],[0,np.sin(theta),np.cos(theta)]]) matrix_2 = np.array([[np.cos(gamma),0,np.sin(gamma)],[0,1,0],[-np.sin(gamma),0,np.cos(gamma)]]) matrix_3 = np.array([[np.cos(beta),-np.sin(beta),0],[np.sin(beta),np.cos(beta),0],[0,0,1]]) new_pc = np.matmul(pointcloud,matrix_1) new_pc = np.matmul(new_pc,matrix_2) new_pc = np.matmul(new_pc,matrix_3).astype('float32') return normalize(new_pc) ''' Shear the point cloud ''' def shear(pointcloud,severity): N, C = pointcloud.shape c = [0.05, 0.1, 0.15, 0.2, 0.25][severity-1] a = np.random.uniform(c-0.05,c+0.05) * np.random.choice([-1,1]) b = np.random.uniform(c-0.05,c+0.05) * np.random.choice([-1,1]) d = np.random.uniform(c-0.05,c+0.05) * np.random.choice([-1,1]) e = np.random.uniform(c-0.05,c+0.05) * np.random.choice([-1,1]) f = np.random.uniform(c-0.05,c+0.05) * np.random.choice([-1,1]) g = np.random.uniform(c-0.05,c+0.05) * np.random.choice([-1,1]) matrix = np.array([[1,0,b],[d,1,e],[f,0,1]]) new_pc = np.matmul(pointcloud,matrix).astype('float32') return normalize(new_pc) ''' Scale the point cloud ''' def scale(pointcloud,severity): #TODO N, C = pointcloud.shape c = [0.1, 0.2, 0.3, 0.4, 0.5][severity-1] a=b=d=1 r = np.random.randint(0,3) t = np.random.choice([-1,1]) if r == 0: a += c * t b += c * (-t) elif r == 1: b += c * t d += c * (-t) elif r == 2: a += c * t d += c * (-t) matrix = np.array([[a,0,0],[0,b,0],[0,0,d]]) new_pc = np.matmul(pointcloud,matrix).astype('float32') return normalize(new_pc) ### Noise ### ''' Add Uniform noise to point cloud ''' def uniform_noise(pointcloud, severity): #TODO N, C = pointcloud.shape c = [0.01, 0.02, 0.03, 0.04, 0.05][severity-1] jitter = np.random.uniform(-c,c,(N, C)) new_pc = (pointcloud + jitter).astype('float32') return normalize(new_pc) ''' Add Gaussian noise to point cloud ''' def gaussian_noise(pointcloud, severity): N, C = pointcloud.shape c = [0.01, 0.015, 0.02, 0.025, 0.03][severity-1] jitter = np.random.normal(size=(N, C)) * c new_pc = (pointcloud + jitter).astype('float32') new_pc = np.clip(new_pc,-1,1) return new_pc ''' Add noise to the edge-length-2 cude ''' def background_noise(pointcloud, severity): N, C = pointcloud.shape c = [N//45, N//40, N//35, N//30, N//20][severity-1] jitter = np.random.uniform(-1,1,(c, C)) new_pc = np.concatenate((pointcloud,jitter),axis=0).astype('float32') return normalize(new_pc) ''' Upsampling ''' def upsampling(pointcloud, severity): N, C = pointcloud.shape c = [N//5, N//4, N//3, N//2, N][severity-1] index = np.random.choice(ORIG_NUM, c, replace=False) add = pointcloud[index] + np.random.uniform(-0.05,0.05,(c, C)) new_pc = np.concatenate((pointcloud,add),axis=0).astype('float32') return normalize(new_pc) ''' Add impulse noise ''' def impulse_noise(pointcloud, severity): N, C = pointcloud.shape c = [N//30, N//25, N//20, N//15, N//10][severity-1] index = np.random.choice(ORIG_NUM, c, replace=False) pointcloud[index] += np.random.choice([-1,1], size=(c,C)) * 0.1 return normalize(pointcloud) ### Point Number Modification ### ''' Cutout several part in the point cloud ''' def cutout(pointcloud, severity): N, C = pointcloud.shape c = [(2,30), (3,30), (5,30), (7,30), (10,30)][severity-1] for _ in range(c[0]): i = np.random.choice(pointcloud.shape[0],1) picked = pointcloud[i] dist = np.sum((pointcloud - picked)**2, axis=1, keepdims=True) idx = np.argpartition(dist, c[1], axis=0)[:c[1]] # pointcloud[idx.squeeze()] = 0 pointcloud = np.delete(pointcloud, idx.squeeze(), axis=0) # print(pointcloud.shape) return pointcloud ''' Uniformly sampling the point cloud ''' def uniform_sampling(pointcloud, severity): N, C = pointcloud.shape c = [N//15, N//10, N//8, N//6, N//2, 3 * N//4][severity-1] index = np.random.choice(ORIG_NUM, ORIG_NUM - c, replace=False) return pointcloud[index] ''' Density-based up-sampling the point cloud ''' def density_inc(pointcloud, severity): N, C = pointcloud.shape c = [(1,100), (2,100), (3,100), (4,100), (5,100)][severity-1] # idx = np.random.choice(N,c[0]) temp = [] for _ in range(c[0]): i = np.random.choice(pointcloud.shape[0],1) picked = pointcloud[i] dist = np.sum((pointcloud - picked)**2, axis=1, keepdims=True) idx = np.argpartition(dist, c[1], axis=0)[:c[1]] # idx_2 = np.random.choice(c[1],int((3/4) * c[1]),replace=False) # idx = idx[idx_2] temp.append(pointcloud[idx.squeeze()]) pointcloud = np.delete(pointcloud, idx.squeeze(), axis=0) idx = np.random.choice(pointcloud.shape[0],1024 - c[0] * c[1]) temp.append(pointcloud[idx.squeeze()]) pointcloud = np.concatenate(temp) # print(pointcloud.shape) return pointcloud ''' Density-based sampling the point cloud ''' def density(pointcloud, severity): N, C = pointcloud.shape c = [(1,100), (2,100), (3,100), (4,100), (5,100)][severity-1] for _ in range(c[0]): i = np.random.choice(pointcloud.shape[0],1) picked = pointcloud[i] dist = np.sum((pointcloud - picked)**2, axis=1, keepdims=True) idx = np.argpartition(dist, c[1], axis=0)[:c[1]] idx_2 = np.random.choice(c[1],int((3/4) * c[1]),replace=False) idx = idx[idx_2] pointcloud = np.delete(pointcloud, idx.squeeze(), axis=0) # pointcloud[idx.squeeze()] = 0 # print(pointcloud.shape) return pointcloud def occlusion(severity): ## severity here does not stand for real severity ## pointcloud = [] f_0 = open("./data/modelnet40_ply_hdf5_2048/ply_data_test_0_id2file.json") f_1 = open("./data/modelnet40_ply_hdf5_2048/ply_data_test_1_id2file.json") lsit_0 = json.load(f_0) lsit_1 = json.load(f_1) f_0.close() f_1.close() for item in lsit_0 + lsit_1: folder = item.split('/')[0] mesh = item.split('/')[1][:-3] + 'off' # print(mesh) original_data = load_mesh("./data/ModelNet40/" + folder + "/test/" + mesh) new_pc = occlusion_1(original_data,'occlusion',severity,n_points=1024) theta = -np.pi / 2. gamma = 0 beta = np.pi matrix_1 = np.array([[1,0,0],[0,np.cos(theta),-np.sin(theta)],[0,np.sin(theta),np.cos(theta)]]) matrix_2 = np.array([[np.cos(gamma),0,np.sin(gamma)],[0,1,0],[-np.sin(gamma),0,np.cos(gamma)]]) matrix_3 = np.array([[np.cos(beta),-np.sin(beta),0],[np.sin(beta),np.cos(beta),0],[0,0,1]]) new_pc = np.matmul(new_pc,matrix_1) new_pc = np.matmul(new_pc,matrix_2) new_pc = normalize(np.matmul(new_pc,matrix_3).astype('float32')) pointcloud.append(new_pc) pointcloud = np.stack(pointcloud,axis=0) np.save("./data/modelnet40_c/data_occlusion_" + str(severity) + ".npy", pointcloud) return def simulate_lidar(pointcloud,pose,severity): pose = pose.transpose() ##################################### # simplify the rotation to I matrix # pose[:3,:3] = 0 pose[0,0] = pose[1,1] = pose[2,2] = 1 # Translate the point cloud # pose[3,[0,1,2]] = -pose[3,[0,1,2]] ##################################### pointcloud_new = np.concatenate([pointcloud,np.ones((pointcloud.shape[0],1))],axis=1) pointcloud_new = np.dot(pointcloud_new,pose) pointcloud_new = appendSpherical_np(pointcloud_new[:,:3]) delta = 1. * np.pi / 180. cur = np.min(pointcloud_new[:,4]) new_pc = [] while cur + delta < np.max(pointcloud_new[:4]): pointcloud_new[(pointcloud_new[:,4] >= cur+delta/4) & (pointcloud_new[:,4] < cur + delta*3/4),4] = cur + delta / 2. new_pc.append(pointcloud_new[(pointcloud_new[:,4] >= cur+delta/4) & (pointcloud_new[:,4] < cur + delta*3/4)]) cur += delta new_pc = np.concatenate(new_pc,axis=0) # pointcloud = np.dot(pointcloud,np.linalg.inv(pose)) new_pc = appendCart_np(new_pc[:,3:]) new_pc = np.concatenate([new_pc[:,3:],np.ones((new_pc.shape[0],1))],axis=1) new_pc = np.dot(new_pc,np.linalg.inv(pose)) index = np.random.choice(new_pc.shape[0],768) new_pc = new_pc[index] return new_pc[:,:3] def lidar(severity): ## severity here does not stand for real severity ## pointcloud = [] f_0 = open("./data/modelnet40_ply_hdf5_2048/ply_data_test_0_id2file.json") f_1 = open("./data/modelnet40_ply_hdf5_2048/ply_data_test_1_id2file.json") lsit_0 = json.load(f_0) lsit_1 = json.load(f_1) f_0.close() f_1.close() for item in lsit_0 + lsit_1: folder = item.split('/')[0] mesh = item.split('/')[1][:-3] + 'off' original_data = load_mesh("./data/ModelNet40/" + folder + "/test/" + mesh) new_pc,pose = occlusion_1(original_data,'lidar',severity,n_points=1024) new_pc = simulate_lidar(new_pc,pose,severity) theta = -np.pi / 2. gamma = 0 beta = np.pi matrix_1 = np.array([[1,0,0],[0,np.cos(theta),-np.sin(theta)],[0,np.sin(theta),np.cos(theta)]]) matrix_2 = np.array([[np.cos(gamma),0,np.sin(gamma)],[0,1,0],[-np.sin(gamma),0,np.cos(gamma)]]) matrix_3 = np.array([[np.cos(beta),-np.sin(beta),0],[np.sin(beta),np.cos(beta),0],[0,0,1]]) new_pc = np.matmul(new_pc,matrix_1) new_pc = np.matmul(new_pc,matrix_2) new_pc = np.matmul(new_pc,matrix_3).astype('float32') pointcloud.append(new_pc) pointcloud = np.stack(pointcloud,axis=0) np.save("./data/modelnet40_c/data_lidar_" + str(severity) + ".npy", pointcloud) return def ffd_distortion(pointcloud, severity): N, C = pointcloud.shape c = [0.1,0.2,0.3,0.4,0.5][severity-1] new_pc = distortion.distortion(pointcloud,severity=c) return normalize(new_pc) def rbf_distortion(pointcloud, severity): N, C = pointcloud.shape c = [(0.025,5),(0.05,5),(0.075,5),(0.1,5),(0.125,5)][severity-1] new_pc = distortion.distortion_2(pointcloud,severity=c,func='multi_quadratic_biharmonic_spline') return normalize(new_pc).astype('float32') def rbf_distortion_inv(pointcloud, severity): N, C = pointcloud.shape c = [(0.025,5),(0.05,5),(0.075,5),(0.1,5),(0.125,5)][severity-1] new_pc = distortion.distortion_2(pointcloud,severity=c,func='inv_multi_quadratic_biharmonic_spline') return normalize(new_pc).astype('float32') def load_data(): os.makedirs("./data/modelnet40_c",exist_ok = True) modelnet40_dir = "./data/modelnet40_ply_hdf5_2048/" modelnet40_test_file = os.path.join(modelnet40_dir, "test_files.txt") with open(modelnet40_test_file, "r") as f: modelnet40_test_paths = [l.strip() for l in f.readlines()] data = [] labels = [] for modelnet40_test_path in modelnet40_test_paths: test_h5 = h5py.File(modelnet40_test_path, "r") data.append(test_h5["data"][:]) labels.append(test_h5["label"][:]) data = np.concatenate(data) labels = np.concatenate(labels) np.save("./data/modelnet40_c/label.npy", labels) return data, labels def save_data(data,corruption,severity): if not MAP[corruption]: np.save("./data/modelnet40_c/data_" + corruption + ".npy", data) return new_data = [] for i in range(data.shape[0]): if corruption in ['occlusion', 'lidar']: new_data.append(MAP[corruption](severity)) else: new_data.append(MAP[corruption](data[i],severity)) new_data = np.stack(new_data,axis=0) np.save("./data/modelnet40_c/data_" + corruption + "_" + str(severity) + ".npy", new_data) MAP = {'uniform': uniform_noise, 'gaussian': gaussian_noise, 'background': background_noise, 'impulse': impulse_noise, 'scale': scale, 'upsampling': upsampling, 'shear': shear, 'rotation': rotation, 'cutout': cutout, 'density': density, 'density_inc': density_inc, 'distortion': ffd_distortion, 'distortion_rbf': rbf_distortion, 'distortion_rbf_inv': rbf_distortion_inv, 'occlusion': occlusion, 'lidar': lidar, 'original': None, } ORIG_NUM = 1024 if __name__ == "__main__": data, labels = load_data() for cor in MAP.keys(): # if cor in ['occlusion', 'lidar']: # continue for sev in [1,2,3,4,5]: if cor == 'density_inc': ORIG_NUM = 2048 else: ORIG_NUM = 1024 index = np.random.choice(data.shape[1],ORIG_NUM,replace=False) save_data(data[:,index,:], cor, sev) print("Done with Corruption: {} with Severity: {}".format(cor,sev)) ================================================ FILE: data/occlusion.py ================================================ import open3d as o3d import numpy as np from util import get_points, set_points, normalize, shuffle_data def random_pose(severity): """generate a random camera pose""" theta = 2 * np.pi * severity / 5 delta = np.pi / 5 angle_x = np.random.uniform(2./3. * np.pi, 5./6. * np.pi) angle_y = 0 angle_z = np.random.uniform(theta-delta,theta+delta) Rx = np.array([[1, 0, 0], [0, np.cos(angle_x), -np.sin(angle_x)], [0, np.sin(angle_x), np.cos(angle_x)]]) Ry = np.array([[np.cos(angle_y), 0, np.sin(angle_y)], [0, 1, 0], [-np.sin(angle_y), 0, np.cos(angle_y)]]) Rz = np.array([[np.cos(angle_z), -np.sin(angle_z), 0], [np.sin(angle_z), np.cos(angle_z), 0], [0, 0, 1]]) R = np.dot(Rz, np.dot(Ry, Rx)) # a rotation matrix with arbitrarily chosen yaw, pitch, roll # Set camera pointing to the origin and 1 unit away from the origin t = np.expand_dims(-R[:, 2] * 3., 1) # select the third column, reshape into (3, 1)-vector matrix = np.concatenate([np.concatenate([R.T, -np.dot(R.T,t)], 1), [[0, 0, 0, 1]]], 0) return matrix def lidar_pose(severity): """generate a random LiDAR pose""" theta = 2 * np.pi * severity / 5 delta = np.pi / 5 angle_x = 5./8. * np.pi angle_y = 0 angle_z = np.random.uniform(theta-delta,theta+delta) Rx = np.array([[1, 0, 0], [0, np.cos(angle_x), -np.sin(angle_x)], [0, np.sin(angle_x), np.cos(angle_x)]]) Ry = np.array([[np.cos(angle_y), 0, np.sin(angle_y)], [0, 1, 0], [-np.sin(angle_y), 0, np.cos(angle_y)]]) Rz = np.array([[np.cos(angle_z), -np.sin(angle_z), 0], [np.sin(angle_z), np.cos(angle_z), 0], [0, 0, 1]]) R = np.dot(Rz, np.dot(Ry, Rx)) # a rotation matrix with arbitrarily chosen yaw, pitch, roll # Set camera pointing to the origin and 1 unit away from the origin t = np.expand_dims(-R[:, 2] * 5, 1) # select the third column, reshape into (3, 1)-vector pose = np.concatenate([np.concatenate([R, t], 1), [[0, 0, 0, 1]]], 0) matrix = np.concatenate([np.concatenate([R.T, -np.dot(R.T,t)], 1), [[0, 0, 0, 1]]], 0) return matrix, pose def get_default_camera_extrinsic(): return np.array([[1,0,0,1], [0,1,0,0], [0,0,1,2], [0,0,0,1]]) def get_default_camera_intrinsic(width=1920, height=1080): return { "width": width, "height": height, "fx": 365, "fy": 365, "cx": width / 2 - 0.5, "cy": height / 2 - 0.5 } def core_occlusion(mesh, type, camera_extrinsic=None, camera_intrinsic=None, window_width=1080, window_height=720, n_points=None, downsample_ratio=None): if camera_extrinsic is None: camera_extrinsic = get_default_camera_extrinsic() if camera_intrinsic is None: camera_intrinsic = get_default_camera_intrinsic() camera_parameters = o3d.camera.PinholeCameraParameters() camera_parameters.extrinsic = camera_extrinsic camera_parameters.intrinsic.set_intrinsics(**camera_intrinsic) viewer = o3d.visualization.Visualizer() viewer.create_window(width=window_width, height=window_height) viewer.add_geometry(mesh) control = viewer.get_view_control() control.convert_from_pinhole_camera_parameters(camera_parameters) # viewer.run() depth = viewer.capture_depth_float_buffer(do_render=True) viewer.destroy_window() pcd = o3d.geometry.PointCloud.create_from_depth_image(depth, camera_parameters.intrinsic, extrinsic=camera_parameters.extrinsic) if downsample_ratio is not None: ratio = int((1 - downsample_ratio) / downsample_ratio) pcd = pcd.uniform_down_sample(ratio) elif n_points is not None: # print(np.asarray(pcd.points).shape[0]) ratio = int(np.asarray(pcd.points).shape[0] / n_points) if ratio > 0: # if type == 'occlusion': set_points(pcd, shuffle_data(np.asarray(pcd.points))) pcd = pcd.uniform_down_sample(ratio) return pcd def occlusion_1(mesh, type, severity, window_width=1080, window_height=720, n_points=None, downsample_ratio=None): points = get_points(mesh) points = normalize(points) set_points(mesh, points) if type == 'occlusion': camera_extrinsic = random_pose(severity) elif type == 'lidar': camera_extrinsic,pose = lidar_pose(severity) camera_intrinsic = get_default_camera_intrinsic(window_width, window_height) pcd = core_occlusion(mesh, type, camera_extrinsic=camera_extrinsic, camera_intrinsic=camera_intrinsic, window_width=window_width, window_height=window_height, n_points=n_points, downsample_ratio=downsample_ratio) points = get_points(pcd) if points.shape[0] < n_points: index = np.random.choice(points.shape[0], n_points) points = points[index] # points = normalize(points) # points = denomalize(points, scale, offset) if type == 'lidar': return points[:n_points,:], pose else: return points[:n_points,:] ================================================ FILE: data/process.py ================================================ import os SHAPE = ["airplane", "bathtub", "bed", "bench", "bookshelf", "bottle", "bowl", "car", "chair", "cone", "cup", "curtain", "desk", "door", "dresser", "flower_pot", "glass_box", "guitar", "keyboard", "lamp", "laptop", "mantel", "monitor", "night_stand", "person", "piano", "plant", "radio", "range_hood", "sink", "sofa", "stairs", "stool", "table", "tent", "toilet", "tv_stand", "vase", "wardrobe", "xbox" ] if __name__ == '__main__': for object in SHAPE: g = os.walk("data/ModelNet40/"+object+"/test") for path,dir_list,file_list in g: for file in file_list: # print(file) with open(os.path.join(path,file), "r") as f: lines = f.readlines() if len(lines[0]) == 4: continue else: lines.insert(0,'OFF\n') lines[1] = lines[1][3:] # print(lines) with open(os.path.join(path,file), "w") as f: for line in lines: f.write(line) ================================================ FILE: data/util.py ================================================ import open3d as o3d import numpy as np import copy def get_points(data): if isinstance(data, o3d.cpu.pybind.geometry.TriangleMesh): return np.asarray(data.vertices) elif isinstance(data, o3d.cpu.pybind.geometry.PointCloud): return np.asarray(data.points) else: raise Exception("Wrong input data format: should be pointcloud or mesh") def set_points(data, points): if isinstance(data, o3d.cpu.pybind.geometry.TriangleMesh): data.vertices = o3d.utility.Vector3dVector(points) return data elif isinstance(data, o3d.cpu.pybind.geometry.PointCloud): data.points = o3d.utility.Vector3dVector(points) return data else: raise Exception("Wrong input data format: should be pointcloud or mesh") def normalize(new_pc): new_pc[:,0] -= (np.max(new_pc[:,0]) + np.min(new_pc[:,0])) / 2 new_pc[:,1] -= (np.max(new_pc[:,1]) + np.min(new_pc[:,1])) / 2 new_pc[:,2] -= (np.max(new_pc[:,2]) + np.min(new_pc[:,2])) / 2 leng_x, leng_y, leng_z = np.max(new_pc[:,0]) - np.min(new_pc[:,0]), np.max(new_pc[:,1]) - np.min(new_pc[:,1]), np.max(new_pc[:,2]) - np.min(new_pc[:,2]) if leng_x >= leng_y and leng_x >= leng_z: ratio = 2.0 / leng_x elif leng_y >= leng_x and leng_y >= leng_z: ratio = 2.0 / leng_y else: ratio = 2.0 / leng_z new_pc *= ratio return new_pc def denomalize(points, scale, offset, hard_copy=False): if hard_copy: new_points = copy.deepcopy(points) else: new_points = points n_points = new_points.shape[0] new_points = new_points * np.tile(scale, (n_points,1)) + np.tile(offset, (n_points,1)) return new_points def shuffle_data(data): idx = np.arange(data.shape[0]) np.random.shuffle(idx) return data[idx, ...] def appendSpherical_np(xyz): ptsnew = np.hstack((xyz, np.zeros(xyz.shape))) xy = xyz[:,0]**2 + xyz[:,1]**2 ptsnew[:,3] = np.sqrt(xy + xyz[:,2]**2) ptsnew[:,4] = np.arctan2(np.sqrt(xy), xyz[:,2]) # for elevation angle defined from Z-axis down #ptsnew[:,4] = np.arctan2(xyz[:,2], np.sqrt(xy)) # for elevation angle defined from XY-plane up ptsnew[:,5] = np.arctan2(xyz[:,1], xyz[:,0]) return ptsnew def appendCart_np(xyz): ptsnew = np.hstack((xyz, np.zeros(xyz.shape))) ptsnew[:,3] = ptsnew[:,0] * np.sin(ptsnew[:,1]) * np.cos(ptsnew[:,2]) ptsnew[:,4] = ptsnew[:,0] * np.sin(ptsnew[:,1]) * np.sin(ptsnew[:,2]) ptsnew[:,5] = ptsnew[:,0] * np.cos(ptsnew[:,1]) return ptsnew ================================================ FILE: dataloader.py ================================================ import numpy as np import torch from torch.utils.data import Dataset, DataLoader from torchvision import transforms import os from pc_utils import (rotate_point_cloud, PointcloudScaleAndTranslate) import rs_cnn.data.data_utils as rscnn_d_utils from rs_cnn.data.ModelNet40Loader import ModelNet40Cls as rscnn_ModelNet40Cls import PCT_Pytorch.pointnet2_ops_lib.pointnet2_ops.pointnet2_utils as pointnet2_utils from pointnet2_tf.modelnet_h5_dataset import ModelNetH5Dataset as pointnet2_ModelNetH5Dataset from dgcnn.pytorch.data import ModelNet40 as dgcnn_ModelNet40 # distilled from the following sources: # https://github.com/Yochengliu/Relation-Shape-CNN/blob/master/data/ModelNet40Loader.py # https://github.com/Yochengliu/Relation-Shape-CNN/blob/master/train_cls.py class ModelNet40Rscnn(Dataset): def __init__(self, split, data_path, train_data_path, valid_data_path, test_data_path, num_points): self.split = split self.num_points = num_points _transforms = transforms.Compose([rscnn_d_utils.PointcloudToTensor()]) rscnn_params = { 'num_points': 1024, # although it does not matter 'root': data_path, 'transforms': _transforms, 'train': (split in ["train", "valid"]), 'data_file': { 'train': train_data_path, 'valid': valid_data_path, 'test': test_data_path }[self.split] } self.rscnn_dataset = rscnn_ModelNet40Cls(**rscnn_params) self.PointcloudScaleAndTranslate = PointcloudScaleAndTranslate() def __len__(self): return self.rscnn_dataset.__len__() def __getitem__(self, idx): point, label = self.rscnn_dataset.__getitem__(idx) # for compatibility with the overall code point = np.array(point) label = label[0].item() return {'pc': point, 'label': label} def batch_proc(self, data_batch, device): point = data_batch['pc'].to(device) if self.split == "train": # (B, npoint) fps_idx = pointnet2_utils.furthest_point_sample(point, 1200) fps_idx = fps_idx[:, np.random.choice(1200, self.num_points, False)] point = pointnet2_utils.gather_operation( point.transpose(1, 2).contiguous(), fps_idx).transpose(1, 2).contiguous() # (B, N, 3) point.data = self.PointcloudScaleAndTranslate(point.data) else: fps_idx = pointnet2_utils.furthest_point_sample( point, self.num_points) # (B, npoint) point = pointnet2_utils.gather_operation( point.transpose(1, 2).contiguous(), fps_idx).transpose(1, 2).contiguous() # to maintain compatibility point = point.cpu() return {'pc': point, 'label': data_batch['label']} # distilled from the following sources: # https://github.com/charlesq34/pointnet2/blob/7961e26e31d0ba5a72020635cee03aac5d0e754a/modelnet_h5_dataset.py # https://github.com/charlesq34/pointnet2/blob/7961e26e31d0ba5a72020635cee03aac5d0e754a/train.py class ModelNet40PN2(Dataset): def __init__(self, split, train_data_path, valid_data_path, test_data_path, num_points): self.split = split self.dataset_name = 'modelnet40_pn2' data_path = { "train": train_data_path, "valid": valid_data_path, "test": test_data_path }[self.split] pointnet2_params = { 'list_filename': data_path, # this has nothing to do with actual dataloader batch size 'batch_size': 32, 'npoints': num_points, 'shuffle': False } # loading all the pointnet2data self._dataset = pointnet2_ModelNetH5Dataset(**pointnet2_params) all_pc = [] all_label = [] while self._dataset.has_next_batch(): # augmentation here has nothing to do with actual data_augmentation pc, label = self._dataset.next_batch(augment=False) all_pc.append(pc) all_label.append(label) self.all_pc = np.concatenate(all_pc) self.all_label = np.concatenate(all_label) def __len__(self): return self.all_pc.shape[0] def __getitem__(self, idx): return {'pc': self.all_pc[idx], 'label': np.int64(self.all_label[idx])} def batch_proc(self, data_batch, device): if self.split == "train": point = np.array(data_batch['pc']) point = self._dataset._augment_batch_data(point) # converted to tensor to maintain compatibility with the other code data_batch['pc'] = torch.tensor(point) else: pass return data_batch class ModelNet40Dgcnn(Dataset): def __init__(self, split, train_data_path, valid_data_path, test_data_path, num_points): self.split = split self.data_path = { "train": train_data_path, "valid": valid_data_path, "test": test_data_path }[self.split] dgcnn_params = { 'partition': 'train' if split in ['train', 'valid'] else 'test', 'num_points': num_points, "data_path": self.data_path } self.dataset = dgcnn_ModelNet40(**dgcnn_params) def __len__(self): return self.dataset.__len__() def __getitem__(self, idx): pc, label = self.dataset.__getitem__(idx) return {'pc': pc, 'label': label.item()} def load_data(data_path,corruption,severity): DATA_DIR = os.path.join(data_path, 'data_' + corruption + '_' +str(severity) + '.npy') # if corruption in ['occlusion']: # LABEL_DIR = os.path.join(data_path, 'label_occlusion.npy') LABEL_DIR = os.path.join(data_path, 'label.npy') all_data = np.load(DATA_DIR) all_label = np.load(LABEL_DIR) return all_data, all_label class ModelNet40C(Dataset): def __init__(self, split, test_data_path,corruption,severity): assert split == 'test' self.split = split self.data_path = { "test": test_data_path }[self.split] self.corruption = corruption self.severity = severity self.data, self.label = load_data(self.data_path, self.corruption, self.severity) # self.num_points = num_points self.partition = 'test' def __getitem__(self, item): pointcloud = self.data[item]#[:self.num_points] label = self.label[item] return {'pc': pointcloud, 'label': label.item()} def __len__(self): return self.data.shape[0] def create_dataloader(split, cfg): num_workers = cfg.DATALOADER.num_workers batch_size = cfg.DATALOADER.batch_size dataset_args = { "split": split } if cfg.EXP.DATASET == "modelnet40_rscnn": dataset_args.update(dict(**cfg.DATALOADER.MODELNET40_RSCNN)) # augmentation directly done in the code so that # it is as similar to the vanilla code as possible dataset = ModelNet40Rscnn(**dataset_args) elif cfg.EXP.DATASET == "modelnet40_pn2": dataset_args.update(dict(**cfg.DATALOADER.MODELNET40_PN2)) dataset = ModelNet40PN2(**dataset_args) elif cfg.EXP.DATASET == "modelnet40_dgcnn": dataset_args.update(dict(**cfg.DATALOADER.MODELNET40_DGCNN)) dataset = ModelNet40Dgcnn(**dataset_args) elif cfg.EXP.DATASET == "modelnet40_c": dataset_args.update(dict(**cfg.DATALOADER.MODELNET40_C)) dataset = ModelNet40C(**dataset_args) else: assert False if "batch_proc" not in dir(dataset): dataset.batch_proc = None return DataLoader( dataset, batch_size, num_workers=num_workers, shuffle=(split == "train"), drop_last=(split == "train"), pin_memory=(torch.cuda.is_available()) and (not num_workers) ) ================================================ FILE: dgcnn/.gitignore ================================================ data/ log/ *.pyc .DS_Store pytorch/pretrained/ pytorch/checkpoints/ tensorflow/part_seg/train_results/ ================================================ FILE: dgcnn/README.md ================================================ # Dynamic Graph CNN for Learning on Point Clouds We propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds including classification and segmentation. EdgeConv is differentiable and can be plugged into existing architectures. [[Project]](https://liuziwei7.github.io/projects/DGCNN) [[Paper]](https://arxiv.org/abs/1801.07829) ## Overview `DGCNN` is the author's re-implementation of Dynamic Graph CNN, which achieves state-of-the-art performance on point-cloud-related high-level tasks including category classification, semantic segmentation and part segmentation. Further information please contact [Yue Wang](https://www.csail.mit.edu/person/yue-wang) and [Yongbin Sun](https://autoid.mit.edu/people-2). ## Author's Implementations The classification experiments in our paper are done with the pytorch implementation. * [tensorflow-dgcnn](./tensorflow) * [pytorch-dgcnn](./pytorch) ## Other Implementations * [pytorch-geometric](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.EdgeConv) * [pytorch-dgcnn](https://github.com/AnTao97/dgcnn.pytorch) (This implementation on S3DIS achieves significant better results than our tensorflow implementation) ## Citation Please cite this paper if you want to use it in your work, @article{dgcnn, title={Dynamic Graph CNN for Learning on Point Clouds}, author={Wang, Yue and Sun, Yongbin and Liu, Ziwei and Sarma, Sanjay E. and Bronstein, Michael M. and Solomon, Justin M.}, journal={ACM Transactions on Graphics (TOG)}, year={2019} } ## License MIT License ## Acknowledgement The structure of this codebase is borrowed from [PointNet](https://github.com/charlesq34/pointnet). ================================================ FILE: dgcnn/pytorch/README.md ================================================ # Dynamic Graph CNN for Learning on Point Clouds (PyTorch) ## Point Cloud Classification * Run the training script: ``` 1024 points python main.py --exp_name=dgcnn_1024 --model=dgcnn --num_points=1024 --k=20 --use_sgd=True ``` ``` 2048 points python main.py --exp_name=dgcnn_2048 --model=dgcnn --num_points=2048 --k=40 --use_sgd=True ``` * Run the evaluation script after training finished: ``` 1024 points python main.py --exp_name=dgcnn_1024_eval --model=dgcnn --num_points=1024 --k=20 --use_sgd=True --eval=True --model_path=checkpoints/dgcnn_1024/models/model.t7 ``` ``` 2048 points python main.py --exp_name=dgcnn_2048_eval --model=dgcnn --num_points=2048 --k=40 --use_sgd=True --eval=True --model_path=checkpoints/dgcnn_2048/models/model.t7 ``` * Run the evaluation script with pretrained models: ``` 1024 points python main.py --exp_name=dgcnn_1024_eval --model=dgcnn --num_points=1024 --k=20 --use_sgd=True --eval=True --model_path=pretrained/model.1024.t7 ``` ``` 2048 points python main.py --exp_name=dgcnn_2048_eval --model=dgcnn --num_points=2048 --k=40 --use_sgd=True --eval=True --model_path=pretrained/model.2048.t7 ``` ================================================ FILE: dgcnn/pytorch/data.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- """ @Author: Yue Wang @Contact: yuewangx@mit.edu @File: data.py @Time: 2018/10/13 6:21 PM """ import os import sys import glob import h5py import numpy as np from torch.utils.data import Dataset def download(): BASE_DIR = os.path.dirname(os.path.abspath(__file__)) DATA_DIR = os.path.join(BASE_DIR, '../../data') if not os.path.exists(DATA_DIR): os.mkdir(DATA_DIR) if not os.path.exists(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048')): www = 'https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip' zipfile = os.path.basename(www) os.system('wget %s; unzip %s' % (www, zipfile)) os.system('mv %s %s' % (zipfile[:-4], DATA_DIR)) os.system('rm %s' % (zipfile)) def load_data(data_path): download() BASE_DIR = os.path.dirname(os.path.abspath(__file__)) DATA_DIR = os.path.join(BASE_DIR, '../../data') all_data = [] all_label = [] with open(data_path, "r") as f: # for h5_name in glob.glob(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048', 'ply_data_%s*.h5'%partition)): for h5_name in f.readlines(): # h5_name = os.path.join(BASE_DIR, "../../", h5_name.strip()) f = h5py.File(h5_name.strip(), 'r') data = f['data'][:].astype('float32') label = f['label'][:].astype('int64') f.close() all_data.append(data) all_label.append(label) all_data = np.concatenate(all_data, axis=0) all_label = np.concatenate(all_label, axis=0) return all_data, all_label def translate_pointcloud(pointcloud): xyz1 = np.random.uniform(low=2./3., high=3./2., size=[3]) xyz2 = np.random.uniform(low=-0.2, high=0.2, size=[3]) translated_pointcloud = np.add(np.multiply(pointcloud, xyz1), xyz2).astype('float32') return translated_pointcloud def jitter_pointcloud(pointcloud, sigma=0.01, clip=0.02): N, C = pointcloud.shape pointcloud += np.clip(sigma * np.random.randn(N, C), -1*clip, clip) return pointcloud class ModelNet40(Dataset): def __init__(self, num_points, data_path, partition='train'): self.data, self.label = load_data(data_path) self.num_points = num_points self.partition = partition def __getitem__(self, item): pointcloud = self.data[item][:self.num_points] label = self.label[item] if self.partition == 'train': pointcloud = translate_pointcloud(pointcloud) np.random.shuffle(pointcloud) return pointcloud, label def __len__(self): return self.data.shape[0] if __name__ == '__main__': train = ModelNet40(1024) test = ModelNet40(1024, 'test') for data, label in train: print(data.shape) print(label.shape) ================================================ FILE: dgcnn/pytorch/main.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- """ @Author: Yue Wang @Contact: yuewangx@mit.edu @File: main.py @Time: 2018/10/13 10:39 PM """ from __future__ import print_function import os import argparse import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.optim.lr_scheduler import CosineAnnealingLR from data import ModelNet40 from model import PointNet, DGCNN import numpy as np from torch.utils.data import DataLoader from util import cal_loss, IOStream import sklearn.metrics as metrics def _init_(): if not os.path.exists('checkpoints'): os.makedirs('checkpoints') if not os.path.exists('checkpoints/'+args.exp_name): os.makedirs('checkpoints/'+args.exp_name) if not os.path.exists('checkpoints/'+args.exp_name+'/'+'models'): os.makedirs('checkpoints/'+args.exp_name+'/'+'models') os.system('cp main.py checkpoints'+'/'+args.exp_name+'/'+'main.py.backup') os.system('cp model.py checkpoints' + '/' + args.exp_name + '/' + 'model.py.backup') os.system('cp util.py checkpoints' + '/' + args.exp_name + '/' + 'util.py.backup') os.system('cp data.py checkpoints' + '/' + args.exp_name + '/' + 'data.py.backup') def train(args, io): train_loader = DataLoader(ModelNet40(partition='train', num_points=args.num_points), num_workers=8, batch_size=args.batch_size, shuffle=True, drop_last=True) test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), num_workers=8, batch_size=args.test_batch_size, shuffle=True, drop_last=False) device = torch.device("cuda" if args.cuda else "cpu") #Try to load models if args.model == 'pointnet': model = PointNet(args).to(device) elif args.model == 'dgcnn': model = DGCNN(args).to(device) else: raise Exception("Not implemented") print(str(model)) model = nn.DataParallel(model) print("Let's use", torch.cuda.device_count(), "GPUs!") if args.use_sgd: print("Use SGD") opt = optim.SGD(model.parameters(), lr=args.lr*100, momentum=args.momentum, weight_decay=1e-4) else: print("Use Adam") opt = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4) scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=args.lr) print(f"Using the smoothing loss {bool(args.smoothing)}") criterion = lambda x,y: cal_loss(x, y, bool(args.smoothing)) best_test_acc = 0 for epoch in range(args.epochs): scheduler.step() #################### # Train #################### train_loss = 0.0 count = 0.0 model.train() train_pred = [] train_true = [] for data, label in train_loader: data, label = data.to(device), label.to(device).squeeze() data = data.permute(0, 2, 1) batch_size = data.size()[0] opt.zero_grad() logits = model(data) loss = criterion(logits, label) loss.backward() opt.step() preds = logits.max(dim=1)[1] count += batch_size train_loss += loss.item() * batch_size train_true.append(label.cpu().numpy()) train_pred.append(preds.detach().cpu().numpy()) train_true = np.concatenate(train_true) train_pred = np.concatenate(train_pred) outstr = 'Train %d, loss: %.6f, train acc: %.6f, train avg acc: %.6f' % (epoch, train_loss*1.0/count, metrics.accuracy_score( train_true, train_pred), metrics.balanced_accuracy_score( train_true, train_pred)) io.cprint(outstr) #################### # Test #################### test_loss = 0.0 count = 0.0 model.eval() test_pred = [] test_true = [] for data, label in test_loader: data, label = data.to(device), label.to(device).squeeze() data = data.permute(0, 2, 1) batch_size = data.size()[0] logits = model(data) loss = criterion(logits, label) preds = logits.max(dim=1)[1] count += batch_size test_loss += loss.item() * batch_size test_true.append(label.cpu().numpy()) test_pred.append(preds.detach().cpu().numpy()) test_true = np.concatenate(test_true) test_pred = np.concatenate(test_pred) test_acc = metrics.accuracy_score(test_true, test_pred) avg_per_class_acc = metrics.balanced_accuracy_score(test_true, test_pred) outstr = 'Test %d, loss: %.6f, test acc: %.6f, test avg acc: %.6f' % (epoch, test_loss*1.0/count, test_acc, avg_per_class_acc) io.cprint(outstr) if test_acc >= best_test_acc: best_test_acc = test_acc torch.save(model.state_dict(), 'checkpoints/%s/models/model.t7' % args.exp_name) def test(args, io): test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), batch_size=args.test_batch_size, shuffle=True, drop_last=False) device = torch.device("cuda" if args.cuda else "cpu") #Try to load models model = DGCNN(args).to(device) model = nn.DataParallel(model) model.load_state_dict(torch.load(args.model_path)) model = model.eval() test_acc = 0.0 count = 0.0 test_true = [] test_pred = [] for data, label in test_loader: data, label = data.to(device), label.to(device).squeeze() data = data.permute(0, 2, 1) batch_size = data.size()[0] logits = model(data) preds = logits.max(dim=1)[1] test_true.append(label.cpu().numpy()) test_pred.append(preds.detach().cpu().numpy()) test_true = np.concatenate(test_true) test_pred = np.concatenate(test_pred) test_acc = metrics.accuracy_score(test_true, test_pred) avg_per_class_acc = metrics.balanced_accuracy_score(test_true, test_pred) outstr = 'Test :: test acc: %.6f, test avg acc: %.6f'%(test_acc, avg_per_class_acc) io.cprint(outstr) if __name__ == "__main__": # Training settings parser = argparse.ArgumentParser(description='Point Cloud Recognition') parser.add_argument('--exp_name', type=str, default='exp', metavar='N', help='Name of the experiment') parser.add_argument('--model', type=str, default='dgcnn', metavar='N', choices=['pointnet', 'dgcnn'], help='Model to use, [pointnet, dgcnn]') parser.add_argument('--dataset', type=str, default='modelnet40', metavar='N', choices=['modelnet40']) parser.add_argument('--batch_size', type=int, default=32, metavar='batch_size', help='Size of batch)') parser.add_argument('--test_batch_size', type=int, default=16, metavar='batch_size', help='Size of batch)') parser.add_argument('--epochs', type=int, default=250, metavar='N', help='number of episode to train ') parser.add_argument('--use_sgd', type=bool, default=True, help='Use SGD') parser.add_argument('--lr', type=float, default=0.001, metavar='LR', help='learning rate (default: 0.001, 0.1 if using sgd)') parser.add_argument('--momentum', type=float, default=0.9, metavar='M', help='SGD momentum (default: 0.9)') parser.add_argument('--no_cuda', type=bool, default=False, help='enables CUDA training') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--eval', type=bool, default=False, help='evaluate the model') parser.add_argument('--num_points', type=int, default=1024, help='num of points to use') parser.add_argument('--dropout', type=float, default=0.5, help='dropout rate') parser.add_argument('--emb_dims', type=int, default=1024, metavar='N', help='Dimension of embeddings') parser.add_argument('--k', type=int, default=20, metavar='N', help='Num of nearest neighbors to use') parser.add_argument('--model_path', type=str, default='', metavar='N', help='Pretrained model path') parser.add_argument('--smoothing', type=int, default=1, help='Whether to use smoothing in the loss') parser.add_argument('--leaky_relu', type=int, default=1, help='Whether to use leaky_relu') args = parser.parse_args() _init_() io = IOStream('checkpoints/' + args.exp_name + '/run.log') io.cprint(str(args)) args.cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) if args.cuda: io.cprint( 'Using GPU : ' + str(torch.cuda.current_device()) + ' from ' + str(torch.cuda.device_count()) + ' devices') torch.cuda.manual_seed(args.seed) else: io.cprint('Using CPU') if not args.eval: train(args, io) else: test(args, io) ================================================ FILE: dgcnn/pytorch/model.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- """ @Author: Yue Wang @Contact: yuewangx@mit.edu @File: model.py @Time: 2018/10/13 6:35 PM """ import os import sys import copy import math import numpy as np import torch import torch.nn as nn import torch.nn.functional as F def knn(x, k): inner = -2*torch.matmul(x.transpose(2, 1), x) xx = torch.sum(x**2, dim=1, keepdim=True) pairwise_distance = -xx - inner - xx.transpose(2, 1) idx = pairwise_distance.topk(k=k, dim=-1)[1] # (batch_size, num_points, k) return idx def get_graph_feature(x, k=20, idx=None): batch_size = x.size(0) num_points = x.size(2) x = x.view(batch_size, -1, num_points) if idx is None: idx = knn(x, k=k) # (batch_size, num_points, k) device = torch.device('cuda') idx_base = torch.arange(0, batch_size, device=device).view(-1, 1, 1)*num_points idx = idx + idx_base idx = idx.view(-1) _, num_dims, _ = x.size() x = x.transpose(2, 1).contiguous() # (batch_size, num_points, num_dims) -> (batch_size*num_points, num_dims) # batch_size * num_points * k + range(0, batch_size*num_points) feature = x.view(batch_size*num_points, -1)[idx, :] feature = feature.view(batch_size, num_points, k, num_dims) x = x.view(batch_size, num_points, 1, num_dims).repeat(1, 1, k, 1) feature = torch.cat((feature-x, x), dim=3).permute(0, 3, 1, 2).contiguous() return feature class PointNet(nn.Module): def __init__(self, args, output_channels=40): super(PointNet, self).__init__() self.args = args self.conv1 = nn.Conv1d(3, 64, kernel_size=1, bias=False) self.conv2 = nn.Conv1d(64, 64, kernel_size=1, bias=False) self.conv3 = nn.Conv1d(64, 64, kernel_size=1, bias=False) self.conv4 = nn.Conv1d(64, 128, kernel_size=1, bias=False) self.conv5 = nn.Conv1d(128, args.emb_dims, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm1d(64) self.bn2 = nn.BatchNorm1d(64) self.bn3 = nn.BatchNorm1d(64) self.bn4 = nn.BatchNorm1d(128) self.bn5 = nn.BatchNorm1d(args.emb_dims) self.linear1 = nn.Linear(args.emb_dims, 512, bias=False) self.bn6 = nn.BatchNorm1d(512) self.dp1 = nn.Dropout() self.linear2 = nn.Linear(512, output_channels) def forward(self, x): x = F.relu(self.bn1(self.conv1(x))) x = F.relu(self.bn2(self.conv2(x))) x = F.relu(self.bn3(self.conv3(x))) x = F.relu(self.bn4(self.conv4(x))) x = F.relu(self.bn5(self.conv5(x))) x = F.adaptive_max_pool1d(x, 1).squeeze() x = F.relu(self.bn6(self.linear1(x))) x = self.dp1(x) x = self.linear2(x) return x class DGCNN(nn.Module): def __init__(self, args, output_channels=40): super(DGCNN, self).__init__() self.args = args self.k = args.k self.leaky_relu = bool(args.leaky_relu) self.bn1 = nn.BatchNorm2d(64) self.bn2 = nn.BatchNorm2d(64) self.bn3 = nn.BatchNorm2d(128) self.bn4 = nn.BatchNorm2d(256) self.bn5 = nn.BatchNorm1d(args.emb_dims) if self.leaky_relu: act_mod = nn.LeakyReLU act_mod_args = {'negative_slope': 0.2} else: act_mod = nn.ReLU act_mod_args = {} self.conv1 = nn.Sequential(nn.Conv2d(6, 64, kernel_size=1, bias=False), self.bn1, act_mod(**act_mod_args)) self.conv2 = nn.Sequential(nn.Conv2d(64*2, 64, kernel_size=1, bias=False), self.bn2, act_mod(**act_mod_args)) self.conv3 = nn.Sequential(nn.Conv2d(64*2, 128, kernel_size=1, bias=False), self.bn3, act_mod(**act_mod_args)) self.conv4 = nn.Sequential(nn.Conv2d(128*2, 256, kernel_size=1, bias=False), self.bn4, act_mod(**act_mod_args)) self.conv5 = nn.Sequential(nn.Conv1d(512, args.emb_dims, kernel_size=1, bias=False), self.bn5, act_mod(**act_mod_args)) self.linear1 = nn.Linear(args.emb_dims*2, 512, bias=False) self.bn6 = nn.BatchNorm1d(512) self.dp1 = nn.Dropout(p=args.dropout) self.linear2 = nn.Linear(512, 256) self.bn7 = nn.BatchNorm1d(256) self.dp2 = nn.Dropout(p=args.dropout) self.linear3 = nn.Linear(256, output_channels) def forward(self, x): batch_size = x.size(0) x = get_graph_feature(x, k=self.k) x = self.conv1(x) x1 = x.max(dim=-1, keepdim=False)[0] x = get_graph_feature(x1, k=self.k) x = self.conv2(x) x2 = x.max(dim=-1, keepdim=False)[0] x = get_graph_feature(x2, k=self.k) x = self.conv3(x) x3 = x.max(dim=-1, keepdim=False)[0] x = get_graph_feature(x3, k=self.k) x = self.conv4(x) x4 = x.max(dim=-1, keepdim=False)[0] x = torch.cat((x1, x2, x3, x4), dim=1) x = self.conv5(x) x1 = F.adaptive_max_pool1d(x, 1).view(batch_size, -1) x2 = F.adaptive_avg_pool1d(x, 1).view(batch_size, -1) x = torch.cat((x1, x2), 1) if self.leaky_relu: act = lambda y: F.leaky_relu(y, negative_slope=0.2) else: act = F.relu x = act(self.bn6(self.linear1(x))) x = self.dp1(x) x = act(self.bn7(self.linear2(x))) x = self.dp2(x) x = self.linear3(x) return x ================================================ FILE: dgcnn/pytorch/util.py ================================================ #!/usr/bin/env python # -*- coding: utf-8 -*- """ @Author: Yue Wang @Contact: yuewangx@mit.edu @File: util @Time: 4/5/19 3:47 PM """ import numpy as np import torch import torch.nn.functional as F def cal_loss(pred, gold, smoothing=True): ''' Calculate cross entropy loss, apply label smoothing if needed. ''' gold = gold.contiguous().view(-1) if smoothing: eps = 0.2 n_class = pred.size(1) one_hot = torch.zeros_like(pred).scatter(1, gold.view(-1, 1), 1) one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1) log_prb = F.log_softmax(pred, dim=1) loss = -(one_hot * log_prb).sum(dim=1).mean() else: loss = F.cross_entropy(pred, gold, reduction='mean') return loss class IOStream(): def __init__(self, path): self.f = open(path, 'a') def cprint(self, text): print(text) self.f.write(text+'\n') self.f.flush() def close(self): self.f.close() ================================================ FILE: dgcnn/tensorflow/README.md ================================================ # Dynamic Graph CNN for Learning on Point Clouds (TensorFlow) ## Point Cloud Classification * Run the training script: ``` bash python train.py ``` * Run the evaluation script after training finished: ``` bash python evalutate.py ``` ================================================ FILE: dgcnn/tensorflow/evaluate.py ================================================ import tensorflow as tf import numpy as np import argparse import socket import importlib import time import os import scipy.misc import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) sys.path.append(os.path.join(BASE_DIR, 'models')) sys.path.append(os.path.join(BASE_DIR, 'utils')) import provider import pc_util parser = argparse.ArgumentParser() parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]') parser.add_argument('--model', default='dgcnn', help='Model name: dgcnn [default: dgcnn]') parser.add_argument('--batch_size', type=int, default=4, help='Batch Size during training [default: 1]') parser.add_argument('--num_point', type=int, default=1024, help='Point Number [256/512/1024/2048] [default: 1024]') parser.add_argument('--model_path', default='log/model.ckpt', help='model checkpoint file path [default: log/model.ckpt]') parser.add_argument('--dump_dir', default='dump', help='dump folder path [dump]') parser.add_argument('--visu', action='store_true', help='Whether to dump image for error case [default: False]') FLAGS = parser.parse_args() BATCH_SIZE = FLAGS.batch_size NUM_POINT = FLAGS.num_point MODEL_PATH = FLAGS.model_path GPU_INDEX = FLAGS.gpu MODEL = importlib.import_module(FLAGS.model) # import network module DUMP_DIR = FLAGS.dump_dir if not os.path.exists(DUMP_DIR): os.mkdir(DUMP_DIR) LOG_FOUT = open(os.path.join(DUMP_DIR, 'log_evaluate.txt'), 'w') LOG_FOUT.write(str(FLAGS)+'\n') NUM_CLASSES = 40 SHAPE_NAMES = [line.rstrip() for line in \ open(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/shape_names.txt'))] HOSTNAME = socket.gethostname() # ModelNet40 official train/test split TRAIN_FILES = provider.getDataFiles( \ os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/train_files.txt')) TEST_FILES = provider.getDataFiles(\ os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/test_files.txt')) def log_string(out_str): LOG_FOUT.write(out_str+'\n') LOG_FOUT.flush() print(out_str) def evaluate(num_votes): is_training = False with tf.device('/gpu:'+str(GPU_INDEX)): pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT) is_training_pl = tf.placeholder(tf.bool, shape=()) # simple model pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl) loss = MODEL.get_loss(pred, labels_pl, end_points) # Add ops to save and restore all the variables. saver = tf.train.Saver() # Create a session config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True config.log_device_placement = True sess = tf.Session(config=config) # Restore variables from disk. saver.restore(sess, MODEL_PATH) log_string("Model restored.") ops = {'pointclouds_pl': pointclouds_pl, 'labels_pl': labels_pl, 'is_training_pl': is_training_pl, 'pred': pred, 'loss': loss} eval_one_epoch(sess, ops, num_votes) def eval_one_epoch(sess, ops, num_votes=1, topk=1): error_cnt = 0 is_training = False total_correct = 0 total_seen = 0 loss_sum = 0 total_seen_class = [0 for _ in range(NUM_CLASSES)] total_correct_class = [0 for _ in range(NUM_CLASSES)] fout = open(os.path.join(DUMP_DIR, 'pred_label.txt'), 'w') for fn in range(len(TEST_FILES)): log_string('----'+str(fn)+'----') current_data, current_label = provider.loadDataFile(TEST_FILES[fn]) current_data = current_data[:,0:NUM_POINT,:] current_label = np.squeeze(current_label) print(current_data.shape) file_size = current_data.shape[0] num_batches = file_size // BATCH_SIZE print(file_size) for batch_idx in range(num_batches): start_idx = batch_idx * BATCH_SIZE end_idx = (batch_idx+1) * BATCH_SIZE cur_batch_size = end_idx - start_idx # Aggregating BEG batch_loss_sum = 0 # sum of losses for the batch batch_pred_sum = np.zeros((cur_batch_size, NUM_CLASSES)) # score for classes batch_pred_classes = np.zeros((cur_batch_size, NUM_CLASSES)) # 0/1 for classes for vote_idx in range(num_votes): rotated_data = provider.rotate_point_cloud_by_angle(current_data[start_idx:end_idx, :, :], vote_idx/float(num_votes) * np.pi * 2) feed_dict = {ops['pointclouds_pl']: rotated_data, ops['labels_pl']: current_label[start_idx:end_idx], ops['is_training_pl']: is_training} loss_val, pred_val = sess.run([ops['loss'], ops['pred']], feed_dict=feed_dict) batch_pred_sum += pred_val batch_pred_val = np.argmax(pred_val, 1) for el_idx in range(cur_batch_size): batch_pred_classes[el_idx, batch_pred_val[el_idx]] += 1 batch_loss_sum += (loss_val * cur_batch_size / float(num_votes)) # pred_val_topk = np.argsort(batch_pred_sum, axis=-1)[:,-1*np.array(range(topk))-1] # pred_val = np.argmax(batch_pred_classes, 1) pred_val = np.argmax(batch_pred_sum, 1) # Aggregating END correct = np.sum(pred_val == current_label[start_idx:end_idx]) # correct = np.sum(pred_val_topk[:,0:topk] == label_val) total_correct += correct total_seen += cur_batch_size loss_sum += batch_loss_sum for i in range(start_idx, end_idx): l = current_label[i] total_seen_class[l] += 1 total_correct_class[l] += (pred_val[i-start_idx] == l) fout.write('%d, %d\n' % (pred_val[i-start_idx], l)) if pred_val[i-start_idx] != l and FLAGS.visu: # ERROR CASE, DUMP! img_filename = '%d_label_%s_pred_%s.jpg' % (error_cnt, SHAPE_NAMES[l], SHAPE_NAMES[pred_val[i-start_idx]]) img_filename = os.path.join(DUMP_DIR, img_filename) output_img = pc_util.point_cloud_three_views(np.squeeze(current_data[i, :, :])) scipy.misc.imsave(img_filename, output_img) error_cnt += 1 log_string('eval mean loss: %f' % (loss_sum / float(total_seen))) log_string('eval accuracy: %f' % (total_correct / float(total_seen))) log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float)))) class_accuracies = np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float) for i, name in enumerate(SHAPE_NAMES): log_string('%10s:\t%0.3f' % (name, class_accuracies[i])) if __name__=='__main__': with tf.Graph().as_default(): evaluate(num_votes=12) LOG_FOUT.close() ================================================ FILE: dgcnn/tensorflow/models/dgcnn.py ================================================ import tensorflow as tf import numpy as np import math import sys import os BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) sys.path.append(os.path.join(BASE_DIR, '../utils')) sys.path.append(os.path.join(BASE_DIR, '../../utils')) import tf_util from transform_nets import input_transform_net def placeholder_inputs(batch_size, num_point): pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3)) labels_pl = tf.placeholder(tf.int32, shape=(batch_size)) return pointclouds_pl, labels_pl def get_model(point_cloud, is_training, bn_decay=None): """ Classification PointNet, input is BxNx3, output Bx40 """ batch_size = point_cloud.get_shape()[0].value num_point = point_cloud.get_shape()[1].value end_points = {} k = 20 adj_matrix = tf_util.pairwise_distance(point_cloud) nn_idx = tf_util.knn(adj_matrix, k=k) edge_feature = tf_util.get_edge_feature(point_cloud, nn_idx=nn_idx, k=k) with tf.variable_scope('transform_net1') as sc: transform = input_transform_net(edge_feature, is_training, bn_decay, K=3) point_cloud_transformed = tf.matmul(point_cloud, transform) adj_matrix = tf_util.pairwise_distance(point_cloud_transformed) nn_idx = tf_util.knn(adj_matrix, k=k) edge_feature = tf_util.get_edge_feature(point_cloud_transformed, nn_idx=nn_idx, k=k) net = tf_util.conv2d(edge_feature, 64, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='dgcnn1', bn_decay=bn_decay) net = tf.reduce_max(net, axis=-2, keep_dims=True) net1 = net adj_matrix = tf_util.pairwise_distance(net) nn_idx = tf_util.knn(adj_matrix, k=k) edge_feature = tf_util.get_edge_feature(net, nn_idx=nn_idx, k=k) net = tf_util.conv2d(edge_feature, 64, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='dgcnn2', bn_decay=bn_decay) net = tf.reduce_max(net, axis=-2, keep_dims=True) net2 = net adj_matrix = tf_util.pairwise_distance(net) nn_idx = tf_util.knn(adj_matrix, k=k) edge_feature = tf_util.get_edge_feature(net, nn_idx=nn_idx, k=k) net = tf_util.conv2d(edge_feature, 64, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='dgcnn3', bn_decay=bn_decay) net = tf.reduce_max(net, axis=-2, keep_dims=True) net3 = net adj_matrix = tf_util.pairwise_distance(net) nn_idx = tf_util.knn(adj_matrix, k=k) edge_feature = tf_util.get_edge_feature(net, nn_idx=nn_idx, k=k) net = tf_util.conv2d(edge_feature, 128, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='dgcnn4', bn_decay=bn_decay) net = tf.reduce_max(net, axis=-2, keep_dims=True) net4 = net net = tf_util.conv2d(tf.concat([net1, net2, net3, net4], axis=-1), 1024, [1, 1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='agg', bn_decay=bn_decay) net = tf.reduce_max(net, axis=1, keep_dims=True) # MLP on global point cloud vector net = tf.reshape(net, [batch_size, -1]) net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay) net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1') net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training, scope='fc2', bn_decay=bn_decay) net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp2') net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3') return net, end_points def get_loss(pred, label, end_points): """ pred: B*NUM_CLASSES, label: B, """ labels = tf.one_hot(indices=label, depth=40) loss = tf.losses.softmax_cross_entropy(onehot_labels=labels, logits=pred, label_smoothing=0.2) classify_loss = tf.reduce_mean(loss) return classify_loss if __name__=='__main__': batch_size = 2 num_pt = 124 pos_dim = 3 input_feed = np.random.rand(batch_size, num_pt, pos_dim) label_feed = np.random.rand(batch_size) label_feed[label_feed>=0.5] = 1 label_feed[label_feed<0.5] = 0 label_feed = label_feed.astype(np.int32) # # np.save('./debug/input_feed.npy', input_feed) # input_feed = np.load('./debug/input_feed.npy') # print input_feed with tf.Graph().as_default(): input_pl, label_pl = placeholder_inputs(batch_size, num_pt) pos, ftr = get_model(input_pl, tf.constant(True)) # loss = get_loss(logits, label_pl, None) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) feed_dict = {input_pl: input_feed, label_pl: label_feed} res1, res2 = sess.run([pos, ftr], feed_dict=feed_dict) print res1.shape print res1 print res2.shape print res2 ================================================ FILE: dgcnn/tensorflow/models/transform_nets.py ================================================ import tensorflow as tf import numpy as np import sys import os BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) sys.path.append(os.path.join(BASE_DIR, '../utils')) import tf_util def input_transform_net(edge_feature, is_training, bn_decay=None, K=3, is_dist=False): """ Input (XYZ) Transform Net, input is BxNx3 gray image Return: Transformation matrix of size 3xK """ batch_size = edge_feature.get_shape()[0].value num_point = edge_feature.get_shape()[1].value # input_image = tf.expand_dims(point_cloud, -1) net = tf_util.conv2d(edge_feature, 64, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='tconv1', bn_decay=bn_decay, is_dist=is_dist) net = tf_util.conv2d(net, 128, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='tconv2', bn_decay=bn_decay, is_dist=is_dist) net = tf.reduce_max(net, axis=-2, keep_dims=True) net = tf_util.conv2d(net, 1024, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='tconv3', bn_decay=bn_decay, is_dist=is_dist) net = tf_util.max_pool2d(net, [num_point,1], padding='VALID', scope='tmaxpool') net = tf.reshape(net, [batch_size, -1]) net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='tfc1', bn_decay=bn_decay,is_dist=is_dist) net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training, scope='tfc2', bn_decay=bn_decay,is_dist=is_dist) with tf.variable_scope('transform_XYZ') as sc: # assert(K==3) with tf.device('/cpu:0'): weights = tf.get_variable('weights', [256, K*K], initializer=tf.constant_initializer(0.0), dtype=tf.float32) biases = tf.get_variable('biases', [K*K], initializer=tf.constant_initializer(0.0), dtype=tf.float32) biases += tf.constant(np.eye(K).flatten(), dtype=tf.float32) transform = tf.matmul(net, weights) transform = tf.nn.bias_add(transform, biases) transform = tf.reshape(transform, [batch_size, K, K]) return transform ================================================ FILE: dgcnn/tensorflow/part_seg/README.md ================================================ ## Part segmentation ### Dataset Load the data for part segmentation. ``` sh +x download_data.sh ``` ### Train Train the model on 2 GPUs, each with 12 GB memeory. ``` python train_multi_gpu.py ``` Model parameters are saved every 5 epochs in "train_results/trained_models/". ### Evaluation To evaluate the model saved after epoch n, ``` python test.py --model_path train_results/trained_models/epoch_n.ckpt ``` For example, if we want to test the model saved after 175 epochs (provided), ``` python test.py --model_path train_results/trained_models/epoch_175.ckpt ``` ================================================ FILE: dgcnn/tensorflow/part_seg/download_data.sh ================================================ #!/bin/bash # Download original ShapeNetPart dataset (around 1GB) ['PartAnnotation'] wget https://shapenet.cs.stanford.edu/ericyi/shapenetcore_partanno_v0.zip unzip shapenetcore_partanno_v0.zip rm shapenetcore_partanno_v0.zip # Download HDF5 for ShapeNet Part segmentation (around 346MB) ['hdf5_data'] wget https://shapenet.cs.stanford.edu/media/shapenet_part_seg_hdf5_data.zip unzip shapenet_part_seg_hdf5_data.zip rm shapenet_part_seg_hdf5_data.zip ================================================ FILE: dgcnn/tensorflow/part_seg/part_seg_model.py ================================================ import tensorflow as tf import numpy as np import math import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(os.path.dirname(BASE_DIR)) sys.path.append(os.path.join(BASE_DIR, '../utils')) sys.path.append(os.path.join(BASE_DIR, '../models')) sys.path.append(os.path.join(BASE_DIR, '../')) import tf_util from transform_nets import input_transform_net def get_model(point_cloud, input_label, is_training, cat_num, part_num, \ batch_size, num_point, weight_decay, bn_decay=None): batch_size = point_cloud.get_shape()[0].value num_point = point_cloud.get_shape()[1].value input_image = tf.expand_dims(point_cloud, -1) k = 20 adj = tf_util.pairwise_distance(point_cloud) nn_idx = tf_util.knn(adj, k=k) edge_feature = tf_util.get_edge_feature(input_image, nn_idx=nn_idx, k=k) with tf.variable_scope('transform_net1') as sc: transform = input_transform_net(edge_feature, is_training, bn_decay, K=3, is_dist=True) point_cloud_transformed = tf.matmul(point_cloud, transform) input_image = tf.expand_dims(point_cloud_transformed, -1) adj = tf_util.pairwise_distance(point_cloud_transformed) nn_idx = tf_util.knn(adj, k=k) edge_feature = tf_util.get_edge_feature(input_image, nn_idx=nn_idx, k=k) out1 = tf_util.conv2d(edge_feature, 64, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, weight_decay=weight_decay, scope='adj_conv1', bn_decay=bn_decay, is_dist=True) out2 = tf_util.conv2d(out1, 64, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, weight_decay=weight_decay, scope='adj_conv2', bn_decay=bn_decay, is_dist=True) net_1 = tf.reduce_max(out2, axis=-2, keep_dims=True) adj = tf_util.pairwise_distance(net_1) nn_idx = tf_util.knn(adj, k=k) edge_feature = tf_util.get_edge_feature(net_1, nn_idx=nn_idx, k=k) out3 = tf_util.conv2d(edge_feature, 64, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, weight_decay=weight_decay, scope='adj_conv3', bn_decay=bn_decay, is_dist=True) out4 = tf_util.conv2d(out3, 64, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, weight_decay=weight_decay, scope='adj_conv4', bn_decay=bn_decay, is_dist=True) net_2 = tf.reduce_max(out4, axis=-2, keep_dims=True) adj = tf_util.pairwise_distance(net_2) nn_idx = tf_util.knn(adj, k=k) edge_feature = tf_util.get_edge_feature(net_2, nn_idx=nn_idx, k=k) out5 = tf_util.conv2d(edge_feature, 64, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, weight_decay=weight_decay, scope='adj_conv5', bn_decay=bn_decay, is_dist=True) # out6 = tf_util.conv2d(out5, 64, [1,1], # padding='VALID', stride=[1,1], # bn=True, is_training=is_training, weight_decay=weight_decay, # scope='adj_conv6', bn_decay=bn_decay, is_dist=True) net_3 = tf.reduce_max(out5, axis=-2, keep_dims=True) out7 = tf_util.conv2d(tf.concat([net_1, net_2, net_3], axis=-1), 1024, [1, 1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='adj_conv7', bn_decay=bn_decay, is_dist=True) out_max = tf_util.max_pool2d(out7, [num_point, 1], padding='VALID', scope='maxpool') one_hot_label_expand = tf.reshape(input_label, [batch_size, 1, 1, cat_num]) one_hot_label_expand = tf_util.conv2d(one_hot_label_expand, 64, [1, 1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='one_hot_label_expand', bn_decay=bn_decay, is_dist=True) out_max = tf.concat(axis=3, values=[out_max, one_hot_label_expand]) expand = tf.tile(out_max, [1, num_point, 1, 1]) concat = tf.concat(axis=3, values=[expand, net_1, net_2, net_3]) net2 = tf_util.conv2d(concat, 256, [1,1], padding='VALID', stride=[1,1], bn_decay=bn_decay, bn=True, is_training=is_training, scope='seg/conv1', weight_decay=weight_decay, is_dist=True) net2 = tf_util.dropout(net2, keep_prob=0.6, is_training=is_training, scope='seg/dp1') net2 = tf_util.conv2d(net2, 256, [1,1], padding='VALID', stride=[1,1], bn_decay=bn_decay, bn=True, is_training=is_training, scope='seg/conv2', weight_decay=weight_decay, is_dist=True) net2 = tf_util.dropout(net2, keep_prob=0.6, is_training=is_training, scope='seg/dp2') net2 = tf_util.conv2d(net2, 128, [1,1], padding='VALID', stride=[1,1], bn_decay=bn_decay, bn=True, is_training=is_training, scope='seg/conv3', weight_decay=weight_decay, is_dist=True) net2 = tf_util.conv2d(net2, part_num, [1,1], padding='VALID', stride=[1,1], activation_fn=None, bn=False, scope='seg/conv4', weight_decay=weight_decay, is_dist=True) net2 = tf.reshape(net2, [batch_size, num_point, part_num]) return net2 def get_loss(seg_pred, seg): per_instance_seg_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=seg_pred, labels=seg), axis=1) seg_loss = tf.reduce_mean(per_instance_seg_loss) per_instance_seg_pred_res = tf.argmax(seg_pred, 2) return seg_loss, per_instance_seg_loss, per_instance_seg_pred_res ================================================ FILE: dgcnn/tensorflow/part_seg/test.py ================================================ import argparse import tensorflow as tf import json import numpy as np import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) sys.path.append(os.path.dirname(BASE_DIR)) import provider import part_seg_model as model parser = argparse.ArgumentParser() parser.add_argument('--model_path', default='train_results/trained_models/epoch_160.ckpt', help='Model checkpoint path') FLAGS = parser.parse_args() # DEFAULT SETTINGS pretrained_model_path = FLAGS.model_path hdf5_data_dir = os.path.join(BASE_DIR, './hdf5_data') ply_data_dir = os.path.join(BASE_DIR, './PartAnnotation') gpu_to_use = 0 output_dir = os.path.join(BASE_DIR, './test_results') output_verbose = False # MAIN SCRIPT point_num = 3000 batch_size = 1 test_file_list = os.path.join(BASE_DIR, 'testing_ply_file_list.txt') oid2cpid = json.load(open(os.path.join(hdf5_data_dir, 'overallid_to_catid_partid.json'), 'r')) object2setofoid = {} for idx in range(len(oid2cpid)): objid, pid = oid2cpid[idx] if not objid in object2setofoid.keys(): object2setofoid[objid] = [] object2setofoid[objid].append(idx) all_obj_cat_file = os.path.join(hdf5_data_dir, 'all_object_categories.txt') fin = open(all_obj_cat_file, 'r') lines = [line.rstrip() for line in fin.readlines()] objcats = [line.split()[1] for line in lines] objnames = [line.split()[0] for line in lines] on2oid = {objcats[i]:i for i in range(len(objcats))} fin.close() color_map_file = os.path.join(hdf5_data_dir, 'part_color_mapping.json') color_map = json.load(open(color_map_file, 'r')) NUM_OBJ_CATS = 16 NUM_PART_CATS = 50 cpid2oid = json.load(open(os.path.join(hdf5_data_dir, 'catid_partid_to_overallid.json'), 'r')) def printout(flog, data): print(data) flog.write(data + '\n') def output_color_point_cloud(data, seg, out_file): with open(out_file, 'w') as f: l = len(seg) for i in range(l): color = color_map[seg[i]] f.write('v %f %f %f %f %f %f\n' % (data[i][0], data[i][1], data[i][2], color[0], color[1], color[2])) def output_color_point_cloud_red_blue(data, seg, out_file): with open(out_file, 'w') as f: l = len(seg) for i in range(l): if seg[i] == 1: color = [0, 0, 1] elif seg[i] == 0: color = [1, 0, 0] else: color = [0, 0, 0] f.write('v %f %f %f %f %f %f\n' % (data[i][0], data[i][1], data[i][2], color[0], color[1], color[2])) def pc_normalize(pc): l = pc.shape[0] centroid = np.mean(pc, axis=0) pc = pc - centroid m = np.max(np.sqrt(np.sum(pc**2, axis=1))) pc = pc / m return pc def placeholder_inputs(): pointclouds_ph = tf.placeholder(tf.float32, shape=(batch_size, point_num, 3)) input_label_ph = tf.placeholder(tf.float32, shape=(batch_size, NUM_OBJ_CATS)) return pointclouds_ph, input_label_ph def output_color_point_cloud(data, seg, out_file): with open(out_file, 'w') as f: l = len(seg) for i in range(l): color = color_map[seg[i]] f.write('v %f %f %f %f %f %f\n' % (data[i][0], data[i][1], data[i][2], color[0], color[1], color[2])) def load_pts_seg_files(pts_file, seg_file, catid): with open(pts_file, 'r') as f: pts_str = [item.rstrip() for item in f.readlines()] pts = np.array([np.float32(s.split()) for s in pts_str], dtype=np.float32) with open(seg_file, 'r') as f: part_ids = np.array([int(item.rstrip()) for item in f.readlines()], dtype=np.uint8) seg = np.array([cpid2oid[catid+'_'+str(x)] for x in part_ids]) return pts, seg def pc_augment_to_point_num(pts, pn): assert(pts.shape[0] <= pn) cur_len = pts.shape[0] res = np.array(pts) while cur_len < pn: res = np.concatenate((res, pts)) cur_len += pts.shape[0] return res[:pn, :] def convert_label_to_one_hot(labels): label_one_hot = np.zeros((labels.shape[0], NUM_OBJ_CATS)) for idx in range(labels.shape[0]): label_one_hot[idx, labels[idx]] = 1 return label_one_hot def predict(): is_training = False with tf.device('/gpu:'+str(gpu_to_use)): pointclouds_ph, input_label_ph = placeholder_inputs() is_training_ph = tf.placeholder(tf.bool, shape=()) seg_pred = model.get_model(pointclouds_ph, input_label_ph, \ cat_num=NUM_OBJ_CATS, part_num=NUM_PART_CATS, is_training=is_training_ph, \ batch_size=batch_size, num_point=point_num, weight_decay=0.0, bn_decay=None) saver = tf.train.Saver() config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True with tf.Session(config=config) as sess: if not os.path.exists(output_dir): os.mkdir(output_dir) flog = open(os.path.join(output_dir, 'log.txt'), 'a') printout(flog, 'Loading model %s' % pretrained_model_path) saver.restore(sess, pretrained_model_path) printout(flog, 'Model restored.') batch_data = np.zeros([batch_size, point_num, 3]).astype(np.float32) total_acc = 0.0 total_seen = 0 total_acc_iou = 0.0 total_per_cat_acc = np.zeros((NUM_OBJ_CATS)).astype(np.float32) total_per_cat_iou = np.zeros((NUM_OBJ_CATS)).astype(np.float32) total_per_cat_seen = np.zeros((NUM_OBJ_CATS)).astype(np.int32) ffiles = open(test_file_list, 'r') lines = [line.rstrip() for line in ffiles.readlines()] pts_files = [line.split()[0] for line in lines] seg_files = [line.split()[1] for line in lines] labels = [line.split()[2] for line in lines] ffiles.close() len_pts_files = len(pts_files) for shape_idx in range(len_pts_files): if shape_idx % 100 == 0: printout(flog, '%d/%d ...' % (shape_idx, len_pts_files)) cur_gt_label = on2oid[labels[shape_idx]] # 0/1/.../15 cur_label_one_hot = np.zeros((1, NUM_OBJ_CATS), dtype=np.float32) cur_label_one_hot[0, cur_gt_label] = 1 pts_file_to_load = os.path.join(ply_data_dir, pts_files[shape_idx]) seg_file_to_load = os.path.join(ply_data_dir, seg_files[shape_idx]) pts, seg = load_pts_seg_files(pts_file_to_load, seg_file_to_load, objcats[cur_gt_label]) ori_point_num = len(seg) batch_data[0, ...] = pc_augment_to_point_num(pc_normalize(pts), point_num) seg_pred_res = sess.run(seg_pred, feed_dict={ pointclouds_ph: batch_data, input_label_ph: cur_label_one_hot, is_training_ph: is_training}) seg_pred_res = seg_pred_res[0, ...] iou_oids = object2setofoid[objcats[cur_gt_label]] non_cat_labels = list(set(np.arange(NUM_PART_CATS)).difference(set(iou_oids))) mini = np.min(seg_pred_res) seg_pred_res[:, non_cat_labels] = mini - 1000 seg_pred_val = np.argmax(seg_pred_res, axis=1)[:ori_point_num] seg_acc = np.mean(seg_pred_val == seg) total_acc += seg_acc total_seen += 1 total_per_cat_seen[cur_gt_label] += 1 total_per_cat_acc[cur_gt_label] += seg_acc mask = np.int32(seg_pred_val == seg) total_iou = 0.0 iou_log = '' for oid in iou_oids: n_pred = np.sum(seg_pred_val == oid) n_gt = np.sum(seg == oid) n_intersect = np.sum(np.int32(seg == oid) * mask) n_union = n_pred + n_gt - n_intersect iou_log += '_' + str(n_pred)+'_'+str(n_gt)+'_'+str(n_intersect)+'_'+str(n_union)+'_' if n_union == 0: total_iou += 1 iou_log += '_1\n' else: total_iou += n_intersect * 1.0 / n_union iou_log += '_'+str(n_intersect * 1.0 / n_union)+'\n' avg_iou = total_iou / len(iou_oids) total_acc_iou += avg_iou total_per_cat_iou[cur_gt_label] += avg_iou if output_verbose: output_color_point_cloud(pts, seg, os.path.join(output_dir, str(shape_idx)+'_gt.obj')) output_color_point_cloud(pts, seg_pred_val, os.path.join(output_dir, str(shape_idx)+'_pred.obj')) output_color_point_cloud_red_blue(pts, np.int32(seg == seg_pred_val), os.path.join(output_dir, str(shape_idx)+'_diff.obj')) with open(os.path.join(output_dir, str(shape_idx)+'.log'), 'w') as fout: fout.write('Total Point: %d\n\n' % ori_point_num) fout.write('Ground Truth: %s\n' % objnames[cur_gt_label]) fout.write('Accuracy: %f\n' % seg_acc) fout.write('IoU: %f\n\n' % avg_iou) fout.write('IoU details: %s\n' % iou_log) printout(flog, 'Accuracy: %f' % (total_acc / total_seen)) printout(flog, 'IoU: %f' % (total_acc_iou / total_seen)) for cat_idx in range(NUM_OBJ_CATS): printout(flog, '\t ' + objcats[cat_idx] + ' Total Number: ' + str(total_per_cat_seen[cat_idx])) if total_per_cat_seen[cat_idx] > 0: printout(flog, '\t ' + objcats[cat_idx] + ' Accuracy: ' + \ str(total_per_cat_acc[cat_idx] / total_per_cat_seen[cat_idx])) printout(flog, '\t ' + objcats[cat_idx] + ' IoU: '+ \ str(total_per_cat_iou[cat_idx] / total_per_cat_seen[cat_idx])) with tf.Graph().as_default(): predict() ================================================ FILE: dgcnn/tensorflow/part_seg/testing_ply_file_list.txt ================================================ 03001627/points/355fa0f35b61fdd7aa74a6b5ee13e775.pts 03001627/expert_verified/points_label/355fa0f35b61fdd7aa74a6b5ee13e775.seg 03001627 04379243/points/408c3db9b4ee6be2e9f3e9c758fef992.pts 04379243/expert_verified/points_label/408c3db9b4ee6be2e9f3e9c758fef992.seg 04379243 02691156/points/a1708ad923f3b51abbf3143b1cb6076a.pts 02691156/expert_verified/points_label/a1708ad923f3b51abbf3143b1cb6076a.seg 02691156 03001627/points/2783a969fa42cdecbe31379a5751d820.pts 03001627/expert_verified/points_label/2783a969fa42cdecbe31379a5751d820.seg 03001627 03001627/points/ed56af61297594bf1c4300651205adf3.pts 03001627/expert_verified/points_label/ed56af61297594bf1c4300651205adf3.seg 03001627 03001627/points/c0857de5101f704f3c5e1addd9922bf2.pts 03001627/expert_verified/points_label/c0857de5101f704f3c5e1addd9922bf2.seg 03001627 02691156/points/b72804a8bd3dbbaca8607f540cc62ba.pts 02691156/expert_verified/points_label/b72804a8bd3dbbaca8607f540cc62ba.seg 02691156 03001627/points/df609533cd186278398c7598b0d2e5d5.pts 03001627/expert_verified/points_label/df609533cd186278398c7598b0d2e5d5.seg 03001627 04379243/points/c24b7a315dbf2f3178ab7c8b395efbfe.pts 04379243/expert_verified/points_label/c24b7a315dbf2f3178ab7c8b395efbfe.seg 04379243 03636649/points/b8c87ad9d4930983a8d82fc8a3e54728.pts 03636649/expert_verified/points_label/b8c87ad9d4930983a8d82fc8a3e54728.seg 03636649 02691156/points/8add45a11c9fcb446eb5821e78d8898a.pts 02691156/expert_verified/points_label/8add45a11c9fcb446eb5821e78d8898a.seg 02691156 04379243/points/94d6518cf1e00eaac013a7bed5288654.pts 04379243/expert_verified/points_label/94d6518cf1e00eaac013a7bed5288654.seg 04379243 04379243/points/1dbb8fd083f96ad279b3e1be3524f72f.pts 04379243/expert_verified/points_label/1dbb8fd083f96ad279b3e1be3524f72f.seg 04379243 03001627/points/452115e132539be4daaaeef365d8f6e5.pts 03001627/expert_verified/points_label/452115e132539be4daaaeef365d8f6e5.seg 03001627 04379243/points/bd25dfa62c3c2cf772bd03149507655d.pts 04379243/expert_verified/points_label/bd25dfa62c3c2cf772bd03149507655d.seg 04379243 03948459/points/b1bbe535a833635d91f9af3df5b0c8fc.pts 03948459/expert_verified/points_label/b1bbe535a833635d91f9af3df5b0c8fc.seg 03948459 04379243/points/d41c8af82fe98a019fb4103277a6b93.pts 04379243/expert_verified/points_label/d41c8af82fe98a019fb4103277a6b93.seg 04379243 03001627/points/3109a0b9f9bc5fecb4cd1bd556007aed.pts 03001627/expert_verified/points_label/3109a0b9f9bc5fecb4cd1bd556007aed.seg 03001627 03001627/points/d38129a3301d31350b1fc43ca5e85e.pts 03001627/expert_verified/points_label/d38129a3301d31350b1fc43ca5e85e.seg 03001627 03636649/points/495af808806f1727a753b1b88fff4abb.pts 03636649/expert_verified/points_label/495af808806f1727a753b1b88fff4abb.seg 03636649 04379243/points/4d3cc502d4444c848cbb8bac2032149c.pts 04379243/expert_verified/points_label/4d3cc502d4444c848cbb8bac2032149c.seg 04379243 02691156/points/ed7e1a38fe33830b87697d3904b168b.pts 02691156/expert_verified/points_label/ed7e1a38fe33830b87697d3904b168b.seg 02691156 04379243/points/cf076ced8264a480cce90f0d61ed7a70.pts 04379243/expert_verified/points_label/cf076ced8264a480cce90f0d61ed7a70.seg 04379243 04379243/points/c04b363fd824528bd42b9650f19dd425.pts 04379243/expert_verified/points_label/c04b363fd824528bd42b9650f19dd425.seg 04379243 04379243/points/9705c2610980d0fdb2d0500bdfc28f70.pts 04379243/expert_verified/points_label/9705c2610980d0fdb2d0500bdfc28f70.seg 04379243 02691156/points/de29a1335c332a5ef7bc9a344bb7bae5.pts 02691156/expert_verified/points_label/de29a1335c332a5ef7bc9a344bb7bae5.seg 02691156 03001627/points/75d0664363f418efe461a9a9741d9415.pts 03001627/expert_verified/points_label/75d0664363f418efe461a9a9741d9415.seg 03001627 03001627/points/3421ad5a45b85f7a4b3c42e318f3affc.pts 03001627/expert_verified/points_label/3421ad5a45b85f7a4b3c42e318f3affc.seg 03001627 03001627/points/c67a255a26e30abb6b9f3980da0b1dff.pts 03001627/expert_verified/points_label/c67a255a26e30abb6b9f3980da0b1dff.seg 03001627 04379243/points/6791c92944c99c029f1deb04fb8ae481.pts 04379243/expert_verified/points_label/6791c92944c99c029f1deb04fb8ae481.seg 04379243 04379243/points/4b5536d2e9c5b9b7febad4f49b26ec52.pts 04379243/expert_verified/points_label/4b5536d2e9c5b9b7febad4f49b26ec52.seg 04379243 04379243/points/c5fc6c1e0d446d37acce1c6e70b58979.pts 04379243/expert_verified/points_label/c5fc6c1e0d446d37acce1c6e70b58979.seg 04379243 03001627/points/9c8d3c5779871705d22218517e73100.pts 03001627/expert_verified/points_label/9c8d3c5779871705d22218517e73100.seg 03001627 04379243/points/4f70d14dc276a9539a83764a2641fc5c.pts 04379243/expert_verified/points_label/4f70d14dc276a9539a83764a2641fc5c.seg 04379243 04379243/points/9d8f0444a8c09adff0d4c8f4dd125299.pts 04379243/expert_verified/points_label/9d8f0444a8c09adff0d4c8f4dd125299.seg 04379243 04379243/points/57fbb082f660c4f7716b680dedf77108.pts 04379243/expert_verified/points_label/57fbb082f660c4f7716b680dedf77108.seg 04379243 02958343/points/cb19594e73992a3d51008e496c6cfd2e.pts 02958343/expert_verified/points_label/cb19594e73992a3d51008e496c6cfd2e.seg 02958343 03624134/points/9d424831d05d363d870906b5178d97bd.pts 03624134/expert_verified/points_label/9d424831d05d363d870906b5178d97bd.seg 03624134 03001627/points/b884ff155c4117a7508dd48e67ad44bc.pts 03001627/expert_verified/points_label/b884ff155c4117a7508dd48e67ad44bc.seg 03001627 02958343/points/7a5eba46ba4cfac35aa429db266f0c30.pts 02958343/expert_verified/points_label/7a5eba46ba4cfac35aa429db266f0c30.seg 02958343 02691156/points/4def53f149137451b0009f08a96f38a9.pts 02691156/expert_verified/points_label/4def53f149137451b0009f08a96f38a9.seg 02691156 03001627/points/fa8f7c225d3b9f1def4a09e7eb872bd9.pts 03001627/expert_verified/points_label/fa8f7c225d3b9f1def4a09e7eb872bd9.seg 03001627 04225987/points/f5d7698b5a57d61226e0640b67de606.pts 04225987/expert_verified/points_label/f5d7698b5a57d61226e0640b67de606.seg 04225987 03001627/points/9aece6c6436cde6fd9ac1bf1eddffd24.pts 03001627/expert_verified/points_label/9aece6c6436cde6fd9ac1bf1eddffd24.seg 03001627 04099429/points/15474cf9caa757a528eba1f0b7744e9.pts 04099429/expert_verified/points_label/15474cf9caa757a528eba1f0b7744e9.seg 04099429 02691156/points/571cfb1da3d5b3704b5910188444efc8.pts 02691156/expert_verified/points_label/571cfb1da3d5b3704b5910188444efc8.seg 02691156 03636649/points/5d97be0e2414bfe0a8930422448288ea.pts 03636649/expert_verified/points_label/5d97be0e2414bfe0a8930422448288ea.seg 03636649 02958343/points/648ceaad362345518a6cf8c6b92417f2.pts 02958343/expert_verified/points_label/648ceaad362345518a6cf8c6b92417f2.seg 02958343 03001627/points/8a845bb67ee8486d6199d6fe090be061.pts 03001627/expert_verified/points_label/8a845bb67ee8486d6199d6fe090be061.seg 03001627 04379243/points/3645a90e02d16f0584aa8fa8b66ba302.pts 04379243/expert_verified/points_label/3645a90e02d16f0584aa8fa8b66ba302.seg 04379243 04379243/points/ecf3d40b14300d3c0c26b04b6b8e17a.pts 04379243/expert_verified/points_label/ecf3d40b14300d3c0c26b04b6b8e17a.seg 04379243 04379243/points/a860e5edcaec268e615bcf72f8385966.pts 04379243/expert_verified/points_label/a860e5edcaec268e615bcf72f8385966.seg 04379243 03001627/points/5edfec789343e0c3319f1c1eee46f332.pts 03001627/expert_verified/points_label/5edfec789343e0c3319f1c1eee46f332.seg 03001627 02691156/points/92fb0d6a866fe7aca8607f540cc62ba.pts 02691156/expert_verified/points_label/92fb0d6a866fe7aca8607f540cc62ba.seg 02691156 02958343/points/e4886a4d0c6ea960fe21694bd5f519d1.pts 02958343/expert_verified/points_label/e4886a4d0c6ea960fe21694bd5f519d1.seg 02958343 03636649/points/e3ee6b31e54e95b7d42b9650f19dd425.pts 03636649/expert_verified/points_label/e3ee6b31e54e95b7d42b9650f19dd425.seg 03636649 03467517/points/d546e034a6c659a425cd348738a8052a.pts 03467517/expert_verified/points_label/d546e034a6c659a425cd348738a8052a.seg 03467517 03001627/points/26a6ce644504c5fa22963ea1e168015d.pts 03001627/expert_verified/points_label/26a6ce644504c5fa22963ea1e168015d.seg 03001627 02691156/points/b2b1c1d5c757af8a7209009cfb89d4bd.pts 02691156/expert_verified/points_label/b2b1c1d5c757af8a7209009cfb89d4bd.seg 02691156 03467517/points/4bd2492d56d6b8c537b5646da91e9ed0.pts 03467517/expert_verified/points_label/4bd2492d56d6b8c537b5646da91e9ed0.seg 03467517 04379243/points/92ed9344484dd026dfd21203bf8b4b46.pts 04379243/expert_verified/points_label/92ed9344484dd026dfd21203bf8b4b46.seg 04379243 04379243/points/2d1d8a2f976387bd3145205f02ff9fc5.pts 04379243/expert_verified/points_label/2d1d8a2f976387bd3145205f02ff9fc5.seg 04379243 03467517/points/5b7fcd85ce6fd1931377689fa4e4b2d6.pts 03467517/expert_verified/points_label/5b7fcd85ce6fd1931377689fa4e4b2d6.seg 03467517 02691156/points/4cee36a2e8dd3b24b87697d3904b168b.pts 02691156/expert_verified/points_label/4cee36a2e8dd3b24b87697d3904b168b.seg 02691156 03001627/points/f23c1bb951fa8909bc01640b1b5116e7.pts 03001627/expert_verified/points_label/f23c1bb951fa8909bc01640b1b5116e7.seg 03001627 04379243/points/370b45eeeb9b11416f04d49e4de95b59.pts 04379243/expert_verified/points_label/370b45eeeb9b11416f04d49e4de95b59.seg 04379243 03001627/points/3885255ca5d75e69da2260dc4a1fc2c6.pts 03001627/expert_verified/points_label/3885255ca5d75e69da2260dc4a1fc2c6.seg 03001627 02691156/points/452c18f8997c53741adbb4c4e06ad649.pts 02691156/expert_verified/points_label/452c18f8997c53741adbb4c4e06ad649.seg 02691156 03001627/points/8b39b501c9fa4d349b9f2eb77f5e247e.pts 03001627/expert_verified/points_label/8b39b501c9fa4d349b9f2eb77f5e247e.seg 03001627 04379243/points/94966aa8a7a6f540f6807434c358ea12.pts 04379243/expert_verified/points_label/94966aa8a7a6f540f6807434c358ea12.seg 04379243 03001627/points/9b6f17ce2db29c4c9ae35d137ece64f9.pts 03001627/expert_verified/points_label/9b6f17ce2db29c4c9ae35d137ece64f9.seg 03001627 03467517/points/85bef84a26a91bff9ce363b13bdd195d.pts 03467517/expert_verified/points_label/85bef84a26a91bff9ce363b13bdd195d.seg 03467517 03624134/points/e98bc872371c852e15b040d25222e627.pts 03624134/expert_verified/points_label/e98bc872371c852e15b040d25222e627.seg 03624134 04379243/points/5dff67091a2f7ef1ab988fe471b1bd06.pts 04379243/expert_verified/points_label/5dff67091a2f7ef1ab988fe471b1bd06.seg 04379243 03001627/points/e6f37dff25ec4ca4f815ebdb2df45512.pts 03001627/expert_verified/points_label/e6f37dff25ec4ca4f815ebdb2df45512.seg 03001627 02691156/points/85a15c26a6e9921ae008cc4902bfe3cd.pts 02691156/expert_verified/points_label/85a15c26a6e9921ae008cc4902bfe3cd.seg 02691156 03001627/points/94371ddd6d62f7b762ec387b772e9e1.pts 03001627/expert_verified/points_label/94371ddd6d62f7b762ec387b772e9e1.seg 03001627 02691156/points/4374a3b4b98e247b398db3ebdf468ed7.pts 02691156/expert_verified/points_label/4374a3b4b98e247b398db3ebdf468ed7.seg 02691156 03948459/points/8fa02aab7237289667fdfbdf64f19325.pts 03948459/expert_verified/points_label/8fa02aab7237289667fdfbdf64f19325.seg 03948459 04379243/points/9f1fcee83cacf964f4b6538438a0b930.pts 04379243/expert_verified/points_label/9f1fcee83cacf964f4b6538438a0b930.seg 04379243 04225987/points/f5643778dbcd653655a834a7aafb0236.pts 04225987/expert_verified/points_label/f5643778dbcd653655a834a7aafb0236.seg 04225987 03636649/points/cdbe11124dbf418167ac0fa90111fad0.pts 03636649/expert_verified/points_label/cdbe11124dbf418167ac0fa90111fad0.seg 03636649 03001627/points/e3d23dc47ddd9620c9be65dfbd21428b.pts 03001627/expert_verified/points_label/e3d23dc47ddd9620c9be65dfbd21428b.seg 03001627 03001627/points/efd0411eaf2396c4de7ed732f5aeea4.pts 03001627/expert_verified/points_label/efd0411eaf2396c4de7ed732f5aeea4.seg 03001627 03636649/points/7ad15667f654fc08664b3b9b23ddfcbc.pts 03636649/expert_verified/points_label/7ad15667f654fc08664b3b9b23ddfcbc.seg 03636649 04379243/points/55d5fce641343449d42b9650f19dd425.pts 04379243/expert_verified/points_label/55d5fce641343449d42b9650f19dd425.seg 04379243 03467517/points/a31ef3a8c70b789b93f0194265a9746c.pts 03467517/expert_verified/points_label/a31ef3a8c70b789b93f0194265a9746c.seg 03467517 03001627/points/ccfc857f35c138ede785b88cc9024b2a.pts 03001627/expert_verified/points_label/ccfc857f35c138ede785b88cc9024b2a.seg 03001627 02691156/points/e3fd510add7b1aa3c19eb6ab3736de88.pts 02691156/expert_verified/points_label/e3fd510add7b1aa3c19eb6ab3736de88.seg 02691156 03636649/points/213d911cc489c352b5db3f95d706a0c9.pts 03636649/expert_verified/points_label/213d911cc489c352b5db3f95d706a0c9.seg 03636649 04225987/points/c171d90db4c4ba56cdb1768065dafd0c.pts 04225987/expert_verified/points_label/c171d90db4c4ba56cdb1768065dafd0c.seg 04225987 03797390/points/10f6e09036350e92b3f21f1137c3c347.pts 03797390/expert_verified/points_label/10f6e09036350e92b3f21f1137c3c347.seg 03797390 02691156/points/a374b0448461438ef3d4cc10d9776c62.pts 02691156/expert_verified/points_label/a374b0448461438ef3d4cc10d9776c62.seg 02691156 03001627/points/b6457a76f24de9f67aa6f8353fce2005.pts 03001627/expert_verified/points_label/b6457a76f24de9f67aa6f8353fce2005.seg 03001627 03001627/points/7fe08cd7a9b76c1dcbde89e0c48a01bf.pts 03001627/expert_verified/points_label/7fe08cd7a9b76c1dcbde89e0c48a01bf.seg 03001627 03001627/points/58867a00409c47c0813a1237d2827540.pts 03001627/expert_verified/points_label/58867a00409c47c0813a1237d2827540.seg 03001627 02958343/points/65e3e2893669a09cc7b48e36e31209b9.pts 02958343/expert_verified/points_label/65e3e2893669a09cc7b48e36e31209b9.seg 02958343 03948459/points/edec08542b9312b712b38b1d99376c0b.pts 03948459/expert_verified/points_label/edec08542b9312b712b38b1d99376c0b.seg 03948459 03636649/points/cd80cc92cf732e8d8a17805dbfb751e2.pts 03636649/expert_verified/points_label/cd80cc92cf732e8d8a17805dbfb751e2.seg 03636649 03467517/points/87650e8ff3d85672381b7fbf79296afb.pts 03467517/expert_verified/points_label/87650e8ff3d85672381b7fbf79296afb.seg 03467517 03636649/points/1e91664763d371937dd73da65dc0e6a7.pts 03636649/expert_verified/points_label/1e91664763d371937dd73da65dc0e6a7.seg 03636649 04379243/points/104c8e90ecf0e5351ed672982b7954af.pts 04379243/expert_verified/points_label/104c8e90ecf0e5351ed672982b7954af.seg 04379243 04379243/points/1834fac2f46a26f91933ffef19678834.pts 04379243/expert_verified/points_label/1834fac2f46a26f91933ffef19678834.seg 04379243 04379243/points/ed0be8928caab4bdab610b0c94236463.pts 04379243/expert_verified/points_label/ed0be8928caab4bdab610b0c94236463.seg 04379243 04379243/points/105f53a6471f3ceb4a420e3c1b966720.pts 04379243/expert_verified/points_label/105f53a6471f3ceb4a420e3c1b966720.seg 04379243 04379243/points/7bf5f689da285153583ff8a5fc7c1869.pts 04379243/expert_verified/points_label/7bf5f689da285153583ff8a5fc7c1869.seg 04379243 02958343/points/eface8341d001e9ceb01ae4a4788bd4f.pts 02958343/expert_verified/points_label/eface8341d001e9ceb01ae4a4788bd4f.seg 02958343 03001627/points/517880899d26080471a782a4379556c7.pts 03001627/expert_verified/points_label/517880899d26080471a782a4379556c7.seg 03001627 03001627/points/5ef3e4abd4386c8871bc6030acc85f1e.pts 03001627/expert_verified/points_label/5ef3e4abd4386c8871bc6030acc85f1e.seg 03001627 03001627/points/3eb60e6679d1df1dde7eedbb2790491b.pts 03001627/expert_verified/points_label/3eb60e6679d1df1dde7eedbb2790491b.seg 03001627 03001627/points/4702e6196503ff84f1c0e03f321d0b20.pts 03001627/expert_verified/points_label/4702e6196503ff84f1c0e03f321d0b20.seg 03001627 02958343/points/b0a7789537663f7ba1ff2929b2f5cf19.pts 02958343/expert_verified/points_label/b0a7789537663f7ba1ff2929b2f5cf19.seg 02958343 03636649/points/2ce7732982343c1d9792f6094a78f8d5.pts 03636649/expert_verified/points_label/2ce7732982343c1d9792f6094a78f8d5.seg 03636649 03467517/points/78a75ce8dc8dc197dc2b574e941c815b.pts 03467517/expert_verified/points_label/78a75ce8dc8dc197dc2b574e941c815b.seg 03467517 03636649/points/348d6ddf9e02cbddf647dc544bb0ab61.pts 03636649/expert_verified/points_label/348d6ddf9e02cbddf647dc544bb0ab61.seg 03636649 03001627/points/e56087cd55cce8b4f41a4361d0ca9bc8.pts 03001627/expert_verified/points_label/e56087cd55cce8b4f41a4361d0ca9bc8.seg 03001627 03642806/points/4d3dde22f529195bc887d5d9a11f3155.pts 03642806/expert_verified/points_label/4d3dde22f529195bc887d5d9a11f3155.seg 03642806 03001627/points/78e1977bc5f0f4041552c6ecbda964b.pts 03001627/expert_verified/points_label/78e1977bc5f0f4041552c6ecbda964b.seg 03001627 04379243/points/44360c91a7e91098d93768e7b9b1eabf.pts 04379243/expert_verified/points_label/44360c91a7e91098d93768e7b9b1eabf.seg 04379243 02691156/points/52ca6970fb09b561f9f7510373841dd9.pts 02691156/expert_verified/points_label/52ca6970fb09b561f9f7510373841dd9.seg 02691156 02958343/points/383f8d508b6f25f565d21723f535417.pts 02958343/expert_verified/points_label/383f8d508b6f25f565d21723f535417.seg 02958343 03001627/points/d6da5457b0682e24696b74614952b2d0.pts 03001627/expert_verified/points_label/d6da5457b0682e24696b74614952b2d0.seg 03001627 02691156/points/9f5dda6f01bbe29bf810506e9ae2dcc2.pts 02691156/expert_verified/points_label/9f5dda6f01bbe29bf810506e9ae2dcc2.seg 02691156 03467517/points/35e77edd3ae6ad4993f0194265a9746c.pts 03467517/expert_verified/points_label/35e77edd3ae6ad4993f0194265a9746c.seg 03467517 03001627/points/590d04438aeffbb58f447453fccbd9d3.pts 03001627/expert_verified/points_label/590d04438aeffbb58f447453fccbd9d3.seg 03001627 03001627/points/cdfa898eadf316122056b4bd5d870b47.pts 03001627/expert_verified/points_label/cdfa898eadf316122056b4bd5d870b47.seg 03001627 03001627/points/8e678a54f2ee4e5e492d9da2668ec34c.pts 03001627/expert_verified/points_label/8e678a54f2ee4e5e492d9da2668ec34c.seg 03001627 04379243/points/1804dd6f5c827c1a4bf8d5f43e57b138.pts 04379243/expert_verified/points_label/1804dd6f5c827c1a4bf8d5f43e57b138.seg 04379243 02691156/points/23eed87ac79f1b152f9c405cf0817830.pts 02691156/expert_verified/points_label/23eed87ac79f1b152f9c405cf0817830.seg 02691156 02691156/points/97bc5fffde64178f43afdb9c81ff2967.pts 02691156/expert_verified/points_label/97bc5fffde64178f43afdb9c81ff2967.seg 02691156 03001627/points/3b1f1913f2bc0dc171dbe96559c7bcae.pts 03001627/expert_verified/points_label/3b1f1913f2bc0dc171dbe96559c7bcae.seg 03001627 04379243/points/82e1c0b874b0a9e035cd53a06b1d2317.pts 04379243/expert_verified/points_label/82e1c0b874b0a9e035cd53a06b1d2317.seg 04379243 03001627/points/e0a0d5c2ba6fdca215b55266697a17be.pts 03001627/expert_verified/points_label/e0a0d5c2ba6fdca215b55266697a17be.seg 03001627 03636649/points/9b558be5e2b60e3eb09f0ca9c143fdfd.pts 03636649/expert_verified/points_label/9b558be5e2b60e3eb09f0ca9c143fdfd.seg 03636649 03001627/points/813be9a8485050571563f0911e3e5fc0.pts 03001627/expert_verified/points_label/813be9a8485050571563f0911e3e5fc0.seg 03001627 02958343/points/6ca9967adcf862a461c6c61410fc904b.pts 02958343/expert_verified/points_label/6ca9967adcf862a461c6c61410fc904b.seg 02958343 03624134/points/5663637633c938d1395331ebe4786cd.pts 03624134/expert_verified/points_label/5663637633c938d1395331ebe4786cd.seg 03624134 03636649/points/ec8dc2311d381a9e3d39d8012919dd25.pts 03636649/expert_verified/points_label/ec8dc2311d381a9e3d39d8012919dd25.seg 03636649 04379243/points/b685208ccf38786a6f1e07a56c129dfc.pts 04379243/expert_verified/points_label/b685208ccf38786a6f1e07a56c129dfc.seg 04379243 03636649/points/ce621e6df1ab9ae35d2cdb96c1afe34.pts 03636649/expert_verified/points_label/ce621e6df1ab9ae35d2cdb96c1afe34.seg 03636649 02691156/points/b092d523bdd320e4ca8607f540cc62ba.pts 02691156/expert_verified/points_label/b092d523bdd320e4ca8607f540cc62ba.seg 02691156 04379243/points/401fe961ec7b0cb5dcfcef693e7ec696.pts 04379243/expert_verified/points_label/401fe961ec7b0cb5dcfcef693e7ec696.seg 04379243 04225987/points/1e5fd1de723cc66cbb1ed6d4d8526a19.pts 04225987/expert_verified/points_label/1e5fd1de723cc66cbb1ed6d4d8526a19.seg 04225987 03001627/points/b987a2ca54c6ddecb74697ced5978572.pts 03001627/expert_verified/points_label/b987a2ca54c6ddecb74697ced5978572.seg 03001627 04379243/points/3e42e3386f4aea9277cf3bb06f394ad.pts 04379243/expert_verified/points_label/3e42e3386f4aea9277cf3bb06f394ad.seg 04379243 02958343/points/1198255e3d20d2f323f3ca54768fe2ee.pts 02958343/expert_verified/points_label/1198255e3d20d2f323f3ca54768fe2ee.seg 02958343 04379243/points/2b564ff0989caf58ab610b0c94236463.pts 04379243/expert_verified/points_label/2b564ff0989caf58ab610b0c94236463.seg 04379243 03636649/points/941271c5d9b192eaccd8f9b9403fd602.pts 03636649/expert_verified/points_label/941271c5d9b192eaccd8f9b9403fd602.seg 03636649 02691156/points/6aeae52e38f892a7e0091ae06332b2d5.pts 02691156/expert_verified/points_label/6aeae52e38f892a7e0091ae06332b2d5.seg 02691156 04379243/points/4cdfd605352adcb0da13974b3533fb59.pts 04379243/expert_verified/points_label/4cdfd605352adcb0da13974b3533fb59.seg 04379243 04379243/points/7c24e4f8778e224799a5e8f6c5baa224.pts 04379243/expert_verified/points_label/7c24e4f8778e224799a5e8f6c5baa224.seg 04379243 03001627/points/6272c21e439e0205c8687ff9b0b4e4ac.pts 03001627/expert_verified/points_label/6272c21e439e0205c8687ff9b0b4e4ac.seg 03001627 02691156/points/acd8f367c36a3d84fc7a6d75b3d807ff.pts 02691156/expert_verified/points_label/acd8f367c36a3d84fc7a6d75b3d807ff.seg 02691156 04379243/points/d58bdda16e6bba6f796740c80be6053.pts 04379243/expert_verified/points_label/d58bdda16e6bba6f796740c80be6053.seg 04379243 03636649/points/f97506704760741b460fa882e24b7e4a.pts 03636649/expert_verified/points_label/f97506704760741b460fa882e24b7e4a.seg 03636649 03636649/points/9f5c3ea9f8254b8bd42b9650f19dd425.pts 03636649/expert_verified/points_label/9f5c3ea9f8254b8bd42b9650f19dd425.seg 03636649 03797390/points/79e673336e836d1333becb3a9550cbb1.pts 03797390/expert_verified/points_label/79e673336e836d1333becb3a9550cbb1.seg 03797390 03948459/points/2d573d37cce5b48b9f433921788191f3.pts 03948459/expert_verified/points_label/2d573d37cce5b48b9f433921788191f3.seg 03948459 04379243/points/7aaad1c5c2be8c24a9ed7bb5b55809f8.pts 04379243/expert_verified/points_label/7aaad1c5c2be8c24a9ed7bb5b55809f8.seg 04379243 04379243/points/c6c412c771ab0ae015a34fa27bdf3d03.pts 04379243/expert_verified/points_label/c6c412c771ab0ae015a34fa27bdf3d03.seg 04379243 03467517/points/819251e11b46438ff6ff9bebca919581.pts 03467517/expert_verified/points_label/819251e11b46438ff6ff9bebca919581.seg 03467517 03001627/points/51f4ea68be319fe8990e5087098e19c.pts 03001627/expert_verified/points_label/51f4ea68be319fe8990e5087098e19c.seg 03001627 03467517/points/66b24797480ba515d57700c05b1862d8.pts 03467517/expert_verified/points_label/66b24797480ba515d57700c05b1862d8.seg 03467517 03790512/points/9d3b07f4475d501e8249f134aca4c817.pts 03790512/expert_verified/points_label/9d3b07f4475d501e8249f134aca4c817.seg 03790512 04379243/points/72cfb60a075369ab7252c133a7e17d94.pts 04379243/expert_verified/points_label/72cfb60a075369ab7252c133a7e17d94.seg 04379243 04379243/points/12a2733fc5f6b31ef8574543281e850f.pts 04379243/expert_verified/points_label/12a2733fc5f6b31ef8574543281e850f.seg 04379243 03636649/points/aed950102f1e9c7a659dda512294c744.pts 03636649/expert_verified/points_label/aed950102f1e9c7a659dda512294c744.seg 03636649 03001627/points/3126c6e9277b775b245ac1812a4e4d0c.pts 03001627/expert_verified/points_label/3126c6e9277b775b245ac1812a4e4d0c.seg 03001627 02958343/points/8decf42b145f98d148d2ba4615e03b21.pts 02958343/expert_verified/points_label/8decf42b145f98d148d2ba4615e03b21.seg 02958343 03467517/points/2f9bd6e61e038d8fd4b4ae2ff4c58b57.pts 03467517/expert_verified/points_label/2f9bd6e61e038d8fd4b4ae2ff4c58b57.seg 03467517 03467517/points/6a983b2ff1b8a42e1285d7bfa3e922e4.pts 03467517/expert_verified/points_label/6a983b2ff1b8a42e1285d7bfa3e922e4.seg 03467517 03261776/points/e33d6e8e39a75268957b6a4f3924d982.pts 03261776/expert_verified/points_label/e33d6e8e39a75268957b6a4f3924d982.seg 03261776 04379243/points/fe2f2b120d84ed909b896cf832106977.pts 04379243/expert_verified/points_label/fe2f2b120d84ed909b896cf832106977.seg 04379243 02958343/points/1328a95d69cefe32f200a72c9245aee7.pts 02958343/expert_verified/points_label/1328a95d69cefe32f200a72c9245aee7.seg 02958343 03001627/points/58409b308683d908ca2bec46a3b47519.pts 03001627/expert_verified/points_label/58409b308683d908ca2bec46a3b47519.seg 03001627 03001627/points/507a5070cde81fd867936ca58e67cec6.pts 03001627/expert_verified/points_label/507a5070cde81fd867936ca58e67cec6.seg 03001627 04379243/points/ec68e1edbb7e9bc7e93cebb6ba9ca43e.pts 04379243/expert_verified/points_label/ec68e1edbb7e9bc7e93cebb6ba9ca43e.seg 04379243 03001627/points/7facccfa81369078a8930422448288ea.pts 03001627/expert_verified/points_label/7facccfa81369078a8930422448288ea.seg 03001627 03001627/points/be0c5a0e91c99e804e1a714ee619465a.pts 03001627/expert_verified/points_label/be0c5a0e91c99e804e1a714ee619465a.seg 03001627 03001627/points/d73e46e07bdb3fe75fe4ecea39e8bd40.pts 03001627/expert_verified/points_label/d73e46e07bdb3fe75fe4ecea39e8bd40.seg 03001627 03636649/points/122fb7bfa09c184ca249f8489bc060dd.pts 03636649/expert_verified/points_label/122fb7bfa09c184ca249f8489bc060dd.seg 03636649 03001627/points/9ef3323c6ced7dfef313a0fb5fd4d79.pts 03001627/expert_verified/points_label/9ef3323c6ced7dfef313a0fb5fd4d79.seg 03001627 02691156/points/d8452d4fe51f2bab3554ccf8c30febe7.pts 02691156/expert_verified/points_label/d8452d4fe51f2bab3554ccf8c30febe7.seg 02691156 02691156/points/d59d75f52ac9b241ae0d772a1c85134a.pts 02691156/expert_verified/points_label/d59d75f52ac9b241ae0d772a1c85134a.seg 02691156 02691156/points/f9e80ce23d9536623fddedb0bf24c68a.pts 02691156/expert_verified/points_label/f9e80ce23d9536623fddedb0bf24c68a.seg 02691156 02691156/points/e69631d34410f99ac4f72bf08dc79a6.pts 02691156/expert_verified/points_label/e69631d34410f99ac4f72bf08dc79a6.seg 02691156 04379243/points/f7196ec7d732af5166decb1b3cdc5557.pts 04379243/expert_verified/points_label/f7196ec7d732af5166decb1b3cdc5557.seg 04379243 03261776/points/c5e47b627cb7818f17e22b7299bb7bc6.pts 03261776/expert_verified/points_label/c5e47b627cb7818f17e22b7299bb7bc6.seg 03261776 03001627/points/5a60c649a221293d72ed554eb3baedcc.pts 03001627/expert_verified/points_label/5a60c649a221293d72ed554eb3baedcc.seg 03001627 04379243/points/b117aac2e13630bb5d23c9bbb429abf9.pts 04379243/expert_verified/points_label/b117aac2e13630bb5d23c9bbb429abf9.seg 04379243 03642806/points/e4c34c87ed1bc2191ef7a71d6e01357e.pts 03642806/expert_verified/points_label/e4c34c87ed1bc2191ef7a71d6e01357e.seg 03642806 02691156/points/3fb7ceab42d7b17219ba010ddb4974fe.pts 02691156/expert_verified/points_label/3fb7ceab42d7b17219ba010ddb4974fe.seg 02691156 04379243/points/fc472163ea149f8e19fb4103277a6b93.pts 04379243/expert_verified/points_label/fc472163ea149f8e19fb4103277a6b93.seg 04379243 03001627/points/5ef73c9bee1b4adcd019a8a03d4a2a3.pts 03001627/expert_verified/points_label/5ef73c9bee1b4adcd019a8a03d4a2a3.seg 03001627 02691156/points/384e72f69e6f24404cb288947cda4a2c.pts 02691156/expert_verified/points_label/384e72f69e6f24404cb288947cda4a2c.seg 02691156 03636649/points/3fca250636e2b47a8d0fc77aab7a8d33.pts 03636649/expert_verified/points_label/3fca250636e2b47a8d0fc77aab7a8d33.seg 03636649 04379243/points/46957ba752c3554bd42b9650f19dd425.pts 04379243/expert_verified/points_label/46957ba752c3554bd42b9650f19dd425.seg 04379243 03001627/points/bce7ff621a5440bb34ee5c94ebdf7f1d.pts 03001627/expert_verified/points_label/bce7ff621a5440bb34ee5c94ebdf7f1d.seg 03001627 02691156/points/66ae19841350ac2d4ba2821676102936.pts 02691156/expert_verified/points_label/66ae19841350ac2d4ba2821676102936.seg 02691156 03001627/points/e53b07b648e8d041107a17cfae0b6df6.pts 03001627/expert_verified/points_label/e53b07b648e8d041107a17cfae0b6df6.seg 03001627 03624134/points/d1c757548ead4a4d8d03ca4865da5b6.pts 03624134/expert_verified/points_label/d1c757548ead4a4d8d03ca4865da5b6.seg 03624134 04379243/points/d19b4bde0766723c9b3bb0ef2a08be04.pts 04379243/expert_verified/points_label/d19b4bde0766723c9b3bb0ef2a08be04.seg 04379243 03001627/points/6ecec258a1b6fe2a6fee8e2140acec9.pts 03001627/expert_verified/points_label/6ecec258a1b6fe2a6fee8e2140acec9.seg 03001627 02691156/points/ab95a4e7f2d3cf9ca8607f540cc62ba.pts 02691156/expert_verified/points_label/ab95a4e7f2d3cf9ca8607f540cc62ba.seg 02691156 03624134/points/b61c9b5f29ad581c860a45e027159a9a.pts 03624134/expert_verified/points_label/b61c9b5f29ad581c860a45e027159a9a.seg 03624134 03001627/points/c7da2d72f9927f1881dff5c2e57ad46e.pts 03001627/expert_verified/points_label/c7da2d72f9927f1881dff5c2e57ad46e.seg 03001627 04379243/points/b9886dd3c4a651f3664b3b9b23ddfcbc.pts 04379243/expert_verified/points_label/b9886dd3c4a651f3664b3b9b23ddfcbc.seg 04379243 02691156/points/abc465975af79827dfb86dddee1d6ac3.pts 02691156/expert_verified/points_label/abc465975af79827dfb86dddee1d6ac3.seg 02691156 03636649/points/7be01530bf43f2ed8a83637b92bdc7.pts 03636649/expert_verified/points_label/7be01530bf43f2ed8a83637b92bdc7.seg 03636649 02691156/points/b81339a2f1dbc0de9598ceb95c7f0752.pts 02691156/expert_verified/points_label/b81339a2f1dbc0de9598ceb95c7f0752.seg 02691156 03001627/points/69709cb300ae3784ee72e5c46412e9a7.pts 03001627/expert_verified/points_label/69709cb300ae3784ee72e5c46412e9a7.seg 03001627 03001627/points/ec25a41ca233ed096e5a467428553af2.pts 03001627/expert_verified/points_label/ec25a41ca233ed096e5a467428553af2.seg 03001627 04379243/points/4e9394f9f64859aef4ef86403cccc399.pts 04379243/expert_verified/points_label/4e9394f9f64859aef4ef86403cccc399.seg 04379243 04379243/points/c477235c02413bfc44d2ca62bee212a0.pts 04379243/expert_verified/points_label/c477235c02413bfc44d2ca62bee212a0.seg 04379243 04379243/points/41b0491fdb14d41bd25ca1a27cf9bdec.pts 04379243/expert_verified/points_label/41b0491fdb14d41bd25ca1a27cf9bdec.seg 04379243 02691156/points/59eecc0a983a27a8130cc35407fba74a.pts 02691156/expert_verified/points_label/59eecc0a983a27a8130cc35407fba74a.seg 02691156 03467517/points/22129fab1497437cc3f912172873d52f.pts 03467517/expert_verified/points_label/22129fab1497437cc3f912172873d52f.seg 03467517 04379243/points/6365205d2324234fc8a1efeb4b91d393.pts 04379243/expert_verified/points_label/6365205d2324234fc8a1efeb4b91d393.seg 04379243 03001627/points/2a75b2bb82d7f77c3f9d6e0ade5188b0.pts 03001627/expert_verified/points_label/2a75b2bb82d7f77c3f9d6e0ade5188b0.seg 03001627 03001627/points/8f226d6b3089d3b7bca860dd9b04c52c.pts 03001627/expert_verified/points_label/8f226d6b3089d3b7bca860dd9b04c52c.seg 03001627 03624134/points/5e515b18ed17a418b056c98b2e5e5e4e.pts 03624134/expert_verified/points_label/5e515b18ed17a418b056c98b2e5e5e4e.seg 03624134 02691156/points/5bc41589eba11a4e15477d594f1fbd99.pts 02691156/expert_verified/points_label/5bc41589eba11a4e15477d594f1fbd99.seg 02691156 03001627/points/2bbf00f0c583fd8a4b3c42e318f3affc.pts 03001627/expert_verified/points_label/2bbf00f0c583fd8a4b3c42e318f3affc.seg 03001627 03790512/points/9e9300a6e1caec217395d58407f193ba.pts 03790512/expert_verified/points_label/9e9300a6e1caec217395d58407f193ba.seg 03790512 03636649/points/81894e0739e3fea9d49b2e04785f8492.pts 03636649/expert_verified/points_label/81894e0739e3fea9d49b2e04785f8492.seg 03636649 02958343/points/cdc8453c63ffc13e20f29d4da2b76f7a.pts 02958343/expert_verified/points_label/cdc8453c63ffc13e20f29d4da2b76f7a.seg 02958343 04379243/points/7a0b6685a30298fb8ae8d7de284e7d2.pts 04379243/expert_verified/points_label/7a0b6685a30298fb8ae8d7de284e7d2.seg 04379243 03001627/points/c5ee6b77f9f84adeed52100e321c9f3e.pts 03001627/expert_verified/points_label/c5ee6b77f9f84adeed52100e321c9f3e.seg 03001627 04379243/points/4e87db85d5dab96822339a4b4aacca6b.pts 04379243/expert_verified/points_label/4e87db85d5dab96822339a4b4aacca6b.seg 04379243 02958343/points/6dbae14e481e8fb9333e0bf0b765fa12.pts 02958343/expert_verified/points_label/6dbae14e481e8fb9333e0bf0b765fa12.seg 02958343 03467517/points/bad8978268948ea3d3eb77b119df6d.pts 03467517/expert_verified/points_label/bad8978268948ea3d3eb77b119df6d.seg 03467517 03001627/points/c552529c54b0612e53041c49040be3d5.pts 03001627/expert_verified/points_label/c552529c54b0612e53041c49040be3d5.seg 03001627 02958343/points/dca8ed788347b28c171cf359a50c99bc.pts 02958343/expert_verified/points_label/dca8ed788347b28c171cf359a50c99bc.seg 02958343 04379243/points/99720647e210078beaf288f952624966.pts 04379243/expert_verified/points_label/99720647e210078beaf288f952624966.seg 04379243 03001627/points/b1f4b2c32f8a2fa77ee217c21e683487.pts 03001627/expert_verified/points_label/b1f4b2c32f8a2fa77ee217c21e683487.seg 03001627 04379243/points/41cdb5b619790d5a74eb542502c2205f.pts 04379243/expert_verified/points_label/41cdb5b619790d5a74eb542502c2205f.seg 04379243 04379243/points/a25141a07c77c25467de2aaf749e5256.pts 04379243/expert_verified/points_label/a25141a07c77c25467de2aaf749e5256.seg 04379243 04379243/points/e9c3a3aa2278608bec15b38012222fa8.pts 04379243/expert_verified/points_label/e9c3a3aa2278608bec15b38012222fa8.seg 04379243 03636649/points/8e025c4aa0b0201a81a172d69c52a28a.pts 03636649/expert_verified/points_label/8e025c4aa0b0201a81a172d69c52a28a.seg 03636649 03001627/points/e175bc785390e8f6c05575120a46cd3b.pts 03001627/expert_verified/points_label/e175bc785390e8f6c05575120a46cd3b.seg 03001627 02691156/points/ecb4ae05d7dd135a619550d2af0b6117.pts 02691156/expert_verified/points_label/ecb4ae05d7dd135a619550d2af0b6117.seg 02691156 02691156/points/87069f21b11c180799a771d197c7b487.pts 02691156/expert_verified/points_label/87069f21b11c180799a771d197c7b487.seg 02691156 02691156/points/ca11efc8928c10908b96ae1a0a8b84ec.pts 02691156/expert_verified/points_label/ca11efc8928c10908b96ae1a0a8b84ec.seg 02691156 03790512/points/365c1f92a54c8cb52a45a87054fa7272.pts 03790512/expert_verified/points_label/365c1f92a54c8cb52a45a87054fa7272.seg 03790512 03636649/points/23040992da19679aaa7cb30470f3273c.pts 03636649/expert_verified/points_label/23040992da19679aaa7cb30470f3273c.seg 03636649 02691156/points/9441549e323552f2f001dddaf44c449b.pts 02691156/expert_verified/points_label/9441549e323552f2f001dddaf44c449b.seg 02691156 02958343/points/17bfc66c6bc0a99d68c415156b102065.pts 02958343/expert_verified/points_label/17bfc66c6bc0a99d68c415156b102065.seg 02958343 03001627/points/671d34c27cc0f1bf2deeb5ec76cf103b.pts 03001627/expert_verified/points_label/671d34c27cc0f1bf2deeb5ec76cf103b.seg 03001627 03642806/points/464edfe14e9fa45c3394926146371698.pts 03642806/expert_verified/points_label/464edfe14e9fa45c3394926146371698.seg 03642806 04379243/points/279c8601278e827dab610b0c94236463.pts 04379243/expert_verified/points_label/279c8601278e827dab610b0c94236463.seg 04379243 04379243/points/29d9c6d84c6a126917b431cae0dd70ed.pts 04379243/expert_verified/points_label/29d9c6d84c6a126917b431cae0dd70ed.seg 04379243 04379243/points/5d3d902051858e56ed1397afd2317e5b.pts 04379243/expert_verified/points_label/5d3d902051858e56ed1397afd2317e5b.seg 04379243 02958343/points/aa78d4465ae18312711f9e3a79a13dcf.pts 02958343/expert_verified/points_label/aa78d4465ae18312711f9e3a79a13dcf.seg 02958343 03001627/points/d561ff6788ab46517b016084e2ae95e.pts 03001627/expert_verified/points_label/d561ff6788ab46517b016084e2ae95e.seg 03001627 03001627/points/b24ed89d85b74771216fff6094e6695c.pts 03001627/expert_verified/points_label/b24ed89d85b74771216fff6094e6695c.seg 03001627 03636649/points/f6eeb5d67c32616648fda83c10428379.pts 03636649/expert_verified/points_label/f6eeb5d67c32616648fda83c10428379.seg 03636649 03001627/points/3b3a9f4e3aa9f2f4d39a194653571dfc.pts 03001627/expert_verified/points_label/3b3a9f4e3aa9f2f4d39a194653571dfc.seg 03001627 03001627/points/bd0b06e158bcee8ac0d89fc15154c9a2.pts 03001627/expert_verified/points_label/bd0b06e158bcee8ac0d89fc15154c9a2.seg 03001627 04379243/points/89251f322490e7047e38640a31d0bc3.pts 04379243/expert_verified/points_label/89251f322490e7047e38640a31d0bc3.seg 04379243 03001627/points/935f5e58e9e15231febad4f49b26ec52.pts 03001627/expert_verified/points_label/935f5e58e9e15231febad4f49b26ec52.seg 03001627 03467517/points/8f59fee745f1e37ea5c8e9fc8b2242fd.pts 03467517/expert_verified/points_label/8f59fee745f1e37ea5c8e9fc8b2242fd.seg 03467517 02691156/points/fddcb2b3d45ce98e641c309f1fd7e183.pts 02691156/expert_verified/points_label/fddcb2b3d45ce98e641c309f1fd7e183.seg 02691156 03001627/points/d915d2f1664bf76e71a70be9f12ce8b0.pts 03001627/expert_verified/points_label/d915d2f1664bf76e71a70be9f12ce8b0.seg 03001627 02958343/points/1ae9732840a315afab2c2809513f396e.pts 02958343/expert_verified/points_label/1ae9732840a315afab2c2809513f396e.seg 02958343 04379243/points/b658e507c84d6202610c2a68437007d6.pts 04379243/expert_verified/points_label/b658e507c84d6202610c2a68437007d6.seg 04379243 02958343/points/707d1e19b465d075adbfb30d8d1b297e.pts 02958343/expert_verified/points_label/707d1e19b465d075adbfb30d8d1b297e.seg 02958343 04379243/points/5b74412eba257e5182b796aa5845e185.pts 04379243/expert_verified/points_label/5b74412eba257e5182b796aa5845e185.seg 04379243 03636649/points/a801be11157a7f243d39d8012919dd25.pts 03636649/expert_verified/points_label/a801be11157a7f243d39d8012919dd25.seg 03636649 02691156/points/26e10058cf9835aaca8607f540cc62ba.pts 02691156/expert_verified/points_label/26e10058cf9835aaca8607f540cc62ba.seg 02691156 03636649/points/bc704db7b62582e5d1cbf3e52b9b6237.pts 03636649/expert_verified/points_label/bc704db7b62582e5d1cbf3e52b9b6237.seg 03636649 02691156/points/d2e2e23f5be557e2d1ab3b031c100cb1.pts 02691156/expert_verified/points_label/d2e2e23f5be557e2d1ab3b031c100cb1.seg 02691156 03001627/points/920af478601258e24762da3a3017ade.pts 03001627/expert_verified/points_label/920af478601258e24762da3a3017ade.seg 03001627 03001627/points/3ffd794e5100258483bc207d8a5912e3.pts 03001627/expert_verified/points_label/3ffd794e5100258483bc207d8a5912e3.seg 03001627 04379243/points/69c536d9e450cb79436e6787c76ef3f0.pts 04379243/expert_verified/points_label/69c536d9e450cb79436e6787c76ef3f0.seg 04379243 04379243/points/6cf6a546e2ecbffe815a7efb12912.pts 04379243/expert_verified/points_label/6cf6a546e2ecbffe815a7efb12912.seg 04379243 03001627/points/815f436a40c28da51f56aa11cd5e0c3e.pts 03001627/expert_verified/points_label/815f436a40c28da51f56aa11cd5e0c3e.seg 03001627 03642806/points/4504a4d244d05ddbf5f79806bd65844f.pts 03642806/expert_verified/points_label/4504a4d244d05ddbf5f79806bd65844f.seg 03642806 04379243/points/8ad9868947e7391113625562b56161f0.pts 04379243/expert_verified/points_label/8ad9868947e7391113625562b56161f0.seg 04379243 03001627/points/6b9c3d42724275cf7a5c8cd74a7bc29a.pts 03001627/expert_verified/points_label/6b9c3d42724275cf7a5c8cd74a7bc29a.seg 03001627 04379243/points/67e32538a35a5011a0ab1d82ef09f78f.pts 04379243/expert_verified/points_label/67e32538a35a5011a0ab1d82ef09f78f.seg 04379243 03624134/points/2743e37a65e198d51592d7a04a86fa53.pts 03624134/expert_verified/points_label/2743e37a65e198d51592d7a04a86fa53.seg 03624134 04379243/points/12df5c215f4364b7fe388cf6c4c3705d.pts 04379243/expert_verified/points_label/12df5c215f4364b7fe388cf6c4c3705d.seg 04379243 02958343/points/55e0897c0ac089a6da5cb3be8feeaadc.pts 02958343/expert_verified/points_label/55e0897c0ac089a6da5cb3be8feeaadc.seg 02958343 02773838/points/4e4fcfffec161ecaed13f430b2941481.pts 02773838/expert_verified/points_label/4e4fcfffec161ecaed13f430b2941481.seg 02773838 04379243/points/8ce70dead5119191cc3492a06e9bd850.pts 04379243/expert_verified/points_label/8ce70dead5119191cc3492a06e9bd850.seg 04379243 02691156/points/e033b6ad34586a86cc1c9e8218bfe7fc.pts 02691156/expert_verified/points_label/e033b6ad34586a86cc1c9e8218bfe7fc.seg 02691156 03636649/points/600b2f00113ad714e2367b9e27f16a71.pts 03636649/expert_verified/points_label/600b2f00113ad714e2367b9e27f16a71.seg 03636649 04379243/points/a74cad1781afed87dcfcef693e7ec696.pts 04379243/expert_verified/points_label/a74cad1781afed87dcfcef693e7ec696.seg 04379243 03001627/points/5402eecc67e489502fa77440dcb93214.pts 03001627/expert_verified/points_label/5402eecc67e489502fa77440dcb93214.seg 03001627 03001627/points/d5bd6ea417eba6ce456cbf78e1e89022.pts 03001627/expert_verified/points_label/d5bd6ea417eba6ce456cbf78e1e89022.seg 03001627 03001627/points/d4edd167061dac5f52a3901fa1436b1a.pts 03001627/expert_verified/points_label/d4edd167061dac5f52a3901fa1436b1a.seg 03001627 03636649/points/9fc3ddc511f4ef62dced62abd38a02b0.pts 03636649/expert_verified/points_label/9fc3ddc511f4ef62dced62abd38a02b0.seg 03636649 02691156/points/92a83ecaa10e8d3f78e919a72d9a39e7.pts 02691156/expert_verified/points_label/92a83ecaa10e8d3f78e919a72d9a39e7.seg 02691156 03001627/points/fee36ec8c8ae503fc68456e8da5b9a30.pts 03001627/expert_verified/points_label/fee36ec8c8ae503fc68456e8da5b9a30.seg 03001627 04379243/points/1df409cfefbb51658b9b51ae4415d5aa.pts 04379243/expert_verified/points_label/1df409cfefbb51658b9b51ae4415d5aa.seg 04379243 03001627/points/76283716a2c6586e266d673a6188bf4c.pts 03001627/expert_verified/points_label/76283716a2c6586e266d673a6188bf4c.seg 03001627 04379243/points/29b2aaca87d19a3c5759f4335ff2e408.pts 04379243/expert_verified/points_label/29b2aaca87d19a3c5759f4335ff2e408.seg 04379243 04379243/points/21ca4d36a0f6fa69b937d98d58545fa.pts 04379243/expert_verified/points_label/21ca4d36a0f6fa69b937d98d58545fa.seg 04379243 02691156/points/da1acb401541235be4d2773f0358b43b.pts 02691156/expert_verified/points_label/da1acb401541235be4d2773f0358b43b.seg 02691156 04379243/points/553c416f33c5e5e18b9b51ae4415d5aa.pts 04379243/expert_verified/points_label/553c416f33c5e5e18b9b51ae4415d5aa.seg 04379243 04379243/points/174832b73cd6d91c9856fa70a578baeb.pts 04379243/expert_verified/points_label/174832b73cd6d91c9856fa70a578baeb.seg 04379243 02691156/points/1c2e9dedbcf511e616a077c4c0fc1181.pts 02691156/expert_verified/points_label/1c2e9dedbcf511e616a077c4c0fc1181.seg 02691156 03001627/points/893c689b192bbe33ebadcdfba7971b71.pts 03001627/expert_verified/points_label/893c689b192bbe33ebadcdfba7971b71.seg 03001627 04379243/points/52037005fbff92d08fa35606145b47dc.pts 04379243/expert_verified/points_label/52037005fbff92d08fa35606145b47dc.seg 04379243 04225987/points/e38a4e6fb32b51a1bebb1fbb949ea955.pts 04225987/expert_verified/points_label/e38a4e6fb32b51a1bebb1fbb949ea955.seg 04225987 03636649/points/42bc0dce81734d892610e2a20d7c4b61.pts 03636649/expert_verified/points_label/42bc0dce81734d892610e2a20d7c4b61.seg 03636649 04379243/points/cb7ebc943b1b424988386fe1512ed26f.pts 04379243/expert_verified/points_label/cb7ebc943b1b424988386fe1512ed26f.seg 04379243 03624134/points/2d6e9b23e171760c3e332fb3cb6ebe50.pts 03624134/expert_verified/points_label/2d6e9b23e171760c3e332fb3cb6ebe50.seg 03624134 04379243/points/d05ff7b47febe58a656db3f863b4b796.pts 04379243/expert_verified/points_label/d05ff7b47febe58a656db3f863b4b796.seg 04379243 03636649/points/e178ab3b967c7fddc901d9dddb735c9f.pts 03636649/expert_verified/points_label/e178ab3b967c7fddc901d9dddb735c9f.seg 03636649 04379243/points/527b2d1e964f056383be1aa5a5ab0c80.pts 04379243/expert_verified/points_label/527b2d1e964f056383be1aa5a5ab0c80.seg 04379243 03001627/points/f1a1bb6ad29d703078d928ba1c4a6f75.pts 03001627/expert_verified/points_label/f1a1bb6ad29d703078d928ba1c4a6f75.seg 03001627 04379243/points/ed9dc0937009dc031311158f08f2982a.pts 04379243/expert_verified/points_label/ed9dc0937009dc031311158f08f2982a.seg 04379243 02691156/points/e41c5719ad09055f1b880c747ee1f83.pts 02691156/expert_verified/points_label/e41c5719ad09055f1b880c747ee1f83.seg 02691156 04379243/points/34bbe284f7499df071a782a4379556c7.pts 04379243/expert_verified/points_label/34bbe284f7499df071a782a4379556c7.seg 04379243 02691156/points/973df01cea43c7f690b1d6deb98feec6.pts 02691156/expert_verified/points_label/973df01cea43c7f690b1d6deb98feec6.seg 02691156 03001627/points/ed97d1c954fca49851ceffe90913a32.pts 03001627/expert_verified/points_label/ed97d1c954fca49851ceffe90913a32.seg 03001627 03001627/points/3a74e3d5172ee94fdef1c01cbd4ae0c.pts 03001627/expert_verified/points_label/3a74e3d5172ee94fdef1c01cbd4ae0c.seg 03001627 04379243/points/194b279c7e892a2d15fa8082e5524f79.pts 04379243/expert_verified/points_label/194b279c7e892a2d15fa8082e5524f79.seg 04379243 04379243/points/23ece3bf871619366ff454af1e8947f3.pts 04379243/expert_verified/points_label/23ece3bf871619366ff454af1e8947f3.seg 04379243 02691156/points/7de379891610f5feaf7dd1bfd65143a9.pts 02691156/expert_verified/points_label/7de379891610f5feaf7dd1bfd65143a9.seg 02691156 04379243/points/54ba7e77a2bf5fe3158b7df020486ff2.pts 04379243/expert_verified/points_label/54ba7e77a2bf5fe3158b7df020486ff2.seg 04379243 03001627/points/39825fb4341ebd1ccb002c1e2b5fc68b.pts 03001627/expert_verified/points_label/39825fb4341ebd1ccb002c1e2b5fc68b.seg 03001627 03001627/points/a32febea4a0ac30171a782a4379556c7.pts 03001627/expert_verified/points_label/a32febea4a0ac30171a782a4379556c7.seg 03001627 02691156/points/b9ba988dd9a6cf426e8b6dd39a855b69.pts 02691156/expert_verified/points_label/b9ba988dd9a6cf426e8b6dd39a855b69.seg 02691156 02691156/points/37b1f7f02c4b87dbca8607f540cc62ba.pts 02691156/expert_verified/points_label/37b1f7f02c4b87dbca8607f540cc62ba.seg 02691156 04379243/points/8ce538a671c6e684d93768e7b9b1eabf.pts 04379243/expert_verified/points_label/8ce538a671c6e684d93768e7b9b1eabf.seg 04379243 04225987/points/48bf45bffab55d7cf14c37b285d25cdf.pts 04225987/expert_verified/points_label/48bf45bffab55d7cf14c37b285d25cdf.seg 04225987 02691156/points/820ba20e5da8325f19ba010ddb4974fe.pts 02691156/expert_verified/points_label/820ba20e5da8325f19ba010ddb4974fe.seg 02691156 02691156/points/ff52c059efaca3c1ca8607f540cc62ba.pts 02691156/expert_verified/points_label/ff52c059efaca3c1ca8607f540cc62ba.seg 02691156 04379243/points/99737ff619cae25d6effbd64ad6b71b8.pts 04379243/expert_verified/points_label/99737ff619cae25d6effbd64ad6b71b8.seg 04379243 04379243/points/e3b7fbed310c2c397c8d78b9aede742.pts 04379243/expert_verified/points_label/e3b7fbed310c2c397c8d78b9aede742.seg 04379243 03001627/points/e8eedd37cb054e37b59d74a7c956bd18.pts 03001627/expert_verified/points_label/e8eedd37cb054e37b59d74a7c956bd18.seg 03001627 03790512/points/8134a965cc0b134bb37378f3c85478b4.pts 03790512/expert_verified/points_label/8134a965cc0b134bb37378f3c85478b4.seg 03790512 03636649/points/da5f13f4048dbd72fcb8d8c6d4df8143.pts 03636649/expert_verified/points_label/da5f13f4048dbd72fcb8d8c6d4df8143.seg 03636649 03001627/points/f5d8dd0309401ebac47a35332c17cce2.pts 03001627/expert_verified/points_label/f5d8dd0309401ebac47a35332c17cce2.seg 03001627 02691156/points/521eab9363fdc2a07209009cfb89d4bd.pts 02691156/expert_verified/points_label/521eab9363fdc2a07209009cfb89d4bd.seg 02691156 03636649/points/b1e552b454366a9d7787152e5befb05b.pts 03636649/expert_verified/points_label/b1e552b454366a9d7787152e5befb05b.seg 03636649 02958343/points/8590a6c8270375e34b5a812ecf553410.pts 02958343/expert_verified/points_label/8590a6c8270375e34b5a812ecf553410.seg 02958343 04379243/points/d46537f513283d6cdcfcef693e7ec696.pts 04379243/expert_verified/points_label/d46537f513283d6cdcfcef693e7ec696.seg 04379243 03001627/points/60a5795c905f3bb157f5033576317e1.pts 03001627/expert_verified/points_label/60a5795c905f3bb157f5033576317e1.seg 03001627 02691156/points/8996445c6d2407c0fb5c1b0f759e2bc1.pts 02691156/expert_verified/points_label/8996445c6d2407c0fb5c1b0f759e2bc1.seg 02691156 03624134/points/5e15d63317014f30ceea8802f71596b5.pts 03624134/expert_verified/points_label/5e15d63317014f30ceea8802f71596b5.seg 03624134 03642806/points/9d48ab8c41174e60888cad7f6c0e6001.pts 03642806/expert_verified/points_label/9d48ab8c41174e60888cad7f6c0e6001.seg 03642806 04379243/points/4cd35d6ec155d39633207e4c3ac155a4.pts 04379243/expert_verified/points_label/4cd35d6ec155d39633207e4c3ac155a4.seg 04379243 04379243/points/884d2cc0d3aa8a72640e544a5d67c33a.pts 04379243/expert_verified/points_label/884d2cc0d3aa8a72640e544a5d67c33a.seg 04379243 03001627/points/8191bad981637a71b356ab8b24c147.pts 03001627/expert_verified/points_label/8191bad981637a71b356ab8b24c147.seg 03001627 03261776/points/de3b9b253e8f1aaf8b15c58b209760b5.pts 03261776/expert_verified/points_label/de3b9b253e8f1aaf8b15c58b209760b5.seg 03261776 03636649/points/5b744ac897fe8bc557f40ff86fe708ff.pts 03636649/expert_verified/points_label/5b744ac897fe8bc557f40ff86fe708ff.seg 03636649 04379243/points/6cd84ff61583805c85e2af9bf984f0b5.pts 04379243/expert_verified/points_label/6cd84ff61583805c85e2af9bf984f0b5.seg 04379243 04379243/points/e65066d6b0b83719c3bd24f986301745.pts 04379243/expert_verified/points_label/e65066d6b0b83719c3bd24f986301745.seg 04379243 04379243/points/f3efcbd9745da90619fb4103277a6b93.pts 04379243/expert_verified/points_label/f3efcbd9745da90619fb4103277a6b93.seg 04379243 04379243/points/8ac4d93e65b9d58d9b937d98d58545fa.pts 04379243/expert_verified/points_label/8ac4d93e65b9d58d9b937d98d58545fa.seg 04379243 03636649/points/b69c3a0a46b932e3d3c1fbbc2200e255.pts 03636649/expert_verified/points_label/b69c3a0a46b932e3d3c1fbbc2200e255.seg 03636649 03636649/points/5c7965b0835a1a241de9bf5a9c22fde.pts 03636649/expert_verified/points_label/5c7965b0835a1a241de9bf5a9c22fde.seg 03636649 03001627/points/27ea798c55699b6d2c528d33bca1ac2.pts 03001627/expert_verified/points_label/27ea798c55699b6d2c528d33bca1ac2.seg 03001627 03467517/points/dc623742d6d1518e19959b248340fafd.pts 03467517/expert_verified/points_label/dc623742d6d1518e19959b248340fafd.seg 03467517 03001627/points/c6cb59e7645dd14d661ff085a0f14b7.pts 03001627/expert_verified/points_label/c6cb59e7645dd14d661ff085a0f14b7.seg 03001627 03948459/points/a3679104af613021912d826efe946a9f.pts 03948459/expert_verified/points_label/a3679104af613021912d826efe946a9f.seg 03948459 03467517/points/b6d2d35747549a5b93f0194265a9746c.pts 03467517/expert_verified/points_label/b6d2d35747549a5b93f0194265a9746c.seg 03467517 02691156/points/2c1fff0653854166e7a636089598229.pts 02691156/expert_verified/points_label/2c1fff0653854166e7a636089598229.seg 02691156 04379243/points/1040cd764facf6981190e285a2cbc9c.pts 04379243/expert_verified/points_label/1040cd764facf6981190e285a2cbc9c.seg 04379243 03001627/points/485831d92925bf03f3d7c13662c10792.pts 03001627/expert_verified/points_label/485831d92925bf03f3d7c13662c10792.seg 03001627 03636649/points/284986b4c72d624abd73284bc3c3cbac.pts 03636649/expert_verified/points_label/284986b4c72d624abd73284bc3c3cbac.seg 03636649 02691156/points/4c008f39378be18bc0909d98a1ff2b4.pts 02691156/expert_verified/points_label/4c008f39378be18bc0909d98a1ff2b4.seg 02691156 04379243/points/9611888ee0db1ecaf7d4d3ced798ad90.pts 04379243/expert_verified/points_label/9611888ee0db1ecaf7d4d3ced798ad90.seg 04379243 03467517/points/12e30808350dd945f4b498e11fb60a4b.pts 03467517/expert_verified/points_label/12e30808350dd945f4b498e11fb60a4b.seg 03467517 03467517/points/3243edb05f5e8803ac61a2f8346a8f.pts 03467517/expert_verified/points_label/3243edb05f5e8803ac61a2f8346a8f.seg 03467517 04379243/points/ec4675f62f6946118cbb8bac2032149c.pts 04379243/expert_verified/points_label/ec4675f62f6946118cbb8bac2032149c.seg 04379243 04379243/points/eb00a4e8b33d257cad16260d4d73b56.pts 04379243/expert_verified/points_label/eb00a4e8b33d257cad16260d4d73b56.seg 04379243 03001627/points/5607b02869c1f8a019fb4103277a6b93.pts 03001627/expert_verified/points_label/5607b02869c1f8a019fb4103277a6b93.seg 03001627 03636649/points/d456beea1501f278f70220cd6be776f7.pts 03636649/expert_verified/points_label/d456beea1501f278f70220cd6be776f7.seg 03636649 02691156/points/3feeb5f8ecbfcb4ba8f0518e94fcfb22.pts 02691156/expert_verified/points_label/3feeb5f8ecbfcb4ba8f0518e94fcfb22.seg 02691156 04379243/points/fe130356df1977499c2a886f3b75f1ff.pts 04379243/expert_verified/points_label/fe130356df1977499c2a886f3b75f1ff.seg 04379243 02958343/points/aa7f127bb8cd9db73755eb267a6f3b6b.pts 02958343/expert_verified/points_label/aa7f127bb8cd9db73755eb267a6f3b6b.seg 02958343 04379243/points/84a3c87bba5a472af51f77a6d7299806.pts 04379243/expert_verified/points_label/84a3c87bba5a472af51f77a6d7299806.seg 04379243 04099429/points/2de8ee55ff69502863098049d14fe32f.pts 04099429/expert_verified/points_label/2de8ee55ff69502863098049d14fe32f.seg 04099429 03624134/points/539ff9b2a7a0329e759e4c424bcdaafe.pts 03624134/expert_verified/points_label/539ff9b2a7a0329e759e4c424bcdaafe.seg 03624134 03948459/points/f3f6678898938575575e33965575974.pts 03948459/expert_verified/points_label/f3f6678898938575575e33965575974.seg 03948459 04379243/points/c26dfd3453d81bf7788eb1f5e7ba6e7b.pts 04379243/expert_verified/points_label/c26dfd3453d81bf7788eb1f5e7ba6e7b.seg 04379243 03001627/points/8117c55b8bbdbbc54c5c5c89015f1980.pts 03001627/expert_verified/points_label/8117c55b8bbdbbc54c5c5c89015f1980.seg 03001627 03624134/points/40ccb8ac250e0ea5880595487ba7a30b.pts 03624134/expert_verified/points_label/40ccb8ac250e0ea5880595487ba7a30b.seg 03624134 04379243/points/a0d2754011acdcc9d8a0e410093d6619.pts 04379243/expert_verified/points_label/a0d2754011acdcc9d8a0e410093d6619.seg 04379243 03790512/points/5bd41c7d3e158ac93ff4d2f5a7608a24.pts 03790512/expert_verified/points_label/5bd41c7d3e158ac93ff4d2f5a7608a24.seg 03790512 04379243/points/8f440a7c0e2af79f3ed0ffd59feeec00.pts 04379243/expert_verified/points_label/8f440a7c0e2af79f3ed0ffd59feeec00.seg 04379243 03001627/points/734ac9809aada180d18df440db206fb1.pts 03001627/expert_verified/points_label/734ac9809aada180d18df440db206fb1.seg 03001627 03001627/points/54f33a7cb3621d5ced98cca8f0ccd5f7.pts 03001627/expert_verified/points_label/54f33a7cb3621d5ced98cca8f0ccd5f7.seg 03001627 03001627/points/d274fc14092387c1e17e1cb731e2fa4f.pts 03001627/expert_verified/points_label/d274fc14092387c1e17e1cb731e2fa4f.seg 03001627 03636649/points/6ccb43088eda061dbfc838749f053cf9.pts 03636649/expert_verified/points_label/6ccb43088eda061dbfc838749f053cf9.seg 03636649 02773838/points/1b9ef45fefefa35ed13f430b2941481.pts 02773838/expert_verified/points_label/1b9ef45fefefa35ed13f430b2941481.seg 02773838 03001627/points/35053caa62eea36c116cc4e115d5fd2.pts 03001627/expert_verified/points_label/35053caa62eea36c116cc4e115d5fd2.seg 03001627 04379243/points/b893c20bfb5d718371a782a4379556c7.pts 04379243/expert_verified/points_label/b893c20bfb5d718371a782a4379556c7.seg 04379243 04379243/points/1a5062241d7903076f88aa1b7f7cc6c6.pts 04379243/expert_verified/points_label/1a5062241d7903076f88aa1b7f7cc6c6.seg 04379243 02958343/points/add26d8f4f91ba04c84b95bddf75b22d.pts 02958343/expert_verified/points_label/add26d8f4f91ba04c84b95bddf75b22d.seg 02958343 03636649/points/f85f26c5a807b22312bea13341a54c3f.pts 03636649/expert_verified/points_label/f85f26c5a807b22312bea13341a54c3f.seg 03636649 03001627/points/8a232028c2b2cfad43649af30eba8304.pts 03001627/expert_verified/points_label/8a232028c2b2cfad43649af30eba8304.seg 03001627 03636649/points/3a5a0f4c78e17b284f0c4075db76b7c.pts 03636649/expert_verified/points_label/3a5a0f4c78e17b284f0c4075db76b7c.seg 03636649 04379243/points/df811f7a858750875634c21965ee6bab.pts 04379243/expert_verified/points_label/df811f7a858750875634c21965ee6bab.seg 04379243 02691156/points/48706d323b9041d5438a95791ca4064d.pts 02691156/expert_verified/points_label/48706d323b9041d5438a95791ca4064d.seg 02691156 03790512/points/170cfc531a4fd09fe6905ba5363784c3.pts 03790512/expert_verified/points_label/170cfc531a4fd09fe6905ba5363784c3.seg 03790512 03467517/points/d4b2ddb52e8dcd3593f0194265a9746c.pts 03467517/expert_verified/points_label/d4b2ddb52e8dcd3593f0194265a9746c.seg 03467517 03636649/points/2af78c0b040634e5881cd5e2fd8f0f3b.pts 03636649/expert_verified/points_label/2af78c0b040634e5881cd5e2fd8f0f3b.seg 03636649 04379243/points/90cd6a48cf2789a9b430d97a45d5824.pts 04379243/expert_verified/points_label/90cd6a48cf2789a9b430d97a45d5824.seg 04379243 03001627/points/43290694390ad1adfc735c9ceab0161a.pts 03001627/expert_verified/points_label/43290694390ad1adfc735c9ceab0161a.seg 03001627 03636649/points/ed57181b9e7644a3f51f77a6d7299806.pts 03636649/expert_verified/points_label/ed57181b9e7644a3f51f77a6d7299806.seg 03636649 03261776/points/a9661a8bb610d902957b6a4f3924d982.pts 03261776/expert_verified/points_label/a9661a8bb610d902957b6a4f3924d982.seg 03261776 02691156/points/b31bbc50a0d3a4366cf1b4a8fc3914e.pts 02691156/expert_verified/points_label/b31bbc50a0d3a4366cf1b4a8fc3914e.seg 02691156 03001627/points/cd5ad4afabaed0d3e762624dc3c8fa2a.pts 03001627/expert_verified/points_label/cd5ad4afabaed0d3e762624dc3c8fa2a.seg 03001627 02958343/points/d2e1dc21db9b45df6436916a86a90ed7.pts 02958343/expert_verified/points_label/d2e1dc21db9b45df6436916a86a90ed7.seg 02958343 02691156/points/de9e093bb17848c3b2bd4a92202f8700.pts 02691156/expert_verified/points_label/de9e093bb17848c3b2bd4a92202f8700.seg 02691156 03467517/points/40cd2cafde62ff7ca24eeca91f583600.pts 03467517/expert_verified/points_label/40cd2cafde62ff7ca24eeca91f583600.seg 03467517 02958343/points/56e0fef0632aed0f1d27be7764701cfe.pts 02958343/expert_verified/points_label/56e0fef0632aed0f1d27be7764701cfe.seg 02958343 04379243/points/a4d149a48607de3d92f4c88fd91c6b1b.pts 04379243/expert_verified/points_label/a4d149a48607de3d92f4c88fd91c6b1b.seg 04379243 03636649/points/45f11cb4099c9c87bbc7a6acbd8f058b.pts 03636649/expert_verified/points_label/45f11cb4099c9c87bbc7a6acbd8f058b.seg 03636649 04379243/points/3558aeeb9698722acf19858fd1963d10.pts 04379243/expert_verified/points_label/3558aeeb9698722acf19858fd1963d10.seg 04379243 03636649/points/2a52bd01472ec7e1589ec67c01f5c1a7.pts 03636649/expert_verified/points_label/2a52bd01472ec7e1589ec67c01f5c1a7.seg 03636649 03467517/points/58bb21c325f021088f01c8e793a6e062.pts 03467517/expert_verified/points_label/58bb21c325f021088f01c8e793a6e062.seg 03467517 04379243/points/3997cdee934a9b238eb3bc6c6d15f9bf.pts 04379243/expert_verified/points_label/3997cdee934a9b238eb3bc6c6d15f9bf.seg 04379243 03001627/points/c4cab2a416a4537e2871cc0b3cc1a485.pts 03001627/expert_verified/points_label/c4cab2a416a4537e2871cc0b3cc1a485.seg 03001627 04379243/points/6aaa78b81528f4846674ff79eed6185a.pts 04379243/expert_verified/points_label/6aaa78b81528f4846674ff79eed6185a.seg 04379243 03636649/points/fd5f6ab819910a66dc7f95a5a82e36f7.pts 03636649/expert_verified/points_label/fd5f6ab819910a66dc7f95a5a82e36f7.seg 03636649 04379243/points/8e3303cae6cc104bad4f8ccb153c24e.pts 04379243/expert_verified/points_label/8e3303cae6cc104bad4f8ccb153c24e.seg 04379243 03001627/points/2f0318b23d899a84493f17f4fe9b9eb2.pts 03001627/expert_verified/points_label/2f0318b23d899a84493f17f4fe9b9eb2.seg 03001627 04379243/points/2406cdcd4c60c84132884c4c87a2e061.pts 04379243/expert_verified/points_label/2406cdcd4c60c84132884c4c87a2e061.seg 04379243 03790512/points/55caf44a43f2c04d468bac13e007a6e9.pts 03790512/expert_verified/points_label/55caf44a43f2c04d468bac13e007a6e9.seg 03790512 03001627/points/ee665ce6679ac8cfb502ac2eb9128f9a.pts 03001627/expert_verified/points_label/ee665ce6679ac8cfb502ac2eb9128f9a.seg 03001627 02691156/points/32edb6ba5788dc12d8ff6111270336a9.pts 02691156/expert_verified/points_label/32edb6ba5788dc12d8ff6111270336a9.seg 02691156 03636649/points/d0fde1daedab10365240248232b90795.pts 03636649/expert_verified/points_label/d0fde1daedab10365240248232b90795.seg 03636649 04379243/points/61b88b501933ebae8f7068c66465c4d6.pts 04379243/expert_verified/points_label/61b88b501933ebae8f7068c66465c4d6.seg 04379243 03001627/points/93556cf01e19f638bf80985a99195eb8.pts 03001627/expert_verified/points_label/93556cf01e19f638bf80985a99195eb8.seg 03001627 04379243/points/f3b8c91c5dd1cb6b8722573b29f0d6d8.pts 04379243/expert_verified/points_label/f3b8c91c5dd1cb6b8722573b29f0d6d8.seg 04379243 04379243/points/eae36b396f6b5f97664b3b9b23ddfcbc.pts 04379243/expert_verified/points_label/eae36b396f6b5f97664b3b9b23ddfcbc.seg 04379243 03624134/points/8bd5c4f395695ebdf40d02cc9d84a93a.pts 03624134/expert_verified/points_label/8bd5c4f395695ebdf40d02cc9d84a93a.seg 03624134 03001627/points/8c81ff18e04584547f409062bafc8e2.pts 03001627/expert_verified/points_label/8c81ff18e04584547f409062bafc8e2.seg 03001627 03001627/points/77e7660d71c6f3befebad4f49b26ec52.pts 03001627/expert_verified/points_label/77e7660d71c6f3befebad4f49b26ec52.seg 03001627 03261776/points/bc404e52bfcd2038538cf6df9faa9b65.pts 03261776/expert_verified/points_label/bc404e52bfcd2038538cf6df9faa9b65.seg 03261776 03001627/points/f09af71bebd4bea8a2651abaf391628e.pts 03001627/expert_verified/points_label/f09af71bebd4bea8a2651abaf391628e.seg 03001627 03001627/points/8c8efbe62a1547942b90a0fb76278f6f.pts 03001627/expert_verified/points_label/8c8efbe62a1547942b90a0fb76278f6f.seg 03001627 04379243/points/aed5697ff59e3d3035478a6869a3602d.pts 04379243/expert_verified/points_label/aed5697ff59e3d3035478a6869a3602d.seg 04379243 02691156/points/5ac00867c7d78b1690b1d6deb98feec6.pts 02691156/expert_verified/points_label/5ac00867c7d78b1690b1d6deb98feec6.seg 02691156 03001627/points/c709aa613431c0538a653a9f65a410f6.pts 03001627/expert_verified/points_label/c709aa613431c0538a653a9f65a410f6.seg 03001627 03624134/points/8facbe9d9f4da233d15a5887ec2183c9.pts 03624134/expert_verified/points_label/8facbe9d9f4da233d15a5887ec2183c9.seg 03624134 03642806/points/dbcd5a88a9d4f1d7579cfe4420588034.pts 03642806/expert_verified/points_label/dbcd5a88a9d4f1d7579cfe4420588034.seg 03642806 03636649/points/f29a94f969dd55ffc35131da26f8061a.pts 03636649/expert_verified/points_label/f29a94f969dd55ffc35131da26f8061a.seg 03636649 02958343/points/5e014eb2bd03daab9fbe97de4a41d527.pts 02958343/expert_verified/points_label/5e014eb2bd03daab9fbe97de4a41d527.seg 02958343 04379243/points/7105bd044f464358beedb4c8fd29e2d1.pts 04379243/expert_verified/points_label/7105bd044f464358beedb4c8fd29e2d1.seg 04379243 04379243/points/c827c0d4ef212f2b30cb1fe6fdc7d605.pts 04379243/expert_verified/points_label/c827c0d4ef212f2b30cb1fe6fdc7d605.seg 04379243 04379243/points/19bc9c781df1da46824080f516909671.pts 04379243/expert_verified/points_label/19bc9c781df1da46824080f516909671.seg 04379243 03001627/points/71b53a5f441d45b742b7e4c0136bdb7e.pts 03001627/expert_verified/points_label/71b53a5f441d45b742b7e4c0136bdb7e.seg 03001627 02958343/points/e7e94f8dbbe8c1e9784da3853aae78cd.pts 02958343/expert_verified/points_label/e7e94f8dbbe8c1e9784da3853aae78cd.seg 02958343 03790512/points/832c4a316c419228b37378f3c85478b4.pts 03790512/expert_verified/points_label/832c4a316c419228b37378f3c85478b4.seg 03790512 02954340/points/c7122c44495a5ac6aceb0fa31f18f016.pts 02954340/expert_verified/points_label/c7122c44495a5ac6aceb0fa31f18f016.seg 02954340 03001627/points/6b32d3a9198f8b03d1dcc55e36186e4e.pts 03001627/expert_verified/points_label/6b32d3a9198f8b03d1dcc55e36186e4e.seg 03001627 03636649/points/7893d0b50a7b6a768ec45924afa4ac91.pts 03636649/expert_verified/points_label/7893d0b50a7b6a768ec45924afa4ac91.seg 03636649 02691156/points/befcb95d80e0e49119ba010ddb4974fe.pts 02691156/expert_verified/points_label/befcb95d80e0e49119ba010ddb4974fe.seg 02691156 03001627/points/b70600293bab55c0593ebeeedbff73b.pts 03001627/expert_verified/points_label/b70600293bab55c0593ebeeedbff73b.seg 03001627 02691156/points/7fedb48b457ee9f31629b98cc1b1b992.pts 02691156/expert_verified/points_label/7fedb48b457ee9f31629b98cc1b1b992.seg 02691156 04099429/points/e04bda8655d9e606ebcdf982796b4fa.pts 04099429/expert_verified/points_label/e04bda8655d9e606ebcdf982796b4fa.seg 04099429 04379243/points/25bcea593e4314c3436e6787c76ef3f0.pts 04379243/expert_verified/points_label/25bcea593e4314c3436e6787c76ef3f0.seg 04379243 03636649/points/f3a9cc3060fd6b0e6e4f8fc909e0d34e.pts 03636649/expert_verified/points_label/f3a9cc3060fd6b0e6e4f8fc909e0d34e.seg 03636649 04379243/points/516928532093f765bababe11fcea8796.pts 04379243/expert_verified/points_label/516928532093f765bababe11fcea8796.seg 04379243 03001627/points/31569815c88e79de4458bae25a4e518a.pts 03001627/expert_verified/points_label/31569815c88e79de4458bae25a4e518a.seg 03001627 03001627/points/a08ad49c281128ea53615647c93fc704.pts 03001627/expert_verified/points_label/a08ad49c281128ea53615647c93fc704.seg 03001627 03642806/points/f5fc954736b06be15fd06491ae919ea3.pts 03642806/expert_verified/points_label/f5fc954736b06be15fd06491ae919ea3.seg 03642806 04379243/points/15b495c101881d96e2367b9e27f16a71.pts 04379243/expert_verified/points_label/15b495c101881d96e2367b9e27f16a71.seg 04379243 02691156/points/ebd991666f177f8f575bf8a4b14be4f4.pts 02691156/expert_verified/points_label/ebd991666f177f8f575bf8a4b14be4f4.seg 02691156 02691156/points/f7739764eb1c78a053f370d353cea84.pts 02691156/expert_verified/points_label/f7739764eb1c78a053f370d353cea84.seg 02691156 03636649/points/8a6d770e6b4942c5ef3a2c64cef919d0.pts 03636649/expert_verified/points_label/8a6d770e6b4942c5ef3a2c64cef919d0.seg 03636649 04379243/points/2fcc875b28c5557dcfcef693e7ec696.pts 04379243/expert_verified/points_label/2fcc875b28c5557dcfcef693e7ec696.seg 04379243 03636649/points/896abd405c79547086485c798787f66b.pts 03636649/expert_verified/points_label/896abd405c79547086485c798787f66b.seg 03636649 02691156/points/356a633ea047c549ca8607f540cc62ba.pts 02691156/expert_verified/points_label/356a633ea047c549ca8607f540cc62ba.seg 02691156 03001627/points/c983108db7fcfa3619fb4103277a6b93.pts 03001627/expert_verified/points_label/c983108db7fcfa3619fb4103277a6b93.seg 03001627 04225987/points/97f85bc59f09a9f455c660e6cd8e92b.pts 04225987/expert_verified/points_label/97f85bc59f09a9f455c660e6cd8e92b.seg 04225987 03636649/points/69a708be7245f4c9786e8e92cc08146.pts 03636649/expert_verified/points_label/69a708be7245f4c9786e8e92cc08146.seg 03636649 04379243/points/f71296c0a7e93ec282db9fca4b68095.pts 04379243/expert_verified/points_label/f71296c0a7e93ec282db9fca4b68095.seg 04379243 02691156/points/33faf711ed54a4d3db22b838c125a50b.pts 02691156/expert_verified/points_label/33faf711ed54a4d3db22b838c125a50b.seg 02691156 03642806/points/5d544ee4b094c6606436916a86a90ed7.pts 03642806/expert_verified/points_label/5d544ee4b094c6606436916a86a90ed7.seg 03642806 02691156/points/a0d63ee7fd87f93619ba010ddb4974fe.pts 02691156/expert_verified/points_label/a0d63ee7fd87f93619ba010ddb4974fe.seg 02691156 03001627/points/e30b412be565a1026efe57da6d3d385e.pts 03001627/expert_verified/points_label/e30b412be565a1026efe57da6d3d385e.seg 03001627 04379243/points/fe5e1df0653804d6ce4670b160b81e9.pts 04379243/expert_verified/points_label/fe5e1df0653804d6ce4670b160b81e9.seg 04379243 02691156/points/fd41d04f1aabbaea3fddedb0bf24c68a.pts 02691156/expert_verified/points_label/fd41d04f1aabbaea3fddedb0bf24c68a.seg 02691156 03624134/points/e79481b2fde3a3ab340fbf70397ab69a.pts 03624134/expert_verified/points_label/e79481b2fde3a3ab340fbf70397ab69a.seg 03624134 04379243/points/d06d27bc9ad1faabd7bf6fb68df7f786.pts 04379243/expert_verified/points_label/d06d27bc9ad1faabd7bf6fb68df7f786.seg 04379243 03001627/points/e4931ffa06d7b05cb04cb542e2c50eb4.pts 03001627/expert_verified/points_label/e4931ffa06d7b05cb04cb542e2c50eb4.seg 03001627 03001627/points/d4b5f8edc72b4676f4175ee3a177350a.pts 03001627/expert_verified/points_label/d4b5f8edc72b4676f4175ee3a177350a.seg 03001627 03636649/points/4f16fffbe480b835276206fae5d3c473.pts 03636649/expert_verified/points_label/4f16fffbe480b835276206fae5d3c473.seg 03636649 03001627/points/8ade914cd21b6e49656f29b05c68d39f.pts 03001627/expert_verified/points_label/8ade914cd21b6e49656f29b05c68d39f.seg 03001627 03001627/points/1e304b967d5253d5dd079f8cece51712.pts 03001627/expert_verified/points_label/1e304b967d5253d5dd079f8cece51712.seg 03001627 04379243/points/6d0ef6312f8af87a53e946fb2184f0c4.pts 04379243/expert_verified/points_label/6d0ef6312f8af87a53e946fb2184f0c4.seg 04379243 03948459/points/79c0cac016998c7cf7ba4a82f8032357.pts 03948459/expert_verified/points_label/79c0cac016998c7cf7ba4a82f8032357.seg 03948459 03642806/points/b51683c6285fa0f69067ac5c9d4ee692.pts 03642806/expert_verified/points_label/b51683c6285fa0f69067ac5c9d4ee692.seg 03642806 04379243/points/93cdfd14889492dd91a4fd87fee47737.pts 04379243/expert_verified/points_label/93cdfd14889492dd91a4fd87fee47737.seg 04379243 03636649/points/da8141b45da808199a06a7de97b096dc.pts 03636649/expert_verified/points_label/da8141b45da808199a06a7de97b096dc.seg 03636649 04379243/points/7d22cd72bf2762b19a4b266ed4d507c9.pts 04379243/expert_verified/points_label/7d22cd72bf2762b19a4b266ed4d507c9.seg 04379243 04225987/points/aa886bed91a13113d5498a74ca9ca78b.pts 04225987/expert_verified/points_label/aa886bed91a13113d5498a74ca9ca78b.seg 04225987 04379243/points/55547d2fae0e3dc21705bfd3afcd10e.pts 04379243/expert_verified/points_label/55547d2fae0e3dc21705bfd3afcd10e.seg 04379243 04379243/points/222c56ff9cddbaf4139eb23f7c8036f.pts 04379243/expert_verified/points_label/222c56ff9cddbaf4139eb23f7c8036f.seg 04379243 03636649/points/292f1f97a543d735dedf3c967c85981a.pts 03636649/expert_verified/points_label/292f1f97a543d735dedf3c967c85981a.seg 03636649 04379243/points/9e2318099f77d3df3527ecfeb345775f.pts 04379243/expert_verified/points_label/9e2318099f77d3df3527ecfeb345775f.seg 04379243 04379243/points/6ace903899706a5819fb4103277a6b93.pts 04379243/expert_verified/points_label/6ace903899706a5819fb4103277a6b93.seg 04379243 03636649/points/c080aefc6cbff8c81185ac82ed4da80d.pts 03636649/expert_verified/points_label/c080aefc6cbff8c81185ac82ed4da80d.seg 03636649 03790512/points/9dd4ae1c34af4766b4f2746c8140d6d6.pts 03790512/expert_verified/points_label/9dd4ae1c34af4766b4f2746c8140d6d6.seg 03790512 03001627/points/e199b1f6a70c9f56df44d20a516c07b3.pts 03001627/expert_verified/points_label/e199b1f6a70c9f56df44d20a516c07b3.seg 03001627 04379243/points/8129d4c51abc3356bababe11fcea8796.pts 04379243/expert_verified/points_label/8129d4c51abc3356bababe11fcea8796.seg 04379243 03001627/points/c9d8573a048c0e959c0ca344f487323e.pts 03001627/expert_verified/points_label/c9d8573a048c0e959c0ca344f487323e.seg 03001627 04379243/points/25eefc5a3c7b30e1f103d473de33521a.pts 04379243/expert_verified/points_label/25eefc5a3c7b30e1f103d473de33521a.seg 04379243 03624134/points/c20cca071ea58e3ef2c542131520d62e.pts 03624134/expert_verified/points_label/c20cca071ea58e3ef2c542131520d62e.seg 03624134 03001627/points/c86cfe147872280463626070a93463cf.pts 03001627/expert_verified/points_label/c86cfe147872280463626070a93463cf.seg 03001627 03001627/points/3853339519aca1bdfcd4910413c446d9.pts 03001627/expert_verified/points_label/3853339519aca1bdfcd4910413c446d9.seg 03001627 03001627/points/8cb44a50906b827615e7ec87bf4cc5ab.pts 03001627/expert_verified/points_label/8cb44a50906b827615e7ec87bf4cc5ab.seg 03001627 02691156/points/fd9f1cdaa381599bca8607f540cc62ba.pts 02691156/expert_verified/points_label/fd9f1cdaa381599bca8607f540cc62ba.seg 02691156 03001627/points/80dabf9ddbdc92f681806e3880250dff.pts 03001627/expert_verified/points_label/80dabf9ddbdc92f681806e3880250dff.seg 03001627 04379243/points/5919dea71f3bcb071d54ab02e78bef2.pts 04379243/expert_verified/points_label/5919dea71f3bcb071d54ab02e78bef2.seg 04379243 03636649/points/292ba732e002629e68c2f5eb1dd4dfaa.pts 03636649/expert_verified/points_label/292ba732e002629e68c2f5eb1dd4dfaa.seg 03636649 04379243/points/5d77e8f6ad3741a0c30ab36bf7b0552.pts 04379243/expert_verified/points_label/5d77e8f6ad3741a0c30ab36bf7b0552.seg 04379243 03467517/points/21a517abc4729e6e352e5d4d2615db5b.pts 03467517/expert_verified/points_label/21a517abc4729e6e352e5d4d2615db5b.seg 03467517 03467517/points/6554f6429eb7b67585e3c97721f726e4.pts 03467517/expert_verified/points_label/6554f6429eb7b67585e3c97721f726e4.seg 03467517 02958343/points/f84ba2039d0a4ec5afe717997470b28d.pts 02958343/expert_verified/points_label/f84ba2039d0a4ec5afe717997470b28d.seg 02958343 02691156/points/29fd29045703ff18b4a8b7176ed97248.pts 02691156/expert_verified/points_label/29fd29045703ff18b4a8b7176ed97248.seg 02691156 03467517/points/a7f449a1f2cd1f1693f0194265a9746c.pts 03467517/expert_verified/points_label/a7f449a1f2cd1f1693f0194265a9746c.seg 03467517 03790512/points/7fcee59a33976221a88e8cb97b773125.pts 03790512/expert_verified/points_label/7fcee59a33976221a88e8cb97b773125.seg 03790512 04099429/points/2407c2684ee757e89c4176ab56cb612.pts 04099429/expert_verified/points_label/2407c2684ee757e89c4176ab56cb612.seg 04099429 04379243/points/f621e2ad900ad48535836c728d324152.pts 04379243/expert_verified/points_label/f621e2ad900ad48535836c728d324152.seg 04379243 03001627/points/9a54daea9071a536bf80985a99195eb8.pts 03001627/expert_verified/points_label/9a54daea9071a536bf80985a99195eb8.seg 03001627 03001627/points/fd9e909b082d8175d319c38340319ae4.pts 03001627/expert_verified/points_label/fd9e909b082d8175d319c38340319ae4.seg 03001627 03001627/points/a8dd9990ecd74c45435897641a7ee684.pts 03001627/expert_verified/points_label/a8dd9990ecd74c45435897641a7ee684.seg 03001627 03636649/points/c6424950ca9447627d8864caa856253b.pts 03636649/expert_verified/points_label/c6424950ca9447627d8864caa856253b.seg 03636649 03948459/points/7f3ec97cfaea31137504cc74f24f0eee.pts 03948459/expert_verified/points_label/7f3ec97cfaea31137504cc74f24f0eee.seg 03948459 02691156/points/43abe330362164e99be82ec29531a70f.pts 02691156/expert_verified/points_label/43abe330362164e99be82ec29531a70f.seg 02691156 03001627/points/499c4b519c708ae84cd08aa7c510fb85.pts 03001627/expert_verified/points_label/499c4b519c708ae84cd08aa7c510fb85.seg 03001627 04379243/points/4c7931492b41f960d50eef20e0914a48.pts 04379243/expert_verified/points_label/4c7931492b41f960d50eef20e0914a48.seg 04379243 03001627/points/3f36e261cc87648ac3bd24f986301745.pts 03001627/expert_verified/points_label/3f36e261cc87648ac3bd24f986301745.seg 03001627 03001627/points/a09a88c11d0b27368821ad3452f1c8c9.pts 03001627/expert_verified/points_label/a09a88c11d0b27368821ad3452f1c8c9.seg 03001627 04379243/points/89cc879f005dcf50f1f50f6a678fb494.pts 04379243/expert_verified/points_label/89cc879f005dcf50f1f50f6a678fb494.seg 04379243 02958343/points/d34b0494fc4d756ab927782fc69a1fbb.pts 02958343/expert_verified/points_label/d34b0494fc4d756ab927782fc69a1fbb.seg 02958343 02958343/points/705840df46a582e2ac826a3c82da491.pts 02958343/expert_verified/points_label/705840df46a582e2ac826a3c82da491.seg 02958343 02691156/points/74a5f937c22aa08a3e70653c1b3170b5.pts 02691156/expert_verified/points_label/74a5f937c22aa08a3e70653c1b3170b5.seg 02691156 03948459/points/a0a1633186261a031274aa253a241db2.pts 03948459/expert_verified/points_label/a0a1633186261a031274aa253a241db2.seg 03948459 03001627/points/2de04227fae28e70b6eb6f056d511fe1.pts 03001627/expert_verified/points_label/2de04227fae28e70b6eb6f056d511fe1.seg 03001627 02691156/points/1e9ef313876bfba7d02c6d35cc802839.pts 02691156/expert_verified/points_label/1e9ef313876bfba7d02c6d35cc802839.seg 02691156 03636649/points/e99793b871d27333d42b9650f19dd425.pts 03636649/expert_verified/points_label/e99793b871d27333d42b9650f19dd425.seg 03636649 03001627/points/7228d43e00af4c1e2746490e2236e9a8.pts 03001627/expert_verified/points_label/7228d43e00af4c1e2746490e2236e9a8.seg 03001627 03636649/points/66111d2c7a23b0feb404555b84577afb.pts 03636649/expert_verified/points_label/66111d2c7a23b0feb404555b84577afb.seg 03636649 03001627/points/2499541ace317cbb8cb5d9909aeb1309.pts 03001627/expert_verified/points_label/2499541ace317cbb8cb5d9909aeb1309.seg 03001627 04379243/points/d151d9f45d8b14536cd661fb5fd95741.pts 04379243/expert_verified/points_label/d151d9f45d8b14536cd661fb5fd95741.seg 04379243 03001627/points/ea7be2b97e78d5b35a4480134e0cdd21.pts 03001627/expert_verified/points_label/ea7be2b97e78d5b35a4480134e0cdd21.seg 03001627 02958343/points/9c35f00f81110738783854950b26f0d3.pts 02958343/expert_verified/points_label/9c35f00f81110738783854950b26f0d3.seg 02958343 03001627/points/e30bd575bbd6c68c9710e093c764abec.pts 03001627/expert_verified/points_label/e30bd575bbd6c68c9710e093c764abec.seg 03001627 03790512/points/61b17f12bec91d057395d58407f193ba.pts 03790512/expert_verified/points_label/61b17f12bec91d057395d58407f193ba.seg 03790512 04379243/points/cd895c35fff495cdd0b93fa304cfa755.pts 04379243/expert_verified/points_label/cd895c35fff495cdd0b93fa304cfa755.seg 04379243 02958343/points/b70d970f8020c25dd141480e2c154d3.pts 02958343/expert_verified/points_label/b70d970f8020c25dd141480e2c154d3.seg 02958343 04379243/points/2642d805c53e243d629f73b53bd7a234.pts 04379243/expert_verified/points_label/2642d805c53e243d629f73b53bd7a234.seg 04379243 04379243/points/1bce2f4937d36446a32c566d71fa585c.pts 04379243/expert_verified/points_label/1bce2f4937d36446a32c566d71fa585c.seg 04379243 04379243/points/7c1bcea89b0037a2d67bd369ec608dad.pts 04379243/expert_verified/points_label/7c1bcea89b0037a2d67bd369ec608dad.seg 04379243 04379243/points/3154c61c595bd600e56ddd87eb888f65.pts 04379243/expert_verified/points_label/3154c61c595bd600e56ddd87eb888f65.seg 04379243 03001627/points/7a1de77ca204eaf28a514cac7cb18507.pts 03001627/expert_verified/points_label/7a1de77ca204eaf28a514cac7cb18507.seg 03001627 04379243/points/77ecc55547840f06d42b9650f19dd425.pts 04379243/expert_verified/points_label/77ecc55547840f06d42b9650f19dd425.seg 04379243 02691156/points/9a8aecab136ce50db7ef47444625afb2.pts 02691156/expert_verified/points_label/9a8aecab136ce50db7ef47444625afb2.seg 02691156 02958343/points/24866846d728484e1d1a964dea8a7aab.pts 02958343/expert_verified/points_label/24866846d728484e1d1a964dea8a7aab.seg 02958343 04099429/points/9b75297c580ff937b61ce5beb9f92726.pts 04099429/expert_verified/points_label/9b75297c580ff937b61ce5beb9f92726.seg 04099429 04225987/points/90dbe261a4d56dcf1082f2ea630bf69e.pts 04225987/expert_verified/points_label/90dbe261a4d56dcf1082f2ea630bf69e.seg 04225987 03001627/points/81b27636162e148bb3fb065fa3089331.pts 03001627/expert_verified/points_label/81b27636162e148bb3fb065fa3089331.seg 03001627 03642806/points/66d47a84a3d522dc9311bf79d4774e73.pts 03642806/expert_verified/points_label/66d47a84a3d522dc9311bf79d4774e73.seg 03642806 03001627/points/2a05ae00b701fda36567137a59cb1a56.pts 03001627/expert_verified/points_label/2a05ae00b701fda36567137a59cb1a56.seg 03001627 04379243/points/79df23303a3192c1cdf1dfd78f33901b.pts 04379243/expert_verified/points_label/79df23303a3192c1cdf1dfd78f33901b.seg 04379243 04379243/points/bf17779bec6abccf161bc5243aab8ea4.pts 04379243/expert_verified/points_label/bf17779bec6abccf161bc5243aab8ea4.seg 04379243 03001627/points/ece1a921c1bfd44947f5e245ee376525.pts 03001627/expert_verified/points_label/ece1a921c1bfd44947f5e245ee376525.seg 03001627 03636649/points/15c51ecb58bf304fef3a2c64cef919d0.pts 03636649/expert_verified/points_label/15c51ecb58bf304fef3a2c64cef919d0.seg 03636649 04379243/points/5d93e285b2006520ab610b0c94236463.pts 04379243/expert_verified/points_label/5d93e285b2006520ab610b0c94236463.seg 04379243 03636649/points/b2d5929e66044aeac7db9c21ccfbc4a1.pts 03636649/expert_verified/points_label/b2d5929e66044aeac7db9c21ccfbc4a1.seg 03636649 04379243/points/f3164e1781a296597f6f00dc967c386.pts 04379243/expert_verified/points_label/f3164e1781a296597f6f00dc967c386.seg 04379243 04379243/points/798a07e42d76013582695d8aaeacccc5.pts 04379243/expert_verified/points_label/798a07e42d76013582695d8aaeacccc5.seg 04379243 03948459/points/cc014e78b5cd9e7ed957eaf7f4edb205.pts 03948459/expert_verified/points_label/cc014e78b5cd9e7ed957eaf7f4edb205.seg 03948459 03636649/points/b3a98808fb1ccd892a5041fadf25a502.pts 03636649/expert_verified/points_label/b3a98808fb1ccd892a5041fadf25a502.seg 03636649 04379243/points/9472c006a5d35b9ab606ece4189242ff.pts 04379243/expert_verified/points_label/9472c006a5d35b9ab606ece4189242ff.seg 04379243 03001627/points/3f04adffb69b5ebee95cd0dc8c2f0e83.pts 03001627/expert_verified/points_label/3f04adffb69b5ebee95cd0dc8c2f0e83.seg 03001627 03001627/points/26aa22bd1da8b8c5b1a5c6ecbc81953c.pts 03001627/expert_verified/points_label/26aa22bd1da8b8c5b1a5c6ecbc81953c.seg 03001627 03001627/points/f68ecc9ec512915f36d8dd30a594b2af.pts 03001627/expert_verified/points_label/f68ecc9ec512915f36d8dd30a594b2af.seg 03001627 03642806/points/6489453e322cdb53f9f3c6290096f50f.pts 03642806/expert_verified/points_label/6489453e322cdb53f9f3c6290096f50f.seg 03642806 03001627/points/c53fa6829ec9a947d13b7d13ee32497.pts 03001627/expert_verified/points_label/c53fa6829ec9a947d13b7d13ee32497.seg 03001627 04379243/points/7f1bd688960e2c1b97f2016c3d6097c9.pts 04379243/expert_verified/points_label/7f1bd688960e2c1b97f2016c3d6097c9.seg 04379243 02958343/points/edb2ab8a1d7e20f36436916a86a90ed7.pts 02958343/expert_verified/points_label/edb2ab8a1d7e20f36436916a86a90ed7.seg 02958343 04379243/points/159a2a760327ca5bababe11fcea8796.pts 04379243/expert_verified/points_label/159a2a760327ca5bababe11fcea8796.seg 04379243 02958343/points/988108a7536d686824065b218dc1b5b9.pts 02958343/expert_verified/points_label/988108a7536d686824065b218dc1b5b9.seg 02958343 03636649/points/c695408a86062c4d242ea50288b3f64.pts 03636649/expert_verified/points_label/c695408a86062c4d242ea50288b3f64.seg 03636649 04379243/points/2e7cb2cbfbbb4d002ee19ebe356c2dcb.pts 04379243/expert_verified/points_label/2e7cb2cbfbbb4d002ee19ebe356c2dcb.seg 04379243 02691156/points/3d23703a618ce7df1e569ed4e4cfe84.pts 02691156/expert_verified/points_label/3d23703a618ce7df1e569ed4e4cfe84.seg 02691156 03636649/points/97b7d9aabe38f91df11c97be803c47d.pts 03636649/expert_verified/points_label/97b7d9aabe38f91df11c97be803c47d.seg 03636649 04379243/points/5be1589df948b227c955e5ed03ef3a2f.pts 04379243/expert_verified/points_label/5be1589df948b227c955e5ed03ef3a2f.seg 04379243 04379243/points/8ea7ca2c8b48eb68ab610b0c94236463.pts 04379243/expert_verified/points_label/8ea7ca2c8b48eb68ab610b0c94236463.seg 04379243 02958343/points/eb56379e243b0e2090da6b3e2ed8b49d.pts 02958343/expert_verified/points_label/eb56379e243b0e2090da6b3e2ed8b49d.seg 02958343 03001627/points/cc30a723aeba69a139e0f39f5249b0ba.pts 03001627/expert_verified/points_label/cc30a723aeba69a139e0f39f5249b0ba.seg 03001627 03001627/points/ff8efd10f5e6c5c7c6c0380e62f2644.pts 03001627/expert_verified/points_label/ff8efd10f5e6c5c7c6c0380e62f2644.seg 03001627 02691156/points/d0ee4253d406b3f05e9e2656aff7dd5b.pts 02691156/expert_verified/points_label/d0ee4253d406b3f05e9e2656aff7dd5b.seg 02691156 02691156/points/9afe827a622d8ca28699933784576e73.pts 02691156/expert_verified/points_label/9afe827a622d8ca28699933784576e73.seg 02691156 03467517/points/d82fc6db200cdf6ea24eeca91f583600.pts 03467517/expert_verified/points_label/d82fc6db200cdf6ea24eeca91f583600.seg 03467517 03642806/points/6123321e3af0b6328204b359ccd3949e.pts 03642806/expert_verified/points_label/6123321e3af0b6328204b359ccd3949e.seg 03642806 03636649/points/e15defcb3dd448094fffb007974c9976.pts 03636649/expert_verified/points_label/e15defcb3dd448094fffb007974c9976.seg 03636649 03001627/points/c7fe45610d10cb108ad3a7d07aac2767.pts 03001627/expert_verified/points_label/c7fe45610d10cb108ad3a7d07aac2767.seg 03001627 04379243/points/bfaa1c23d2622422ad16260d4d73b56.pts 04379243/expert_verified/points_label/bfaa1c23d2622422ad16260d4d73b56.seg 04379243 04379243/points/8e3fc5f1f8e9658ce8b2b8dc0c816caf.pts 04379243/expert_verified/points_label/8e3fc5f1f8e9658ce8b2b8dc0c816caf.seg 04379243 03467517/points/1a96f73d0929bd4793f0194265a9746c.pts 03467517/expert_verified/points_label/1a96f73d0929bd4793f0194265a9746c.seg 03467517 02691156/points/86b11ae736659136ca8607f540cc62ba.pts 02691156/expert_verified/points_label/86b11ae736659136ca8607f540cc62ba.seg 02691156 04379243/points/4c4c719ac4b61d8f812c9aaa38f9a422.pts 04379243/expert_verified/points_label/4c4c719ac4b61d8f812c9aaa38f9a422.seg 04379243 04379243/points/443eca86041e57ab1e99b149cff6a230.pts 04379243/expert_verified/points_label/443eca86041e57ab1e99b149cff6a230.seg 04379243 03948459/points/6b2d89a7f2b173f0d9deb3f829cc2475.pts 03948459/expert_verified/points_label/6b2d89a7f2b173f0d9deb3f829cc2475.seg 03948459 04379243/points/8d84471c4af977d917271868b642acd3.pts 04379243/expert_verified/points_label/8d84471c4af977d917271868b642acd3.seg 04379243 03636649/points/b78bef16d4f44844931e98da3a93e73e.pts 03636649/expert_verified/points_label/b78bef16d4f44844931e98da3a93e73e.seg 03636649 03636649/points/29985e44b73051d923500a5b036df62e.pts 03636649/expert_verified/points_label/29985e44b73051d923500a5b036df62e.seg 03636649 03642806/points/4f3575df3821e08c466909b3e9553909.pts 03642806/expert_verified/points_label/4f3575df3821e08c466909b3e9553909.seg 03642806 03001627/points/3774a2b8c71e70b9f18a36d57b7cced0.pts 03001627/expert_verified/points_label/3774a2b8c71e70b9f18a36d57b7cced0.seg 03001627 03001627/points/3ea40a75f22515557dcf230d8b7d162e.pts 03001627/expert_verified/points_label/3ea40a75f22515557dcf230d8b7d162e.seg 03001627 03001627/points/33c4f94e97c3fefd19fb4103277a6b93.pts 03001627/expert_verified/points_label/33c4f94e97c3fefd19fb4103277a6b93.seg 03001627 03636649/points/d7760d5f9e1e6a622cd2160e449d45ae.pts 03636649/expert_verified/points_label/d7760d5f9e1e6a622cd2160e449d45ae.seg 03636649 02954340/points/7f9ddfff396634f17790cd6f6e8952aa.pts 02954340/expert_verified/points_label/7f9ddfff396634f17790cd6f6e8952aa.seg 02954340 03001627/points/5e706e87ca60bd19ecb01bc908e8cea6.pts 03001627/expert_verified/points_label/5e706e87ca60bd19ecb01bc908e8cea6.seg 03001627 04379243/points/90c19c729cabdb864b8710a3469971b1.pts 04379243/expert_verified/points_label/90c19c729cabdb864b8710a3469971b1.seg 04379243 02691156/points/d08471df3e76602427743256ca3834f.pts 02691156/expert_verified/points_label/d08471df3e76602427743256ca3834f.seg 02691156 02958343/points/67c229c70e64a25e69c2e0a91b39f742.pts 02958343/expert_verified/points_label/67c229c70e64a25e69c2e0a91b39f742.seg 02958343 04379243/points/1011e1c9812b84d2a9ed7bb5b55809f8.pts 04379243/expert_verified/points_label/1011e1c9812b84d2a9ed7bb5b55809f8.seg 04379243 03636649/points/3e2d51c40b37c9c086052e834fbd2c4a.pts 03636649/expert_verified/points_label/3e2d51c40b37c9c086052e834fbd2c4a.seg 03636649 03001627/points/6b385a32489bab4abbc7a6acbd8f058b.pts 03001627/expert_verified/points_label/6b385a32489bab4abbc7a6acbd8f058b.seg 03001627 03001627/points/61d29e8133da0b58d1fd43e2bf80195.pts 03001627/expert_verified/points_label/61d29e8133da0b58d1fd43e2bf80195.seg 03001627 04379243/points/d5f2968e4b7254ccf4104961857ca9c.pts 04379243/expert_verified/points_label/d5f2968e4b7254ccf4104961857ca9c.seg 04379243 04379243/points/30c9865cfc4294a7ad16260d4d73b56.pts 04379243/expert_verified/points_label/30c9865cfc4294a7ad16260d4d73b56.seg 04379243 03001627/points/76919a456a23b9779368d1198f406e7.pts 03001627/expert_verified/points_label/76919a456a23b9779368d1198f406e7.seg 03001627 03001627/points/c12da8acb2c7973597e755dddca14449.pts 03001627/expert_verified/points_label/c12da8acb2c7973597e755dddca14449.seg 03001627 02958343/points/a5dcd1196a1ffa9739f20966eb25504f.pts 02958343/expert_verified/points_label/a5dcd1196a1ffa9739f20966eb25504f.seg 02958343 02691156/points/1deb997079e0b3cd6c1cd53dbc9f7b8e.pts 02691156/expert_verified/points_label/1deb997079e0b3cd6c1cd53dbc9f7b8e.seg 02691156 03636649/points/afb7cc3bbc3595a4e9b3dff83c7ff715.pts 03636649/expert_verified/points_label/afb7cc3bbc3595a4e9b3dff83c7ff715.seg 03636649 03636649/points/b4aee889d5e2a826f6747912091f1965.pts 03636649/expert_verified/points_label/b4aee889d5e2a826f6747912091f1965.seg 03636649 03636649/points/ea71ba1d8d8c8e5888a1de3dc61bfeef.pts 03636649/expert_verified/points_label/ea71ba1d8d8c8e5888a1de3dc61bfeef.seg 03636649 02958343/points/b0c2225ab347e28f1a48cf85d161a723.pts 02958343/expert_verified/points_label/b0c2225ab347e28f1a48cf85d161a723.seg 02958343 03001627/points/1ab8a3b55c14a7b27eaeab1f0c9120b7.pts 03001627/expert_verified/points_label/1ab8a3b55c14a7b27eaeab1f0c9120b7.seg 03001627 03261776/points/c6d19db35f69bae7b6d9c2cee7f2f72b.pts 03261776/expert_verified/points_label/c6d19db35f69bae7b6d9c2cee7f2f72b.seg 03261776 03001627/points/6d6e634ff34bd350c511e6b9b3b344f3.pts 03001627/expert_verified/points_label/6d6e634ff34bd350c511e6b9b3b344f3.seg 03001627 02691156/points/ce682d7a2bbf77b6fc4b92d3d335214a.pts 02691156/expert_verified/points_label/ce682d7a2bbf77b6fc4b92d3d335214a.seg 02691156 03261776/points/943048e64cc2bc980a070963925e308.pts 03261776/expert_verified/points_label/943048e64cc2bc980a070963925e308.seg 03261776 03642806/points/5a63c5f29f0bc0eb12d8efb2f101da03.pts 03642806/expert_verified/points_label/5a63c5f29f0bc0eb12d8efb2f101da03.seg 03642806 04379243/points/19678fdb9bc926505e4b35ff1ea95f37.pts 04379243/expert_verified/points_label/19678fdb9bc926505e4b35ff1ea95f37.seg 04379243 02958343/points/52f2a2472411fe2e6b418c7d9fedcaa9.pts 02958343/expert_verified/points_label/52f2a2472411fe2e6b418c7d9fedcaa9.seg 02958343 03001627/points/1ee92a9d78cccbda98d2e7dbe701ca48.pts 03001627/expert_verified/points_label/1ee92a9d78cccbda98d2e7dbe701ca48.seg 03001627 03001627/points/795f38ce5d8519938077cafed2bb8242.pts 03001627/expert_verified/points_label/795f38ce5d8519938077cafed2bb8242.seg 03001627 03001627/points/5e5121cc58c4fea78ce66f12ba927a2b.pts 03001627/expert_verified/points_label/5e5121cc58c4fea78ce66f12ba927a2b.seg 03001627 03001627/points/b998016472e9dd7a9b9f2eb77f5e247e.pts 03001627/expert_verified/points_label/b998016472e9dd7a9b9f2eb77f5e247e.seg 03001627 04379243/points/30b506e5e1fc282afdfcfddf24fb29ec.pts 04379243/expert_verified/points_label/30b506e5e1fc282afdfcfddf24fb29ec.seg 04379243 03624134/points/bcd7ed830358dbd6d58ea69ee1ced10e.pts 03624134/expert_verified/points_label/bcd7ed830358dbd6d58ea69ee1ced10e.seg 03624134 03001627/points/40d202afdcc49c6d35836c728d324152.pts 03001627/expert_verified/points_label/40d202afdcc49c6d35836c728d324152.seg 03001627 03467517/points/fdb74c27462dfd837c481698bd5233b4.pts 03467517/expert_verified/points_label/fdb74c27462dfd837c481698bd5233b4.seg 03467517 02691156/points/dc7c5d12854b9467b96212c8f6cd06e.pts 02691156/expert_verified/points_label/dc7c5d12854b9467b96212c8f6cd06e.seg 02691156 02691156/points/48e9c61de4db838d84b83051fa0ae5d2.pts 02691156/expert_verified/points_label/48e9c61de4db838d84b83051fa0ae5d2.seg 02691156 04379243/points/d187561a6b0cbd0acaed5ce7390f30b7.pts 04379243/expert_verified/points_label/d187561a6b0cbd0acaed5ce7390f30b7.seg 04379243 04379243/points/ae9e04d050f5cba1492d9da2668ec34c.pts 04379243/expert_verified/points_label/ae9e04d050f5cba1492d9da2668ec34c.seg 04379243 04379243/points/72c884f3b9b9119966f379f51753f72b.pts 04379243/expert_verified/points_label/72c884f3b9b9119966f379f51753f72b.seg 04379243 02691156/points/917694a71164f2148e8405d6c51a908.pts 02691156/expert_verified/points_label/917694a71164f2148e8405d6c51a908.seg 02691156 03001627/points/a2441f03fed7c13def31f91fe6afc8fa.pts 03001627/expert_verified/points_label/a2441f03fed7c13def31f91fe6afc8fa.seg 03001627 03001627/points/49c955a80749d2e1a5ffdf44ff86b795.pts 03001627/expert_verified/points_label/49c955a80749d2e1a5ffdf44ff86b795.seg 03001627 03636649/points/c43c89d862e10552b24ecc319936dfe2.pts 03636649/expert_verified/points_label/c43c89d862e10552b24ecc319936dfe2.seg 03636649 03636649/points/e5ff9311bee487f5ca4aaad7dc0e3a16.pts 03636649/expert_verified/points_label/e5ff9311bee487f5ca4aaad7dc0e3a16.seg 03636649 02958343/points/ba0ac1d1e25d3fad63f2c3a55558a78f.pts 02958343/expert_verified/points_label/ba0ac1d1e25d3fad63f2c3a55558a78f.seg 02958343 04379243/points/2f58b1ca8634a6b48b9b51ae4415d5aa.pts 04379243/expert_verified/points_label/2f58b1ca8634a6b48b9b51ae4415d5aa.seg 04379243 03001627/points/c585ee093bfd52af6512b7b24f3d84.pts 03001627/expert_verified/points_label/c585ee093bfd52af6512b7b24f3d84.seg 03001627 03001627/points/46f6a6e0f239282fc8687ff9b0b4e4ac.pts 03001627/expert_verified/points_label/46f6a6e0f239282fc8687ff9b0b4e4ac.seg 03001627 03642806/points/f72dc1ffeae0168aadcfd37206a0d18b.pts 03642806/expert_verified/points_label/f72dc1ffeae0168aadcfd37206a0d18b.seg 03642806 03948459/points/1e83ef6ed5d0b78b7efb854782e23566.pts 03948459/expert_verified/points_label/1e83ef6ed5d0b78b7efb854782e23566.seg 03948459 03001627/points/95e5f6e550761aefe65b629e4a22f51e.pts 03001627/expert_verified/points_label/95e5f6e550761aefe65b629e4a22f51e.seg 03001627 03001627/points/b38d05caee69c7ac8fc6229eb64e56a.pts 03001627/expert_verified/points_label/b38d05caee69c7ac8fc6229eb64e56a.seg 03001627 02691156/points/4ff50b9f815c58acca8607f540cc62ba.pts 02691156/expert_verified/points_label/4ff50b9f815c58acca8607f540cc62ba.seg 02691156 03636649/points/78a11c0b8e964c9b41657e31b569b105.pts 03636649/expert_verified/points_label/78a11c0b8e964c9b41657e31b569b105.seg 03636649 02958343/points/b1f75a8e8b9e921a8a6cf8c6b92417f2.pts 02958343/expert_verified/points_label/b1f75a8e8b9e921a8a6cf8c6b92417f2.seg 02958343 02958343/points/a836fc66c01eccca58c27e607f6e2d4c.pts 02958343/expert_verified/points_label/a836fc66c01eccca58c27e607f6e2d4c.seg 02958343 02691156/points/fac4af109beb0108b4f192eea1889928.pts 02691156/expert_verified/points_label/fac4af109beb0108b4f192eea1889928.seg 02691156 03467517/points/b9c10bf6fc2095f93f0194265a9746c.pts 03467517/expert_verified/points_label/b9c10bf6fc2095f93f0194265a9746c.seg 03467517 02691156/points/b976a48c015d6ced5e9e2656aff7dd5b.pts 02691156/expert_verified/points_label/b976a48c015d6ced5e9e2656aff7dd5b.seg 02691156 04379243/points/889f48aa85accd2ee73947fdf756a329.pts 04379243/expert_verified/points_label/889f48aa85accd2ee73947fdf756a329.seg 04379243 02691156/points/b6d61068ef2bf2d46059aeb39e538eb2.pts 02691156/expert_verified/points_label/b6d61068ef2bf2d46059aeb39e538eb2.seg 02691156 04379243/points/d94de64641651a2079b3e1be3524f72f.pts 04379243/expert_verified/points_label/d94de64641651a2079b3e1be3524f72f.seg 04379243 03001627/points/117bd6da01905949a81116f5456ee312.pts 03001627/expert_verified/points_label/117bd6da01905949a81116f5456ee312.seg 03001627 03636649/points/845542d0f578a9db1ec48bc3c478566d.pts 03636649/expert_verified/points_label/845542d0f578a9db1ec48bc3c478566d.seg 03636649 04379243/points/9391dcc782fa7f6bfdad344760a9dafd.pts 04379243/expert_verified/points_label/9391dcc782fa7f6bfdad344760a9dafd.seg 04379243 04379243/points/fe99a1127734f7852b70eac6546e93fd.pts 04379243/expert_verified/points_label/fe99a1127734f7852b70eac6546e93fd.seg 04379243 03001627/points/4e358c2dc0513971f98c0761af40e04.pts 03001627/expert_verified/points_label/4e358c2dc0513971f98c0761af40e04.seg 03001627 03636649/points/53afad2e573b26b141657e31b569b105.pts 03636649/expert_verified/points_label/53afad2e573b26b141657e31b569b105.seg 03636649 04379243/points/3e51742cb382aa1f79b3e1be3524f72f.pts 04379243/expert_verified/points_label/3e51742cb382aa1f79b3e1be3524f72f.seg 04379243 02958343/points/4f17af1ca7ae689d409b2c4484d833cc.pts 02958343/expert_verified/points_label/4f17af1ca7ae689d409b2c4484d833cc.seg 02958343 03467517/points/c739664436ac5237aa0c867d5b070a5d.pts 03467517/expert_verified/points_label/c739664436ac5237aa0c867d5b070a5d.seg 03467517 03797390/points/61c10dccfa8e508e2d66cbf6a91063.pts 03797390/expert_verified/points_label/61c10dccfa8e508e2d66cbf6a91063.seg 03797390 03467517/points/aa86d20d03b2303593f0194265a9746c.pts 03467517/expert_verified/points_label/aa86d20d03b2303593f0194265a9746c.seg 03467517 04379243/points/2f98d5e721e84debaa8081a7009091db.pts 04379243/expert_verified/points_label/2f98d5e721e84debaa8081a7009091db.seg 04379243 04379243/points/2a0f853dadd841f96f1e07a56c129dfc.pts 04379243/expert_verified/points_label/2a0f853dadd841f96f1e07a56c129dfc.seg 04379243 03001627/points/8031478c3fe31ddcc337647acafe65f0.pts 03001627/expert_verified/points_label/8031478c3fe31ddcc337647acafe65f0.seg 03001627 03636649/points/a53112591be182b9d93768e7b9b1eabf.pts 03636649/expert_verified/points_label/a53112591be182b9d93768e7b9b1eabf.seg 03636649 03001627/points/5bc916f8b9d0a7c6b40f0ac0fb9a650d.pts 03001627/expert_verified/points_label/5bc916f8b9d0a7c6b40f0ac0fb9a650d.seg 03001627 02691156/points/f2d4b8440d4bde5330afbcb38d77d0c3.pts 02691156/expert_verified/points_label/f2d4b8440d4bde5330afbcb38d77d0c3.seg 02691156 03001627/points/e4274fc2b9e4a5511882515d09f3979e.pts 03001627/expert_verified/points_label/e4274fc2b9e4a5511882515d09f3979e.seg 03001627 03001627/points/9ab18a33335373b2659dda512294c744.pts 03001627/expert_verified/points_label/9ab18a33335373b2659dda512294c744.seg 03001627 04379243/points/32ea6609eb659a2cec3367bccf60e518.pts 04379243/expert_verified/points_label/32ea6609eb659a2cec3367bccf60e518.seg 04379243 04379243/points/759cb93134fd5efde76bc197b3a3ffc0.pts 04379243/expert_verified/points_label/759cb93134fd5efde76bc197b3a3ffc0.seg 04379243 03001627/points/b8b5e172ee58899df2d9e72ba502035.pts 03001627/expert_verified/points_label/b8b5e172ee58899df2d9e72ba502035.seg 03001627 03001627/points/1886b3e3f3d4af3ace522e6dda26fb51.pts 03001627/expert_verified/points_label/1886b3e3f3d4af3ace522e6dda26fb51.seg 03001627 03948459/points/3f5f657bec9a21814ce6ac98dc4781fe.pts 03948459/expert_verified/points_label/3f5f657bec9a21814ce6ac98dc4781fe.seg 03948459 04379243/points/5adf5a7173e588ad76e9713f57a5fcb6.pts 04379243/expert_verified/points_label/5adf5a7173e588ad76e9713f57a5fcb6.seg 04379243 03001627/points/f33b6f791e9d64387d01b77e04a0bc7b.pts 03001627/expert_verified/points_label/f33b6f791e9d64387d01b77e04a0bc7b.seg 03001627 04379243/points/4e928377ae98ed8d99e8bf807e902261.pts 04379243/expert_verified/points_label/4e928377ae98ed8d99e8bf807e902261.seg 04379243 03001627/points/d7867d215f52107ba5e8cf3aa1686d66.pts 03001627/expert_verified/points_label/d7867d215f52107ba5e8cf3aa1686d66.seg 03001627 02691156/points/bddc2c1a4fae008947a1dbf5fd48a4dd.pts 02691156/expert_verified/points_label/bddc2c1a4fae008947a1dbf5fd48a4dd.seg 02691156 02958343/points/bafacc7f28509d4157abc6fa0d632bc7.pts 02958343/expert_verified/points_label/bafacc7f28509d4157abc6fa0d632bc7.seg 02958343 02691156/points/a14b262838529c2c81e1d9f6b27f1a92.pts 02691156/expert_verified/points_label/a14b262838529c2c81e1d9f6b27f1a92.seg 02691156 03001627/points/38afa26a419ea3abed040525648fc6d7.pts 03001627/expert_verified/points_label/38afa26a419ea3abed040525648fc6d7.seg 03001627 04379243/points/79f63a1564928af071a782a4379556c7.pts 04379243/expert_verified/points_label/79f63a1564928af071a782a4379556c7.seg 04379243 04379243/points/cbd1cd9b5423f890beedb4c8fd29e2d1.pts 04379243/expert_verified/points_label/cbd1cd9b5423f890beedb4c8fd29e2d1.seg 04379243 02691156/points/d74767519393a937f73e5bc170b7e2be.pts 02691156/expert_verified/points_label/d74767519393a937f73e5bc170b7e2be.seg 02691156 03001627/points/9a82269e56737217e16571f1d370cad9.pts 03001627/expert_verified/points_label/9a82269e56737217e16571f1d370cad9.seg 03001627 03001627/points/6e1e73e14637a28da1c367d7a459a9b7.pts 03001627/expert_verified/points_label/6e1e73e14637a28da1c367d7a459a9b7.seg 03001627 03797390/points/eecb13f61a93b4048f58d8b19de93f99.pts 03797390/expert_verified/points_label/eecb13f61a93b4048f58d8b19de93f99.seg 03797390 03001627/points/4f7523a3d276bfae4b3c42e318f3affc.pts 03001627/expert_verified/points_label/4f7523a3d276bfae4b3c42e318f3affc.seg 03001627 03624134/points/f19fe19693937db1cb03b57fca000b1f.pts 03624134/expert_verified/points_label/f19fe19693937db1cb03b57fca000b1f.seg 03624134 02958343/points/c3858a8b73dcb137e3bdba9430565083.pts 02958343/expert_verified/points_label/c3858a8b73dcb137e3bdba9430565083.seg 02958343 04379243/points/3ce930bb150aef8a69fb38085fbc320c.pts 04379243/expert_verified/points_label/3ce930bb150aef8a69fb38085fbc320c.seg 04379243 04379243/points/75e3cbf4b1ef0df971a782a4379556c7.pts 04379243/expert_verified/points_label/75e3cbf4b1ef0df971a782a4379556c7.seg 04379243 04379243/points/5040f8f3e2293db448e116352760c52d.pts 04379243/expert_verified/points_label/5040f8f3e2293db448e116352760c52d.seg 04379243 04379243/points/edaf24be15738ea2c5d1c45cadcaa3eb.pts 04379243/expert_verified/points_label/edaf24be15738ea2c5d1c45cadcaa3eb.seg 04379243 04379243/points/6fb52c296531dc17beedb4c8fd29e2d1.pts 04379243/expert_verified/points_label/6fb52c296531dc17beedb4c8fd29e2d1.seg 04379243 04379243/points/e777df6ffb40e3a1853d412328e7e7a6.pts 04379243/expert_verified/points_label/e777df6ffb40e3a1853d412328e7e7a6.seg 04379243 03001627/points/9c103621101bcf9919fb4103277a6b93.pts 03001627/expert_verified/points_label/9c103621101bcf9919fb4103277a6b93.seg 03001627 03001627/points/5d20adaf6d8f89fa2f1c10544d7d6f.pts 03001627/expert_verified/points_label/5d20adaf6d8f89fa2f1c10544d7d6f.seg 03001627 02691156/points/b80bd34ab330babbc8727b27ee96a4b7.pts 02691156/expert_verified/points_label/b80bd34ab330babbc8727b27ee96a4b7.seg 02691156 04379243/points/50d898f6d1c05cee2d99129afd32edf4.pts 04379243/expert_verified/points_label/50d898f6d1c05cee2d99129afd32edf4.seg 04379243 04379243/points/c0c836c630cdb4bb664b3b9b23ddfcbc.pts 04379243/expert_verified/points_label/c0c836c630cdb4bb664b3b9b23ddfcbc.seg 04379243 03790512/points/a1553e0bb7897a7ace0bf41e5f45753d.pts 03790512/expert_verified/points_label/a1553e0bb7897a7ace0bf41e5f45753d.seg 03790512 03467517/points/7701180906a0aa156a7ae841f1f88f87.pts 03467517/expert_verified/points_label/7701180906a0aa156a7ae841f1f88f87.seg 03467517 03467517/points/3ef569c13f4ab5f83ac61a2f8346a8f.pts 03467517/expert_verified/points_label/3ef569c13f4ab5f83ac61a2f8346a8f.seg 03467517 03636649/points/3834d7f376879c03eca29403b7226aa1.pts 03636649/expert_verified/points_label/3834d7f376879c03eca29403b7226aa1.seg 03636649 02958343/points/34ab29cea66952f16f48edd113a40fce.pts 02958343/expert_verified/points_label/34ab29cea66952f16f48edd113a40fce.seg 02958343 02958343/points/e24f388736f4e6fd2cdd250493632937.pts 02958343/expert_verified/points_label/e24f388736f4e6fd2cdd250493632937.seg 02958343 03001627/points/3ae022522800685c610195e4fb10d1de.pts 03001627/expert_verified/points_label/3ae022522800685c610195e4fb10d1de.seg 03001627 02691156/points/49660fd24e5c2fbab87697d3904b168b.pts 02691156/expert_verified/points_label/49660fd24e5c2fbab87697d3904b168b.seg 02691156 03642806/points/2d5d4d79cd464298566636e42679cc7f.pts 03642806/expert_verified/points_label/2d5d4d79cd464298566636e42679cc7f.seg 03642806 04379243/points/7988dedacce42552ab610b0c94236463.pts 04379243/expert_verified/points_label/7988dedacce42552ab610b0c94236463.seg 04379243 04379243/points/91ed62f2b3fd5919f12d7184a2ad3430.pts 04379243/expert_verified/points_label/91ed62f2b3fd5919f12d7184a2ad3430.seg 04379243 03001627/points/a5898fefb1733333a82b0d8d157287f5.pts 03001627/expert_verified/points_label/a5898fefb1733333a82b0d8d157287f5.seg 03001627 04379243/points/b4ef1de99422b08768661782af60b711.pts 04379243/expert_verified/points_label/b4ef1de99422b08768661782af60b711.seg 04379243 03001627/points/df2b7e697ab6ca0f155d75bbf62b80.pts 03001627/expert_verified/points_label/df2b7e697ab6ca0f155d75bbf62b80.seg 03001627 03467517/points/408a8e1b51266b9ccc34b900bb2492e.pts 03467517/expert_verified/points_label/408a8e1b51266b9ccc34b900bb2492e.seg 03467517 03001627/points/597f2b2153af0c544aabcf2a7cb640f9.pts 03001627/expert_verified/points_label/597f2b2153af0c544aabcf2a7cb640f9.seg 03001627 03001627/points/6870fbd4a7b733b0674f1c30a8cad95a.pts 03001627/expert_verified/points_label/6870fbd4a7b733b0674f1c30a8cad95a.seg 03001627 03001627/points/e35d7d19dcdc9e5c30e06a011e63236a.pts 03001627/expert_verified/points_label/e35d7d19dcdc9e5c30e06a011e63236a.seg 03001627 04225987/points/58ade10f7f87edc6e860048d7ced02e3.pts 04225987/expert_verified/points_label/58ade10f7f87edc6e860048d7ced02e3.seg 04225987 04379243/points/39cf5ae2b497715a84253b2030fab070.pts 04379243/expert_verified/points_label/39cf5ae2b497715a84253b2030fab070.seg 04379243 04379243/points/ab7b0db92f96381f8cbb8bac2032149c.pts 04379243/expert_verified/points_label/ab7b0db92f96381f8cbb8bac2032149c.seg 04379243 03001627/points/b117b01ab380362db8134b0fbf68257d.pts 03001627/expert_verified/points_label/b117b01ab380362db8134b0fbf68257d.seg 03001627 03467517/points/913f3c90f5b78256e98e318d424a4bb9.pts 03467517/expert_verified/points_label/913f3c90f5b78256e98e318d424a4bb9.seg 03467517 04379243/points/831985fb385a5b2a9ae2d75b4fc35b7.pts 04379243/expert_verified/points_label/831985fb385a5b2a9ae2d75b4fc35b7.seg 04379243 03467517/points/482b8b9a225b6ca1d57700c05b1862d8.pts 03467517/expert_verified/points_label/482b8b9a225b6ca1d57700c05b1862d8.seg 03467517 03001627/points/93a6876247c7a015d84b8ba651dfb8ac.pts 03001627/expert_verified/points_label/93a6876247c7a015d84b8ba651dfb8ac.seg 03001627 04379243/points/a78273aa10b2dfb0bc8d334f99e7f52.pts 04379243/expert_verified/points_label/a78273aa10b2dfb0bc8d334f99e7f52.seg 04379243 04379243/points/3c686ac317c496f9a71c812e027f94d9.pts 04379243/expert_verified/points_label/3c686ac317c496f9a71c812e027f94d9.seg 04379243 02691156/points/50755e616df58fe566cf1b4a8fc3914e.pts 02691156/expert_verified/points_label/50755e616df58fe566cf1b4a8fc3914e.seg 02691156 03001627/points/8cedc8e684d60ff42a06d8c81262ef96.pts 03001627/expert_verified/points_label/8cedc8e684d60ff42a06d8c81262ef96.seg 03001627 04379243/points/f74c321042dbc8e684d78f017ff73fd6.pts 04379243/expert_verified/points_label/f74c321042dbc8e684d78f017ff73fd6.seg 04379243 02958343/points/5130947e5f18e73a8321b7d65a99d2a.pts 02958343/expert_verified/points_label/5130947e5f18e73a8321b7d65a99d2a.seg 02958343 03261776/points/f5d210ff14ca9d29b6d9c2cee7f2f72b.pts 03261776/expert_verified/points_label/f5d210ff14ca9d29b6d9c2cee7f2f72b.seg 03261776 03001627/points/d36de0f850783d8fd6b3090036b71698.pts 03001627/expert_verified/points_label/d36de0f850783d8fd6b3090036b71698.seg 03001627 03001627/points/6897c2665267cca39eea64ae4d2b4158.pts 03001627/expert_verified/points_label/6897c2665267cca39eea64ae4d2b4158.seg 03001627 03001627/points/6e98c5d61e008b4c2871cc0b3cc1a485.pts 03001627/expert_verified/points_label/6e98c5d61e008b4c2871cc0b3cc1a485.seg 03001627 02958343/points/92f697d036addb55ed576c2966428f.pts 02958343/expert_verified/points_label/92f697d036addb55ed576c2966428f.seg 02958343 04379243/points/f3fd419f725aa894ba5342d638d0c267.pts 04379243/expert_verified/points_label/f3fd419f725aa894ba5342d638d0c267.seg 04379243 04379243/points/62eff79cf2e75bc2765ee729adbdf968.pts 04379243/expert_verified/points_label/62eff79cf2e75bc2765ee729adbdf968.seg 04379243 03001627/points/98a1f8651c962402492d9da2668ec34c.pts 03001627/expert_verified/points_label/98a1f8651c962402492d9da2668ec34c.seg 03001627 03636649/points/d90639e69c82f864eb2d9895648d1206.pts 03636649/expert_verified/points_label/d90639e69c82f864eb2d9895648d1206.seg 03636649 02954340/points/a1494210f6774b87b3e0e60b857dde8f.pts 02954340/expert_verified/points_label/a1494210f6774b87b3e0e60b857dde8f.seg 02954340 03467517/points/d528407fe43b5df193f0194265a9746c.pts 03467517/expert_verified/points_label/d528407fe43b5df193f0194265a9746c.seg 03467517 03636649/points/776e4b38023091002cd2160e449d45ae.pts 03636649/expert_verified/points_label/776e4b38023091002cd2160e449d45ae.seg 03636649 04379243/points/91df49ec00f2c5ce73f1ca2ca101a20d.pts 04379243/expert_verified/points_label/91df49ec00f2c5ce73f1ca2ca101a20d.seg 04379243 04379243/points/47f25d5b367326ceaaf15b62af6b513f.pts 04379243/expert_verified/points_label/47f25d5b367326ceaaf15b62af6b513f.seg 04379243 04379243/points/f5d6579b3a1f5a879d2be74cfb51ade1.pts 04379243/expert_verified/points_label/f5d6579b3a1f5a879d2be74cfb51ade1.seg 04379243 02691156/points/f6ea6663b48bf78261f1ef59130c405d.pts 02691156/expert_verified/points_label/f6ea6663b48bf78261f1ef59130c405d.seg 02691156 03001627/points/63da17eda9d415b5319c5e90e9cc9126.pts 03001627/expert_verified/points_label/63da17eda9d415b5319c5e90e9cc9126.seg 03001627 02691156/points/9fb60716f0f5a2b84408eb298433d643.pts 02691156/expert_verified/points_label/9fb60716f0f5a2b84408eb298433d643.seg 02691156 02773838/points/5161d9adede671d6edc32c5c9ec9f827.pts 02773838/expert_verified/points_label/5161d9adede671d6edc32c5c9ec9f827.seg 02773838 04379243/points/696beb1883be838cc955e5ed03ef3a2f.pts 04379243/expert_verified/points_label/696beb1883be838cc955e5ed03ef3a2f.seg 04379243 03001627/points/bc184c3cbe3349b19fb4103277a6b93.pts 03001627/expert_verified/points_label/bc184c3cbe3349b19fb4103277a6b93.seg 03001627 03642806/points/28fbfd8b8c9c6f16e1e44e2fc05361d9.pts 03642806/expert_verified/points_label/28fbfd8b8c9c6f16e1e44e2fc05361d9.seg 03642806 04379243/points/506e4e67efe1794c1dacbc3d67b5a11a.pts 04379243/expert_verified/points_label/506e4e67efe1794c1dacbc3d67b5a11a.seg 04379243 02691156/points/a48676cfe44fd9bee40acb87a6be88b3.pts 02691156/expert_verified/points_label/a48676cfe44fd9bee40acb87a6be88b3.seg 02691156 04379243/points/9e5926bfdc7f01749e65a3d2929a9516.pts 04379243/expert_verified/points_label/9e5926bfdc7f01749e65a3d2929a9516.seg 04379243 04379243/points/dc47d49db6ac670635d498476a30ff0e.pts 04379243/expert_verified/points_label/dc47d49db6ac670635d498476a30ff0e.seg 04379243 04379243/points/33c6e3b21a67b750e78d7b497732dce1.pts 04379243/expert_verified/points_label/33c6e3b21a67b750e78d7b497732dce1.seg 04379243 04379243/points/27295a6f585b7817febad4f49b26ec52.pts 04379243/expert_verified/points_label/27295a6f585b7817febad4f49b26ec52.seg 04379243 03624134/points/6f8b660661269406504c6b6d62466c67.pts 03624134/expert_verified/points_label/6f8b660661269406504c6b6d62466c67.seg 03624134 03642806/points/dbc61cbed5f7f2b33c1abb78f1519c49.pts 03642806/expert_verified/points_label/dbc61cbed5f7f2b33c1abb78f1519c49.seg 03642806 03001627/points/374bec02e71fe06528b4c5ec471dc963.pts 03001627/expert_verified/points_label/374bec02e71fe06528b4c5ec471dc963.seg 03001627 03001627/points/b41aaea5754adae0444b41d6d7f557fa.pts 03001627/expert_verified/points_label/b41aaea5754adae0444b41d6d7f557fa.seg 03001627 03001627/points/7f4f73ad1b3f882ba14472becb07b261.pts 03001627/expert_verified/points_label/7f4f73ad1b3f882ba14472becb07b261.seg 03001627 03001627/points/b80122c3a0543a7b7eaeab1f0c9120b7.pts 03001627/expert_verified/points_label/b80122c3a0543a7b7eaeab1f0c9120b7.seg 03001627 04379243/points/2e4fbab46e264616d93768e7b9b1eabf.pts 04379243/expert_verified/points_label/2e4fbab46e264616d93768e7b9b1eabf.seg 04379243 03001627/points/4a12589099b05c51e13b3410f3683610.pts 03001627/expert_verified/points_label/4a12589099b05c51e13b3410f3683610.seg 03001627 03001627/points/bc523df998d94c7223ac0bd64c9cb255.pts 03001627/expert_verified/points_label/bc523df998d94c7223ac0bd64c9cb255.seg 03001627 02691156/points/218caa58819e10d1fe40308d822f996c.pts 02691156/expert_verified/points_label/218caa58819e10d1fe40308d822f996c.seg 02691156 04379243/points/a5e951c9d7a9a93f8cbb8bac2032149c.pts 04379243/expert_verified/points_label/a5e951c9d7a9a93f8cbb8bac2032149c.seg 04379243 03636649/points/f228f6cd86162beb659dda512294c744.pts 03636649/expert_verified/points_label/f228f6cd86162beb659dda512294c744.seg 03636649 03467517/points/648a820e550bdfd093f0194265a9746c.pts 03467517/expert_verified/points_label/648a820e550bdfd093f0194265a9746c.seg 03467517 03624134/points/8f61777bf6b57fedc13545c5b1a2e607.pts 03624134/expert_verified/points_label/8f61777bf6b57fedc13545c5b1a2e607.seg 03624134 03001627/points/bb9efb4912a018b3c329e2758ab09ecb.pts 03001627/expert_verified/points_label/bb9efb4912a018b3c329e2758ab09ecb.seg 03001627 03001627/points/fdac1f9c0b030841c8687ff9b0b4e4ac.pts 03001627/expert_verified/points_label/fdac1f9c0b030841c8687ff9b0b4e4ac.seg 03001627 02691156/points/8ac8c21b63ff535fca8607f540cc62ba.pts 02691156/expert_verified/points_label/8ac8c21b63ff535fca8607f540cc62ba.seg 02691156 03467517/points/4e4d180e78d8b52a93f0194265a9746c.pts 03467517/expert_verified/points_label/4e4d180e78d8b52a93f0194265a9746c.seg 03467517 03636649/points/7bc1b202ebf000625949e084b65603cf.pts 03636649/expert_verified/points_label/7bc1b202ebf000625949e084b65603cf.seg 03636649 03001627/points/3c8362c1e57c30d7e6c5cd45aa112726.pts 03001627/expert_verified/points_label/3c8362c1e57c30d7e6c5cd45aa112726.seg 03001627 03001627/points/5510d5af1ab5714b3c42e318f3affc.pts 03001627/expert_verified/points_label/5510d5af1ab5714b3c42e318f3affc.seg 03001627 04379243/points/4d393b562df7cfad9a16b095d67f7209.pts 04379243/expert_verified/points_label/4d393b562df7cfad9a16b095d67f7209.seg 04379243 03797390/points/e984fd7e97c2be347eaeab1f0c9120b7.pts 03797390/expert_verified/points_label/e984fd7e97c2be347eaeab1f0c9120b7.seg 03797390 03001627/points/483d22dbbee32ee54e5c7d89bdfc49a3.pts 03001627/expert_verified/points_label/483d22dbbee32ee54e5c7d89bdfc49a3.seg 03001627 02691156/points/a5cd14be786fc8175e9e2656aff7dd5b.pts 02691156/expert_verified/points_label/a5cd14be786fc8175e9e2656aff7dd5b.seg 02691156 03636649/points/d4bbd93c0d85e77d7934a0d24a61231.pts 03636649/expert_verified/points_label/d4bbd93c0d85e77d7934a0d24a61231.seg 03636649 03467517/points/7027bc171baae1d663e148e250c0340d.pts 03467517/expert_verified/points_label/7027bc171baae1d663e148e250c0340d.seg 03467517 03636649/points/1a44dd6ee873d443da13974b3533fb59.pts 03636649/expert_verified/points_label/1a44dd6ee873d443da13974b3533fb59.seg 03636649 04379243/points/2e3037a285fd8b5c1be2a853ec4f9e8.pts 04379243/expert_verified/points_label/2e3037a285fd8b5c1be2a853ec4f9e8.seg 04379243 04379243/points/e3b585b15506fa7113f96345312df593.pts 04379243/expert_verified/points_label/e3b585b15506fa7113f96345312df593.seg 04379243 02958343/points/ee1d28a50a2b71e129348d14ca881f7d.pts 02958343/expert_verified/points_label/ee1d28a50a2b71e129348d14ca881f7d.seg 02958343 03001627/points/22af872ac796ed26ff8d7c1096fae070.pts 03001627/expert_verified/points_label/22af872ac796ed26ff8d7c1096fae070.seg 03001627 03642806/points/9b4ab67eb448c49c11ced4a54f2e6229.pts 03642806/expert_verified/points_label/9b4ab67eb448c49c11ced4a54f2e6229.seg 03642806 03624134/points/1640911b9dc0ef0da95c6095f89cd899.pts 03624134/expert_verified/points_label/1640911b9dc0ef0da95c6095f89cd899.seg 03624134 03001627/points/f6810de4042cc5ce57bd4bc6eae9b341.pts 03001627/expert_verified/points_label/f6810de4042cc5ce57bd4bc6eae9b341.seg 03001627 03001627/points/c46eb7460be602b6bf80985a99195eb8.pts 03001627/expert_verified/points_label/c46eb7460be602b6bf80985a99195eb8.seg 03001627 03624134/points/debbbf239d59d8724662dc124dd336ed.pts 03624134/expert_verified/points_label/debbbf239d59d8724662dc124dd336ed.seg 03624134 04379243/points/5b51e63726f21bb6a75d03186a0409e2.pts 04379243/expert_verified/points_label/5b51e63726f21bb6a75d03186a0409e2.seg 04379243 02691156/points/b59a7cab8e95f6eaf3a7414a84b5637.pts 02691156/expert_verified/points_label/b59a7cab8e95f6eaf3a7414a84b5637.seg 02691156 03001627/points/52c32b187590e8f3bba5aaac798c64af.pts 03001627/expert_verified/points_label/52c32b187590e8f3bba5aaac798c64af.seg 03001627 03001627/points/1c173d970e21e9a8be95ff480950e9ef.pts 03001627/expert_verified/points_label/1c173d970e21e9a8be95ff480950e9ef.seg 03001627 03624134/points/7238d0009faeacb5fd770de1635caa0.pts 03624134/expert_verified/points_label/7238d0009faeacb5fd770de1635caa0.seg 03624134 04379243/points/cc554812025dc498e7ed5b5b11f935c9.pts 04379243/expert_verified/points_label/cc554812025dc498e7ed5b5b11f935c9.seg 04379243 04379243/points/fff492e352c8cb336240c88cd4684446.pts 04379243/expert_verified/points_label/fff492e352c8cb336240c88cd4684446.seg 04379243 03636649/points/e0a2948797cc33b2e19a0cc107ada7cd.pts 03636649/expert_verified/points_label/e0a2948797cc33b2e19a0cc107ada7cd.seg 03636649 03636649/points/fe02f6594ed8b96ae85a3dc26b76b2ae.pts 03636649/expert_verified/points_label/fe02f6594ed8b96ae85a3dc26b76b2ae.seg 03636649 04379243/points/d4a7a1dc0f1a51986f15d61c214769af.pts 04379243/expert_verified/points_label/d4a7a1dc0f1a51986f15d61c214769af.seg 04379243 03624134/points/3dbda789bc59a5f99246ea0301684d80.pts 03624134/expert_verified/points_label/3dbda789bc59a5f99246ea0301684d80.seg 03624134 04379243/points/b82e068c2c18cd67b09f0ca9c143fdfd.pts 04379243/expert_verified/points_label/b82e068c2c18cd67b09f0ca9c143fdfd.seg 04379243 03001627/points/b360f2264526521f1dee989d1177ef4e.pts 03001627/expert_verified/points_label/b360f2264526521f1dee989d1177ef4e.seg 03001627 02691156/points/8ff8f3c845e7ae8443afdb9c81ff2967.pts 02691156/expert_verified/points_label/8ff8f3c845e7ae8443afdb9c81ff2967.seg 02691156 03001627/points/ea87765cf9dbe2fe55f46d55537192b6.pts 03001627/expert_verified/points_label/ea87765cf9dbe2fe55f46d55537192b6.seg 03001627 03001627/points/df23ca11080bb439676c272956dad3c2.pts 03001627/expert_verified/points_label/df23ca11080bb439676c272956dad3c2.seg 03001627 03790512/points/a3dfeae5bced3533b37378f3c85478b4.pts 03790512/expert_verified/points_label/a3dfeae5bced3533b37378f3c85478b4.seg 03790512 04379243/points/9af7a071bbd432baa5526f91aecc0c37.pts 04379243/expert_verified/points_label/9af7a071bbd432baa5526f91aecc0c37.seg 04379243 03001627/points/a8b5f5b6bf0cb2d6876b399a99a15c0f.pts 03001627/expert_verified/points_label/a8b5f5b6bf0cb2d6876b399a99a15c0f.seg 03001627 03001627/points/c7e590c0390e8d5debe67d9b32c3ddf8.pts 03001627/expert_verified/points_label/c7e590c0390e8d5debe67d9b32c3ddf8.seg 03001627 03790512/points/4f30742005b7c20e883158c0007ed9ba.pts 03790512/expert_verified/points_label/4f30742005b7c20e883158c0007ed9ba.seg 03790512 04379243/points/40b632472f8e69a7664b3b9b23ddfcbc.pts 04379243/expert_verified/points_label/40b632472f8e69a7664b3b9b23ddfcbc.seg 04379243 03467517/points/d71c17b4d1ffa131f10a27cbb87f3a5.pts 03467517/expert_verified/points_label/d71c17b4d1ffa131f10a27cbb87f3a5.seg 03467517 04379243/points/f563e9cd92a0dbe5a07b1c1d0ca9cf45.pts 04379243/expert_verified/points_label/f563e9cd92a0dbe5a07b1c1d0ca9cf45.seg 04379243 03797390/points/1a97f3c83016abca21d0de04f408950f.pts 03797390/expert_verified/points_label/1a97f3c83016abca21d0de04f408950f.seg 03797390 04379243/points/c3135e3b21b42e132449009b96f8a6ed.pts 04379243/expert_verified/points_label/c3135e3b21b42e132449009b96f8a6ed.seg 04379243 03636649/points/89b168160388c29da996f5a90dae9cac.pts 03636649/expert_verified/points_label/89b168160388c29da996f5a90dae9cac.seg 03636649 02958343/points/8bbbfdbec9251733ace5721ccacba16.pts 02958343/expert_verified/points_label/8bbbfdbec9251733ace5721ccacba16.seg 02958343 04379243/points/db5a895ae7358c97b66213207f46bee7.pts 04379243/expert_verified/points_label/db5a895ae7358c97b66213207f46bee7.seg 04379243 03001627/points/6a28919186eb55ecf69d0cf4fdc89b12.pts 03001627/expert_verified/points_label/6a28919186eb55ecf69d0cf4fdc89b12.seg 03001627 04379243/points/e7169243daef074dc82dc2efb3363de1.pts 04379243/expert_verified/points_label/e7169243daef074dc82dc2efb3363de1.seg 04379243 03467517/points/4ae5a491c3ffb473462c6cdd250c26bb.pts 03467517/expert_verified/points_label/4ae5a491c3ffb473462c6cdd250c26bb.seg 03467517 04379243/points/e1a8e9e2059f4792fbb8cbddab1c2002.pts 04379243/expert_verified/points_label/e1a8e9e2059f4792fbb8cbddab1c2002.seg 04379243 03467517/points/364f85832427992343820c03f9f59458.pts 03467517/expert_verified/points_label/364f85832427992343820c03f9f59458.seg 03467517 02958343/points/4822076e48b366371f0d59cde6139796.pts 02958343/expert_verified/points_label/4822076e48b366371f0d59cde6139796.seg 02958343 03636649/points/d34a10201a5448a253cf897b7fc1d12.pts 03636649/expert_verified/points_label/d34a10201a5448a253cf897b7fc1d12.seg 03636649 03467517/points/77095861248c816693f0194265a9746c.pts 03467517/expert_verified/points_label/77095861248c816693f0194265a9746c.seg 03467517 04379243/points/dacde6546ca2e07f66dc6ea1ac82d91f.pts 04379243/expert_verified/points_label/dacde6546ca2e07f66dc6ea1ac82d91f.seg 04379243 03636649/points/670ad2964ad5a98c9f1a71e46bbde97c.pts 03636649/expert_verified/points_label/670ad2964ad5a98c9f1a71e46bbde97c.seg 03636649 02691156/points/77c9fd0f0c6b0e9fca8607f540cc62ba.pts 02691156/expert_verified/points_label/77c9fd0f0c6b0e9fca8607f540cc62ba.seg 02691156 03001627/points/5fc6b04623ae6a9963ed57e35c972b4b.pts 03001627/expert_verified/points_label/5fc6b04623ae6a9963ed57e35c972b4b.seg 03001627 02958343/points/f18093ac0242d439f500cc506a763c18.pts 02958343/expert_verified/points_label/f18093ac0242d439f500cc506a763c18.seg 02958343 03001627/points/2fed64c67552aa689c1db271ad9472a7.pts 03001627/expert_verified/points_label/2fed64c67552aa689c1db271ad9472a7.seg 03001627 03001627/points/bf7e8e0dc4f4038cc2567be77cb7ab45.pts 03001627/expert_verified/points_label/bf7e8e0dc4f4038cc2567be77cb7ab45.seg 03001627 04379243/points/690e073a4000c7ae540e292bd26f307a.pts 04379243/expert_verified/points_label/690e073a4000c7ae540e292bd26f307a.seg 04379243 03467517/points/5fc56e6d220d775e381b7fbf79296afb.pts 03467517/expert_verified/points_label/5fc56e6d220d775e381b7fbf79296afb.seg 03467517 04379243/points/8af3fd230ea7ac6518101790733ed6b2.pts 04379243/expert_verified/points_label/8af3fd230ea7ac6518101790733ed6b2.seg 04379243 03636649/points/80436dff2a30721849655ac7c771b113.pts 03636649/expert_verified/points_label/80436dff2a30721849655ac7c771b113.seg 03636649 03790512/points/b767982d38b5171e429f1c522640e6f0.pts 03790512/expert_verified/points_label/b767982d38b5171e429f1c522640e6f0.seg 03790512 03001627/points/40e6fb27aeb9c9ab44f999802029a79a.pts 03001627/expert_verified/points_label/40e6fb27aeb9c9ab44f999802029a79a.seg 03001627 04379243/points/59e1afdec89de9442b70eac6546e93fd.pts 04379243/expert_verified/points_label/59e1afdec89de9442b70eac6546e93fd.seg 04379243 02691156/points/43d8125d940bb2ae850f318836ee7512.pts 02691156/expert_verified/points_label/43d8125d940bb2ae850f318836ee7512.seg 02691156 02691156/points/cbc9d6ae9d22fcc57f3efc94c2d31dc5.pts 02691156/expert_verified/points_label/cbc9d6ae9d22fcc57f3efc94c2d31dc5.seg 02691156 04379243/points/f585560965413925d706ecb3379aa341.pts 04379243/expert_verified/points_label/f585560965413925d706ecb3379aa341.seg 04379243 04379243/points/adee49b8f5251efeaade78cbbf8fad3b.pts 04379243/expert_verified/points_label/adee49b8f5251efeaade78cbbf8fad3b.seg 04379243 03261776/points/ccf84f2cbd3ebeb247ba1bc05b9a0f37.pts 03261776/expert_verified/points_label/ccf84f2cbd3ebeb247ba1bc05b9a0f37.seg 03261776 03001627/points/2343e2c4fa69f33a2ff834514c92e8fd.pts 03001627/expert_verified/points_label/2343e2c4fa69f33a2ff834514c92e8fd.seg 03001627 03636649/points/1d89da4ac1538ada9c949ae6274aa016.pts 03636649/expert_verified/points_label/1d89da4ac1538ada9c949ae6274aa016.seg 03636649 03001627/points/51e14c516e45ec3b18ed59365c9648a7.pts 03001627/expert_verified/points_label/51e14c516e45ec3b18ed59365c9648a7.seg 03001627 03001627/points/1e276a016b664e424d678187b8261d95.pts 03001627/expert_verified/points_label/1e276a016b664e424d678187b8261d95.seg 03001627 03636649/points/4deef34d95367b58c0d95250e682f6ee.pts 03636649/expert_verified/points_label/4deef34d95367b58c0d95250e682f6ee.seg 03636649 03001627/points/5d3eff6a1b9a119da011ccf7cbabf68e.pts 03001627/expert_verified/points_label/5d3eff6a1b9a119da011ccf7cbabf68e.seg 03001627 04379243/points/9afaf5ab87a889f67acae9ce58893de5.pts 04379243/expert_verified/points_label/9afaf5ab87a889f67acae9ce58893de5.seg 04379243 04379243/points/5431993203dfcf797ec12e029bc725db.pts 04379243/expert_verified/points_label/5431993203dfcf797ec12e029bc725db.seg 04379243 03001627/points/6a01eed3a575987211e48e4bcdc4a2a3.pts 03001627/expert_verified/points_label/6a01eed3a575987211e48e4bcdc4a2a3.seg 03001627 02958343/points/a8f2c3adc0671c15c64e95fc6a597455.pts 02958343/expert_verified/points_label/a8f2c3adc0671c15c64e95fc6a597455.seg 02958343 04379243/points/f60960ae4dc8e293c8ce22a41ea48e48.pts 04379243/expert_verified/points_label/f60960ae4dc8e293c8ce22a41ea48e48.seg 04379243 03624134/points/3a4f0118a57093cbf7c4ed45ce654123.pts 03624134/expert_verified/points_label/3a4f0118a57093cbf7c4ed45ce654123.seg 03624134 03636649/points/52783aa89adf06f3250c527721570ba0.pts 03636649/expert_verified/points_label/52783aa89adf06f3250c527721570ba0.seg 03636649 03001627/points/b13a4df698183bf9afb6676a5cd782b6.pts 03001627/expert_verified/points_label/b13a4df698183bf9afb6676a5cd782b6.seg 03001627 03636649/points/26f725bb6578936cd247b9308cd5c441.pts 03636649/expert_verified/points_label/26f725bb6578936cd247b9308cd5c441.seg 03636649 03001627/points/6df1ecffaa0abdbf327289c00b6dc9ca.pts 03001627/expert_verified/points_label/6df1ecffaa0abdbf327289c00b6dc9ca.seg 03001627 04379243/points/3c475d9f0433a7eaad2650d014e970a5.pts 04379243/expert_verified/points_label/3c475d9f0433a7eaad2650d014e970a5.seg 04379243 02958343/points/fee1c13922c07e8711b978ff9450f61b.pts 02958343/expert_verified/points_label/fee1c13922c07e8711b978ff9450f61b.seg 02958343 04379243/points/6bc941dbd290c7f21acdac000802e11c.pts 04379243/expert_verified/points_label/6bc941dbd290c7f21acdac000802e11c.seg 04379243 02958343/points/6333b9c777384ad14362be10a3fc8255.pts 02958343/expert_verified/points_label/6333b9c777384ad14362be10a3fc8255.seg 02958343 03001627/points/9a35f15e924e19db637adadafee6f182.pts 03001627/expert_verified/points_label/9a35f15e924e19db637adadafee6f182.seg 03001627 03001627/points/b0531a0d44fc22144224ee0743294f79.pts 03001627/expert_verified/points_label/b0531a0d44fc22144224ee0743294f79.seg 03001627 03636649/points/913ff6452d0ea43c9d62807daf4a2134.pts 03636649/expert_verified/points_label/913ff6452d0ea43c9d62807daf4a2134.seg 03636649 03467517/points/e45f323ce7ecab8393f0194265a9746c.pts 03467517/expert_verified/points_label/e45f323ce7ecab8393f0194265a9746c.seg 03467517 02691156/points/aa2af754642256c08699933784576e73.pts 02691156/expert_verified/points_label/aa2af754642256c08699933784576e73.seg 02691156 04379243/points/75b308ba45762ad499e8bf807e902261.pts 04379243/expert_verified/points_label/75b308ba45762ad499e8bf807e902261.seg 04379243 03001627/points/3622d983fd6d7b98e3a73d090627e9ba.pts 03001627/expert_verified/points_label/3622d983fd6d7b98e3a73d090627e9ba.seg 03001627 04225987/points/db4c8bf323465e4c537d393009a79347.pts 04225987/expert_verified/points_label/db4c8bf323465e4c537d393009a79347.seg 04225987 04379243/points/132bfde1fabe9ab771a782a4379556c7.pts 04379243/expert_verified/points_label/132bfde1fabe9ab771a782a4379556c7.seg 04379243 03001627/points/3dc8243b17bc790620768660cf080d12.pts 03001627/expert_verified/points_label/3dc8243b17bc790620768660cf080d12.seg 03001627 04379243/points/ccb96ea5f047c97f278d386bfa54545.pts 04379243/expert_verified/points_label/ccb96ea5f047c97f278d386bfa54545.seg 04379243 04379243/points/14ae5631e7dfa10430bbd4cddd04c77b.pts 04379243/expert_verified/points_label/14ae5631e7dfa10430bbd4cddd04c77b.seg 04379243 04379243/points/78a81cbd2a5720d93a938fdd57fac3b4.pts 04379243/expert_verified/points_label/78a81cbd2a5720d93a938fdd57fac3b4.seg 04379243 04379243/points/307bdd2a06137694a10ff7fd5e43a633.pts 04379243/expert_verified/points_label/307bdd2a06137694a10ff7fd5e43a633.seg 04379243 03001627/points/f3573756e64259f2b29d280b4e59c527.pts 03001627/expert_verified/points_label/f3573756e64259f2b29d280b4e59c527.seg 03001627 04379243/points/1815c6431b06dfb4f008d8a3590fb522.pts 04379243/expert_verified/points_label/1815c6431b06dfb4f008d8a3590fb522.seg 04379243 04379243/points/7fda06ada2d897baadab4c26397edfab.pts 04379243/expert_verified/points_label/7fda06ada2d897baadab4c26397edfab.seg 04379243 04379243/points/86b48365b2bd587e61830bc1b4d6c5ea.pts 04379243/expert_verified/points_label/86b48365b2bd587e61830bc1b4d6c5ea.seg 04379243 03948459/points/6aae44dd39fb9476f059c10da31213ea.pts 03948459/expert_verified/points_label/6aae44dd39fb9476f059c10da31213ea.seg 03948459 04379243/points/424c77a1f39ac41620dd2dd4d7d7656c.pts 04379243/expert_verified/points_label/424c77a1f39ac41620dd2dd4d7d7656c.seg 04379243 03001627/points/8778c23fd21bdebf8a80d99ff4e76c20.pts 03001627/expert_verified/points_label/8778c23fd21bdebf8a80d99ff4e76c20.seg 03001627 03001627/points/257deb231ce652169f2349486c570dd4.pts 03001627/expert_verified/points_label/257deb231ce652169f2349486c570dd4.seg 03001627 03642806/points/e5559cd005d5c4942a7b0c74c5f22fc4.pts 03642806/expert_verified/points_label/e5559cd005d5c4942a7b0c74c5f22fc4.seg 03642806 03001627/points/986e49bd8314d7424addf6a5f8726274.pts 03001627/expert_verified/points_label/986e49bd8314d7424addf6a5f8726274.seg 03001627 04379243/points/b3fc5247186936f1dcfcef693e7ec696.pts 04379243/expert_verified/points_label/b3fc5247186936f1dcfcef693e7ec696.seg 04379243 02691156/points/da9d111e1175d318bbf3143b1cb6076a.pts 02691156/expert_verified/points_label/da9d111e1175d318bbf3143b1cb6076a.seg 02691156 04379243/points/54b26954e478b1a34ea8d5f5f27d7ce3.pts 04379243/expert_verified/points_label/54b26954e478b1a34ea8d5f5f27d7ce3.seg 04379243 03001627/points/2d44744a7ea0bf724b3c42e318f3affc.pts 03001627/expert_verified/points_label/2d44744a7ea0bf724b3c42e318f3affc.seg 03001627 04379243/points/9dd63148e5b0a4f79eaa55bb236fb6e1.pts 04379243/expert_verified/points_label/9dd63148e5b0a4f79eaa55bb236fb6e1.seg 04379243 04379243/points/6ab7ebf9b94176456f1e07a56c129dfc.pts 04379243/expert_verified/points_label/6ab7ebf9b94176456f1e07a56c129dfc.seg 04379243 03001627/points/6aaa9bd6e835eb0f9b9f2eb77f5e247e.pts 03001627/expert_verified/points_label/6aaa9bd6e835eb0f9b9f2eb77f5e247e.seg 03001627 03636649/points/34020466b4342812218c9f1216abefd.pts 03636649/expert_verified/points_label/34020466b4342812218c9f1216abefd.seg 03636649 03001627/points/df7735e2bce09a511f98c0761af40e04.pts 03001627/expert_verified/points_label/df7735e2bce09a511f98c0761af40e04.seg 03001627 03636649/points/1d963d5c54613202b0aa15078ea6f391.pts 03636649/expert_verified/points_label/1d963d5c54613202b0aa15078ea6f391.seg 03636649 03636649/points/5a9e0dd068e2436bd7ebac63aa51083.pts 03636649/expert_verified/points_label/5a9e0dd068e2436bd7ebac63aa51083.seg 03636649 03001627/points/b1f50d8d41a8c53b6197fd390b16d14d.pts 03001627/expert_verified/points_label/b1f50d8d41a8c53b6197fd390b16d14d.seg 03001627 03001627/points/285931af369b12c2ccd42a2d6eea63ed.pts 03001627/expert_verified/points_label/285931af369b12c2ccd42a2d6eea63ed.seg 03001627 03636649/points/69429d8ffb5009a82060e7309fc3fc6.pts 03636649/expert_verified/points_label/69429d8ffb5009a82060e7309fc3fc6.seg 03636649 04379243/points/63b53646b3562677d395837145ded71.pts 04379243/expert_verified/points_label/63b53646b3562677d395837145ded71.seg 04379243 03001627/points/ee5ee3f6759aabacf2f43e6f841bd32b.pts 03001627/expert_verified/points_label/ee5ee3f6759aabacf2f43e6f841bd32b.seg 03001627 02691156/points/bdfbf1c555dacd9d325212819caa597d.pts 02691156/expert_verified/points_label/bdfbf1c555dacd9d325212819caa597d.seg 02691156 04379243/points/9f321f05a7808719ab610b0c94236463.pts 04379243/expert_verified/points_label/9f321f05a7808719ab610b0c94236463.seg 04379243 03624134/points/fb1f385d487d13d7aa0079d6fb0f853c.pts 03624134/expert_verified/points_label/fb1f385d487d13d7aa0079d6fb0f853c.seg 03624134 04379243/points/109738784a0a6129a02c88fe01f2b9c1.pts 04379243/expert_verified/points_label/109738784a0a6129a02c88fe01f2b9c1.seg 04379243 03467517/points/65e3bdc247b3ce3d4de904d1abbce016.pts 03467517/expert_verified/points_label/65e3bdc247b3ce3d4de904d1abbce016.seg 03467517 02691156/points/94ce3a5ad2576e73a5cac89017eae8d1.pts 02691156/expert_verified/points_label/94ce3a5ad2576e73a5cac89017eae8d1.seg 02691156 03001627/points/80fab0c55a60abb7dafb0be26f6b45d5.pts 03001627/expert_verified/points_label/80fab0c55a60abb7dafb0be26f6b45d5.seg 03001627 04379243/points/e6ee101d3cb13bdd16a2b5862518c93.pts 04379243/expert_verified/points_label/e6ee101d3cb13bdd16a2b5862518c93.seg 04379243 04379243/points/6f2ffe8c014a6a458af30108ea9ccb6c.pts 04379243/expert_verified/points_label/6f2ffe8c014a6a458af30108ea9ccb6c.seg 04379243 02958343/points/504793ed2da6cf7eba3e2415e22cd45c.pts 02958343/expert_verified/points_label/504793ed2da6cf7eba3e2415e22cd45c.seg 02958343 03467517/points/9e26dcbac33f056c343b0b12983b9982.pts 03467517/expert_verified/points_label/9e26dcbac33f056c343b0b12983b9982.seg 03467517 03467517/points/a92cd0b5d559075daa9518d76daaca23.pts 03467517/expert_verified/points_label/a92cd0b5d559075daa9518d76daaca23.seg 03467517 03636649/points/b6989c99bba1226539b3360f500ac52a.pts 03636649/expert_verified/points_label/b6989c99bba1226539b3360f500ac52a.seg 03636649 03624134/points/cc38f97557029b2a2b5fd8277662be97.pts 03624134/expert_verified/points_label/cc38f97557029b2a2b5fd8277662be97.seg 03624134 03790512/points/41cc9674e700c3fdb37378f3c85478b4.pts 03790512/expert_verified/points_label/41cc9674e700c3fdb37378f3c85478b4.seg 03790512 03001627/points/56b171b1f1521d27291d12adef12641b.pts 03001627/expert_verified/points_label/56b171b1f1521d27291d12adef12641b.seg 03001627 03636649/points/ddc2d39dac6e84506c5b8009db95f66f.pts 03636649/expert_verified/points_label/ddc2d39dac6e84506c5b8009db95f66f.seg 03636649 02691156/points/edc185566c1df89c35fc197bbabcd5bd.pts 02691156/expert_verified/points_label/edc185566c1df89c35fc197bbabcd5bd.seg 02691156 04379243/points/fb5e8a6361262c26acf7920879052e93.pts 04379243/expert_verified/points_label/fb5e8a6361262c26acf7920879052e93.seg 04379243 04379243/points/8862cddf90fddb3119fb4103277a6b93.pts 04379243/expert_verified/points_label/8862cddf90fddb3119fb4103277a6b93.seg 04379243 02691156/points/d5a94c9f09d238c4c3a35cee92bb95b.pts 02691156/expert_verified/points_label/d5a94c9f09d238c4c3a35cee92bb95b.seg 02691156 03636649/points/1682d4404196cf127588e2ca59b15f8.pts 03636649/expert_verified/points_label/1682d4404196cf127588e2ca59b15f8.seg 03636649 04379243/points/2f33abdfe147813e44949d7685cb63ea.pts 04379243/expert_verified/points_label/2f33abdfe147813e44949d7685cb63ea.seg 04379243 03001627/points/e158f7ba6828db5c654ea6737b0d3597.pts 03001627/expert_verified/points_label/e158f7ba6828db5c654ea6737b0d3597.seg 03001627 04379243/points/564474f25a4400c5dc20930e6fc85682.pts 04379243/expert_verified/points_label/564474f25a4400c5dc20930e6fc85682.seg 04379243 04379243/points/eb379b2b95e76502e258d1c3e7302e7b.pts 04379243/expert_verified/points_label/eb379b2b95e76502e258d1c3e7302e7b.seg 04379243 03001627/points/3a1b54325b3565e72ca4b544d68c52.pts 03001627/expert_verified/points_label/3a1b54325b3565e72ca4b544d68c52.seg 03001627 04225987/points/393ca71bd734f3071082f2ea630bf69e.pts 04225987/expert_verified/points_label/393ca71bd734f3071082f2ea630bf69e.seg 04225987 03636649/points/bd1cbcb990375022b45fed2806c331ab.pts 03636649/expert_verified/points_label/bd1cbcb990375022b45fed2806c331ab.seg 03636649 03001627/points/6a9dce6566cd61652b339ec555ba3bfc.pts 03001627/expert_verified/points_label/6a9dce6566cd61652b339ec555ba3bfc.seg 03001627 02691156/points/94379090010cd6bb874c9ce092a813ef.pts 02691156/expert_verified/points_label/94379090010cd6bb874c9ce092a813ef.seg 02691156 02773838/points/d3bd250ca3cb8e29976855a35549333.pts 02773838/expert_verified/points_label/d3bd250ca3cb8e29976855a35549333.seg 02773838 03001627/points/36cb782fbc164ac312591a3ac05fadf1.pts 03001627/expert_verified/points_label/36cb782fbc164ac312591a3ac05fadf1.seg 03001627 03642806/points/2211a40cc77a085362c091e763f81d3.pts 03642806/expert_verified/points_label/2211a40cc77a085362c091e763f81d3.seg 03642806 04379243/points/5cbd726c3ffd8fc49b458816be7a3962.pts 04379243/expert_verified/points_label/5cbd726c3ffd8fc49b458816be7a3962.seg 04379243 02691156/points/72aee7d0e998a68aca8607f540cc62ba.pts 02691156/expert_verified/points_label/72aee7d0e998a68aca8607f540cc62ba.seg 02691156 04379243/points/1c3310f4c05ce1f6a192483aa282f8e5.pts 04379243/expert_verified/points_label/1c3310f4c05ce1f6a192483aa282f8e5.seg 04379243 04379243/points/4ced745f960f7439b91767277279ac70.pts 04379243/expert_verified/points_label/4ced745f960f7439b91767277279ac70.seg 04379243 03642806/points/8d70fb6adc63e21eb7e0383b9609fa5.pts 03642806/expert_verified/points_label/8d70fb6adc63e21eb7e0383b9609fa5.seg 03642806 03001627/points/2bd6800d64c01d677721fafb59ea099.pts 03001627/expert_verified/points_label/2bd6800d64c01d677721fafb59ea099.seg 03001627 03467517/points/1abe78447898821e93f0194265a9746c.pts 03467517/expert_verified/points_label/1abe78447898821e93f0194265a9746c.seg 03467517 02691156/points/9bf3c126d5918c41f5c7319b71bdce6e.pts 02691156/expert_verified/points_label/9bf3c126d5918c41f5c7319b71bdce6e.seg 02691156 03642806/points/1312ea502b4e9b51701c1f58e22b85e8.pts 03642806/expert_verified/points_label/1312ea502b4e9b51701c1f58e22b85e8.seg 03642806 04379243/points/a9cc8112fb8c4ed5dfd21203bf8b4b46.pts 04379243/expert_verified/points_label/a9cc8112fb8c4ed5dfd21203bf8b4b46.seg 04379243 03642806/points/62b25a5e3119b8409023147b38c03c9f.pts 03642806/expert_verified/points_label/62b25a5e3119b8409023147b38c03c9f.seg 03642806 04379243/points/a4fcd8afe8b6de585beaf00da5b709c2.pts 04379243/expert_verified/points_label/a4fcd8afe8b6de585beaf00da5b709c2.seg 04379243 03636649/points/907fd296708ae71dd5fab5deb286066.pts 03636649/expert_verified/points_label/907fd296708ae71dd5fab5deb286066.seg 03636649 04379243/points/c5ae96124c15c734e6c5cd45aa112726.pts 04379243/expert_verified/points_label/c5ae96124c15c734e6c5cd45aa112726.seg 04379243 03642806/points/ef6d43add46d0cae4e07b09c086cc5c4.pts 03642806/expert_verified/points_label/ef6d43add46d0cae4e07b09c086cc5c4.seg 03642806 04379243/points/8d07df2bf706cda58c5591114064d173.pts 04379243/expert_verified/points_label/8d07df2bf706cda58c5591114064d173.seg 04379243 02958343/points/5316fab78a6732f0428df271ebc70bc0.pts 02958343/expert_verified/points_label/5316fab78a6732f0428df271ebc70bc0.seg 02958343 03467517/points/7946e354e342f560c5a468097fc791e4.pts 03467517/expert_verified/points_label/7946e354e342f560c5a468097fc791e4.seg 03467517 03467517/points/d3684d071dcb6bffd3193ed047bef161.pts 03467517/expert_verified/points_label/d3684d071dcb6bffd3193ed047bef161.seg 03467517 04379243/points/33b081062b2195e71771ee930e861b13.pts 04379243/expert_verified/points_label/33b081062b2195e71771ee930e861b13.seg 04379243 02958343/points/511962626501e4abf500cc506a763c18.pts 02958343/expert_verified/points_label/511962626501e4abf500cc506a763c18.seg 02958343 03797390/points/c82b9f1b98f044fc15cf6e5ad80f2da.pts 03797390/expert_verified/points_label/c82b9f1b98f044fc15cf6e5ad80f2da.seg 03797390 04379243/points/49f625856c796254d249abd69334079c.pts 04379243/expert_verified/points_label/49f625856c796254d249abd69334079c.seg 04379243 03001627/points/ca4900c42b8016ef8397cd720acaa508.pts 03001627/expert_verified/points_label/ca4900c42b8016ef8397cd720acaa508.seg 03001627 03636649/points/31a15957bd4f32f87eedf2c7d21f7cfa.pts 03636649/expert_verified/points_label/31a15957bd4f32f87eedf2c7d21f7cfa.seg 03636649 03797390/points/928a383f79698c3fb6d9bc28c8d8a2c4.pts 03797390/expert_verified/points_label/928a383f79698c3fb6d9bc28c8d8a2c4.seg 03797390 04379243/points/17e5a64889ca085fa5526f91aecc0c37.pts 04379243/expert_verified/points_label/17e5a64889ca085fa5526f91aecc0c37.seg 04379243 02958343/points/cbe2dc469c47bb80425b2c354eccabaf.pts 02958343/expert_verified/points_label/cbe2dc469c47bb80425b2c354eccabaf.seg 02958343 03001627/points/19c8189116dd7cd3e95c611687989498.pts 03001627/expert_verified/points_label/19c8189116dd7cd3e95c611687989498.seg 03001627 03636649/points/7f518fe982aae1b5940c8a2639c8747.pts 03636649/expert_verified/points_label/7f518fe982aae1b5940c8a2639c8747.seg 03636649 03636649/points/7b1fef0071908d4bd93768e7b9b1eabf.pts 03636649/expert_verified/points_label/7b1fef0071908d4bd93768e7b9b1eabf.seg 03636649 03001627/points/475e2c8f7a2c1bbd9acf9a86c283d1a2.pts 03001627/expert_verified/points_label/475e2c8f7a2c1bbd9acf9a86c283d1a2.seg 03001627 03467517/points/5c805aca7aa8bdd3ac61a2f8346a8f.pts 03467517/expert_verified/points_label/5c805aca7aa8bdd3ac61a2f8346a8f.seg 03467517 03790512/points/8032295bd3851d75468bac13e007a6e9.pts 03790512/expert_verified/points_label/8032295bd3851d75468bac13e007a6e9.seg 03790512 02691156/points/3e0561d70c7fd4f51c6e4e20f2b76086.pts 02691156/expert_verified/points_label/3e0561d70c7fd4f51c6e4e20f2b76086.seg 02691156 02691156/points/e5610bbacaf098508b96ae1a0a8b84ec.pts 02691156/expert_verified/points_label/e5610bbacaf098508b96ae1a0a8b84ec.seg 02691156 03467517/points/97e8ee1b6df404bd57700c05b1862d8.pts 03467517/expert_verified/points_label/97e8ee1b6df404bd57700c05b1862d8.seg 03467517 03636649/points/981b55897cee64403c8d0fdfb1cc2535.pts 03636649/expert_verified/points_label/981b55897cee64403c8d0fdfb1cc2535.seg 03636649 04379243/points/204d9ecc196990ebe8479ad2eabcbab4.pts 04379243/expert_verified/points_label/204d9ecc196990ebe8479ad2eabcbab4.seg 04379243 04379243/points/9d039675f4d51869f3edd695842c6d58.pts 04379243/expert_verified/points_label/9d039675f4d51869f3edd695842c6d58.seg 04379243 03467517/points/cb5b2e3f499e4fdecc571cd3cf8f17a1.pts 03467517/expert_verified/points_label/cb5b2e3f499e4fdecc571cd3cf8f17a1.seg 03467517 04379243/points/5243b5491a4f8a16a2b5862518c93.pts 04379243/expert_verified/points_label/5243b5491a4f8a16a2b5862518c93.seg 04379243 04379243/points/efbf0d75648b7c7d5792b99b8245d225.pts 04379243/expert_verified/points_label/efbf0d75648b7c7d5792b99b8245d225.seg 04379243 03001627/points/c8265e04c94bcb5a1346e336f65f96f6.pts 03001627/expert_verified/points_label/c8265e04c94bcb5a1346e336f65f96f6.seg 03001627 02958343/points/94cfcfb74e246f938acb0ff76f4aec7d.pts 02958343/expert_verified/points_label/94cfcfb74e246f938acb0ff76f4aec7d.seg 02958343 03467517/points/a0b6f040538d26e3ac61a2f8346a8f.pts 03467517/expert_verified/points_label/a0b6f040538d26e3ac61a2f8346a8f.seg 03467517 03001627/points/70f1f85d47c970bb78dd615a59de5f05.pts 03001627/expert_verified/points_label/70f1f85d47c970bb78dd615a59de5f05.seg 03001627 04379243/points/f4976e80b8533bcf85518f8659f21d56.pts 04379243/expert_verified/points_label/f4976e80b8533bcf85518f8659f21d56.seg 04379243 03636649/points/9fdaafde365beafc37f7ce56c66316ea.pts 03636649/expert_verified/points_label/9fdaafde365beafc37f7ce56c66316ea.seg 03636649 03467517/points/22033c6d7e5a90f193f0194265a9746c.pts 03467517/expert_verified/points_label/22033c6d7e5a90f193f0194265a9746c.seg 03467517 02691156/points/c1b5dc92221bcdad5fc84bf2b9ef981.pts 02691156/expert_verified/points_label/c1b5dc92221bcdad5fc84bf2b9ef981.seg 02691156 04379243/points/79d0985603f7ff3be6c5cd45aa112726.pts 04379243/expert_verified/points_label/79d0985603f7ff3be6c5cd45aa112726.seg 04379243 03467517/points/5d6c1516b83dec8663e148e250c0340d.pts 03467517/expert_verified/points_label/5d6c1516b83dec8663e148e250c0340d.seg 03467517 04379243/points/79c5df613523a462d42b9650f19dd425.pts 04379243/expert_verified/points_label/79c5df613523a462d42b9650f19dd425.seg 04379243 03001627/points/f19e8da9d8f369c531e63f1270e2b445.pts 03001627/expert_verified/points_label/f19e8da9d8f369c531e63f1270e2b445.seg 03001627 03001627/points/9a711bb7070ae88de948e3d64826c640.pts 03001627/expert_verified/points_label/9a711bb7070ae88de948e3d64826c640.seg 03001627 03467517/points/2adbf6c3f8f2d9ca7fe36b1f0a632ed8.pts 03467517/expert_verified/points_label/2adbf6c3f8f2d9ca7fe36b1f0a632ed8.seg 03467517 03001627/points/837ba605a4ab4a4f19fb4103277a6b93.pts 03001627/expert_verified/points_label/837ba605a4ab4a4f19fb4103277a6b93.seg 03001627 03001627/points/807f08096308af5e28c0cecb7de2397a.pts 03001627/expert_verified/points_label/807f08096308af5e28c0cecb7de2397a.seg 03001627 03467517/points/275c4f98ef07f2b393f0194265a9746c.pts 03467517/expert_verified/points_label/275c4f98ef07f2b393f0194265a9746c.seg 03467517 04379243/points/57afaabf994feb305512673aa47c7e3d.pts 04379243/expert_verified/points_label/57afaabf994feb305512673aa47c7e3d.seg 04379243 03001627/points/d9156f5552178de2713decb1a0563b12.pts 03001627/expert_verified/points_label/d9156f5552178de2713decb1a0563b12.seg 03001627 03948459/points/fe62130ce6fcd9b77754fed890b42399.pts 03948459/expert_verified/points_label/fe62130ce6fcd9b77754fed890b42399.seg 03948459 03261776/points/1757fe64e76a9630fc176230c2f2d294.pts 03261776/expert_verified/points_label/1757fe64e76a9630fc176230c2f2d294.seg 03261776 03790512/points/3fd1bff496b369f71765540024eb9fef.pts 03790512/expert_verified/points_label/3fd1bff496b369f71765540024eb9fef.seg 03790512 02958343/points/a6d494af391a97686436916a86a90ed7.pts 02958343/expert_verified/points_label/a6d494af391a97686436916a86a90ed7.seg 02958343 04099429/points/59389aac7b1ea9b09b28f5f9cf8893b5.pts 04099429/expert_verified/points_label/59389aac7b1ea9b09b28f5f9cf8893b5.seg 04099429 04379243/points/c399ed276ed35cb9a6ce08f0d82ba063.pts 04379243/expert_verified/points_label/c399ed276ed35cb9a6ce08f0d82ba063.seg 04379243 03624134/points/e4f610f36ba3c6f69246ea0301684d80.pts 03624134/expert_verified/points_label/e4f610f36ba3c6f69246ea0301684d80.seg 03624134 03636649/points/90b0f9a1ac2e54ecbc7f58784fda27b5.pts 03636649/expert_verified/points_label/90b0f9a1ac2e54ecbc7f58784fda27b5.seg 03636649 03636649/points/e5e9ff118631c2a3ee088de33038f12a.pts 03636649/expert_verified/points_label/e5e9ff118631c2a3ee088de33038f12a.seg 03636649 04099429/points/4936716925b1cd6428eba1f0b7744e9.pts 04099429/expert_verified/points_label/4936716925b1cd6428eba1f0b7744e9.seg 04099429 04379243/points/6e446bb5adf14b0b6121178eafd002fd.pts 04379243/expert_verified/points_label/6e446bb5adf14b0b6121178eafd002fd.seg 04379243 03001627/points/7ea38c936513f5df3772b104757a4809.pts 03001627/expert_verified/points_label/7ea38c936513f5df3772b104757a4809.seg 03001627 04379243/points/23d68e01b77089ae76ad4f5e7c7020eb.pts 04379243/expert_verified/points_label/23d68e01b77089ae76ad4f5e7c7020eb.seg 04379243 03636649/points/4d6bced89943df73b4edf02c99e16daa.pts 03636649/expert_verified/points_label/4d6bced89943df73b4edf02c99e16daa.seg 03636649 04379243/points/3459eec8eb56fa312bac236fe109e385.pts 04379243/expert_verified/points_label/3459eec8eb56fa312bac236fe109e385.seg 04379243 03261776/points/1a5e2a7cddc8e46aa681aea7976a4565.pts 03261776/expert_verified/points_label/1a5e2a7cddc8e46aa681aea7976a4565.seg 03261776 03001627/points/ed0d65c68a1fa5c485e2f8b1d3a373fe.pts 03001627/expert_verified/points_label/ed0d65c68a1fa5c485e2f8b1d3a373fe.seg 03001627 03636649/points/7b005e23eae2768eb08c032bedc99529.pts 03636649/expert_verified/points_label/7b005e23eae2768eb08c032bedc99529.seg 03636649 04379243/points/3f2e9c14ab1d26a0ebead06af665220.pts 04379243/expert_verified/points_label/3f2e9c14ab1d26a0ebead06af665220.seg 04379243 03001627/points/383ab6330284af461fc4ae93e00c18e5.pts 03001627/expert_verified/points_label/383ab6330284af461fc4ae93e00c18e5.seg 03001627 02691156/points/fc7387d630c84bb9c863ab010b80d9ed.pts 02691156/expert_verified/points_label/fc7387d630c84bb9c863ab010b80d9ed.seg 02691156 04225987/points/344e9402d06bd94031145076011658c5.pts 04225987/expert_verified/points_label/344e9402d06bd94031145076011658c5.seg 04225987 04379243/points/745a2b060d0f692bf4b6538438a0b930.pts 04379243/expert_verified/points_label/745a2b060d0f692bf4b6538438a0b930.seg 04379243 04379243/points/928ea87878a7bbe26cf876b69450cd4e.pts 04379243/expert_verified/points_label/928ea87878a7bbe26cf876b69450cd4e.seg 04379243 03001627/points/5fe56a4a9d5508c3b2373df00b89e5d.pts 03001627/expert_verified/points_label/5fe56a4a9d5508c3b2373df00b89e5d.seg 03001627 02691156/points/6a75658fb8242b9c590874dcd9dc8481.pts 02691156/expert_verified/points_label/6a75658fb8242b9c590874dcd9dc8481.seg 02691156 03948459/points/f377665c5b17d0ce61b636d79e46a7e9.pts 03948459/expert_verified/points_label/f377665c5b17d0ce61b636d79e46a7e9.seg 03948459 03642806/points/ab21f75b97d6b1054f22ce0a3592d5.pts 03642806/expert_verified/points_label/ab21f75b97d6b1054f22ce0a3592d5.seg 03642806 04379243/points/a2baf45f001e118e2c79f7f31759bfa7.pts 04379243/expert_verified/points_label/a2baf45f001e118e2c79f7f31759bfa7.seg 04379243 02691156/points/19ff8fce1658f864ca8607f540cc62ba.pts 02691156/expert_verified/points_label/19ff8fce1658f864ca8607f540cc62ba.seg 02691156 04379243/points/8bb3a7e1cb24fe6febad4f49b26ec52.pts 04379243/expert_verified/points_label/8bb3a7e1cb24fe6febad4f49b26ec52.seg 04379243 04379243/points/dbc5a4d1dc3a6e8271a782a4379556c7.pts 04379243/expert_verified/points_label/dbc5a4d1dc3a6e8271a782a4379556c7.seg 04379243 03001627/points/e6c11fed9469141ace8fba09dd640742.pts 03001627/expert_verified/points_label/e6c11fed9469141ace8fba09dd640742.seg 03001627 03797390/points/f99e19b8c4a729353deb88581ea8417a.pts 03797390/expert_verified/points_label/f99e19b8c4a729353deb88581ea8417a.seg 03797390 03001627/points/d454f99b99248bf337c99625b0c170be.pts 03001627/expert_verified/points_label/d454f99b99248bf337c99625b0c170be.seg 03001627 03636649/points/7c23362b39f318cbb18d6f615cb18bdd.pts 03636649/expert_verified/points_label/7c23362b39f318cbb18d6f615cb18bdd.seg 03636649 03001627/points/d8e2e2a923b372731cf97e154cc62f43.pts 03001627/expert_verified/points_label/d8e2e2a923b372731cf97e154cc62f43.seg 03001627 03642806/points/621882a4afd2a126369873c1090720a1.pts 03642806/expert_verified/points_label/621882a4afd2a126369873c1090720a1.seg 03642806 04379243/points/d5d1e750bb492dd5391e4d6c585a697a.pts 04379243/expert_verified/points_label/d5d1e750bb492dd5391e4d6c585a697a.seg 04379243 03467517/points/42f3172b8770d2fd2200c35bfa7099ee.pts 03467517/expert_verified/points_label/42f3172b8770d2fd2200c35bfa7099ee.seg 03467517 03624134/points/a2288d5f3a44233bc40c6b891c4913bd.pts 03624134/expert_verified/points_label/a2288d5f3a44233bc40c6b891c4913bd.seg 03624134 02691156/points/90612205109d7458e84aab2e1d454e3c.pts 02691156/expert_verified/points_label/90612205109d7458e84aab2e1d454e3c.seg 02691156 03001627/points/2c03bcb2a133ce28bb6caad47eee6580.pts 03001627/expert_verified/points_label/2c03bcb2a133ce28bb6caad47eee6580.seg 03001627 03001627/points/f23d3a85baabd7ae32d9baba75737e72.pts 03001627/expert_verified/points_label/f23d3a85baabd7ae32d9baba75737e72.seg 03001627 04379243/points/90be5de0faef91ef3f7e27638e63d848.pts 04379243/expert_verified/points_label/90be5de0faef91ef3f7e27638e63d848.seg 04379243 02691156/points/d5f01e2aa54bbf28ca8607f540cc62ba.pts 02691156/expert_verified/points_label/d5f01e2aa54bbf28ca8607f540cc62ba.seg 02691156 02691156/points/4f0bf26c62bb7c8b7e1c97634acf0214.pts 02691156/expert_verified/points_label/4f0bf26c62bb7c8b7e1c97634acf0214.seg 02691156 03001627/points/4246c8c293c56ea34b3c42e318f3affc.pts 03001627/expert_verified/points_label/4246c8c293c56ea34b3c42e318f3affc.seg 03001627 04379243/points/9b42cb91ccead6d42f6d10c5d1d56320.pts 04379243/expert_verified/points_label/9b42cb91ccead6d42f6d10c5d1d56320.seg 04379243 03001627/points/c67b7b62e529295dfc30525e763ef5eb.pts 03001627/expert_verified/points_label/c67b7b62e529295dfc30525e763ef5eb.seg 03001627 04379243/points/394c63a5658ef759b515d1675be6b5d3.pts 04379243/expert_verified/points_label/394c63a5658ef759b515d1675be6b5d3.seg 04379243 03636649/points/13ba3fbe8fbc53f3ef3a2c64cef919d0.pts 03636649/expert_verified/points_label/13ba3fbe8fbc53f3ef3a2c64cef919d0.seg 03636649 04379243/points/cb860d60db8f3d18febad4f49b26ec52.pts 04379243/expert_verified/points_label/cb860d60db8f3d18febad4f49b26ec52.seg 04379243 04379243/points/657aad273d665f5dd9823f45c4411583.pts 04379243/expert_verified/points_label/657aad273d665f5dd9823f45c4411583.seg 04379243 03001627/points/64fcd1ba0df5d54d79b3e1be3524f72f.pts 03001627/expert_verified/points_label/64fcd1ba0df5d54d79b3e1be3524f72f.seg 03001627 03642806/points/8489cb783d249651b674654e7bbe623d.pts 03642806/expert_verified/points_label/8489cb783d249651b674654e7bbe623d.seg 03642806 03467517/points/3824a2336972d144a24eeca91f583600.pts 03467517/expert_verified/points_label/3824a2336972d144a24eeca91f583600.seg 03467517 03797390/points/99eaa69cf6fe8811dec712af445786fe.pts 03797390/expert_verified/points_label/99eaa69cf6fe8811dec712af445786fe.seg 03797390 03001627/points/e31d71ed32273fede42ac999db581f5e.pts 03001627/expert_verified/points_label/e31d71ed32273fede42ac999db581f5e.seg 03001627 03001627/points/9a42cff883cbd358106f706dac6c58f0.pts 03001627/expert_verified/points_label/9a42cff883cbd358106f706dac6c58f0.seg 03001627 04379243/points/b515a107aa3a3fd0e3dff0d5ebb43915.pts 04379243/expert_verified/points_label/b515a107aa3a3fd0e3dff0d5ebb43915.seg 04379243 03001627/points/bd6a8b133fa4d269491d6cee03fef2a9.pts 03001627/expert_verified/points_label/bd6a8b133fa4d269491d6cee03fef2a9.seg 03001627 03001627/points/51c8f249e778e84a5bae8923b29985ad.pts 03001627/expert_verified/points_label/51c8f249e778e84a5bae8923b29985ad.seg 03001627 02691156/points/f12eefbbefabe566ca8607f540cc62ba.pts 02691156/expert_verified/points_label/f12eefbbefabe566ca8607f540cc62ba.seg 02691156 02691156/points/ad6e93a1db3e1da5977e4bb19a62128e.pts 02691156/expert_verified/points_label/ad6e93a1db3e1da5977e4bb19a62128e.seg 02691156 03001627/points/efa83c67ce47bfca304edcf7c4314468.pts 03001627/expert_verified/points_label/efa83c67ce47bfca304edcf7c4314468.seg 03001627 03624134/points/d6e9e4e07bafca0fa37f3fc191551700.pts 03624134/expert_verified/points_label/d6e9e4e07bafca0fa37f3fc191551700.seg 03624134 03642806/points/e083105e9c2a28bb0c3a03d0a1f182f.pts 03642806/expert_verified/points_label/e083105e9c2a28bb0c3a03d0a1f182f.seg 03642806 03001627/points/d2992fd5e6715bad3bbf93f83cbaf271.pts 03001627/expert_verified/points_label/d2992fd5e6715bad3bbf93f83cbaf271.seg 03001627 04379243/points/4a27cb9384782ce33e95c55cb020b7e6.pts 04379243/expert_verified/points_label/4a27cb9384782ce33e95c55cb020b7e6.seg 04379243 04379243/points/cf046edeff204b81cdf7280ff8af6720.pts 04379243/expert_verified/points_label/cf046edeff204b81cdf7280ff8af6720.seg 04379243 03001627/points/6534f04a1c349a3c8c6540fe6bc16d6f.pts 03001627/expert_verified/points_label/6534f04a1c349a3c8c6540fe6bc16d6f.seg 03001627 03636649/points/1917888a2b6901091735ea0e092a805a.pts 03636649/expert_verified/points_label/1917888a2b6901091735ea0e092a805a.seg 03636649 03636649/points/b37e07ac31fa4f311735ea0e092a805a.pts 03636649/expert_verified/points_label/b37e07ac31fa4f311735ea0e092a805a.seg 03636649 03636649/points/2f6f1fe66631572c6c5b8009db95f66f.pts 03636649/expert_verified/points_label/2f6f1fe66631572c6c5b8009db95f66f.seg 03636649 03467517/points/feab270427cee00a24eeca91f583600.pts 03467517/expert_verified/points_label/feab270427cee00a24eeca91f583600.seg 03467517 02691156/points/e30e25fe047ce1ea10b08ceced9a0113.pts 02691156/expert_verified/points_label/e30e25fe047ce1ea10b08ceced9a0113.seg 02691156 03636649/points/b2347fe81bd2db6a4b3c42e318f3affc.pts 03636649/expert_verified/points_label/b2347fe81bd2db6a4b3c42e318f3affc.seg 03636649 03001627/points/bb7755090f984ba85dd1bba5b1310523.pts 03001627/expert_verified/points_label/bb7755090f984ba85dd1bba5b1310523.seg 03001627 02691156/points/bc7ead8b45952ab8822054a0a020bf4a.pts 02691156/expert_verified/points_label/bc7ead8b45952ab8822054a0a020bf4a.seg 02691156 02691156/points/5a1d4af1f417d28566cf1b4a8fc3914e.pts 02691156/expert_verified/points_label/5a1d4af1f417d28566cf1b4a8fc3914e.seg 02691156 02691156/points/a6cbada42d1a30d0f5c7319b71bdce6e.pts 02691156/expert_verified/points_label/a6cbada42d1a30d0f5c7319b71bdce6e.seg 02691156 02691156/points/b785b39d10c33b5de9f07d25f575b2d4.pts 02691156/expert_verified/points_label/b785b39d10c33b5de9f07d25f575b2d4.seg 02691156 03001627/points/2df8d2af1bc4b9972056b4bd5d870b47.pts 03001627/expert_verified/points_label/2df8d2af1bc4b9972056b4bd5d870b47.seg 03001627 03797390/points/d46b98f63a017578ea456f4bbbc96af9.pts 03797390/expert_verified/points_label/d46b98f63a017578ea456f4bbbc96af9.seg 03797390 04379243/points/1adf96850963550f19fb4103277a6b93.pts 04379243/expert_verified/points_label/1adf96850963550f19fb4103277a6b93.seg 04379243 03001627/points/cb7a4324fdfa690e96dd43aa0ec847c9.pts 03001627/expert_verified/points_label/cb7a4324fdfa690e96dd43aa0ec847c9.seg 03001627 03624134/points/c19088b4c32c0f1d22b38218e60be05.pts 03624134/expert_verified/points_label/c19088b4c32c0f1d22b38218e60be05.seg 03624134 04379243/points/1acf7b0939f3eea2eafdf94e5032b200.pts 04379243/expert_verified/points_label/1acf7b0939f3eea2eafdf94e5032b200.seg 04379243 03467517/points/d50d06b159363b1693f0194265a9746c.pts 03467517/expert_verified/points_label/d50d06b159363b1693f0194265a9746c.seg 03467517 02691156/points/dacb447d7820e7f7ca8607f540cc62ba.pts 02691156/expert_verified/points_label/dacb447d7820e7f7ca8607f540cc62ba.seg 02691156 04379243/points/c3a9dc47c5bf10aac3bd24f986301745.pts 04379243/expert_verified/points_label/c3a9dc47c5bf10aac3bd24f986301745.seg 04379243 04379243/points/4791914b3bcaf57efebad4f49b26ec52.pts 04379243/expert_verified/points_label/4791914b3bcaf57efebad4f49b26ec52.seg 04379243 03001627/points/bf3f14225e8f899db62f9fb4b7f0626.pts 03001627/expert_verified/points_label/bf3f14225e8f899db62f9fb4b7f0626.seg 03001627 04379243/points/4f5c111a89b3fd27aa29e9f0529e8ef7.pts 04379243/expert_verified/points_label/4f5c111a89b3fd27aa29e9f0529e8ef7.seg 04379243 03001627/points/6af8d7bfa508b8d23759750e8db40476.pts 03001627/expert_verified/points_label/6af8d7bfa508b8d23759750e8db40476.seg 03001627 02691156/points/427030abcc0f11a8947bbeb9022263b8.pts 02691156/expert_verified/points_label/427030abcc0f11a8947bbeb9022263b8.seg 02691156 03642806/points/367fbaea8743ec1cc98452c8fce6b43.pts 03642806/expert_verified/points_label/367fbaea8743ec1cc98452c8fce6b43.seg 03642806 04379243/points/419412b927d11c7d8312881285c04cb3.pts 04379243/expert_verified/points_label/419412b927d11c7d8312881285c04cb3.seg 04379243 03001627/points/56cc047440e7c999a23949c21eddef76.pts 03001627/expert_verified/points_label/56cc047440e7c999a23949c21eddef76.seg 03001627 03790512/points/fdb6223c286cb653cc9e7530f9d8e186.pts 03790512/expert_verified/points_label/fdb6223c286cb653cc9e7530f9d8e186.seg 03790512 03636649/points/6b2a590446ad5794b10e111f2d30684d.pts 03636649/expert_verified/points_label/6b2a590446ad5794b10e111f2d30684d.seg 03636649 03001627/points/a3ce9ba74ab50352e6fe3612af521500.pts 03001627/expert_verified/points_label/a3ce9ba74ab50352e6fe3612af521500.seg 03001627 02958343/points/9986dd19b2c459152470de2774d6099.pts 02958343/expert_verified/points_label/9986dd19b2c459152470de2774d6099.seg 02958343 03642806/points/b806daf849a5dba289c212008d2a390e.pts 03642806/expert_verified/points_label/b806daf849a5dba289c212008d2a390e.seg 03642806 04379243/points/2eb503dde3cc027d86c701087a194026.pts 04379243/expert_verified/points_label/2eb503dde3cc027d86c701087a194026.seg 04379243 03001627/points/c4a4710012ee39bd19f4b416b31c46e0.pts 03001627/expert_verified/points_label/c4a4710012ee39bd19f4b416b31c46e0.seg 03001627 02958343/points/bd8654fbca233e41ddb8f37b1865d989.pts 02958343/expert_verified/points_label/bd8654fbca233e41ddb8f37b1865d989.seg 02958343 03001627/points/6fd485a2345c3dd69233bf560301e53.pts 03001627/expert_verified/points_label/6fd485a2345c3dd69233bf560301e53.seg 03001627 02691156/points/aebc4c46b3cb7c3bca8607f540cc62ba.pts 02691156/expert_verified/points_label/aebc4c46b3cb7c3bca8607f540cc62ba.seg 02691156 03001627/points/9343df9a7ed6cbba1923501fcdd899bb.pts 03001627/expert_verified/points_label/9343df9a7ed6cbba1923501fcdd899bb.seg 03001627 04379243/points/7fadae39394c5622c3bd24f986301745.pts 04379243/expert_verified/points_label/7fadae39394c5622c3bd24f986301745.seg 04379243 03001627/points/d619fd50c4d0fb46dea83bbf303af433.pts 03001627/expert_verified/points_label/d619fd50c4d0fb46dea83bbf303af433.seg 03001627 04379243/points/ef02c88a34b3888a1b1a00a31bfed97b.pts 04379243/expert_verified/points_label/ef02c88a34b3888a1b1a00a31bfed97b.seg 04379243 03467517/points/71d0016078dea05a94ca7929d4ba6d2d.pts 03467517/expert_verified/points_label/71d0016078dea05a94ca7929d4ba6d2d.seg 03467517 03001627/points/5623d0ec9efedbc9d4da89766e80607a.pts 03001627/expert_verified/points_label/5623d0ec9efedbc9d4da89766e80607a.seg 03001627 04379243/points/21486e6d0bd896ad5cca18918d24f6cd.pts 04379243/expert_verified/points_label/21486e6d0bd896ad5cca18918d24f6cd.seg 04379243 03636649/points/978df83c1cee012729a60d6ab40898d.pts 03636649/expert_verified/points_label/978df83c1cee012729a60d6ab40898d.seg 03636649 02691156/points/350d12f5290908c7f446f92b52bbd82a.pts 02691156/expert_verified/points_label/350d12f5290908c7f446f92b52bbd82a.seg 02691156 03636649/points/86d7a728dc35d634f800b597bc1c1eb5.pts 03636649/expert_verified/points_label/86d7a728dc35d634f800b597bc1c1eb5.seg 03636649 03001627/points/3b4292989394ba62f51f77a6d7299806.pts 03001627/expert_verified/points_label/3b4292989394ba62f51f77a6d7299806.seg 03001627 03001627/points/f5f18fccf9e16800dbd185de408ea209.pts 03001627/expert_verified/points_label/f5f18fccf9e16800dbd185de408ea209.seg 03001627 04379243/points/4d873bf1a658dcd523eb3ad3d378722a.pts 04379243/expert_verified/points_label/4d873bf1a658dcd523eb3ad3d378722a.seg 04379243 03001627/points/a3e4639ff201f69b22a3043dcd383f68.pts 03001627/expert_verified/points_label/a3e4639ff201f69b22a3043dcd383f68.seg 03001627 04379243/points/8d247c6f6aaf805a2530bfb25087f2b0.pts 04379243/expert_verified/points_label/8d247c6f6aaf805a2530bfb25087f2b0.seg 04379243 03467517/points/511fc5ccf4f1c857a24eeca91f583600.pts 03467517/expert_verified/points_label/511fc5ccf4f1c857a24eeca91f583600.seg 03467517 02691156/points/4635326bc4fdc3e9297cd7e2ef7dfa80.pts 02691156/expert_verified/points_label/4635326bc4fdc3e9297cd7e2ef7dfa80.seg 02691156 03001627/points/525776b59266140381dff5c2e57ad46e.pts 03001627/expert_verified/points_label/525776b59266140381dff5c2e57ad46e.seg 03001627 03001627/points/f1d6552ca66b2e37713decb1a0563b12.pts 03001627/expert_verified/points_label/f1d6552ca66b2e37713decb1a0563b12.seg 03001627 04379243/points/40ff8ae39ad13d014a873bbe35452b88.pts 04379243/expert_verified/points_label/40ff8ae39ad13d014a873bbe35452b88.seg 04379243 02691156/points/59f258b7aa7c1f7aa7d0c1e4eb8db7dc.pts 02691156/expert_verified/points_label/59f258b7aa7c1f7aa7d0c1e4eb8db7dc.seg 02691156 04379243/points/63aa14915f59ed8671a782a4379556c7.pts 04379243/expert_verified/points_label/63aa14915f59ed8671a782a4379556c7.seg 04379243 02691156/points/e16f9cc7dedcacdb9b0435532743fd43.pts 02691156/expert_verified/points_label/e16f9cc7dedcacdb9b0435532743fd43.seg 02691156 04379243/points/c5b83c681c085f2195493ccf8f26ab2c.pts 04379243/expert_verified/points_label/c5b83c681c085f2195493ccf8f26ab2c.seg 04379243 03001627/points/b2ba1569509cdb439451566a8c6563ed.pts 03001627/expert_verified/points_label/b2ba1569509cdb439451566a8c6563ed.seg 03001627 02691156/points/265f5348ab2320b2148672750a1a335.pts 02691156/expert_verified/points_label/265f5348ab2320b2148672750a1a335.seg 02691156 03001627/points/47da08d9c7cd7e104b3c42e318f3affc.pts 03001627/expert_verified/points_label/47da08d9c7cd7e104b3c42e318f3affc.seg 03001627 03001627/points/458356b9c5a8d7bd7cc86734cb2f5062.pts 03001627/expert_verified/points_label/458356b9c5a8d7bd7cc86734cb2f5062.seg 03001627 02691156/points/d20e3ed9b3430672bbf3143b1cb6076a.pts 02691156/expert_verified/points_label/d20e3ed9b3430672bbf3143b1cb6076a.seg 02691156 04379243/points/c45e6ceae72c7a97be8908669c476d49.pts 04379243/expert_verified/points_label/c45e6ceae72c7a97be8908669c476d49.seg 04379243 03001627/points/d9bbd1a1eaf6d2259d3ea1c6b57a0095.pts 03001627/expert_verified/points_label/d9bbd1a1eaf6d2259d3ea1c6b57a0095.seg 03001627 02958343/points/8242b114695b68286f522b2bb8ded829.pts 02958343/expert_verified/points_label/8242b114695b68286f522b2bb8ded829.seg 02958343 03001627/points/e4b40369894a16ce6821a1e68ba5ebab.pts 03001627/expert_verified/points_label/e4b40369894a16ce6821a1e68ba5ebab.seg 03001627 03636649/points/dfe800d8d8642e9647bc3701b998a7d5.pts 03636649/expert_verified/points_label/dfe800d8d8642e9647bc3701b998a7d5.seg 03636649 04379243/points/bdf7606e8d493149664b3b9b23ddfcbc.pts 04379243/expert_verified/points_label/bdf7606e8d493149664b3b9b23ddfcbc.seg 04379243 03001627/points/6015aaa9ef170d9bfdef1c01cbd4ae0c.pts 03001627/expert_verified/points_label/6015aaa9ef170d9bfdef1c01cbd4ae0c.seg 03001627 03624134/points/df7a65224f295122ed9c5b25fef60d04.pts 03624134/expert_verified/points_label/df7a65224f295122ed9c5b25fef60d04.seg 03624134 03467517/points/df959f68bb22e402a24eeca91f583600.pts 03467517/expert_verified/points_label/df959f68bb22e402a24eeca91f583600.seg 03467517 04379243/points/69604fc24b7976d69ccce4c6d5bb195f.pts 04379243/expert_verified/points_label/69604fc24b7976d69ccce4c6d5bb195f.seg 04379243 04379243/points/23aca164c7b2e2d4ad8af6714b643432.pts 04379243/expert_verified/points_label/23aca164c7b2e2d4ad8af6714b643432.seg 04379243 03636649/points/e37796d40348fa5fd8013bb984303089.pts 03636649/expert_verified/points_label/e37796d40348fa5fd8013bb984303089.seg 03636649 04379243/points/8cb6a2e9ba365c94593ebeeedbff73b.pts 04379243/expert_verified/points_label/8cb6a2e9ba365c94593ebeeedbff73b.seg 04379243 03001627/points/d6f2d44c693d2e857062f2d72cde5c95.pts 03001627/expert_verified/points_label/d6f2d44c693d2e857062f2d72cde5c95.seg 03001627 03948459/points/ed29dd43ad28f042d1987c07c912c6e1.pts 03948459/expert_verified/points_label/ed29dd43ad28f042d1987c07c912c6e1.seg 03948459 03001627/points/ca01fd0de2534323c594a0e804f37c1a.pts 03001627/expert_verified/points_label/ca01fd0de2534323c594a0e804f37c1a.seg 03001627 03636649/points/e7b719516449701362525a4d857f099d.pts 03636649/expert_verified/points_label/e7b719516449701362525a4d857f099d.seg 03636649 02691156/points/bd48d0beb5d1acf1d2106c9042f1bde9.pts 02691156/expert_verified/points_label/bd48d0beb5d1acf1d2106c9042f1bde9.seg 02691156 03636649/points/7cb828eb3b8e424b1e88064118b89a3e.pts 03636649/expert_verified/points_label/7cb828eb3b8e424b1e88064118b89a3e.seg 03636649 03001627/points/fdd21f7f2ca9f0bcbdcbca499b446e89.pts 03001627/expert_verified/points_label/fdd21f7f2ca9f0bcbdcbca499b446e89.seg 03001627 03636649/points/d779977c2417752b815c6de5374a8dd2.pts 03636649/expert_verified/points_label/d779977c2417752b815c6de5374a8dd2.seg 03636649 02691156/points/f3e2df468c15795872517bb0a6b4d3ef.pts 02691156/expert_verified/points_label/f3e2df468c15795872517bb0a6b4d3ef.seg 02691156 04379243/points/e3cc0b06be2c972cab610b0c94236463.pts 04379243/expert_verified/points_label/e3cc0b06be2c972cab610b0c94236463.seg 04379243 03261776/points/ca1c1c9aba8f4491a656de49935d2359.pts 03261776/expert_verified/points_label/ca1c1c9aba8f4491a656de49935d2359.seg 03261776 03001627/points/c535629f9661293dc16ef5c633c71b56.pts 03001627/expert_verified/points_label/c535629f9661293dc16ef5c633c71b56.seg 03001627 03636649/points/699fcda4f4e9166ec5eb7aae719027b2.pts 03636649/expert_verified/points_label/699fcda4f4e9166ec5eb7aae719027b2.seg 03636649 03001627/points/8a5d60067de905336c183a120a388982.pts 03001627/expert_verified/points_label/8a5d60067de905336c183a120a388982.seg 03001627 02691156/points/4ad92be763c2ded8fca1f1143bb6bc17.pts 02691156/expert_verified/points_label/4ad92be763c2ded8fca1f1143bb6bc17.seg 02691156 04379243/points/14d6b4b09dfc54e9d679a95896f75103.pts 04379243/expert_verified/points_label/14d6b4b09dfc54e9d679a95896f75103.seg 04379243 02691156/points/5e9129782c45b26992e39b8eae3e6b15.pts 02691156/expert_verified/points_label/5e9129782c45b26992e39b8eae3e6b15.seg 02691156 02691156/points/2aec6e6096e640add00d52e62bf14ee9.pts 02691156/expert_verified/points_label/2aec6e6096e640add00d52e62bf14ee9.seg 02691156 03642806/points/7b4260884a1dfd76b080af510dd640b.pts 03642806/expert_verified/points_label/7b4260884a1dfd76b080af510dd640b.seg 03642806 03636649/points/3a0edfd418e020b97f32712aef0efc5a.pts 03636649/expert_verified/points_label/3a0edfd418e020b97f32712aef0efc5a.seg 03636649 03467517/points/1c374a198daaddc493f0194265a9746c.pts 03467517/expert_verified/points_label/1c374a198daaddc493f0194265a9746c.seg 03467517 04379243/points/9d90a58677e619f94b8710a3469971b1.pts 04379243/expert_verified/points_label/9d90a58677e619f94b8710a3469971b1.seg 04379243 02691156/points/26f8a11864fd6bf7b68211fcc7956ac6.pts 02691156/expert_verified/points_label/26f8a11864fd6bf7b68211fcc7956ac6.seg 02691156 02773838/points/f5108ede5ca11f041f6736765dee4fa9.pts 02773838/expert_verified/points_label/f5108ede5ca11f041f6736765dee4fa9.seg 02773838 03001627/points/41ce60d5443c203eb31c248b8665b2e7.pts 03001627/expert_verified/points_label/41ce60d5443c203eb31c248b8665b2e7.seg 03001627 03797390/points/a637500654ca8d16c97cfc3e8a6b1d16.pts 03797390/expert_verified/points_label/a637500654ca8d16c97cfc3e8a6b1d16.seg 03797390 03001627/points/9ee4b9c97bcf4b3715dec43ae6a12831.pts 03001627/expert_verified/points_label/9ee4b9c97bcf4b3715dec43ae6a12831.seg 03001627 03001627/points/e2dbad7996e7e13430c589758b4b5646.pts 03001627/expert_verified/points_label/e2dbad7996e7e13430c589758b4b5646.seg 03001627 03001627/points/ec9f1fc13f2e4ae2c3bd24f986301745.pts 03001627/expert_verified/points_label/ec9f1fc13f2e4ae2c3bd24f986301745.seg 03001627 03624134/points/172b9a77462dcdeaed90ead9558ee6cb.pts 03624134/expert_verified/points_label/172b9a77462dcdeaed90ead9558ee6cb.seg 03624134 04379243/points/713a4be770bb19b9586b2526565371c0.pts 04379243/expert_verified/points_label/713a4be770bb19b9586b2526565371c0.seg 04379243 04379243/points/f2e6820ca69d9b7719fb4103277a6b93.pts 04379243/expert_verified/points_label/f2e6820ca69d9b7719fb4103277a6b93.seg 04379243 03001627/points/11a06e6f68b1d99c8687ff9b0b4e4ac.pts 03001627/expert_verified/points_label/11a06e6f68b1d99c8687ff9b0b4e4ac.seg 03001627 04379243/points/cfd7e354a5ae982aa0ab1d82ef09f78f.pts 04379243/expert_verified/points_label/cfd7e354a5ae982aa0ab1d82ef09f78f.seg 04379243 03797390/points/8012f52dd0a4d2f718a93a45bf780820.pts 03797390/expert_verified/points_label/8012f52dd0a4d2f718a93a45bf780820.seg 03797390 03636649/points/57c1bc69df779d87bbc7a6acbd8f058b.pts 03636649/expert_verified/points_label/57c1bc69df779d87bbc7a6acbd8f058b.seg 03636649 03948459/points/664579680dc09267e1f2a1daf140ac9f.pts 03948459/expert_verified/points_label/664579680dc09267e1f2a1daf140ac9f.seg 03948459 03001627/points/ca032d3b6dcbe1cea3056fa1e8da3997.pts 03001627/expert_verified/points_label/ca032d3b6dcbe1cea3056fa1e8da3997.seg 03001627 02691156/points/4a837740b388aa45d8ff6111270336a9.pts 02691156/expert_verified/points_label/4a837740b388aa45d8ff6111270336a9.seg 02691156 04099429/points/64803bab9799d0e698d2d2b2ae2563b0.pts 04099429/expert_verified/points_label/64803bab9799d0e698d2d2b2ae2563b0.seg 04099429 04379243/points/c2c36909e461e10adaaaeef365d8f6e5.pts 04379243/expert_verified/points_label/c2c36909e461e10adaaaeef365d8f6e5.seg 04379243 04379243/points/bc842e548e68a3cbb48513409ae7c51d.pts 04379243/expert_verified/points_label/bc842e548e68a3cbb48513409ae7c51d.seg 04379243 03467517/points/4709e55a82a63f64d57700c05b1862d8.pts 03467517/expert_verified/points_label/4709e55a82a63f64d57700c05b1862d8.seg 03467517 04379243/points/dc6f030d9ee566a5dcfcef693e7ec696.pts 04379243/expert_verified/points_label/dc6f030d9ee566a5dcfcef693e7ec696.seg 04379243 03001627/points/8be8093e99b94bd9cf320c31965db5a1.pts 03001627/expert_verified/points_label/8be8093e99b94bd9cf320c31965db5a1.seg 03001627 02958343/points/a0a1b0377d72e86bab3dd76bf33b0f5e.pts 02958343/expert_verified/points_label/a0a1b0377d72e86bab3dd76bf33b0f5e.seg 02958343 03001627/points/efc684ff4dc6ff49ccd42a2d6eea63ed.pts 03001627/expert_verified/points_label/efc684ff4dc6ff49ccd42a2d6eea63ed.seg 03001627 03001627/points/ff2223a085d32243696b74614952b2d0.pts 03001627/expert_verified/points_label/ff2223a085d32243696b74614952b2d0.seg 03001627 02954340/points/8b2951e32e0906bb5f6cb4951755315c.pts 02954340/expert_verified/points_label/8b2951e32e0906bb5f6cb4951755315c.seg 02954340 04379243/points/82b69c9b72a5159ce76bc197b3a3ffc0.pts 04379243/expert_verified/points_label/82b69c9b72a5159ce76bc197b3a3ffc0.seg 04379243 03642806/points/5b5247b13d5b21bdad2954b86711abbd.pts 03642806/expert_verified/points_label/5b5247b13d5b21bdad2954b86711abbd.seg 03642806 03636649/points/44e442591f82cd4cab0ac374f450cdc.pts 03636649/expert_verified/points_label/44e442591f82cd4cab0ac374f450cdc.seg 03636649 03001627/points/2a1184b04dd8f30e3e92f39ce48d644.pts 03001627/expert_verified/points_label/2a1184b04dd8f30e3e92f39ce48d644.seg 03001627 03636649/points/bc49fe3559e18fcb7d910d51d878f708.pts 03636649/expert_verified/points_label/bc49fe3559e18fcb7d910d51d878f708.seg 03636649 03624134/points/c50af8af50613e822bf26da672b84220.pts 03624134/expert_verified/points_label/c50af8af50613e822bf26da672b84220.seg 03624134 04225987/points/c0280aaad5473e8398c63cb68f11df34.pts 04225987/expert_verified/points_label/c0280aaad5473e8398c63cb68f11df34.seg 04225987 03636649/points/5849d1a237cb493c659dda512294c744.pts 03636649/expert_verified/points_label/5849d1a237cb493c659dda512294c744.seg 03636649 02958343/points/fcd90d547fdeb629f200a72c9245aee7.pts 02958343/expert_verified/points_label/fcd90d547fdeb629f200a72c9245aee7.seg 02958343 03001627/points/34898c36e711fbde713decb1a0563b12.pts 03001627/expert_verified/points_label/34898c36e711fbde713decb1a0563b12.seg 03001627 02691156/points/af696fc30a96a0c8bc0909d98a1ff2b4.pts 02691156/expert_verified/points_label/af696fc30a96a0c8bc0909d98a1ff2b4.seg 02691156 04379243/points/f28e030e715b9d3e318462aca9e62b6b.pts 04379243/expert_verified/points_label/f28e030e715b9d3e318462aca9e62b6b.seg 04379243 02691156/points/3c7e4628a9ea201bbf3143b1cb6076a.pts 02691156/expert_verified/points_label/3c7e4628a9ea201bbf3143b1cb6076a.seg 02691156 03636649/points/f092117adb1e9254d1cbf3e52b9b6237.pts 03636649/expert_verified/points_label/f092117adb1e9254d1cbf3e52b9b6237.seg 03636649 04379243/points/7dd881a26eea656d193afeeca14e3baa.pts 04379243/expert_verified/points_label/7dd881a26eea656d193afeeca14e3baa.seg 04379243 03001627/points/79a3115a6f96eef7c151419181ef256.pts 03001627/expert_verified/points_label/79a3115a6f96eef7c151419181ef256.seg 03001627 04379243/points/fc51355d4d03ff4ae6c5cd45aa112726.pts 04379243/expert_verified/points_label/fc51355d4d03ff4ae6c5cd45aa112726.seg 04379243 04379243/points/34121f5cc12135148c1cf3f7d7f0373.pts 04379243/expert_verified/points_label/34121f5cc12135148c1cf3f7d7f0373.seg 04379243 03624134/points/d5167211e757e79f012465c621a63e3.pts 03624134/expert_verified/points_label/d5167211e757e79f012465c621a63e3.seg 03624134 04379243/points/5b375eacdbe49cfaaa539cd22945e538.pts 04379243/expert_verified/points_label/5b375eacdbe49cfaaa539cd22945e538.seg 04379243 02691156/points/d3d788c1fb35227619ba010ddb4974fe.pts 02691156/expert_verified/points_label/d3d788c1fb35227619ba010ddb4974fe.seg 02691156 02691156/points/f26ea1a00455f44fb88e2a19106395c2.pts 02691156/expert_verified/points_label/f26ea1a00455f44fb88e2a19106395c2.seg 02691156 03001627/points/798a46965d9e0edfcea003eff0268278.pts 03001627/expert_verified/points_label/798a46965d9e0edfcea003eff0268278.seg 03001627 02691156/points/3069d990d52051eb3a34c2907e8f3f1f.pts 02691156/expert_verified/points_label/3069d990d52051eb3a34c2907e8f3f1f.seg 02691156 02691156/points/8c42e3042a4beaa7d5c40787c7bb7824.pts 02691156/expert_verified/points_label/8c42e3042a4beaa7d5c40787c7bb7824.seg 02691156 04379243/points/45c5ee611c73b90a509330ce00eb0b20.pts 04379243/expert_verified/points_label/45c5ee611c73b90a509330ce00eb0b20.seg 04379243 03001627/points/22ada577361ed0374b3c42e318f3affc.pts 03001627/expert_verified/points_label/22ada577361ed0374b3c42e318f3affc.seg 03001627 04379243/points/b6ad7be371729438dcfcef693e7ec696.pts 04379243/expert_verified/points_label/b6ad7be371729438dcfcef693e7ec696.seg 04379243 03636649/points/4c266f2b866c59e761fef32872c6fa53.pts 03636649/expert_verified/points_label/4c266f2b866c59e761fef32872c6fa53.seg 03636649 04379243/points/812dd06fc99f174e9f2349486c570dd4.pts 04379243/expert_verified/points_label/812dd06fc99f174e9f2349486c570dd4.seg 04379243 02691156/points/36a5bd4ca6a0b191532d23702363f9a5.pts 02691156/expert_verified/points_label/36a5bd4ca6a0b191532d23702363f9a5.seg 02691156 03001627/points/be0890a6a0f3fcf841f91bc9e1dece3b.pts 03001627/expert_verified/points_label/be0890a6a0f3fcf841f91bc9e1dece3b.seg 03001627 03642806/points/6008f256f3beafd9988abef1fd117e7.pts 03642806/expert_verified/points_label/6008f256f3beafd9988abef1fd117e7.seg 03642806 03001627/points/490941bf4a532b62492d9da2668ec34c.pts 03001627/expert_verified/points_label/490941bf4a532b62492d9da2668ec34c.seg 03001627 03636649/points/94940283714fdff6244ba644cf33cb2e.pts 03636649/expert_verified/points_label/94940283714fdff6244ba644cf33cb2e.seg 03636649 03642806/points/6227e7dd1a391e8d54f22ce0a3592d5.pts 03642806/expert_verified/points_label/6227e7dd1a391e8d54f22ce0a3592d5.seg 03642806 02691156/points/b2ceeee3c5b75962ac4f72bf08dc79a6.pts 02691156/expert_verified/points_label/b2ceeee3c5b75962ac4f72bf08dc79a6.seg 02691156 03642806/points/55a05b33f34e7211f71cb38553f14917.pts 03642806/expert_verified/points_label/55a05b33f34e7211f71cb38553f14917.seg 03642806 02773838/points/74c548ef3ca7b1987515e7bb7dba4019.pts 02773838/expert_verified/points_label/74c548ef3ca7b1987515e7bb7dba4019.seg 02773838 03467517/points/defcf80fcef4b51b3f431ca2c1260d62.pts 03467517/expert_verified/points_label/defcf80fcef4b51b3f431ca2c1260d62.seg 03467517 04379243/points/eaea1cf98b61abd043383304411cc9ec.pts 04379243/expert_verified/points_label/eaea1cf98b61abd043383304411cc9ec.seg 04379243 03001627/points/7f6858bd9d4af9df97316612e1a4343a.pts 03001627/expert_verified/points_label/7f6858bd9d4af9df97316612e1a4343a.seg 03001627 03001627/points/3c27660aacbcf99886327adaa986dff.pts 03001627/expert_verified/points_label/3c27660aacbcf99886327adaa986dff.seg 03001627 04379243/points/229d510bace435811572ee5ddf1b55b.pts 04379243/expert_verified/points_label/229d510bace435811572ee5ddf1b55b.seg 04379243 03636649/points/83c0ad378b5802b73d39d8012919dd25.pts 03636649/expert_verified/points_label/83c0ad378b5802b73d39d8012919dd25.seg 03636649 02691156/points/f009f3112625ee00b8cf782e8c539948.pts 02691156/expert_verified/points_label/f009f3112625ee00b8cf782e8c539948.seg 02691156 02691156/points/f13827d156628467b4cdad9a5bf52dd5.pts 02691156/expert_verified/points_label/f13827d156628467b4cdad9a5bf52dd5.seg 02691156 03636649/points/526251a7530426a4b3c42e318f3affc.pts 03636649/expert_verified/points_label/526251a7530426a4b3c42e318f3affc.seg 03636649 03001627/points/a1133464132d65fcfce0ccdae30f97db.pts 03001627/expert_verified/points_label/a1133464132d65fcfce0ccdae30f97db.seg 03001627 02691156/points/d844094b073a0452b04b2d1c5ce9783b.pts 02691156/expert_verified/points_label/d844094b073a0452b04b2d1c5ce9783b.seg 02691156 03948459/points/2f5b4bcb8d4dd901609e2d916fa0da27.pts 03948459/expert_verified/points_label/2f5b4bcb8d4dd901609e2d916fa0da27.seg 03948459 03636649/points/a4c06cd5032733af543df75232f6ff2b.pts 03636649/expert_verified/points_label/a4c06cd5032733af543df75232f6ff2b.seg 03636649 03636649/points/64eaa45bd2e01db8991ff09eca5b27a8.pts 03636649/expert_verified/points_label/64eaa45bd2e01db8991ff09eca5b27a8.seg 03636649 03636649/points/5bc478e9c4e0bb8180936c51aa7ffcf5.pts 03636649/expert_verified/points_label/5bc478e9c4e0bb8180936c51aa7ffcf5.seg 03636649 03636649/points/b02bd8e5ef9cfe354b3c42e318f3affc.pts 03636649/expert_verified/points_label/b02bd8e5ef9cfe354b3c42e318f3affc.seg 03636649 03636649/points/cf6c082b9534049494db33559ec0df30.pts 03636649/expert_verified/points_label/cf6c082b9534049494db33559ec0df30.seg 03636649 04225987/points/af4343c5b78b70b11082f2ea630bf69e.pts 04225987/expert_verified/points_label/af4343c5b78b70b11082f2ea630bf69e.seg 04225987 03467517/points/c084022f2ddbf95493f0194265a9746c.pts 03467517/expert_verified/points_label/c084022f2ddbf95493f0194265a9746c.seg 03467517 03001627/points/550dd11407c28f9f3bd04286517a8395.pts 03001627/expert_verified/points_label/550dd11407c28f9f3bd04286517a8395.seg 03001627 04379243/points/702cebffa33a19f019f079d1b712f46f.pts 04379243/expert_verified/points_label/702cebffa33a19f019f079d1b712f46f.seg 04379243 04379243/points/388d9e7b2b8a8f909492fbce0bd54e2e.pts 04379243/expert_verified/points_label/388d9e7b2b8a8f909492fbce0bd54e2e.seg 04379243 03636649/points/7634fbdcaa6b304d62c83ac1e3a4ebaa.pts 03636649/expert_verified/points_label/7634fbdcaa6b304d62c83ac1e3a4ebaa.seg 03636649 03636649/points/14d3d2418165ec86bba785994a529f86.pts 03636649/expert_verified/points_label/14d3d2418165ec86bba785994a529f86.seg 03636649 04379243/points/13e19274b358ec867aa3000697a75d55.pts 04379243/expert_verified/points_label/13e19274b358ec867aa3000697a75d55.seg 04379243 03467517/points/727fcc85add981325e683993f34d42f2.pts 03467517/expert_verified/points_label/727fcc85add981325e683993f34d42f2.seg 03467517 02691156/points/947d6b9cd1966e2e719b5362fe06bbb.pts 02691156/expert_verified/points_label/947d6b9cd1966e2e719b5362fe06bbb.seg 02691156 04379243/points/ee5f85db427865e63e5399147a5b4763.pts 04379243/expert_verified/points_label/ee5f85db427865e63e5399147a5b4763.seg 04379243 02691156/points/1678946724380812de689e373096b0e3.pts 02691156/expert_verified/points_label/1678946724380812de689e373096b0e3.seg 02691156 03001627/points/3fdef0a7606c397331ad067823a3f0ce.pts 03001627/expert_verified/points_label/3fdef0a7606c397331ad067823a3f0ce.seg 03001627 03636649/points/1bb465b8f22315d1116f219d90a571c2.pts 03636649/expert_verified/points_label/1bb465b8f22315d1116f219d90a571c2.seg 03636649 04379243/points/9dd5b7e6f90ee322b56d92c5d7b06038.pts 04379243/expert_verified/points_label/9dd5b7e6f90ee322b56d92c5d7b06038.seg 04379243 03467517/points/7eee3b79e053759143891ae68a82472e.pts 03467517/expert_verified/points_label/7eee3b79e053759143891ae68a82472e.seg 03467517 03001627/points/f4b6bf9253918b52944d8f8e13d63fde.pts 03001627/expert_verified/points_label/f4b6bf9253918b52944d8f8e13d63fde.seg 03001627 03636649/points/92e0f64c08f0c8ac3c8d0fdfb1cc2535.pts 03636649/expert_verified/points_label/92e0f64c08f0c8ac3c8d0fdfb1cc2535.seg 03636649 03624134/points/d63521a0dfac9c1f342494fa6f09f376.pts 03624134/expert_verified/points_label/d63521a0dfac9c1f342494fa6f09f376.seg 03624134 04379243/points/c7ff0afab4b7885a52160ba64fb535b2.pts 04379243/expert_verified/points_label/c7ff0afab4b7885a52160ba64fb535b2.seg 04379243 02958343/points/89765af115d9a4955591fcdffe729c55.pts 02958343/expert_verified/points_label/89765af115d9a4955591fcdffe729c55.seg 02958343 03636649/points/70bf2aaedbf9499ec889c00efdaf9928.pts 03636649/expert_verified/points_label/70bf2aaedbf9499ec889c00efdaf9928.seg 03636649 02958343/points/ef15b938dcfa9893c4d922e8a1141322.pts 02958343/expert_verified/points_label/ef15b938dcfa9893c4d922e8a1141322.seg 02958343 03636649/points/4bb676c497969016de98d10ab5975b59.pts 03636649/expert_verified/points_label/4bb676c497969016de98d10ab5975b59.seg 03636649 04379243/points/1c8121e1ad6cd6fc7a480f3f1d55ed3f.pts 04379243/expert_verified/points_label/1c8121e1ad6cd6fc7a480f3f1d55ed3f.seg 04379243 04379243/points/83b8e64089968ae8fd3feb4581507302.pts 04379243/expert_verified/points_label/83b8e64089968ae8fd3feb4581507302.seg 04379243 03636649/points/a4c0f3aed58f0e092fdae21c212bf119.pts 03636649/expert_verified/points_label/a4c0f3aed58f0e092fdae21c212bf119.seg 03636649 04379243/points/e02925509615eb5a4eaf5bbf36d243d4.pts 04379243/expert_verified/points_label/e02925509615eb5a4eaf5bbf36d243d4.seg 04379243 04379243/points/c5087fce38b009ae30bbd4cddd04c77b.pts 04379243/expert_verified/points_label/c5087fce38b009ae30bbd4cddd04c77b.seg 04379243 03001627/points/5107542cfbf142f36209799e55a657c.pts 03001627/expert_verified/points_label/5107542cfbf142f36209799e55a657c.seg 03001627 04379243/points/94a62cfdb84e88ca9a3528690d225ee1.pts 04379243/expert_verified/points_label/94a62cfdb84e88ca9a3528690d225ee1.seg 04379243 04379243/points/80ad1f839582d183fbf6f493308acc40.pts 04379243/expert_verified/points_label/80ad1f839582d183fbf6f493308acc40.seg 04379243 03001627/points/91819d15c2c044ebd47ffa500636d198.pts 03001627/expert_verified/points_label/91819d15c2c044ebd47ffa500636d198.seg 03001627 03636649/points/77a5a12147a6624d786810c22b062a88.pts 03636649/expert_verified/points_label/77a5a12147a6624d786810c22b062a88.seg 03636649 03001627/points/beb4c42cfa1c3b282811d30bba54859.pts 03001627/expert_verified/points_label/beb4c42cfa1c3b282811d30bba54859.seg 03001627 03636649/points/e529fc190753cc9df647dc544bb0ab61.pts 03636649/expert_verified/points_label/e529fc190753cc9df647dc544bb0ab61.seg 03636649 04379243/points/680d4a8b5a30601a4b3c42e318f3affc.pts 04379243/expert_verified/points_label/680d4a8b5a30601a4b3c42e318f3affc.seg 04379243 03001627/points/1d6f4020cab4ec1962d6a66a1a314d66.pts 03001627/expert_verified/points_label/1d6f4020cab4ec1962d6a66a1a314d66.seg 03001627 03001627/points/5b3fd3199d1bc950c1ae25a29e9d46d3.pts 03001627/expert_verified/points_label/5b3fd3199d1bc950c1ae25a29e9d46d3.seg 03001627 03001627/points/17e916fc863540ee3def89b32cef8e45.pts 03001627/expert_verified/points_label/17e916fc863540ee3def89b32cef8e45.seg 03001627 04379243/points/a5d5fc6b0bb7881419fb4103277a6b93.pts 04379243/expert_verified/points_label/a5d5fc6b0bb7881419fb4103277a6b93.seg 04379243 03001627/points/eafec1b145972dcd815b2b467e8e2eac.pts 03001627/expert_verified/points_label/eafec1b145972dcd815b2b467e8e2eac.seg 03001627 04379243/points/1fb2be490f45ec6e19fb4103277a6b93.pts 04379243/expert_verified/points_label/1fb2be490f45ec6e19fb4103277a6b93.seg 04379243 02691156/points/8b61ba80d9e487deca8607f540cc62ba.pts 02691156/expert_verified/points_label/8b61ba80d9e487deca8607f540cc62ba.seg 02691156 03467517/points/2d767b3fbb8a3053b8836869016d1afd.pts 03467517/expert_verified/points_label/2d767b3fbb8a3053b8836869016d1afd.seg 03467517 04379243/points/e0940f2229e42007d98e761e6d91dfc8.pts 04379243/expert_verified/points_label/e0940f2229e42007d98e761e6d91dfc8.seg 04379243 03001627/points/bb90094030f369e4305a3b2fd9173d6f.pts 03001627/expert_verified/points_label/bb90094030f369e4305a3b2fd9173d6f.seg 03001627 02958343/points/c6e3d9cf26016b5752aa494042b7c9db.pts 02958343/expert_verified/points_label/c6e3d9cf26016b5752aa494042b7c9db.seg 02958343 03001627/points/bd0fab2e72b445bd1e722bceee6e83aa.pts 03001627/expert_verified/points_label/bd0fab2e72b445bd1e722bceee6e83aa.seg 03001627 02691156/points/e86fd13a49f0ee0a62b600da24e0965.pts 02691156/expert_verified/points_label/e86fd13a49f0ee0a62b600da24e0965.seg 02691156 03001627/points/eeebe3fe14ee4d3aebefe6b1d594ad2e.pts 03001627/expert_verified/points_label/eeebe3fe14ee4d3aebefe6b1d594ad2e.seg 03001627 04379243/points/398dbb0a34ca527871a782a4379556c7.pts 04379243/expert_verified/points_label/398dbb0a34ca527871a782a4379556c7.seg 04379243 04379243/points/737cc2beda4a023619fb4103277a6b93.pts 04379243/expert_verified/points_label/737cc2beda4a023619fb4103277a6b93.seg 04379243 03001627/points/3895b96949fd81c5f07fee5fc5c45ee2.pts 03001627/expert_verified/points_label/3895b96949fd81c5f07fee5fc5c45ee2.seg 03001627 04379243/points/bba5ce8555c8fa89ba18ade30e563d37.pts 04379243/expert_verified/points_label/bba5ce8555c8fa89ba18ade30e563d37.seg 04379243 04379243/points/cab027dd0162c5b7f1426260885dd0ef.pts 04379243/expert_verified/points_label/cab027dd0162c5b7f1426260885dd0ef.seg 04379243 04379243/points/75f2bc98aecf198974984b9cd0997a52.pts 04379243/expert_verified/points_label/75f2bc98aecf198974984b9cd0997a52.seg 04379243 04379243/points/8d4fe49d942ec85ff4b6538438a0b930.pts 04379243/expert_verified/points_label/8d4fe49d942ec85ff4b6538438a0b930.seg 04379243 03001627/points/89dd53d0377c28207f7114254c4286d2.pts 03001627/expert_verified/points_label/89dd53d0377c28207f7114254c4286d2.seg 03001627 03636649/points/a37695d83a39adb52866fbd701f50f71.pts 03636649/expert_verified/points_label/a37695d83a39adb52866fbd701f50f71.seg 03636649 04379243/points/f99ebf0f053140525a0e5699b3040a35.pts 04379243/expert_verified/points_label/f99ebf0f053140525a0e5699b3040a35.seg 04379243 03624134/points/bbfd2df3edce576e1e652fa812161367.pts 03624134/expert_verified/points_label/bbfd2df3edce576e1e652fa812161367.seg 03624134 04379243/points/f0d8620b49ea76db83130614d8020b3.pts 04379243/expert_verified/points_label/f0d8620b49ea76db83130614d8020b3.seg 04379243 04379243/points/d01a6b35a54c8f77dd986a55e273fa14.pts 04379243/expert_verified/points_label/d01a6b35a54c8f77dd986a55e273fa14.seg 04379243 03001627/points/2f6b0ddf12d1311795bea7c29e873d16.pts 03001627/expert_verified/points_label/2f6b0ddf12d1311795bea7c29e873d16.seg 03001627 03001627/points/5695fd37d1e673cebf964fc57f6a7d6d.pts 03001627/expert_verified/points_label/5695fd37d1e673cebf964fc57f6a7d6d.seg 03001627 03636649/points/746b82746c6a02cca5f600ed2cf472ac.pts 03636649/expert_verified/points_label/746b82746c6a02cca5f600ed2cf472ac.seg 03636649 03001627/points/bcc4ea0133864bfe4d4c0769270d8651.pts 03001627/expert_verified/points_label/bcc4ea0133864bfe4d4c0769270d8651.seg 03001627 03624134/points/81ba3f06ec38eaa46016d22b1dfacd4b.pts 03624134/expert_verified/points_label/81ba3f06ec38eaa46016d22b1dfacd4b.seg 03624134 04379243/points/2a2d6560f14a01c6afac72146bbc9d59.pts 04379243/expert_verified/points_label/2a2d6560f14a01c6afac72146bbc9d59.seg 04379243 04379243/points/856e86709df98497dcfcef693e7ec696.pts 04379243/expert_verified/points_label/856e86709df98497dcfcef693e7ec696.seg 04379243 03948459/points/7418810de4b13e8430b6ca3ac82edfa3.pts 03948459/expert_verified/points_label/7418810de4b13e8430b6ca3ac82edfa3.seg 03948459 03001627/points/11e0f0dfd3d0b22130ddb6ead95f49cc.pts 03001627/expert_verified/points_label/11e0f0dfd3d0b22130ddb6ead95f49cc.seg 03001627 04379243/points/5c6748b094725d9af008d8a3590fb522.pts 04379243/expert_verified/points_label/5c6748b094725d9af008d8a3590fb522.seg 04379243 04379243/points/17f3a2945d6550cbf7628281ecb18112.pts 04379243/expert_verified/points_label/17f3a2945d6550cbf7628281ecb18112.seg 04379243 04379243/points/889c9aedc4ba47592fb02b79d375eea5.pts 04379243/expert_verified/points_label/889c9aedc4ba47592fb02b79d375eea5.seg 04379243 04379243/points/c0b74c61865b563067dc358060e3c47b.pts 04379243/expert_verified/points_label/c0b74c61865b563067dc358060e3c47b.seg 04379243 03636649/points/783b81aa54a69a26d42b9650f19dd425.pts 03636649/expert_verified/points_label/783b81aa54a69a26d42b9650f19dd425.seg 03636649 03467517/points/8b8b084109eef6d81082f2ea630bf69e.pts 03467517/expert_verified/points_label/8b8b084109eef6d81082f2ea630bf69e.seg 03467517 03001627/points/8a9af7d8a83d90fcd53e36731300f5b4.pts 03001627/expert_verified/points_label/8a9af7d8a83d90fcd53e36731300f5b4.seg 03001627 03001627/points/47aca56ff3a7b8a71a782a4379556c7.pts 03001627/expert_verified/points_label/47aca56ff3a7b8a71a782a4379556c7.seg 03001627 03001627/points/9fae8d94a028e9ec2818b21315fe1bde.pts 03001627/expert_verified/points_label/9fae8d94a028e9ec2818b21315fe1bde.seg 03001627 03001627/points/9a41550ba7dd31e3bf80985a99195eb8.pts 03001627/expert_verified/points_label/9a41550ba7dd31e3bf80985a99195eb8.seg 03001627 03001627/points/184b4797cea77beb5ca1c42bb8ac17a.pts 03001627/expert_verified/points_label/184b4797cea77beb5ca1c42bb8ac17a.seg 03001627 04379243/points/bc1ff7fc750617d690f7bef12e52ac08.pts 04379243/expert_verified/points_label/bc1ff7fc750617d690f7bef12e52ac08.seg 04379243 02691156/points/5fb64e3fc0abe449ca8607f540cc62ba.pts 02691156/expert_verified/points_label/5fb64e3fc0abe449ca8607f540cc62ba.seg 02691156 03001627/points/2e0beb3b6927a2b7e45ef4135c266a12.pts 03001627/expert_verified/points_label/2e0beb3b6927a2b7e45ef4135c266a12.seg 03001627 03467517/points/a38684b166ce2c77c155f88004a92bc8.pts 03467517/expert_verified/points_label/a38684b166ce2c77c155f88004a92bc8.seg 03467517 02691156/points/b590adb6d3486f6e90b1d6deb98feec6.pts 02691156/expert_verified/points_label/b590adb6d3486f6e90b1d6deb98feec6.seg 02691156 03636649/points/9d41e23f00d11d153033d35b49a20c8.pts 03636649/expert_verified/points_label/9d41e23f00d11d153033d35b49a20c8.seg 03636649 03001627/points/f4b141ab64a6c4e771a782a4379556c7.pts 03001627/expert_verified/points_label/f4b141ab64a6c4e771a782a4379556c7.seg 03001627 03948459/points/19e45672a3109f18be4927dbd39f74e9.pts 03948459/expert_verified/points_label/19e45672a3109f18be4927dbd39f74e9.seg 03948459 04379243/points/58475b1b20ece0c5eeb8d422649e5f2b.pts 04379243/expert_verified/points_label/58475b1b20ece0c5eeb8d422649e5f2b.seg 04379243 04379243/points/400393a56fc243c442c39a4fb8d01418.pts 04379243/expert_verified/points_label/400393a56fc243c442c39a4fb8d01418.seg 04379243 03001627/points/a128eda00983dd01fb7d9615be5ab4b0.pts 03001627/expert_verified/points_label/a128eda00983dd01fb7d9615be5ab4b0.seg 03001627 04379243/points/6af9a593129b028eb67e68783d58425a.pts 04379243/expert_verified/points_label/6af9a593129b028eb67e68783d58425a.seg 04379243 03001627/points/40f188600cf8362b654ea6737b0d3597.pts 03001627/expert_verified/points_label/40f188600cf8362b654ea6737b0d3597.seg 03001627 04379243/points/a4af8f822fa8d95456c08464b83f209e.pts 04379243/expert_verified/points_label/a4af8f822fa8d95456c08464b83f209e.seg 04379243 03001627/points/d9558dccfe8e3381e45ef4135c266a12.pts 03001627/expert_verified/points_label/d9558dccfe8e3381e45ef4135c266a12.seg 03001627 04379243/points/631028ddb76eed4dbb0085d0daabdaea.pts 04379243/expert_verified/points_label/631028ddb76eed4dbb0085d0daabdaea.seg 04379243 03001627/points/8967e65c1541d1874aa7f42ef07f614e.pts 03001627/expert_verified/points_label/8967e65c1541d1874aa7f42ef07f614e.seg 03001627 04379243/points/38feb6b209579f6faadbf8208284c675.pts 04379243/expert_verified/points_label/38feb6b209579f6faadbf8208284c675.seg 04379243 03624134/points/60277f4060b8703e4e18d7136dc2dc80.pts 03624134/expert_verified/points_label/60277f4060b8703e4e18d7136dc2dc80.seg 03624134 03467517/points/a78c3356a5dca4e7670b811945485012.pts 03467517/expert_verified/points_label/a78c3356a5dca4e7670b811945485012.seg 03467517 03797390/points/645b0e2ef3b95979204df312eabf367f.pts 03797390/expert_verified/points_label/645b0e2ef3b95979204df312eabf367f.seg 03797390 03467517/points/bd6057c7ac1ef31193f0194265a9746c.pts 03467517/expert_verified/points_label/bd6057c7ac1ef31193f0194265a9746c.seg 03467517 03790512/points/bcbcfdad5e0e1d9ba88e8cb97b773125.pts 03790512/expert_verified/points_label/bcbcfdad5e0e1d9ba88e8cb97b773125.seg 03790512 03636649/points/761fb0822bb05bc8ee0cd746086d989.pts 03636649/expert_verified/points_label/761fb0822bb05bc8ee0cd746086d989.seg 03636649 03636649/points/be13324c84d2a9d72b151d8b52c53b90.pts 03636649/expert_verified/points_label/be13324c84d2a9d72b151d8b52c53b90.seg 03636649 04379243/points/7b3dfbd70333485d219a1300d9489f4e.pts 04379243/expert_verified/points_label/7b3dfbd70333485d219a1300d9489f4e.seg 04379243 04379243/points/22c5cbe6271736bffebad4f49b26ec52.pts 04379243/expert_verified/points_label/22c5cbe6271736bffebad4f49b26ec52.seg 04379243 02958343/points/4b7b3b54dc04df53c19f1e8ed99ac2fa.pts 02958343/expert_verified/points_label/4b7b3b54dc04df53c19f1e8ed99ac2fa.seg 02958343 03636649/points/947c6753d77d8082290e2f84c414e6be.pts 03636649/expert_verified/points_label/947c6753d77d8082290e2f84c414e6be.seg 03636649 02958343/points/36c2770d00fdd0bdf1ee968c9039cc3.pts 02958343/expert_verified/points_label/36c2770d00fdd0bdf1ee968c9039cc3.seg 02958343 03001627/points/4ac17ecd78880859e302b6082b0ffc09.pts 03001627/expert_verified/points_label/4ac17ecd78880859e302b6082b0ffc09.seg 03001627 03636649/points/70b78b9439a9de7530f6e0ede20c4525.pts 03636649/expert_verified/points_label/70b78b9439a9de7530f6e0ede20c4525.seg 03636649 04379243/points/d8be4b45afb21cf1616fb9ab42452112.pts 04379243/expert_verified/points_label/d8be4b45afb21cf1616fb9ab42452112.seg 04379243 02691156/points/fe266c740580c102ff9ce0c50c2cd25a.pts 02691156/expert_verified/points_label/fe266c740580c102ff9ce0c50c2cd25a.seg 02691156 02958343/points/30f4617775480bcce27281f3b76d1f5.pts 02958343/expert_verified/points_label/30f4617775480bcce27281f3b76d1f5.seg 02958343 03467517/points/34874708b51c7ed493f0194265a9746c.pts 03467517/expert_verified/points_label/34874708b51c7ed493f0194265a9746c.seg 03467517 04225987/points/abdc4a823b1f78c397f47f3057557cbe.pts 04225987/expert_verified/points_label/abdc4a823b1f78c397f47f3057557cbe.seg 04225987 03948459/points/14fe99eb0c105a90fc9c56fb43681c11.pts 03948459/expert_verified/points_label/14fe99eb0c105a90fc9c56fb43681c11.seg 03948459 04379243/points/f5aecb6607876495e03eb69820d1aaf2.pts 04379243/expert_verified/points_label/f5aecb6607876495e03eb69820d1aaf2.seg 04379243 03001627/points/3c81fab5678a3872327289c00b6dc9ca.pts 03001627/expert_verified/points_label/3c81fab5678a3872327289c00b6dc9ca.seg 03001627 04379243/points/fe3351c94fbab8ce3002761e7a3ba3bd.pts 04379243/expert_verified/points_label/fe3351c94fbab8ce3002761e7a3ba3bd.seg 04379243 04379243/points/5f0c33039269b7a9f0e84b9d9ad447e2.pts 04379243/expert_verified/points_label/5f0c33039269b7a9f0e84b9d9ad447e2.seg 04379243 03001627/points/fa7347547e290732bf65e1af50b5b7d4.pts 03001627/expert_verified/points_label/fa7347547e290732bf65e1af50b5b7d4.seg 03001627 04379243/points/9c33336af33fd905776d8bc79b9caa2c.pts 04379243/expert_verified/points_label/9c33336af33fd905776d8bc79b9caa2c.seg 04379243 03001627/points/1d828c69106609f8cd783766d090e665.pts 03001627/expert_verified/points_label/1d828c69106609f8cd783766d090e665.seg 03001627 04379243/points/5fbb7a5f01f646ca5830980abc1c717a.pts 04379243/expert_verified/points_label/5fbb7a5f01f646ca5830980abc1c717a.seg 04379243 03636649/points/777a686890d74b350359b4e03cfdfa.pts 03636649/expert_verified/points_label/777a686890d74b350359b4e03cfdfa.seg 03636649 02773838/points/3077a9b76724b6d35de21284bb515a83.pts 02773838/expert_verified/points_label/3077a9b76724b6d35de21284bb515a83.seg 02773838 03642806/points/b233163860361eda8cfacef5204026d6.pts 03642806/expert_verified/points_label/b233163860361eda8cfacef5204026d6.seg 03642806 02958343/points/f10f279643fbb3276a78cd0552215cff.pts 02958343/expert_verified/points_label/f10f279643fbb3276a78cd0552215cff.seg 02958343 02691156/points/2c64c521c114df40e51f766854841067.pts 02691156/expert_verified/points_label/2c64c521c114df40e51f766854841067.seg 02691156 03001627/points/3b8f2b955ee9a904b3c42e318f3affc.pts 03001627/expert_verified/points_label/3b8f2b955ee9a904b3c42e318f3affc.seg 03001627 04379243/points/2a64bd38a4e42f33dc43fde5155b3946.pts 04379243/expert_verified/points_label/2a64bd38a4e42f33dc43fde5155b3946.seg 04379243 03001627/points/52310bca00e6a3671201d487ecde379e.pts 03001627/expert_verified/points_label/52310bca00e6a3671201d487ecde379e.seg 03001627 03001627/points/5346017af72c1843169d299c5f567c18.pts 03001627/expert_verified/points_label/5346017af72c1843169d299c5f567c18.seg 03001627 02954340/points/c1436c38beba0005284432ce2f42f498.pts 02954340/expert_verified/points_label/c1436c38beba0005284432ce2f42f498.seg 02954340 03636649/points/34ce1de178694f87e76bc197b3a3ffc0.pts 03636649/expert_verified/points_label/34ce1de178694f87e76bc197b3a3ffc0.seg 03636649 03001627/points/8e7714615a4b1e6f82390c5f604e0d9b.pts 03001627/expert_verified/points_label/8e7714615a4b1e6f82390c5f604e0d9b.seg 03001627 03948459/points/a3e6dcfc074489fd8ec2966c0323533e.pts 03948459/expert_verified/points_label/a3e6dcfc074489fd8ec2966c0323533e.seg 03948459 02691156/points/3ad337dcef167024fe6302fece358e4a.pts 02691156/expert_verified/points_label/3ad337dcef167024fe6302fece358e4a.seg 02691156 04379243/points/124cc3b92266c2767156f312cf4e035e.pts 04379243/expert_verified/points_label/124cc3b92266c2767156f312cf4e035e.seg 04379243 04379243/points/ee5f0411fcff59951105a3fc18779f13.pts 04379243/expert_verified/points_label/ee5f0411fcff59951105a3fc18779f13.seg 04379243 04379243/points/b1117a83ebf5a4c9c337a931444a5063.pts 04379243/expert_verified/points_label/b1117a83ebf5a4c9c337a931444a5063.seg 04379243 03001627/points/fb847cd696ec711197f2016c3d6097c9.pts 03001627/expert_verified/points_label/fb847cd696ec711197f2016c3d6097c9.seg 03001627 02691156/points/50da48c8e7644508fca1f1143bb6bc17.pts 02691156/expert_verified/points_label/50da48c8e7644508fca1f1143bb6bc17.seg 02691156 02958343/points/78c0bec338fa1c01d6b98bf27ff43caf.pts 02958343/expert_verified/points_label/78c0bec338fa1c01d6b98bf27ff43caf.seg 02958343 02691156/points/37fbd275a734ec1b66cf1b4a8fc3914e.pts 02691156/expert_verified/points_label/37fbd275a734ec1b66cf1b4a8fc3914e.seg 02691156 03636649/points/e053e531fc4341b5fcb8d8c6d4df8143.pts 03636649/expert_verified/points_label/e053e531fc4341b5fcb8d8c6d4df8143.seg 03636649 02691156/points/3db61220251b3c9de719b5362fe06bbb.pts 02691156/expert_verified/points_label/3db61220251b3c9de719b5362fe06bbb.seg 02691156 03642806/points/a7f983f1d0642745135a402b573354e4.pts 03642806/expert_verified/points_label/a7f983f1d0642745135a402b573354e4.seg 03642806 03001627/points/4e26eab28703c12bdd5f3f2440a93d21.pts 03001627/expert_verified/points_label/4e26eab28703c12bdd5f3f2440a93d21.seg 03001627 04225987/points/24e46e195f4907887a70e5e6aa241c88.pts 04225987/expert_verified/points_label/24e46e195f4907887a70e5e6aa241c88.seg 04225987 02691156/points/3ab1e94b6c3a1730c56cc5a87f567365.pts 02691156/expert_verified/points_label/3ab1e94b6c3a1730c56cc5a87f567365.seg 02691156 03001627/points/61b984febe54b752d61420a53a0cb96d.pts 03001627/expert_verified/points_label/61b984febe54b752d61420a53a0cb96d.seg 03001627 04379243/points/adf574f947f00bdd548b2639ebc3e759.pts 04379243/expert_verified/points_label/adf574f947f00bdd548b2639ebc3e759.seg 04379243 03001627/points/ef76b9cbf76bad40586ef70b3cee4240.pts 03001627/expert_verified/points_label/ef76b9cbf76bad40586ef70b3cee4240.seg 03001627 04379243/points/abef0c609ad3e9c2edea4b985280bcc1.pts 04379243/expert_verified/points_label/abef0c609ad3e9c2edea4b985280bcc1.seg 04379243 02773838/points/1b84dededd445058e44a5473032f38f.pts 02773838/expert_verified/points_label/1b84dededd445058e44a5473032f38f.seg 02773838 04379243/points/cd09a9641ea97d873823cce3247aa03b.pts 04379243/expert_verified/points_label/cd09a9641ea97d873823cce3247aa03b.seg 04379243 03636649/points/6aa1ce4e245001589f1a71e46bbde97c.pts 03636649/expert_verified/points_label/6aa1ce4e245001589f1a71e46bbde97c.seg 03636649 04379243/points/bb1aa2cdf216d348e76bc197b3a3ffc0.pts 04379243/expert_verified/points_label/bb1aa2cdf216d348e76bc197b3a3ffc0.seg 04379243 04379243/points/da1e75a8647bfd919778416969ddad32.pts 04379243/expert_verified/points_label/da1e75a8647bfd919778416969ddad32.seg 04379243 02958343/points/3d0308da43d52e3ef56f8ea3d9016e55.pts 02958343/expert_verified/points_label/3d0308da43d52e3ef56f8ea3d9016e55.seg 02958343 04379243/points/1ca75076bcebfac76c3484ac7eef025f.pts 04379243/expert_verified/points_label/1ca75076bcebfac76c3484ac7eef025f.seg 04379243 02691156/points/97ec5b82d9757b639cb1b92881e8e76.pts 02691156/expert_verified/points_label/97ec5b82d9757b639cb1b92881e8e76.seg 02691156 02691156/points/75db11c354c6342aad01ec966c80ac91.pts 02691156/expert_verified/points_label/75db11c354c6342aad01ec966c80ac91.seg 02691156 02691156/points/caf80ecbad22a7384e1799d9d4d697c3.pts 02691156/expert_verified/points_label/caf80ecbad22a7384e1799d9d4d697c3.seg 02691156 03001627/points/d6e0a95f00c7af6fbae0ffb97058b7cc.pts 03001627/expert_verified/points_label/d6e0a95f00c7af6fbae0ffb97058b7cc.seg 03001627 04379243/points/fa72e9cf7308066b1c072ac0b83fe07a.pts 04379243/expert_verified/points_label/fa72e9cf7308066b1c072ac0b83fe07a.seg 04379243 03790512/points/455485399ab75f93429f1c522640e6f0.pts 03790512/expert_verified/points_label/455485399ab75f93429f1c522640e6f0.seg 03790512 03642806/points/241ec8a746dd1cfc78f71a335ebabfa5.pts 03642806/expert_verified/points_label/241ec8a746dd1cfc78f71a335ebabfa5.seg 03642806 04379243/points/c6575b4c39a341c698d5fc0473d00a1c.pts 04379243/expert_verified/points_label/c6575b4c39a341c698d5fc0473d00a1c.seg 04379243 02958343/points/219a0021526791d18bb5c0bf5eec83fc.pts 02958343/expert_verified/points_label/219a0021526791d18bb5c0bf5eec83fc.seg 02958343 02691156/points/49917fb82beca4beca8607f540cc62ba.pts 02691156/expert_verified/points_label/49917fb82beca4beca8607f540cc62ba.seg 02691156 03636649/points/dac278ab197b5efefaa6996ece0d86f4.pts 03636649/expert_verified/points_label/dac278ab197b5efefaa6996ece0d86f4.seg 03636649 03467517/points/f146c58eaa06f5e4d57700c05b1862d8.pts 03467517/expert_verified/points_label/f146c58eaa06f5e4d57700c05b1862d8.seg 03467517 04379243/points/aaf6be1d92a8c61fdcfcef693e7ec696.pts 04379243/expert_verified/points_label/aaf6be1d92a8c61fdcfcef693e7ec696.seg 04379243 03001627/points/46789c1fb150dfaf51f77a6d7299806.pts 03001627/expert_verified/points_label/46789c1fb150dfaf51f77a6d7299806.seg 03001627 03790512/points/4a2f0b20ef680347395d58407f193ba.pts 03790512/expert_verified/points_label/4a2f0b20ef680347395d58407f193ba.seg 03790512 04379243/points/28ce06aa6f25b39f2d19175e7d19b7cb.pts 04379243/expert_verified/points_label/28ce06aa6f25b39f2d19175e7d19b7cb.seg 04379243 02958343/points/1710ff46ca275e171df27141dea8c9a.pts 02958343/expert_verified/points_label/1710ff46ca275e171df27141dea8c9a.seg 02958343 03636649/points/b57bcdb88c669663ec2a7a1f5fe7365d.pts 03636649/expert_verified/points_label/b57bcdb88c669663ec2a7a1f5fe7365d.seg 03636649 04379243/points/c348d279fd22730a9741b7ee128375de.pts 04379243/expert_verified/points_label/c348d279fd22730a9741b7ee128375de.seg 04379243 03001627/points/76fe7cf10c5dbf1edcb466b6f48b5810.pts 03001627/expert_verified/points_label/76fe7cf10c5dbf1edcb466b6f48b5810.seg 03001627 04379243/points/7727cc0cb47705632dfc2f8d5d30193c.pts 04379243/expert_verified/points_label/7727cc0cb47705632dfc2f8d5d30193c.seg 04379243 03797390/points/586e67c53f181dc22adf8abaa25e0215.pts 03797390/expert_verified/points_label/586e67c53f181dc22adf8abaa25e0215.seg 03797390 04379243/points/d9b418e6ec14dbf50efffb055ed6bd1.pts 04379243/expert_verified/points_label/d9b418e6ec14dbf50efffb055ed6bd1.seg 04379243 04379243/points/f52e52094d8240b2dcfcef693e7ec696.pts 04379243/expert_verified/points_label/f52e52094d8240b2dcfcef693e7ec696.seg 04379243 02691156/points/821309c2037b49135fab3f99161dc2c2.pts 02691156/expert_verified/points_label/821309c2037b49135fab3f99161dc2c2.seg 02691156 02954340/points/254e230d31a62470a52821bf1aa3b19a.pts 02954340/expert_verified/points_label/254e230d31a62470a52821bf1aa3b19a.seg 02954340 02691156/points/e8de6c58f4a772d771d03b466c72ce41.pts 02691156/expert_verified/points_label/e8de6c58f4a772d771d03b466c72ce41.seg 02691156 03642806/points/f1c6801e84c85a07bfb149497503af.pts 03642806/expert_verified/points_label/f1c6801e84c85a07bfb149497503af.seg 03642806 02691156/points/a04d10b24ede5e9a3de778e85611513b.pts 02691156/expert_verified/points_label/a04d10b24ede5e9a3de778e85611513b.seg 02691156 03467517/points/c8acdfaec5008118343b0b12983b9982.pts 03467517/expert_verified/points_label/c8acdfaec5008118343b0b12983b9982.seg 03467517 03001627/points/9c3e53d9d1e653c0bf80985a99195eb8.pts 03001627/expert_verified/points_label/9c3e53d9d1e653c0bf80985a99195eb8.seg 03001627 02691156/points/123bd9e948881939c38a1d3458dafa1b.pts 02691156/expert_verified/points_label/123bd9e948881939c38a1d3458dafa1b.seg 02691156 03948459/points/abc7a1373f4b30291adcc40d88daf7c8.pts 03948459/expert_verified/points_label/abc7a1373f4b30291adcc40d88daf7c8.seg 03948459 03636649/points/c906a9c7ae536a0c7fb7f79251dd7727.pts 03636649/expert_verified/points_label/c906a9c7ae536a0c7fb7f79251dd7727.seg 03636649 03797390/points/e71102b6da1d63f3a363b55cbd344baa.pts 03797390/expert_verified/points_label/e71102b6da1d63f3a363b55cbd344baa.seg 03797390 03642806/points/22389f9c3c049ce757c29983a611b1c6.pts 03642806/expert_verified/points_label/22389f9c3c049ce757c29983a611b1c6.seg 03642806 04379243/points/5c2c29fd07c365afe5c65540d3456093.pts 04379243/expert_verified/points_label/5c2c29fd07c365afe5c65540d3456093.seg 04379243 03001627/points/9a8dfc7a6831749f504721639e19f609.pts 03001627/expert_verified/points_label/9a8dfc7a6831749f504721639e19f609.seg 03001627 03001627/points/d49ce87d43cf4c8f1679065e1c457f94.pts 03001627/expert_verified/points_label/d49ce87d43cf4c8f1679065e1c457f94.seg 03001627 02691156/points/dfa36bffe436a98ee0534173b9189765.pts 02691156/expert_verified/points_label/dfa36bffe436a98ee0534173b9189765.seg 02691156 04379243/points/987b7b49a1435a4b1b17743c18fb63dc.pts 04379243/expert_verified/points_label/987b7b49a1435a4b1b17743c18fb63dc.seg 04379243 04379243/points/8d0d7787f4babee7e66285d36ebb986.pts 04379243/expert_verified/points_label/8d0d7787f4babee7e66285d36ebb986.seg 04379243 04379243/points/4f06092100d0164013d2510999d0f1d2.pts 04379243/expert_verified/points_label/4f06092100d0164013d2510999d0f1d2.seg 04379243 02958343/points/fce2b933f93d132f4f45033b2f001552.pts 02958343/expert_verified/points_label/fce2b933f93d132f4f45033b2f001552.seg 02958343 04379243/points/3817a222e96acc4ca78510b72d2281ea.pts 04379243/expert_verified/points_label/3817a222e96acc4ca78510b72d2281ea.seg 04379243 03001627/points/7ee09fdece7d9142afdb9a672b7d3b8a.pts 03001627/expert_verified/points_label/7ee09fdece7d9142afdb9a672b7d3b8a.seg 03001627 04379243/points/676d05aaaeecb8a04b3c42e318f3affc.pts 04379243/expert_verified/points_label/676d05aaaeecb8a04b3c42e318f3affc.seg 04379243 03624134/points/6813197ad5e7011fcc34b900bb2492e.pts 03624134/expert_verified/points_label/6813197ad5e7011fcc34b900bb2492e.seg 03624134 04379243/points/ea367e390741fc38dcfcef693e7ec696.pts 04379243/expert_verified/points_label/ea367e390741fc38dcfcef693e7ec696.seg 04379243 04379243/points/2e5ac0552fa296c43bbab77a66bc3671.pts 04379243/expert_verified/points_label/2e5ac0552fa296c43bbab77a66bc3671.seg 04379243 03467517/points/32a337387527f39193f0194265a9746c.pts 03467517/expert_verified/points_label/32a337387527f39193f0194265a9746c.seg 03467517 03001627/points/97cd4ed02e022ce7174150bd56e389a8.pts 03001627/expert_verified/points_label/97cd4ed02e022ce7174150bd56e389a8.seg 03001627 04379243/points/88e06a85e2a0f99fa7e7cb173e141227.pts 04379243/expert_verified/points_label/88e06a85e2a0f99fa7e7cb173e141227.seg 04379243 04379243/points/c5a02d586ea431a1e76bc197b3a3ffc0.pts 04379243/expert_verified/points_label/c5a02d586ea431a1e76bc197b3a3ffc0.seg 04379243 03001627/points/bcdcb4928e07e4174a623eb2e3317415.pts 03001627/expert_verified/points_label/bcdcb4928e07e4174a623eb2e3317415.seg 03001627 02691156/points/934dd5529c22cd05bc0909d98a1ff2b4.pts 02691156/expert_verified/points_label/934dd5529c22cd05bc0909d98a1ff2b4.seg 02691156 03001627/points/e696f4c7cd88b8b52ff834514c92e8fd.pts 03001627/expert_verified/points_label/e696f4c7cd88b8b52ff834514c92e8fd.seg 03001627 02691156/points/93ba822e84586999e3375a6b96a1d765.pts 02691156/expert_verified/points_label/93ba822e84586999e3375a6b96a1d765.seg 02691156 02958343/points/3ac664a7486a0bdff200a72c9245aee7.pts 02958343/expert_verified/points_label/3ac664a7486a0bdff200a72c9245aee7.seg 02958343 02691156/points/545cadae487b55bbc46ba5100bcdc520.pts 02691156/expert_verified/points_label/545cadae487b55bbc46ba5100bcdc520.seg 02691156 03001627/points/c47f71319ead4eb8a4fb72f4f3b0e317.pts 03001627/expert_verified/points_label/c47f71319ead4eb8a4fb72f4f3b0e317.seg 03001627 04379243/points/39bb09201e0cd201c17e7f250c5222bd.pts 04379243/expert_verified/points_label/39bb09201e0cd201c17e7f250c5222bd.seg 04379243 04379243/points/13782b95eeefcedacf004563556ddb36.pts 04379243/expert_verified/points_label/13782b95eeefcedacf004563556ddb36.seg 04379243 03001627/points/3cc90d903e0ec7aa61e11d707ecb7fa0.pts 03001627/expert_verified/points_label/3cc90d903e0ec7aa61e11d707ecb7fa0.seg 03001627 04379243/points/4079aaabaa6451a2765ca89770f206ec.pts 04379243/expert_verified/points_label/4079aaabaa6451a2765ca89770f206ec.seg 04379243 04379243/points/4bbf789edb243cafc955e5ed03ef3a2f.pts 04379243/expert_verified/points_label/4bbf789edb243cafc955e5ed03ef3a2f.seg 04379243 02773838/points/6187bd900c3bc002ed13f430b2941481.pts 02773838/expert_verified/points_label/6187bd900c3bc002ed13f430b2941481.seg 02773838 04379243/points/6dc6bb97c387b2f3af4e8812cf1b9e1.pts 04379243/expert_verified/points_label/6dc6bb97c387b2f3af4e8812cf1b9e1.seg 04379243 03467517/points/9c260623916034b6f7d037d5768b173f.pts 03467517/expert_verified/points_label/9c260623916034b6f7d037d5768b173f.seg 03467517 02691156/points/8d5c3d38de9c3685f2e77d54f4da142.pts 02691156/expert_verified/points_label/8d5c3d38de9c3685f2e77d54f4da142.seg 02691156 04379243/points/6152e14b042aa17546f41dc2aaef556b.pts 04379243/expert_verified/points_label/6152e14b042aa17546f41dc2aaef556b.seg 04379243 03467517/points/68a8bf89972cd337a77e8142614cdaae.pts 03467517/expert_verified/points_label/68a8bf89972cd337a77e8142614cdaae.seg 03467517 02691156/points/3d5354863690ac7eca27bba175814d1.pts 02691156/expert_verified/points_label/3d5354863690ac7eca27bba175814d1.seg 02691156 04379243/points/3411daa955306811d93768e7b9b1eabf.pts 04379243/expert_verified/points_label/3411daa955306811d93768e7b9b1eabf.seg 04379243 04379243/points/8594658920d6ea7b23656ce81843.pts 04379243/expert_verified/points_label/8594658920d6ea7b23656ce81843.seg 04379243 02691156/points/a074750e28ed3818203936772104a82d.pts 02691156/expert_verified/points_label/a074750e28ed3818203936772104a82d.seg 02691156 04379243/points/fcd4d0e1777f4841dcfcef693e7ec696.pts 04379243/expert_verified/points_label/fcd4d0e1777f4841dcfcef693e7ec696.seg 04379243 03948459/points/708e38e7b733fd22bfae4699de9cb91a.pts 03948459/expert_verified/points_label/708e38e7b733fd22bfae4699de9cb91a.seg 03948459 04379243/points/3c4e1361b066ea3b8ca998f0f87d0c84.pts 04379243/expert_verified/points_label/3c4e1361b066ea3b8ca998f0f87d0c84.seg 04379243 03624134/points/38798b7013607bbf1e0b76f10c6e38af.pts 03624134/expert_verified/points_label/38798b7013607bbf1e0b76f10c6e38af.seg 03624134 02691156/points/2176fa9f69e5e1dcca8607f540cc62ba.pts 02691156/expert_verified/points_label/2176fa9f69e5e1dcca8607f540cc62ba.seg 02691156 03467517/points/8dd7df733a5ba17acae98171fea031ef.pts 03467517/expert_verified/points_label/8dd7df733a5ba17acae98171fea031ef.seg 03467517 03001627/points/d3f31fd0fc99f45e8b3f6b4a44a70e52.pts 03001627/expert_verified/points_label/d3f31fd0fc99f45e8b3f6b4a44a70e52.seg 03001627 02691156/points/118e8142a8cb1fe19a4a28ef635593ce.pts 02691156/expert_verified/points_label/118e8142a8cb1fe19a4a28ef635593ce.seg 02691156 03624134/points/de62211649b4cced49384f9741ad64d8.pts 03624134/expert_verified/points_label/de62211649b4cced49384f9741ad64d8.seg 03624134 03642806/points/7a4342f61ed7b153341aafe10fd0cbd4.pts 03642806/expert_verified/points_label/7a4342f61ed7b153341aafe10fd0cbd4.seg 03642806 03001627/points/ba56f02dee485974c242632b2a8c3129.pts 03001627/expert_verified/points_label/ba56f02dee485974c242632b2a8c3129.seg 03001627 04379243/points/97b7baeb8a172de42f56f09e5bc67bee.pts 04379243/expert_verified/points_label/97b7baeb8a172de42f56f09e5bc67bee.seg 04379243 04379243/points/7b2af227264af938d42b9650f19dd425.pts 04379243/expert_verified/points_label/7b2af227264af938d42b9650f19dd425.seg 04379243 04379243/points/e25fdb977fb867fdc3bd24f986301745.pts 04379243/expert_verified/points_label/e25fdb977fb867fdc3bd24f986301745.seg 04379243 03467517/points/33da9c54f43be3e17693a84bff425e3.pts 03467517/expert_verified/points_label/33da9c54f43be3e17693a84bff425e3.seg 03467517 02691156/points/e1e5cfcabcbe26a03087f84b199fd297.pts 02691156/expert_verified/points_label/e1e5cfcabcbe26a03087f84b199fd297.seg 02691156 03636649/points/ba05811f301cdd791735ea0e092a805a.pts 03636649/expert_verified/points_label/ba05811f301cdd791735ea0e092a805a.seg 03636649 03001627/points/6678f63c9b584a549d9e5580ae9f8738.pts 03001627/expert_verified/points_label/6678f63c9b584a549d9e5580ae9f8738.seg 03001627 04379243/points/b6b8ede77085c0a95bea7c29e873d16.pts 04379243/expert_verified/points_label/b6b8ede77085c0a95bea7c29e873d16.seg 04379243 02691156/points/d81042a53dd1cc5bd90bfc986bc4c94d.pts 02691156/expert_verified/points_label/d81042a53dd1cc5bd90bfc986bc4c94d.seg 02691156 03001627/points/37b432326fecc8a1327289c00b6dc9ca.pts 03001627/expert_verified/points_label/37b432326fecc8a1327289c00b6dc9ca.seg 03001627 03636649/points/c898f9b1dddbb8801735ea0e092a805a.pts 03636649/expert_verified/points_label/c898f9b1dddbb8801735ea0e092a805a.seg 03636649 03001627/points/5d02aed0e9c93e829b9f2eb77f5e247e.pts 03001627/expert_verified/points_label/5d02aed0e9c93e829b9f2eb77f5e247e.seg 03001627 03001627/points/9a864d5de972a8c7cb686b8b855fed61.pts 03001627/expert_verified/points_label/9a864d5de972a8c7cb686b8b855fed61.seg 03001627 04379243/points/b14a14cc2f3c38c9e3def9c422df2282.pts 04379243/expert_verified/points_label/b14a14cc2f3c38c9e3def9c422df2282.seg 04379243 04379243/points/f2893a87ec37f8b3781cb4570305e329.pts 04379243/expert_verified/points_label/f2893a87ec37f8b3781cb4570305e329.seg 04379243 02691156/points/3fa511e1882e41eeca8607f540cc62ba.pts 02691156/expert_verified/points_label/3fa511e1882e41eeca8607f540cc62ba.seg 02691156 02691156/points/444d67950ff9a4cc1139bebb00fe5be8.pts 02691156/expert_verified/points_label/444d67950ff9a4cc1139bebb00fe5be8.seg 02691156 03001627/points/3d3b7f63f5525b1ae37f5a622d383617.pts 03001627/expert_verified/points_label/3d3b7f63f5525b1ae37f5a622d383617.seg 03001627 03001627/points/30beaf15d2d2beb1febad4f49b26ec52.pts 03001627/expert_verified/points_label/30beaf15d2d2beb1febad4f49b26ec52.seg 03001627 04379243/points/59f04ddbd896f4f5430644dfe647c381.pts 04379243/expert_verified/points_label/59f04ddbd896f4f5430644dfe647c381.seg 04379243 04379243/points/eb9b9b8d186a974a7afee304cce81d6f.pts 04379243/expert_verified/points_label/eb9b9b8d186a974a7afee304cce81d6f.seg 04379243 03790512/points/7c4fc3a05d5fc8b1d0f568c31c1cd62a.pts 03790512/expert_verified/points_label/7c4fc3a05d5fc8b1d0f568c31c1cd62a.seg 03790512 04379243/points/68142013a4f5e7c2febad4f49b26ec52.pts 04379243/expert_verified/points_label/68142013a4f5e7c2febad4f49b26ec52.seg 04379243 02958343/points/8053e014516531ddc3f500d7b182f6.pts 02958343/expert_verified/points_label/8053e014516531ddc3f500d7b182f6.seg 02958343 02958343/points/1a3782ae4bd711b66b418c7d9fedcaa9.pts 02958343/expert_verified/points_label/1a3782ae4bd711b66b418c7d9fedcaa9.seg 02958343 04379243/points/cc58de930acd321fac242c3aebc81b2f.pts 04379243/expert_verified/points_label/cc58de930acd321fac242c3aebc81b2f.seg 04379243 02691156/points/d4dac019726e980e203936772104a82d.pts 02691156/expert_verified/points_label/d4dac019726e980e203936772104a82d.seg 02691156 02954340/points/6e983d20e0bf80296829cd4082fbdbdf.pts 02954340/expert_verified/points_label/6e983d20e0bf80296829cd4082fbdbdf.seg 02954340 03636649/points/fad026744a6abb1937cf479d4bb58d.pts 03636649/expert_verified/points_label/fad026744a6abb1937cf479d4bb58d.seg 03636649 02958343/points/4d2d4e26349be1f3be2cbcda9b6dc9b2.pts 02958343/expert_verified/points_label/4d2d4e26349be1f3be2cbcda9b6dc9b2.seg 02958343 03636649/points/280fa01686e780ba3501c961e91ff6d7.pts 03636649/expert_verified/points_label/280fa01686e780ba3501c961e91ff6d7.seg 03636649 04379243/points/f02907c5c42e1e766f1e07a56c129dfc.pts 04379243/expert_verified/points_label/f02907c5c42e1e766f1e07a56c129dfc.seg 04379243 04379243/points/5f100571ffd90f8252b4875f731f71cd.pts 04379243/expert_verified/points_label/5f100571ffd90f8252b4875f731f71cd.seg 04379243 04379243/points/f718cb5d6202341dc183308b9aafe2ca.pts 04379243/expert_verified/points_label/f718cb5d6202341dc183308b9aafe2ca.seg 04379243 03642806/points/b436271050d647052f8d6d501b18a4b5.pts 03642806/expert_verified/points_label/b436271050d647052f8d6d501b18a4b5.seg 03642806 03001627/points/6dddf2b95ca09bf5febad4f49b26ec52.pts 03001627/expert_verified/points_label/6dddf2b95ca09bf5febad4f49b26ec52.seg 03001627 02691156/points/b812c2df636aa0218b96ae1a0a8b84ec.pts 02691156/expert_verified/points_label/b812c2df636aa0218b96ae1a0a8b84ec.seg 02691156 02958343/points/89edb3d434f4c983afe1d4530f4c6e24.pts 02958343/expert_verified/points_label/89edb3d434f4c983afe1d4530f4c6e24.seg 02958343 02958343/points/80ac9cc0d4c9dde3b7a7bc444c2d756b.pts 02958343/expert_verified/points_label/80ac9cc0d4c9dde3b7a7bc444c2d756b.seg 02958343 04379243/points/b62d45745434ac46c4cfe384be4426c3.pts 04379243/expert_verified/points_label/b62d45745434ac46c4cfe384be4426c3.seg 04379243 04379243/points/9c4afb731e910d3723500a5b036df62e.pts 04379243/expert_verified/points_label/9c4afb731e910d3723500a5b036df62e.seg 04379243 04379243/points/43fcddd5232a6021a56e8b79ca4e2911.pts 04379243/expert_verified/points_label/43fcddd5232a6021a56e8b79ca4e2911.seg 04379243 04379243/points/6724ae69c0bde4c09b7dad6c9c46bcf1.pts 04379243/expert_verified/points_label/6724ae69c0bde4c09b7dad6c9c46bcf1.seg 04379243 03001627/points/323fc7b1d2b44cb7ff2b8acf844d34d2.pts 03001627/expert_verified/points_label/323fc7b1d2b44cb7ff2b8acf844d34d2.seg 03001627 03001627/points/434cee44934612a81f98c0761af40e04.pts 03001627/expert_verified/points_label/434cee44934612a81f98c0761af40e04.seg 03001627 03636649/points/31dee666120727b0be78c8b300d2a963.pts 03636649/expert_verified/points_label/31dee666120727b0be78c8b300d2a963.seg 03636649 02958343/points/48f5446e6ac9c1b51f1446551412bde4.pts 02958343/expert_verified/points_label/48f5446e6ac9c1b51f1446551412bde4.seg 02958343 04379243/points/aa3eb180a4f6d8d42de421c2ab5cfb52.pts 04379243/expert_verified/points_label/aa3eb180a4f6d8d42de421c2ab5cfb52.seg 04379243 04379243/points/14e5e4db3246dacff12d7184a2ad3430.pts 04379243/expert_verified/points_label/14e5e4db3246dacff12d7184a2ad3430.seg 04379243 03001627/points/96c0ecd1ef80e818c8687ff9b0b4e4ac.pts 03001627/expert_verified/points_label/96c0ecd1ef80e818c8687ff9b0b4e4ac.seg 03001627 04225987/points/d4c042d11f29dffa1082f2ea630bf69e.pts 04225987/expert_verified/points_label/d4c042d11f29dffa1082f2ea630bf69e.seg 04225987 03642806/points/7ebff305b2e93504239603972bcd2e7b.pts 03642806/expert_verified/points_label/7ebff305b2e93504239603972bcd2e7b.seg 03642806 03467517/points/369fc7f8d880e1b793f0194265a9746c.pts 03467517/expert_verified/points_label/369fc7f8d880e1b793f0194265a9746c.seg 03467517 04379243/points/25f69a74efbff4d071a782a4379556c7.pts 04379243/expert_verified/points_label/25f69a74efbff4d071a782a4379556c7.seg 04379243 04379243/points/7cd4844def36a9f5bc7589eefbdbc3c5.pts 04379243/expert_verified/points_label/7cd4844def36a9f5bc7589eefbdbc3c5.seg 04379243 03467517/points/5852a24dde24a8ef93f0194265a9746c.pts 03467517/expert_verified/points_label/5852a24dde24a8ef93f0194265a9746c.seg 03467517 03001627/points/df8440d8678f3a91c8687ff9b0b4e4ac.pts 03001627/expert_verified/points_label/df8440d8678f3a91c8687ff9b0b4e4ac.seg 03001627 04379243/points/49bf25ff4401946524c10ba1eb690638.pts 04379243/expert_verified/points_label/49bf25ff4401946524c10ba1eb690638.seg 04379243 03001627/points/7eedcb6d76b8c23a9cdb421f6af95e5f.pts 03001627/expert_verified/points_label/7eedcb6d76b8c23a9cdb421f6af95e5f.seg 03001627 03797390/points/ff1a44e1c1785d618bca309f2c51966a.pts 03797390/expert_verified/points_label/ff1a44e1c1785d618bca309f2c51966a.seg 03797390 02958343/points/85f3dc3318f5200c8672c9b355cd2075.pts 02958343/expert_verified/points_label/85f3dc3318f5200c8672c9b355cd2075.seg 02958343 02691156/points/c9be9f07f5ae7c375d7629390efe0a2.pts 02691156/expert_verified/points_label/c9be9f07f5ae7c375d7629390efe0a2.seg 02691156 02691156/points/14cd2f1de7f68bf3ab550998f901c8e1.pts 02691156/expert_verified/points_label/14cd2f1de7f68bf3ab550998f901c8e1.seg 02691156 02958343/points/81fad64b8fd8f010b17445a1c29f6d34.pts 02958343/expert_verified/points_label/81fad64b8fd8f010b17445a1c29f6d34.seg 02958343 02958343/points/fe2ce22107693354f1cc1cb691702a23.pts 02958343/expert_verified/points_label/fe2ce22107693354f1cc1cb691702a23.seg 02958343 02691156/points/74cbf170c5f2fb587d9c9c8a8ba32919.pts 02691156/expert_verified/points_label/74cbf170c5f2fb587d9c9c8a8ba32919.seg 02691156 02691156/points/67dbb0de722cf5cd7a734abc5ba1db0f.pts 02691156/expert_verified/points_label/67dbb0de722cf5cd7a734abc5ba1db0f.seg 02691156 04379243/points/fa345f8f107d93b9ba70f71694a4b74c.pts 04379243/expert_verified/points_label/fa345f8f107d93b9ba70f71694a4b74c.seg 04379243 04379243/points/a45a7ba9a2842a55634c21965ee6bab.pts 04379243/expert_verified/points_label/a45a7ba9a2842a55634c21965ee6bab.seg 04379243 04379243/points/8d7ac6078989980fad16260d4d73b56.pts 04379243/expert_verified/points_label/8d7ac6078989980fad16260d4d73b56.seg 04379243 03001627/points/e803b31e2185d0405784b22e1081a3e1.pts 03001627/expert_verified/points_label/e803b31e2185d0405784b22e1081a3e1.seg 03001627 04379243/points/aaf3aeda0f848344b87028a4b477349f.pts 04379243/expert_verified/points_label/aaf3aeda0f848344b87028a4b477349f.seg 04379243 03636649/points/e94aab17400945413225afab722d9fd2.pts 03636649/expert_verified/points_label/e94aab17400945413225afab722d9fd2.seg 03636649 03001627/points/d2c465e85d2e8f1fcea003eff0268278.pts 03001627/expert_verified/points_label/d2c465e85d2e8f1fcea003eff0268278.seg 03001627 03001627/points/88376e3d3a23d263de29d28278a34a18.pts 03001627/expert_verified/points_label/88376e3d3a23d263de29d28278a34a18.seg 03001627 04379243/points/4775e71d37374444febad4f49b26ec52.pts 04379243/expert_verified/points_label/4775e71d37374444febad4f49b26ec52.seg 04379243 03636649/points/f12822778713f5e35b36bbc16e99b441.pts 03636649/expert_verified/points_label/f12822778713f5e35b36bbc16e99b441.seg 03636649 03636649/points/963e6743370d5c5c9b5d51fa8cce1753.pts 03636649/expert_verified/points_label/963e6743370d5c5c9b5d51fa8cce1753.seg 03636649 04379243/points/13c51c08c3695a09eda47978b73f5994.pts 04379243/expert_verified/points_label/13c51c08c3695a09eda47978b73f5994.seg 04379243 04379243/points/89827ac677337629ab610b0c94236463.pts 04379243/expert_verified/points_label/89827ac677337629ab610b0c94236463.seg 04379243 04379243/points/89b478643e53d3d6285c99063fc6fcf8.pts 04379243/expert_verified/points_label/89b478643e53d3d6285c99063fc6fcf8.seg 04379243 04379243/points/401cd99ace3b92fadf6cfab91d65bb91.pts 04379243/expert_verified/points_label/401cd99ace3b92fadf6cfab91d65bb91.seg 04379243 04379243/points/74c3d551e32a1cca664b3b9b23ddfcbc.pts 04379243/expert_verified/points_label/74c3d551e32a1cca664b3b9b23ddfcbc.seg 04379243 04379243/points/db64db160fd13a514e1a714ee619465a.pts 04379243/expert_verified/points_label/db64db160fd13a514e1a714ee619465a.seg 04379243 03001627/points/8e664a0bcaf9d2a45ca1aaa0789db621.pts 03001627/expert_verified/points_label/8e664a0bcaf9d2a45ca1aaa0789db621.seg 03001627 03001627/points/43897195d7f893d759c257be4c612509.pts 03001627/expert_verified/points_label/43897195d7f893d759c257be4c612509.seg 03001627 04379243/points/e6d8569c0957e7453002761e7a3ba3bd.pts 04379243/expert_verified/points_label/e6d8569c0957e7453002761e7a3ba3bd.seg 04379243 03636649/points/ead77648c9c7dbf8d42b9650f19dd425.pts 03636649/expert_verified/points_label/ead77648c9c7dbf8d42b9650f19dd425.seg 03636649 03636649/points/c54d3a5a9c8a655e46407779dbd69b2d.pts 03636649/expert_verified/points_label/c54d3a5a9c8a655e46407779dbd69b2d.seg 03636649 03001627/points/379f0efc898d7a7e9fe74a48bbc553d7.pts 03001627/expert_verified/points_label/379f0efc898d7a7e9fe74a48bbc553d7.seg 03001627 04379243/points/c1d44782ac45d6fe3671949e4f99cc76.pts 04379243/expert_verified/points_label/c1d44782ac45d6fe3671949e4f99cc76.seg 04379243 04379243/points/7b3b160dafe7e122d93768e7b9b1eabf.pts 04379243/expert_verified/points_label/7b3b160dafe7e122d93768e7b9b1eabf.seg 04379243 03001627/points/7f271ecbdeb7610d637adadafee6f182.pts 03001627/expert_verified/points_label/7f271ecbdeb7610d637adadafee6f182.seg 03001627 02958343/points/df34c25a1e1abe9428044fe9244db50a.pts 02958343/expert_verified/points_label/df34c25a1e1abe9428044fe9244db50a.seg 02958343 03948459/points/98c0bd351e275b3c96893524e607761d.pts 03948459/expert_verified/points_label/98c0bd351e275b3c96893524e607761d.seg 03948459 03636649/points/b96c8cc6529167bfcb8d8c6d4df8143.pts 03636649/expert_verified/points_label/b96c8cc6529167bfcb8d8c6d4df8143.seg 03636649 03624134/points/a33847e9c32c1afc93ac017b81605788.pts 03624134/expert_verified/points_label/a33847e9c32c1afc93ac017b81605788.seg 03624134 03001627/points/594d5b7f3e705a1ab3234e0da44b11e4.pts 03001627/expert_verified/points_label/594d5b7f3e705a1ab3234e0da44b11e4.seg 03001627 03001627/points/f0f04644e071d9348ca588a3264b9f86.pts 03001627/expert_verified/points_label/f0f04644e071d9348ca588a3264b9f86.seg 03001627 02691156/points/4bdb2c4fc6701174ca8607f540cc62ba.pts 02691156/expert_verified/points_label/4bdb2c4fc6701174ca8607f540cc62ba.seg 02691156 03001627/points/fc2a1c4c332f7731e45ef4135c266a12.pts 03001627/expert_verified/points_label/fc2a1c4c332f7731e45ef4135c266a12.seg 03001627 02691156/points/df68b8fb9f4531b42e690fa6dfd5d610.pts 02691156/expert_verified/points_label/df68b8fb9f4531b42e690fa6dfd5d610.seg 02691156 03642806/points/517de75577ac6e8a42b9615216f9a30d.pts 03642806/expert_verified/points_label/517de75577ac6e8a42b9615216f9a30d.seg 03642806 03001627/points/74cc57ea0e2e06dbe4106b1d06dc89b3.pts 03001627/expert_verified/points_label/74cc57ea0e2e06dbe4106b1d06dc89b3.seg 03001627 02691156/points/d72a483cf8a0cf2bbbf3143b1cb6076a.pts 02691156/expert_verified/points_label/d72a483cf8a0cf2bbbf3143b1cb6076a.seg 02691156 03001627/points/9c7b2ed3770d1a6ea6fee8e2140acec9.pts 03001627/expert_verified/points_label/9c7b2ed3770d1a6ea6fee8e2140acec9.seg 03001627 04379243/points/28fb9a81898f88c4ae8375def5e736d8.pts 04379243/expert_verified/points_label/28fb9a81898f88c4ae8375def5e736d8.seg 04379243 03636649/points/c0b0d7e15d3dfab1733c22d8b8e1c33d.pts 03636649/expert_verified/points_label/c0b0d7e15d3dfab1733c22d8b8e1c33d.seg 03636649 03001627/points/bb04dc0b336abf4b263915c09bc4854f.pts 03001627/expert_verified/points_label/bb04dc0b336abf4b263915c09bc4854f.seg 03001627 03001627/points/6caccdad9f8d4f0a7f1cdfc0a8f38f2e.pts 03001627/expert_verified/points_label/6caccdad9f8d4f0a7f1cdfc0a8f38f2e.seg 03001627 04379243/points/86ad91ef08c53dd77189b31b3e8c8ef3.pts 04379243/expert_verified/points_label/86ad91ef08c53dd77189b31b3e8c8ef3.seg 04379243 03790512/points/80e717f07645a4a0b37378f3c85478b4.pts 03790512/expert_verified/points_label/80e717f07645a4a0b37378f3c85478b4.seg 03790512 02691156/points/7d226c520a29c7705e28caa3b26a73fd.pts 02691156/expert_verified/points_label/7d226c520a29c7705e28caa3b26a73fd.seg 02691156 04379243/points/89c095a52766ecb05d2ac47f638a4ea4.pts 04379243/expert_verified/points_label/89c095a52766ecb05d2ac47f638a4ea4.seg 04379243 04379243/points/7b92f6facc2a27bc84cc0348a73b80c3.pts 04379243/expert_verified/points_label/7b92f6facc2a27bc84cc0348a73b80c3.seg 04379243 04379243/points/d578287c4a9452efa9af104529ef47c3.pts 04379243/expert_verified/points_label/d578287c4a9452efa9af104529ef47c3.seg 04379243 03636649/points/1475fe59961fc726f096eadaad23f93d.pts 03636649/expert_verified/points_label/1475fe59961fc726f096eadaad23f93d.seg 03636649 03790512/points/7d75e8200565ffa7b37378f3c85478b4.pts 03790512/expert_verified/points_label/7d75e8200565ffa7b37378f3c85478b4.seg 03790512 04379243/points/852826a94cce36ea9f1deb04fb8ae481.pts 04379243/expert_verified/points_label/852826a94cce36ea9f1deb04fb8ae481.seg 04379243 03001627/points/9c50878c91aeb8126bb6bc0db07c71e8.pts 03001627/expert_verified/points_label/9c50878c91aeb8126bb6bc0db07c71e8.seg 03001627 02691156/points/ce827e4c857d553f71d03b466c72ce41.pts 02691156/expert_verified/points_label/ce827e4c857d553f71d03b466c72ce41.seg 02691156 03001627/points/3aab16309520fb21dc0a8cba62d9a78a.pts 03001627/expert_verified/points_label/3aab16309520fb21dc0a8cba62d9a78a.seg 03001627 03001627/points/697cfbe6e043136b737a00f007529fbf.pts 03001627/expert_verified/points_label/697cfbe6e043136b737a00f007529fbf.seg 03001627 04379243/points/fd7769d0eba554c53def89b32cef8e45.pts 04379243/expert_verified/points_label/fd7769d0eba554c53def89b32cef8e45.seg 04379243 03948459/points/d7e86e0e5b1982d4bf0ab4d7096d87f2.pts 03948459/expert_verified/points_label/d7e86e0e5b1982d4bf0ab4d7096d87f2.seg 03948459 03001627/points/70cb8d70d961ca48b04cb542e2c50eb4.pts 03001627/expert_verified/points_label/70cb8d70d961ca48b04cb542e2c50eb4.seg 03001627 03636649/points/c26b7862f2afb7ee4b3c42e318f3affc.pts 03636649/expert_verified/points_label/c26b7862f2afb7ee4b3c42e318f3affc.seg 03636649 03624134/points/906b20dc0a5a5022714112b147c95c8b.pts 03624134/expert_verified/points_label/906b20dc0a5a5022714112b147c95c8b.seg 03624134 03001627/points/f5caa9b5ada31a8b3cf15c77de45986.pts 03001627/expert_verified/points_label/f5caa9b5ada31a8b3cf15c77de45986.seg 03001627 04379243/points/6110d87def4fa88c154c6bbaeb7d331f.pts 04379243/expert_verified/points_label/6110d87def4fa88c154c6bbaeb7d331f.seg 04379243 03642806/points/b5f6fd84a3f44ddb1aa47689117a61e1.pts 03642806/expert_verified/points_label/b5f6fd84a3f44ddb1aa47689117a61e1.seg 03642806 03001627/points/95317d46812e4ed4df5aea2392d894b4.pts 03001627/expert_verified/points_label/95317d46812e4ed4df5aea2392d894b4.seg 03001627 02691156/points/471ca950dbdf0c6c5f80f808704d6409.pts 02691156/expert_verified/points_label/471ca950dbdf0c6c5f80f808704d6409.seg 02691156 04379243/points/c9f85a671d551086d61f9b2773e1d72a.pts 04379243/expert_verified/points_label/c9f85a671d551086d61f9b2773e1d72a.seg 04379243 04379243/points/70f1b5f74faa9bda664b3b9b23ddfcbc.pts 04379243/expert_verified/points_label/70f1b5f74faa9bda664b3b9b23ddfcbc.seg 04379243 02691156/points/9a266b3a734e374687bf26680c510802.pts 02691156/expert_verified/points_label/9a266b3a734e374687bf26680c510802.seg 02691156 03001627/points/4c0983329afcd06f730e89ca0d2d13c3.pts 03001627/expert_verified/points_label/4c0983329afcd06f730e89ca0d2d13c3.seg 03001627 04379243/points/a7172fa4177661f4858699aaad4acee4.pts 04379243/expert_verified/points_label/a7172fa4177661f4858699aaad4acee4.seg 04379243 04379243/points/504d908a55f3e0c764810cc21086da42.pts 04379243/expert_verified/points_label/504d908a55f3e0c764810cc21086da42.seg 04379243 03948459/points/7ba9f65e926d5e3e6fe695987d47043.pts 03948459/expert_verified/points_label/7ba9f65e926d5e3e6fe695987d47043.seg 03948459 04379243/points/5b546ef5de5d10f3ecc9201d3d846bc1.pts 04379243/expert_verified/points_label/5b546ef5de5d10f3ecc9201d3d846bc1.seg 04379243 04379243/points/80f986ae572fce791429f9a19502375a.pts 04379243/expert_verified/points_label/80f986ae572fce791429f9a19502375a.seg 04379243 04379243/points/fd7a579772b195532de421c2ab5cfb52.pts 04379243/expert_verified/points_label/fd7a579772b195532de421c2ab5cfb52.seg 04379243 03001627/points/e09466e9c122dbfdf51f77a6d7299806.pts 03001627/expert_verified/points_label/e09466e9c122dbfdf51f77a6d7299806.seg 03001627 04379243/points/2a80c95b4bbcb73d87ed2480ebb0f3d2.pts 04379243/expert_verified/points_label/2a80c95b4bbcb73d87ed2480ebb0f3d2.seg 04379243 03467517/points/e0d74618e316b0f16d9376f644442e99.pts 03467517/expert_verified/points_label/e0d74618e316b0f16d9376f644442e99.seg 03467517 03001627/points/587ebb2aa71acfe644dd3aaee16d3f4c.pts 03001627/expert_verified/points_label/587ebb2aa71acfe644dd3aaee16d3f4c.seg 03001627 03467517/points/10d2c216c70b788485b61f146daff2fb.pts 03467517/expert_verified/points_label/10d2c216c70b788485b61f146daff2fb.seg 03467517 04379243/points/3c72ddd0dca19bbedcfcef693e7ec696.pts 04379243/expert_verified/points_label/3c72ddd0dca19bbedcfcef693e7ec696.seg 04379243 03001627/points/2742c0a5e984d92fa0dcc52ca811e565.pts 03001627/expert_verified/points_label/2742c0a5e984d92fa0dcc52ca811e565.seg 03001627 03624134/points/792f252dcb06f042dd56c1edf3f6e336.pts 03624134/expert_verified/points_label/792f252dcb06f042dd56c1edf3f6e336.seg 03624134 02691156/points/8fa9e2e8dbed43911f32208e53f871eb.pts 02691156/expert_verified/points_label/8fa9e2e8dbed43911f32208e53f871eb.seg 02691156 03001627/points/d4f5c3e3eab52d0a3334fb6668ccd834.pts 03001627/expert_verified/points_label/d4f5c3e3eab52d0a3334fb6668ccd834.seg 03001627 03642806/points/520d98e360cf44ec8139dd63d55edc44.pts 03642806/expert_verified/points_label/520d98e360cf44ec8139dd63d55edc44.seg 03642806 03467517/points/2eba922263fc1580cc010a80df5d3c87.pts 03467517/expert_verified/points_label/2eba922263fc1580cc010a80df5d3c87.seg 03467517 04379243/points/53c11596c3fc36a8a5094cb6d104b35.pts 04379243/expert_verified/points_label/53c11596c3fc36a8a5094cb6d104b35.seg 04379243 03467517/points/265009e163bf5c6f69da8e7f9a803d12.pts 03467517/expert_verified/points_label/265009e163bf5c6f69da8e7f9a803d12.seg 03467517 04379243/points/fbdf9bffeb353474c3a767747b75e56.pts 04379243/expert_verified/points_label/fbdf9bffeb353474c3a767747b75e56.seg 04379243 03636649/points/b4af7e9a7338a9a3225afab722d9fd2.pts 03636649/expert_verified/points_label/b4af7e9a7338a9a3225afab722d9fd2.seg 03636649 03001627/points/55eeb952519ceb87c3bd24f986301745.pts 03001627/expert_verified/points_label/55eeb952519ceb87c3bd24f986301745.seg 03001627 04379243/points/2259e09ebd0ed2befebad4f49b26ec52.pts 04379243/expert_verified/points_label/2259e09ebd0ed2befebad4f49b26ec52.seg 04379243 04379243/points/63fedc0334f5552dbec3a71604e140e3.pts 04379243/expert_verified/points_label/63fedc0334f5552dbec3a71604e140e3.seg 04379243 03001627/points/70ac5cb405df84575e62305d14755686.pts 03001627/expert_verified/points_label/70ac5cb405df84575e62305d14755686.seg 03001627 03001627/points/3f41b4339ebd59c1c397356311cbeea4.pts 03001627/expert_verified/points_label/3f41b4339ebd59c1c397356311cbeea4.seg 03001627 04379243/points/10bb44a54a12a74e4719088c8e42c6ab.pts 04379243/expert_verified/points_label/10bb44a54a12a74e4719088c8e42c6ab.seg 04379243 04379243/points/a83cda80e5c5a0fc3719086e0b4ab8be.pts 04379243/expert_verified/points_label/a83cda80e5c5a0fc3719086e0b4ab8be.seg 04379243 04379243/points/74983e99e7606eb114708467db3d00e2.pts 04379243/expert_verified/points_label/74983e99e7606eb114708467db3d00e2.seg 04379243 03001627/points/e052eaa1d5bbe795ded10515704c9720.pts 03001627/expert_verified/points_label/e052eaa1d5bbe795ded10515704c9720.seg 03001627 02691156/points/35892510dcd7cebb87bf26680c510802.pts 02691156/expert_verified/points_label/35892510dcd7cebb87bf26680c510802.seg 02691156 03001627/points/7f73cc6c1c9121a9b9f2eb77f5e247e.pts 03001627/expert_verified/points_label/7f73cc6c1c9121a9b9f2eb77f5e247e.seg 03001627 03001627/points/2a8554af80cfa5e719fb4103277a6b93.pts 03001627/expert_verified/points_label/2a8554af80cfa5e719fb4103277a6b93.seg 03001627 04379243/points/f82a5f3c2a57655d825da2b9ec9c8c29.pts 04379243/expert_verified/points_label/f82a5f3c2a57655d825da2b9ec9c8c29.seg 04379243 02691156/points/319cf93077118d19f64801ad2940cdd5.pts 02691156/expert_verified/points_label/319cf93077118d19f64801ad2940cdd5.seg 02691156 03790512/points/5bb3597d49c58017b37378f3c85478b4.pts 03790512/expert_verified/points_label/5bb3597d49c58017b37378f3c85478b4.seg 03790512 02958343/points/17926c1ef484b73e6758a098566bc94e.pts 02958343/expert_verified/points_label/17926c1ef484b73e6758a098566bc94e.seg 02958343 04379243/points/345c1bb95b12ff8c013a7bed5288654.pts 04379243/expert_verified/points_label/345c1bb95b12ff8c013a7bed5288654.seg 04379243 03001627/points/3b788994cd578990c35131da26f8061a.pts 03001627/expert_verified/points_label/3b788994cd578990c35131da26f8061a.seg 03001627 03636649/points/c25cc72cd06852e75bbea6ee257e41cc.pts 03636649/expert_verified/points_label/c25cc72cd06852e75bbea6ee257e41cc.seg 03636649 03001627/points/4e4570768f981ca7b95617254e8005c0.pts 03001627/expert_verified/points_label/4e4570768f981ca7b95617254e8005c0.seg 03001627 03642806/points/ef6d92c90aeabf5becae27d182a3e41c.pts 03642806/expert_verified/points_label/ef6d92c90aeabf5becae27d182a3e41c.seg 03642806 04379243/points/97718e2651d22b3a74740f837351e7eb.pts 04379243/expert_verified/points_label/97718e2651d22b3a74740f837351e7eb.seg 04379243 03948459/points/1f646ff59cabdddcd810dcd63f342aca.pts 03948459/expert_verified/points_label/1f646ff59cabdddcd810dcd63f342aca.seg 03948459 02958343/points/74f7b559d6af926012f2e446484bbaf7.pts 02958343/expert_verified/points_label/74f7b559d6af926012f2e446484bbaf7.seg 02958343 03001627/points/8b3619396de4df10db8860d0872e9c55.pts 03001627/expert_verified/points_label/8b3619396de4df10db8860d0872e9c55.seg 03001627 03001627/points/44ddb3d46266bb0ffebad4f49b26ec52.pts 03001627/expert_verified/points_label/44ddb3d46266bb0ffebad4f49b26ec52.seg 03001627 03001627/points/a5f300f3975497fa9dcf2183c858e6e5.pts 03001627/expert_verified/points_label/a5f300f3975497fa9dcf2183c858e6e5.seg 03001627 03467517/points/113b65f0e68314737c481698bd5233b4.pts 03467517/expert_verified/points_label/113b65f0e68314737c481698bd5233b4.seg 03467517 03001627/points/49795a9ebd9a9c6d2c697f0a1454869.pts 03001627/expert_verified/points_label/49795a9ebd9a9c6d2c697f0a1454869.seg 03001627 03001627/points/5822ae77b06bea3091da37ff8bdd2524.pts 03001627/expert_verified/points_label/5822ae77b06bea3091da37ff8bdd2524.seg 03001627 03467517/points/15222c5926c7058cc6df7dab8e567ef6.pts 03467517/expert_verified/points_label/15222c5926c7058cc6df7dab8e567ef6.seg 03467517 02691156/points/14d9c576d06622198f52dc705c3109b9.pts 02691156/expert_verified/points_label/14d9c576d06622198f52dc705c3109b9.seg 02691156 04379243/points/62ae9ded861138be9d2be74cfb51ade1.pts 04379243/expert_verified/points_label/62ae9ded861138be9d2be74cfb51ade1.seg 04379243 02958343/points/7b067be3aa39b1a124853ec273f6c1d2.pts 02958343/expert_verified/points_label/7b067be3aa39b1a124853ec273f6c1d2.seg 02958343 03636649/points/66cf69a98ff895e2b55fde51a411949f.pts 03636649/expert_verified/points_label/66cf69a98ff895e2b55fde51a411949f.seg 03636649 04379243/points/3253f2c59e6bd2a119fb4103277a6b93.pts 04379243/expert_verified/points_label/3253f2c59e6bd2a119fb4103277a6b93.seg 04379243 02691156/points/fe0c4db38fb6399990b1d6deb98feec6.pts 02691156/expert_verified/points_label/fe0c4db38fb6399990b1d6deb98feec6.seg 02691156 02691156/points/6d93492543d1087eb87697d3904b168b.pts 02691156/expert_verified/points_label/6d93492543d1087eb87697d3904b168b.seg 02691156 03636649/points/402f7ce2b87e7d1ac066b9622c005c53.pts 03636649/expert_verified/points_label/402f7ce2b87e7d1ac066b9622c005c53.seg 03636649 04379243/points/272a4cf3cfff3eb1e173cee47fbaa88.pts 04379243/expert_verified/points_label/272a4cf3cfff3eb1e173cee47fbaa88.seg 04379243 02691156/points/6420a3ff5e526d59e16519c843f95ce0.pts 02691156/expert_verified/points_label/6420a3ff5e526d59e16519c843f95ce0.seg 02691156 03001627/points/487040c5fdc68fdfe6cfc789522bfbab.pts 03001627/expert_verified/points_label/487040c5fdc68fdfe6cfc789522bfbab.seg 03001627 04379243/points/8f48ccd17a15baf5ce01c07526cf2aa4.pts 04379243/expert_verified/points_label/8f48ccd17a15baf5ce01c07526cf2aa4.seg 04379243 03001627/points/40e5d8e71ee3902a31358207d42bcb21.pts 03001627/expert_verified/points_label/40e5d8e71ee3902a31358207d42bcb21.seg 03001627 03636649/points/68491d576b5d35aade8e7376ce4e111f.pts 03636649/expert_verified/points_label/68491d576b5d35aade8e7376ce4e111f.seg 03636649 03467517/points/80aa2f0d66100844925eded29d6897b9.pts 03467517/expert_verified/points_label/80aa2f0d66100844925eded29d6897b9.seg 03467517 03001627/points/7929676e756dcd41577b5d737869717e.pts 03001627/expert_verified/points_label/7929676e756dcd41577b5d737869717e.seg 03001627 03001627/points/2cf7ccf97b09187fcb7547c95fbdff26.pts 03001627/expert_verified/points_label/2cf7ccf97b09187fcb7547c95fbdff26.seg 03001627 02691156/points/e8409b544c626028a9b2becd26dc2fc1.pts 02691156/expert_verified/points_label/e8409b544c626028a9b2becd26dc2fc1.seg 02691156 02691156/points/1e2de00cf19a0a33554ccf8c30febe7.pts 02691156/expert_verified/points_label/1e2de00cf19a0a33554ccf8c30febe7.seg 02691156 02691156/points/8f40518bd30467151e5ae32cb9e3711f.pts 02691156/expert_verified/points_label/8f40518bd30467151e5ae32cb9e3711f.seg 02691156 02958343/points/4f0147c8a158087a4c19dab9f2c7c52d.pts 02958343/expert_verified/points_label/4f0147c8a158087a4c19dab9f2c7c52d.seg 02958343 03624134/points/954fb0819736737a1b9c8e2fdbfc1118.pts 03624134/expert_verified/points_label/954fb0819736737a1b9c8e2fdbfc1118.seg 03624134 04379243/points/415a08a66b8527519f803a8da27dd9a9.pts 04379243/expert_verified/points_label/415a08a66b8527519f803a8da27dd9a9.seg 04379243 03001627/points/4bdbecfbc925219157915a20ae9ec6b6.pts 03001627/expert_verified/points_label/4bdbecfbc925219157915a20ae9ec6b6.seg 03001627 03624134/points/2f74196bd5cb462727c767f081f1365a.pts 03624134/expert_verified/points_label/2f74196bd5cb462727c767f081f1365a.seg 03624134 02958343/points/b5b6b09711cbee6daa44bfa127abe4bb.pts 02958343/expert_verified/points_label/b5b6b09711cbee6daa44bfa127abe4bb.seg 02958343 03001627/points/43e74f15a986eb626a90f735365ac29e.pts 03001627/expert_verified/points_label/43e74f15a986eb626a90f735365ac29e.seg 03001627 03624134/points/385bb539629cd6991dd89e5fcd05911a.pts 03624134/expert_verified/points_label/385bb539629cd6991dd89e5fcd05911a.seg 03624134 03642806/points/fdec2b8af5dd988cef56c22fd326c67.pts 03642806/expert_verified/points_label/fdec2b8af5dd988cef56c22fd326c67.seg 03642806 02958343/points/244a8476648bd073834daea73aa18748.pts 02958343/expert_verified/points_label/244a8476648bd073834daea73aa18748.seg 02958343 03467517/points/d91b0745e57f6508dc6782957fd2f5d2.pts 03467517/expert_verified/points_label/d91b0745e57f6508dc6782957fd2f5d2.seg 03467517 04379243/points/83f1ff21744e71ad2690c0a5b39562ad.pts 04379243/expert_verified/points_label/83f1ff21744e71ad2690c0a5b39562ad.seg 04379243 03001627/points/49aa713bec70ee1f1104b8f54582c707.pts 03001627/expert_verified/points_label/49aa713bec70ee1f1104b8f54582c707.seg 03001627 03001627/points/9231ef07326eae09b04cb542e2c50eb4.pts 03001627/expert_verified/points_label/9231ef07326eae09b04cb542e2c50eb4.seg 03001627 03642806/points/b211cfb105e9f97e6436916a86a90ed7.pts 03642806/expert_verified/points_label/b211cfb105e9f97e6436916a86a90ed7.seg 03642806 03001627/points/fdfedb5bb8cd35374233148ffd345970.pts 03001627/expert_verified/points_label/fdfedb5bb8cd35374233148ffd345970.seg 03001627 04379243/points/3037fac5bc67207e23fa92d98173c06f.pts 04379243/expert_verified/points_label/3037fac5bc67207e23fa92d98173c06f.seg 04379243 04379243/points/40d0dd3fe786e120d75c27ddd792e41a.pts 04379243/expert_verified/points_label/40d0dd3fe786e120d75c27ddd792e41a.seg 04379243 03001627/points/e6ea5e70c2f29d881e8fd793667dc14f.pts 03001627/expert_verified/points_label/e6ea5e70c2f29d881e8fd793667dc14f.seg 03001627 04379243/points/9502eecc3a057115b129901f80d24b7b.pts 04379243/expert_verified/points_label/9502eecc3a057115b129901f80d24b7b.seg 04379243 03001627/points/e68bb6f55e2454fac7f1f7c0570e288d.pts 03001627/expert_verified/points_label/e68bb6f55e2454fac7f1f7c0570e288d.seg 03001627 02691156/points/9bd8d0fa75bc21c5e3375a6b96a1d765.pts 02691156/expert_verified/points_label/9bd8d0fa75bc21c5e3375a6b96a1d765.seg 02691156 02958343/points/1714b6e57c8c4983fb1aad5dae793ff4.pts 02958343/expert_verified/points_label/1714b6e57c8c4983fb1aad5dae793ff4.seg 02958343 02691156/points/8a84a26158da1db7668586dcfb752ad.pts 02691156/expert_verified/points_label/8a84a26158da1db7668586dcfb752ad.seg 02691156 02691156/points/36d8c865f766e3e097872638b21438e3.pts 02691156/expert_verified/points_label/36d8c865f766e3e097872638b21438e3.seg 02691156 03001627/points/96e8a51b1680b756e99481ddc3bbddfb.pts 03001627/expert_verified/points_label/96e8a51b1680b756e99481ddc3bbddfb.seg 03001627 02958343/points/37ad66d0433beb633df8f4ac45647158.pts 02958343/expert_verified/points_label/37ad66d0433beb633df8f4ac45647158.seg 02958343 04379243/points/56a57ef7c3385c9f2f38c0d2792fb5e.pts 04379243/expert_verified/points_label/56a57ef7c3385c9f2f38c0d2792fb5e.seg 04379243 03467517/points/dbdf45cab0adbded1f260c1b356c52ce.pts 03467517/expert_verified/points_label/dbdf45cab0adbded1f260c1b356c52ce.seg 03467517 04379243/points/868bab5194e93577858699aaad4acee4.pts 04379243/expert_verified/points_label/868bab5194e93577858699aaad4acee4.seg 04379243 04379243/points/2bbd62449b56abee659dda512294c744.pts 04379243/expert_verified/points_label/2bbd62449b56abee659dda512294c744.seg 04379243 04379243/points/a18aa2d20d516333daf1f22b6daf05ed.pts 04379243/expert_verified/points_label/a18aa2d20d516333daf1f22b6daf05ed.seg 04379243 03636649/points/7a2362fbddbee9a4d197f67767b32741.pts 03636649/expert_verified/points_label/7a2362fbddbee9a4d197f67767b32741.seg 03636649 03636649/points/f9259d31df38bd5decd204cd7180226d.pts 03636649/expert_verified/points_label/f9259d31df38bd5decd204cd7180226d.seg 03636649 04379243/points/54e85b248576c4eb57cd80d4b17e7e11.pts 04379243/expert_verified/points_label/54e85b248576c4eb57cd80d4b17e7e11.seg 04379243 04379243/points/1299579419252fa954b02959579aa6bb.pts 04379243/expert_verified/points_label/1299579419252fa954b02959579aa6bb.seg 04379243 04379243/points/49ad167497a2af8c9672e39f89e4622e.pts 04379243/expert_verified/points_label/49ad167497a2af8c9672e39f89e4622e.seg 04379243 04379243/points/55221b101eec29dc656a19d1d18fdbac.pts 04379243/expert_verified/points_label/55221b101eec29dc656a19d1d18fdbac.seg 04379243 04379243/points/e8870f3190f6b8d4bd1025bd755a15aa.pts 04379243/expert_verified/points_label/e8870f3190f6b8d4bd1025bd755a15aa.seg 04379243 02691156/points/9818f0b88fed05b24b0a1bcf2fb497ec.pts 02691156/expert_verified/points_label/9818f0b88fed05b24b0a1bcf2fb497ec.seg 02691156 02691156/points/9ba460913d86466f62347b4731688b0f.pts 02691156/expert_verified/points_label/9ba460913d86466f62347b4731688b0f.seg 02691156 04379243/points/574447022c4473d455f46d55537192b6.pts 04379243/expert_verified/points_label/574447022c4473d455f46d55537192b6.seg 04379243 04379243/points/7b5b7bfa8580e913e2580b23e60e4674.pts 04379243/expert_verified/points_label/7b5b7bfa8580e913e2580b23e60e4674.seg 04379243 04225987/points/48f26ddc704fec2f379c6a1d59ef7283.pts 04225987/expert_verified/points_label/48f26ddc704fec2f379c6a1d59ef7283.seg 04225987 04379243/points/b7821e69687d767aab610b0c94236463.pts 04379243/expert_verified/points_label/b7821e69687d767aab610b0c94236463.seg 04379243 02691156/points/e42443669339a6c1a5a118bd15e6e34f.pts 02691156/expert_verified/points_label/e42443669339a6c1a5a118bd15e6e34f.seg 02691156 04379243/points/2444551d00693a0fab610b0c94236463.pts 04379243/expert_verified/points_label/2444551d00693a0fab610b0c94236463.seg 04379243 03467517/points/5e452914684ea7fc398707f20de9db08.pts 03467517/expert_verified/points_label/5e452914684ea7fc398707f20de9db08.seg 03467517 03001627/points/cc6840207c0cf55db30e42459dcb06f.pts 03001627/expert_verified/points_label/cc6840207c0cf55db30e42459dcb06f.seg 03001627 04379243/points/9046b2e610065fe5a5d95e73eecd308a.pts 04379243/expert_verified/points_label/9046b2e610065fe5a5d95e73eecd308a.seg 04379243 03467517/points/c651a91562b86ed8edb9371445f615ae.pts 03467517/expert_verified/points_label/c651a91562b86ed8edb9371445f615ae.seg 03467517 03001627/points/9bb6d3d76d4f5ba94b3c42e318f3affc.pts 03001627/expert_verified/points_label/9bb6d3d76d4f5ba94b3c42e318f3affc.seg 03001627 03001627/points/7fb336186da77367962800be79c6e52.pts 03001627/expert_verified/points_label/7fb336186da77367962800be79c6e52.seg 03001627 04379243/points/b69b2ff85d0ec661d8f9dd7647048a0c.pts 04379243/expert_verified/points_label/b69b2ff85d0ec661d8f9dd7647048a0c.seg 04379243 03001627/points/d2815e678f173616e6cfc789522bfbab.pts 03001627/expert_verified/points_label/d2815e678f173616e6cfc789522bfbab.seg 03001627 03636649/points/b8350fcf08ff0b2ca950bf8f33cff658.pts 03636649/expert_verified/points_label/b8350fcf08ff0b2ca950bf8f33cff658.seg 03636649 04379243/points/202e7b5c3ec079e299e8bf807e902261.pts 04379243/expert_verified/points_label/202e7b5c3ec079e299e8bf807e902261.seg 04379243 03001627/points/c8938f54fecab41e77cd061c90fcdb44.pts 03001627/expert_verified/points_label/c8938f54fecab41e77cd061c90fcdb44.seg 03001627 04379243/points/894e095c7036c8411933ffef19678834.pts 04379243/expert_verified/points_label/894e095c7036c8411933ffef19678834.seg 04379243 03001627/points/4362e715455f42ba9b9f2eb77f5e247e.pts 03001627/expert_verified/points_label/4362e715455f42ba9b9f2eb77f5e247e.seg 03001627 04379243/points/8963760f8bec0fee7f807d3c406ee.pts 04379243/expert_verified/points_label/8963760f8bec0fee7f807d3c406ee.seg 04379243 03948459/points/4acb6494e3aaeb39998978df244b5bd.pts 03948459/expert_verified/points_label/4acb6494e3aaeb39998978df244b5bd.seg 03948459 03636649/points/c1b939cc403a0662664b3b9b23ddfcbc.pts 03636649/expert_verified/points_label/c1b939cc403a0662664b3b9b23ddfcbc.seg 03636649 04379243/points/e64876f5590e6fb7c3bd24f986301745.pts 04379243/expert_verified/points_label/e64876f5590e6fb7c3bd24f986301745.seg 04379243 02691156/points/b8ce3803485b620b2c674305897e1782.pts 02691156/expert_verified/points_label/b8ce3803485b620b2c674305897e1782.seg 02691156 03636649/points/a60c6cf7d4893f2ba26bf7a8fd4719ad.pts 03636649/expert_verified/points_label/a60c6cf7d4893f2ba26bf7a8fd4719ad.seg 03636649 04379243/points/6ca66a443e651c1423500a5b036df62e.pts 04379243/expert_verified/points_label/6ca66a443e651c1423500a5b036df62e.seg 04379243 04379243/points/51930b149cf6125373fa072a624ce947.pts 04379243/expert_verified/points_label/51930b149cf6125373fa072a624ce947.seg 04379243 02691156/points/eb658ff31f0becea1d0f8853f6d023e3.pts 02691156/expert_verified/points_label/eb658ff31f0becea1d0f8853f6d023e3.seg 02691156 03642806/points/3f45cde6f7a13138e256fb3794905772.pts 03642806/expert_verified/points_label/3f45cde6f7a13138e256fb3794905772.seg 03642806 03001627/points/ea572cc193b804399c66df0f068d2a36.pts 03001627/expert_verified/points_label/ea572cc193b804399c66df0f068d2a36.seg 03001627 03001627/points/9e0a0ad80be6df7789d2595edb5088ee.pts 03001627/expert_verified/points_label/9e0a0ad80be6df7789d2595edb5088ee.seg 03001627 04379243/points/8eed35fd5b777acf58316b27df6c8e87.pts 04379243/expert_verified/points_label/8eed35fd5b777acf58316b27df6c8e87.seg 04379243 03642806/points/5baaa726f51cd09b507f3bf1d3472684.pts 03642806/expert_verified/points_label/5baaa726f51cd09b507f3bf1d3472684.seg 03642806 02691156/points/789f032dccc6092977b7d0d4764c121d.pts 02691156/expert_verified/points_label/789f032dccc6092977b7d0d4764c121d.seg 02691156 03001627/points/9682d28e03acd2e3735013f3db728e20.pts 03001627/expert_verified/points_label/9682d28e03acd2e3735013f3db728e20.seg 03001627 02958343/points/b50f9931670e25ef44ccce632b473b8c.pts 02958343/expert_verified/points_label/b50f9931670e25ef44ccce632b473b8c.seg 02958343 03467517/points/d3972d599036251369da8e7f9a803d12.pts 03467517/expert_verified/points_label/d3972d599036251369da8e7f9a803d12.seg 03467517 02691156/points/329987191cce68bfe64acd170567d820.pts 02691156/expert_verified/points_label/329987191cce68bfe64acd170567d820.seg 02691156 03636649/points/ab3e153cd23e992b576a354bb9319732.pts 03636649/expert_verified/points_label/ab3e153cd23e992b576a354bb9319732.seg 03636649 04379243/points/f850a69b0d308fbc19fb4103277a6b93.pts 04379243/expert_verified/points_label/f850a69b0d308fbc19fb4103277a6b93.seg 04379243 04379243/points/1645b28322131b6258c407efcf93be6b.pts 04379243/expert_verified/points_label/1645b28322131b6258c407efcf93be6b.seg 04379243 03001627/points/195464ae11f6bfe1cba091e036bf65ed.pts 03001627/expert_verified/points_label/195464ae11f6bfe1cba091e036bf65ed.seg 03001627 02691156/points/edd9583988b62c90328f15e6c60d0e90.pts 02691156/expert_verified/points_label/edd9583988b62c90328f15e6c60d0e90.seg 02691156 04225987/points/36aaae334d636ec28043db94fbc8c982.pts 04225987/expert_verified/points_label/36aaae334d636ec28043db94fbc8c982.seg 04225987 04379243/points/c3c467718eb9b2a313f96345312df593.pts 04379243/expert_verified/points_label/c3c467718eb9b2a313f96345312df593.seg 04379243 02691156/points/a1848a4a69b14704ca8607f540cc62ba.pts 02691156/expert_verified/points_label/a1848a4a69b14704ca8607f540cc62ba.seg 02691156 02958343/points/c8bd4d0ac34266ffaaa232d0915adae9.pts 02958343/expert_verified/points_label/c8bd4d0ac34266ffaaa232d0915adae9.seg 02958343 04379243/points/ad61a5bc7cba29b88cc413950b617e8f.pts 04379243/expert_verified/points_label/ad61a5bc7cba29b88cc413950b617e8f.seg 04379243 03642806/points/466ea85bb4653ba3a715ae636b111d77.pts 03642806/expert_verified/points_label/466ea85bb4653ba3a715ae636b111d77.seg 03642806 03001627/points/e93714e5553f63619215045784774049.pts 03001627/expert_verified/points_label/e93714e5553f63619215045784774049.seg 03001627 03636649/points/b88c9a7aaab268fb42b08fbc749346d6.pts 03636649/expert_verified/points_label/b88c9a7aaab268fb42b08fbc749346d6.seg 03636649 03636649/points/6ba931adfa36c7965208aab875b932bc.pts 03636649/expert_verified/points_label/6ba931adfa36c7965208aab875b932bc.seg 03636649 03001627/points/e3479f55f5894bb3c7f1f7c0570e288d.pts 03001627/expert_verified/points_label/e3479f55f5894bb3c7f1f7c0570e288d.seg 03001627 03467517/points/4c5288cc18896f8f352e5d4d2615db5b.pts 03467517/expert_verified/points_label/4c5288cc18896f8f352e5d4d2615db5b.seg 03467517 03001627/points/631e102e9a689339b0ec386df15ab64f.pts 03001627/expert_verified/points_label/631e102e9a689339b0ec386df15ab64f.seg 03001627 04379243/points/6daed91ae491c9cbe22ea6d770699e4b.pts 04379243/expert_verified/points_label/6daed91ae491c9cbe22ea6d770699e4b.seg 04379243 03001627/points/40e73a326cf95d0361c93c4994c91bd1.pts 03001627/expert_verified/points_label/40e73a326cf95d0361c93c4994c91bd1.seg 03001627 03467517/points/dc7708c870000008a24eeca91f583600.pts 03467517/expert_verified/points_label/dc7708c870000008a24eeca91f583600.seg 03467517 03001627/points/1ac6531a337de85f2f7628d6bf38bcc4.pts 03001627/expert_verified/points_label/1ac6531a337de85f2f7628d6bf38bcc4.seg 03001627 04379243/points/5191d64e9a1b9664bfdcc70dcc16baa1.pts 04379243/expert_verified/points_label/5191d64e9a1b9664bfdcc70dcc16baa1.seg 04379243 03636649/points/c4dc0ac169c91ff29f8c3d2002c77ddb.pts 03636649/expert_verified/points_label/c4dc0ac169c91ff29f8c3d2002c77ddb.seg 03636649 03624134/points/b8648ae17fb9937949f73a97204d432b.pts 03624134/expert_verified/points_label/b8648ae17fb9937949f73a97204d432b.seg 03624134 04379243/points/a465210c23b0136d7afee304cce81d6f.pts 04379243/expert_verified/points_label/a465210c23b0136d7afee304cce81d6f.seg 04379243 03001627/points/513686d6d63a1d8e577b5d737869717e.pts 03001627/expert_verified/points_label/513686d6d63a1d8e577b5d737869717e.seg 03001627 03624134/points/bee1a473472639e25ca3862a7efa6401.pts 03624134/expert_verified/points_label/bee1a473472639e25ca3862a7efa6401.seg 03624134 02691156/points/adb3ea03d7b954255e9e2656aff7dd5b.pts 02691156/expert_verified/points_label/adb3ea03d7b954255e9e2656aff7dd5b.seg 02691156 02691156/points/959f28c6724979ef9a6e43b878d5b335.pts 02691156/expert_verified/points_label/959f28c6724979ef9a6e43b878d5b335.seg 02691156 04379243/points/dec1d2cf8a4563d36cb02543e4df83bf.pts 04379243/expert_verified/points_label/dec1d2cf8a4563d36cb02543e4df83bf.seg 04379243 03790512/points/a9c432d1dc4034762a45a87054fa7272.pts 03790512/expert_verified/points_label/a9c432d1dc4034762a45a87054fa7272.seg 03790512 03001627/points/1b5e876f3559c231532a8e162f399205.pts 03001627/expert_verified/points_label/1b5e876f3559c231532a8e162f399205.seg 03001627 04379243/points/82e5309809e455d5f15fed2243deb166.pts 04379243/expert_verified/points_label/82e5309809e455d5f15fed2243deb166.seg 04379243 03467517/points/8f1f54d337bf6ccac782e6226a4f593e.pts 03467517/expert_verified/points_label/8f1f54d337bf6ccac782e6226a4f593e.seg 03467517 04379243/points/67d97102f9c54cc95512673aa47c7e3d.pts 04379243/expert_verified/points_label/67d97102f9c54cc95512673aa47c7e3d.seg 04379243 02691156/points/e0cc4f538a8da2d65d3bbd70fc7759b7.pts 02691156/expert_verified/points_label/e0cc4f538a8da2d65d3bbd70fc7759b7.seg 02691156 04379243/points/d0008b042256fb5f7ab911835312d4f1.pts 04379243/expert_verified/points_label/d0008b042256fb5f7ab911835312d4f1.seg 04379243 03467517/points/44c05e219618a6395b3335548350bdee.pts 03467517/expert_verified/points_label/44c05e219618a6395b3335548350bdee.seg 03467517 03001627/points/3f7808c221b01668b4d174e5c61f344.pts 03001627/expert_verified/points_label/3f7808c221b01668b4d174e5c61f344.seg 03001627 03467517/points/51abcb617b2faf3a24eeca91f583600.pts 03467517/expert_verified/points_label/51abcb617b2faf3a24eeca91f583600.seg 03467517 03636649/points/f38370fc4c112017a6e7138fdd58748.pts 03636649/expert_verified/points_label/f38370fc4c112017a6e7138fdd58748.seg 03636649 03001627/points/37607ea19e352af4fffc97a61124b1a9.pts 03001627/expert_verified/points_label/37607ea19e352af4fffc97a61124b1a9.seg 03001627 02958343/points/2cb6de89f5b6e702b626f6a649199824.pts 02958343/expert_verified/points_label/2cb6de89f5b6e702b626f6a649199824.seg 02958343 04099429/points/d781243cc1d1d2e91a0ec553feb1c2c3.pts 04099429/expert_verified/points_label/d781243cc1d1d2e91a0ec553feb1c2c3.seg 04099429 04379243/points/900afcc9f0f5fbfd858699aaad4acee4.pts 04379243/expert_verified/points_label/900afcc9f0f5fbfd858699aaad4acee4.seg 04379243 03001627/points/d13eb19745344ae5fb0eb7e753c06942.pts 03001627/expert_verified/points_label/d13eb19745344ae5fb0eb7e753c06942.seg 03001627 02958343/points/5785192c95cdd67b704715417c0f83c1.pts 02958343/expert_verified/points_label/5785192c95cdd67b704715417c0f83c1.seg 02958343 03001627/points/5bb5b15807158f71504721639e19f609.pts 03001627/expert_verified/points_label/5bb5b15807158f71504721639e19f609.seg 03001627 03636649/points/ba05f660341b7b7b70be09f44cb2fef5.pts 03636649/expert_verified/points_label/ba05f660341b7b7b70be09f44cb2fef5.seg 03636649 02691156/points/97066012fbca5983c74417871493eae8.pts 02691156/expert_verified/points_label/97066012fbca5983c74417871493eae8.seg 02691156 03001627/points/4499729e53c858ae71a782a4379556c7.pts 03001627/expert_verified/points_label/4499729e53c858ae71a782a4379556c7.seg 03001627 04379243/points/41d280b7db61ebddfebad4f49b26ec52.pts 04379243/expert_verified/points_label/41d280b7db61ebddfebad4f49b26ec52.seg 04379243 02773838/points/30bf69aa24dbb3fc9de193e488fc4dce.pts 02773838/expert_verified/points_label/30bf69aa24dbb3fc9de193e488fc4dce.seg 02773838 03467517/points/6c9a9c0e2af9d5b35f713e773d664ec2.pts 03467517/expert_verified/points_label/6c9a9c0e2af9d5b35f713e773d664ec2.seg 03467517 04379243/points/f979c7a650d29ea819fb4103277a6b93.pts 04379243/expert_verified/points_label/f979c7a650d29ea819fb4103277a6b93.seg 04379243 03001627/points/b631b78c2dcc748cba5342d638d0c267.pts 03001627/expert_verified/points_label/b631b78c2dcc748cba5342d638d0c267.seg 03001627 03467517/points/d2ad57f36e00c602baba3b7560fe62f4.pts 03467517/expert_verified/points_label/d2ad57f36e00c602baba3b7560fe62f4.seg 03467517 04379243/points/5771d5a3084b3ca3a2d7b309863cb1b.pts 04379243/expert_verified/points_label/5771d5a3084b3ca3a2d7b309863cb1b.seg 04379243 03636649/points/2d638c6b6b2feb9248da169d95204ce2.pts 03636649/expert_verified/points_label/2d638c6b6b2feb9248da169d95204ce2.seg 03636649 02958343/points/63a4e46bbbd855fc2b63d3b2a8c4e8b.pts 02958343/expert_verified/points_label/63a4e46bbbd855fc2b63d3b2a8c4e8b.seg 02958343 04379243/points/8c67fd5a15e8d9defebad4f49b26ec52.pts 04379243/expert_verified/points_label/8c67fd5a15e8d9defebad4f49b26ec52.seg 04379243 03467517/points/28c3903b29f6b38363e148e250c0340d.pts 03467517/expert_verified/points_label/28c3903b29f6b38363e148e250c0340d.seg 03467517 04379243/points/ab2967188299bea54cb0654f4cfa9684.pts 04379243/expert_verified/points_label/ab2967188299bea54cb0654f4cfa9684.seg 04379243 02691156/points/a9a7f21271b3efbaf446f92b52bbd82a.pts 02691156/expert_verified/points_label/a9a7f21271b3efbaf446f92b52bbd82a.seg 02691156 04379243/points/c3e43144fd61c56f19fb4103277a6b93.pts 04379243/expert_verified/points_label/c3e43144fd61c56f19fb4103277a6b93.seg 04379243 03001627/points/7fcde5fc8e023dd2a6fee8e2140acec9.pts 03001627/expert_verified/points_label/7fcde5fc8e023dd2a6fee8e2140acec9.seg 03001627 03790512/points/70d9cc5115bfedeeab548456bc75847f.pts 03790512/expert_verified/points_label/70d9cc5115bfedeeab548456bc75847f.seg 03790512 03001627/points/3c0dd3719baecf3319fb4103277a6b93.pts 03001627/expert_verified/points_label/3c0dd3719baecf3319fb4103277a6b93.seg 03001627 03636649/points/55077c2175d97b8889ab11a408196888.pts 03636649/expert_verified/points_label/55077c2175d97b8889ab11a408196888.seg 03636649 04379243/points/71fc8c7cdb48978282fa4d4f2c19b2ce.pts 04379243/expert_verified/points_label/71fc8c7cdb48978282fa4d4f2c19b2ce.seg 04379243 04379243/points/f0d5eefef970fa4b9f2349486c570dd4.pts 04379243/expert_verified/points_label/f0d5eefef970fa4b9f2349486c570dd4.seg 04379243 03642806/points/90c01fd78513bb99c9b20aa1b8066c46.pts 03642806/expert_verified/points_label/90c01fd78513bb99c9b20aa1b8066c46.seg 03642806 04379243/points/ca6c07357ba5125b8e2adb29857f8a1.pts 04379243/expert_verified/points_label/ca6c07357ba5125b8e2adb29857f8a1.seg 04379243 04379243/points/634bcd3197e337aafe4e4de1adda2150.pts 04379243/expert_verified/points_label/634bcd3197e337aafe4e4de1adda2150.seg 04379243 04379243/points/7b411de42d4960eb6e25f3efedf6785f.pts 04379243/expert_verified/points_label/7b411de42d4960eb6e25f3efedf6785f.seg 04379243 04379243/points/878414eb6e86494d9a8ef44e1d2c5b75.pts 04379243/expert_verified/points_label/878414eb6e86494d9a8ef44e1d2c5b75.seg 04379243 03001627/points/f3fa7bd00b76f6a87a8a6b9421844d96.pts 03001627/expert_verified/points_label/f3fa7bd00b76f6a87a8a6b9421844d96.seg 03001627 03467517/points/a2c1ee6a7ddb50a493f0194265a9746c.pts 03467517/expert_verified/points_label/a2c1ee6a7ddb50a493f0194265a9746c.seg 03467517 04379243/points/25bc205f6de491f4ccde40b1205ec7ff.pts 04379243/expert_verified/points_label/25bc205f6de491f4ccde40b1205ec7ff.seg 04379243 03636649/points/771d4def2e44bc169eb34048e600e1ea.pts 03636649/expert_verified/points_label/771d4def2e44bc169eb34048e600e1ea.seg 03636649 03624134/points/6ebe2a22b8d9d70862a95b942081dfee.pts 03624134/expert_verified/points_label/6ebe2a22b8d9d70862a95b942081dfee.seg 03624134 02691156/points/9b1fc3881a5335cb44012f72ba1e15a8.pts 02691156/expert_verified/points_label/9b1fc3881a5335cb44012f72ba1e15a8.seg 02691156 03001627/points/3dc252fd90d82b18c9be65dfbd21428b.pts 03001627/expert_verified/points_label/3dc252fd90d82b18c9be65dfbd21428b.seg 03001627 04379243/points/f6f180c3e72caacb5077539b37310c29.pts 04379243/expert_verified/points_label/f6f180c3e72caacb5077539b37310c29.seg 04379243 03642806/points/25bc168b214b54799e28e9cf32e5157.pts 03642806/expert_verified/points_label/25bc168b214b54799e28e9cf32e5157.seg 03642806 04379243/points/ac9fae8af57729945eee45c00c4de9d3.pts 04379243/expert_verified/points_label/ac9fae8af57729945eee45c00c4de9d3.seg 04379243 03001627/points/e8126f9e2d106620d2f33aaf794b5932.pts 03001627/expert_verified/points_label/e8126f9e2d106620d2f33aaf794b5932.seg 03001627 03624134/points/3dc5a6d79ed591bda709dec9a148b2fe.pts 03624134/expert_verified/points_label/3dc5a6d79ed591bda709dec9a148b2fe.seg 03624134 04379243/points/8f73278956fecb80327289c00b6dc9ca.pts 04379243/expert_verified/points_label/8f73278956fecb80327289c00b6dc9ca.seg 04379243 03948459/points/5f46578efd2c65e5d4ac2f5fcaa742ac.pts 03948459/expert_verified/points_label/5f46578efd2c65e5d4ac2f5fcaa742ac.seg 03948459 03624134/points/a05ea45d396c86784e52b614e584a543.pts 03624134/expert_verified/points_label/a05ea45d396c86784e52b614e584a543.seg 03624134 03001627/points/cd939609247df917d9d3572bbd9cf789.pts 03001627/expert_verified/points_label/cd939609247df917d9d3572bbd9cf789.seg 03001627 03261776/points/17c9866b42ae1831df4cfe396cee719e.pts 03261776/expert_verified/points_label/17c9866b42ae1831df4cfe396cee719e.seg 03261776 03797390/points/3d3e993f7baa4d7ef1ff24a8b1564a36.pts 03797390/expert_verified/points_label/3d3e993f7baa4d7ef1ff24a8b1564a36.seg 03797390 03467517/points/36b49aff54f6d7e893f0194265a9746c.pts 03467517/expert_verified/points_label/36b49aff54f6d7e893f0194265a9746c.seg 03467517 02691156/points/48df2496242053da4ee0fb6a51564c3.pts 02691156/expert_verified/points_label/48df2496242053da4ee0fb6a51564c3.seg 02691156 04379243/points/7ad23def902ea4f37b7a2c2624e46d0a.pts 04379243/expert_verified/points_label/7ad23def902ea4f37b7a2c2624e46d0a.seg 04379243 04379243/points/1a8fe5baa2d4b5f7ee84261b3d20656.pts 04379243/expert_verified/points_label/1a8fe5baa2d4b5f7ee84261b3d20656.seg 04379243 03467517/points/d685415d4fcd3205a24eeca91f583600.pts 03467517/expert_verified/points_label/d685415d4fcd3205a24eeca91f583600.seg 03467517 02958343/points/8e308d28d463427f43f0e92e826556b8.pts 02958343/expert_verified/points_label/8e308d28d463427f43f0e92e826556b8.seg 02958343 04379243/points/dc68436ab1a576f6573d2c9ac4b23fdf.pts 04379243/expert_verified/points_label/dc68436ab1a576f6573d2c9ac4b23fdf.seg 04379243 04379243/points/1a153612bcdab3e23cc149415a408229.pts 04379243/expert_verified/points_label/1a153612bcdab3e23cc149415a408229.seg 04379243 03001627/points/19ce953da9aa8065d747a43c11e738e9.pts 03001627/expert_verified/points_label/19ce953da9aa8065d747a43c11e738e9.seg 03001627 04379243/points/db2d4f781756e687d8864caa856253b.pts 04379243/expert_verified/points_label/db2d4f781756e687d8864caa856253b.seg 04379243 04379243/points/d8f851bbc98dccc23fa92d98173c06f.pts 04379243/expert_verified/points_label/d8f851bbc98dccc23fa92d98173c06f.seg 04379243 03467517/points/e585e31db7568c4cf0e1c0df18936d05.pts 03467517/expert_verified/points_label/e585e31db7568c4cf0e1c0df18936d05.seg 03467517 03001627/points/98ac0106ad244505e04fc3fcc1c852e0.pts 03001627/expert_verified/points_label/98ac0106ad244505e04fc3fcc1c852e0.seg 03001627 03001627/points/1b81441b7e597235d61420a53a0cb96d.pts 03001627/expert_verified/points_label/1b81441b7e597235d61420a53a0cb96d.seg 03001627 03001627/points/918145be863f7aeaf050758b903e6054.pts 03001627/expert_verified/points_label/918145be863f7aeaf050758b903e6054.seg 03001627 02691156/points/1af4b32eafffb0f7ee60c37cbf99c1c.pts 02691156/expert_verified/points_label/1af4b32eafffb0f7ee60c37cbf99c1c.seg 02691156 03636649/points/f4e1a4032b1686cec35131da26f8061a.pts 03636649/expert_verified/points_label/f4e1a4032b1686cec35131da26f8061a.seg 03636649 04379243/points/9c4dfafdbd7f9b76c955e5ed03ef3a2f.pts 04379243/expert_verified/points_label/9c4dfafdbd7f9b76c955e5ed03ef3a2f.seg 04379243 02691156/points/80b8f4da6b77eb66d208f79049825a82.pts 02691156/expert_verified/points_label/80b8f4da6b77eb66d208f79049825a82.seg 02691156 03642806/points/de2e95eac460c361e862e3cac45aa769.pts 03642806/expert_verified/points_label/de2e95eac460c361e862e3cac45aa769.seg 03642806 04379243/points/e2571e4eba2d9f5eab610b0c94236463.pts 04379243/expert_verified/points_label/e2571e4eba2d9f5eab610b0c94236463.seg 04379243 04379243/points/a0445e4888d56666b9d7c2fc41e80228.pts 04379243/expert_verified/points_label/a0445e4888d56666b9d7c2fc41e80228.seg 04379243 03001627/points/873c017f35957717b56a13a4b2372aa4.pts 03001627/expert_verified/points_label/873c017f35957717b56a13a4b2372aa4.seg 03001627 03001627/points/3af90da238ac4ddbf91663a74ccd2338.pts 03001627/expert_verified/points_label/3af90da238ac4ddbf91663a74ccd2338.seg 03001627 02958343/points/9698be0fd3516f01fbeda5389ab05f5f.pts 02958343/expert_verified/points_label/9698be0fd3516f01fbeda5389ab05f5f.seg 02958343 03790512/points/655b9dd9425cc3a12a45a87054fa7272.pts 03790512/expert_verified/points_label/655b9dd9425cc3a12a45a87054fa7272.seg 03790512 04379243/points/ec1c92efffb9ee78beedb4c8fd29e2d1.pts 04379243/expert_verified/points_label/ec1c92efffb9ee78beedb4c8fd29e2d1.seg 04379243 04379243/points/3b7fc97192e483ebb0bf045ee98272fc.pts 04379243/expert_verified/points_label/3b7fc97192e483ebb0bf045ee98272fc.seg 04379243 03467517/points/8c3d3e69d03d3443e84e459fb01822f.pts 03467517/expert_verified/points_label/8c3d3e69d03d3443e84e459fb01822f.seg 03467517 02691156/points/e0058b4948f87d3b87697d3904b168b.pts 02691156/expert_verified/points_label/e0058b4948f87d3b87697d3904b168b.seg 02691156 03001627/points/4428b7dc4b6696812905b6e26038a78.pts 03001627/expert_verified/points_label/4428b7dc4b6696812905b6e26038a78.seg 03001627 03636649/points/f7093dd024fd09fc7219d6d5c4afbaff.pts 03636649/expert_verified/points_label/f7093dd024fd09fc7219d6d5c4afbaff.seg 03636649 04379243/points/7d0c5e28089c2b7bd99e852ee772dfa4.pts 04379243/expert_verified/points_label/7d0c5e28089c2b7bd99e852ee772dfa4.seg 04379243 03636649/points/4916f793d87dd184d42b9650f19dd425.pts 03636649/expert_verified/points_label/4916f793d87dd184d42b9650f19dd425.seg 03636649 04379243/points/1ffcbc064f473b7de7c13848b2d8f5ec.pts 04379243/expert_verified/points_label/1ffcbc064f473b7de7c13848b2d8f5ec.seg 04379243 03636649/points/e180510d07b65fff571108a6d1e94edd.pts 03636649/expert_verified/points_label/e180510d07b65fff571108a6d1e94edd.seg 03636649 03636649/points/d9f6bd064c9fd456fcb8d8c6d4df8143.pts 03636649/expert_verified/points_label/d9f6bd064c9fd456fcb8d8c6d4df8143.seg 03636649 04379243/points/ec81c49ee12e8a70fd06de9ba37d44bd.pts 04379243/expert_verified/points_label/ec81c49ee12e8a70fd06de9ba37d44bd.seg 04379243 03636649/points/4a868756ae6404a5c0bc57897eddf6f.pts 03636649/expert_verified/points_label/4a868756ae6404a5c0bc57897eddf6f.seg 03636649 02958343/points/9c827e532de4967285089a13cc567dbd.pts 02958343/expert_verified/points_label/9c827e532de4967285089a13cc567dbd.seg 02958343 03797390/points/1c9f9e25c654cbca3c71bf3f4dd78475.pts 03797390/expert_verified/points_label/1c9f9e25c654cbca3c71bf3f4dd78475.seg 03797390 03001627/points/ca3670f77268f899febad4f49b26ec52.pts 03001627/expert_verified/points_label/ca3670f77268f899febad4f49b26ec52.seg 03001627 04379243/points/9b8e6eb835f0c8bcf37af16b2893f1d4.pts 04379243/expert_verified/points_label/9b8e6eb835f0c8bcf37af16b2893f1d4.seg 04379243 03001627/points/5c9d582488732ee0d7f7a4c4609b0913.pts 03001627/expert_verified/points_label/5c9d582488732ee0d7f7a4c4609b0913.seg 03001627 04379243/points/684ccc0f629ee45cab610b0c94236463.pts 04379243/expert_verified/points_label/684ccc0f629ee45cab610b0c94236463.seg 04379243 03001627/points/4913388a4c94547a81806e3880250dff.pts 03001627/expert_verified/points_label/4913388a4c94547a81806e3880250dff.seg 03001627 03636649/points/73378b714c5bfed2b922d818b19db1e.pts 03636649/expert_verified/points_label/73378b714c5bfed2b922d818b19db1e.seg 03636649 03001627/points/4a89a789f817ab5414038d588fd1342f.pts 03001627/expert_verified/points_label/4a89a789f817ab5414038d588fd1342f.seg 03001627 04379243/points/df7761a3b4ac638c9eaceb124b71b7be.pts 04379243/expert_verified/points_label/df7761a3b4ac638c9eaceb124b71b7be.seg 04379243 03001627/points/46557f689f4cf5dd2acd2bb6205825cb.pts 03001627/expert_verified/points_label/46557f689f4cf5dd2acd2bb6205825cb.seg 03001627 04379243/points/2db1f557e247ded7e907b6d9dc1d71b7.pts 04379243/expert_verified/points_label/2db1f557e247ded7e907b6d9dc1d71b7.seg 04379243 04379243/points/b69d9e876e7a80a29f2349486c570dd4.pts 04379243/expert_verified/points_label/b69d9e876e7a80a29f2349486c570dd4.seg 04379243 04379243/points/a94ea7183f27073248c0c0980e363341.pts 04379243/expert_verified/points_label/a94ea7183f27073248c0c0980e363341.seg 04379243 03636649/points/8f85c2195890ccf671f0940f5ed452dc.pts 03636649/expert_verified/points_label/8f85c2195890ccf671f0940f5ed452dc.seg 03636649 02691156/points/cc80380c511ec8e2c91a9d486db717.pts 02691156/expert_verified/points_label/cc80380c511ec8e2c91a9d486db717.seg 02691156 03642806/points/6b61ef17b4f45050b598e8984f11eb0c.pts 03642806/expert_verified/points_label/6b61ef17b4f45050b598e8984f11eb0c.seg 03642806 04379243/points/d9ce0b512e0420f8be95ff480950e9ef.pts 04379243/expert_verified/points_label/d9ce0b512e0420f8be95ff480950e9ef.seg 04379243 04379243/points/c27a1c6a26642c907ecc778b34d42f32.pts 04379243/expert_verified/points_label/c27a1c6a26642c907ecc778b34d42f32.seg 04379243 04379243/points/debd06d3176a5b728cbb8bac2032149c.pts 04379243/expert_verified/points_label/debd06d3176a5b728cbb8bac2032149c.seg 04379243 04099429/points/fa07813a89527d195d1df55cbe0874aa.pts 04099429/expert_verified/points_label/fa07813a89527d195d1df55cbe0874aa.seg 04099429 03001627/points/2a98a638f675f46e7d44dc16af152638.pts 03001627/expert_verified/points_label/2a98a638f675f46e7d44dc16af152638.seg 03001627 03624134/points/ec1eb959cc203f1de5a365227cfe63ec.pts 03624134/expert_verified/points_label/ec1eb959cc203f1de5a365227cfe63ec.seg 03624134 04379243/points/db0c430a51ac45c19d2be74cfb51ade1.pts 04379243/expert_verified/points_label/db0c430a51ac45c19d2be74cfb51ade1.seg 04379243 04379243/points/26b2a15646f6a3a06f1e07a56c129dfc.pts 04379243/expert_verified/points_label/26b2a15646f6a3a06f1e07a56c129dfc.seg 04379243 04379243/points/90343e416528b576f41d9ea5f63b1b05.pts 04379243/expert_verified/points_label/90343e416528b576f41d9ea5f63b1b05.seg 04379243 03001627/points/43d38ad2f5d103adf9b9977a2406713a.pts 03001627/expert_verified/points_label/43d38ad2f5d103adf9b9977a2406713a.seg 03001627 03001627/points/e279758e8a5b6a8d492d9da2668ec34c.pts 03001627/expert_verified/points_label/e279758e8a5b6a8d492d9da2668ec34c.seg 03001627 03642806/points/71907a4a567dce3bb0de1e7a6809fd90.pts 03642806/expert_verified/points_label/71907a4a567dce3bb0de1e7a6809fd90.seg 03642806 03636649/points/2958cd9fd799bf02cfbcbf340cec6da1.pts 03636649/expert_verified/points_label/2958cd9fd799bf02cfbcbf340cec6da1.seg 03636649 04379243/points/bd7c71ca15b0d4e56c252f74b6220e29.pts 04379243/expert_verified/points_label/bd7c71ca15b0d4e56c252f74b6220e29.seg 04379243 04379243/points/51c6a7298408c3f19730cb37c9a5f63b.pts 04379243/expert_verified/points_label/51c6a7298408c3f19730cb37c9a5f63b.seg 04379243 02691156/points/e3de366a0cfb59ed38294c37c250d7cd.pts 02691156/expert_verified/points_label/e3de366a0cfb59ed38294c37c250d7cd.seg 02691156 03467517/points/f288cd2146b8f4c1f0e1c0df18936d05.pts 03467517/expert_verified/points_label/f288cd2146b8f4c1f0e1c0df18936d05.seg 03467517 04379243/points/270430ab9efb9d85c0f947750540fb22.pts 04379243/expert_verified/points_label/270430ab9efb9d85c0f947750540fb22.seg 04379243 04379243/points/f5ad10e6a938aa80e85c7a030ebdf69a.pts 04379243/expert_verified/points_label/f5ad10e6a938aa80e85c7a030ebdf69a.seg 04379243 04379243/points/8343d98e3710f5bee1b32bbe69d5bc15.pts 04379243/expert_verified/points_label/8343d98e3710f5bee1b32bbe69d5bc15.seg 04379243 03790512/points/40b7a63fd9ede0cf48272812609617e2.pts 03790512/expert_verified/points_label/40b7a63fd9ede0cf48272812609617e2.seg 03790512 03467517/points/16bc13ee237ebeb38460585fe283a1c9.pts 03467517/expert_verified/points_label/16bc13ee237ebeb38460585fe283a1c9.seg 03467517 02691156/points/a56143efe74ee89ebbf3143b1cb6076a.pts 02691156/expert_verified/points_label/a56143efe74ee89ebbf3143b1cb6076a.seg 02691156 04379243/points/9a6ab25d91c92a5a35acfdef2ece21c0.pts 04379243/expert_verified/points_label/9a6ab25d91c92a5a35acfdef2ece21c0.seg 04379243 03467517/points/c9b60abdc17708fb78ad94b294a9faa6.pts 03467517/expert_verified/points_label/c9b60abdc17708fb78ad94b294a9faa6.seg 03467517 04379243/points/cde67434193a2a6f19fb4103277a6b93.pts 04379243/expert_verified/points_label/cde67434193a2a6f19fb4103277a6b93.seg 04379243 04379243/points/6b62c85b16e300557005dacb6907e37d.pts 04379243/expert_verified/points_label/6b62c85b16e300557005dacb6907e37d.seg 04379243 04379243/points/7956ac7aba6295d1c2fd07f66cbad0f7.pts 04379243/expert_verified/points_label/7956ac7aba6295d1c2fd07f66cbad0f7.seg 04379243 04379243/points/dcda90e411cb4e35506d1e1cc84da713.pts 04379243/expert_verified/points_label/dcda90e411cb4e35506d1e1cc84da713.seg 04379243 02691156/points/c494f446954523a8a32748a9f843a0bf.pts 02691156/expert_verified/points_label/c494f446954523a8a32748a9f843a0bf.seg 02691156 02691156/points/18e6f319062ccb49ca8607f540cc62ba.pts 02691156/expert_verified/points_label/18e6f319062ccb49ca8607f540cc62ba.seg 02691156 04379243/points/b7cead95e18b570d2c97486f63c12d76.pts 04379243/expert_verified/points_label/b7cead95e18b570d2c97486f63c12d76.seg 04379243 03948459/points/f6d52684720d52a01ab78426351eea4a.pts 03948459/expert_verified/points_label/f6d52684720d52a01ab78426351eea4a.seg 03948459 04379243/points/7eeceefed2b3aa2794f3bda96cf548cc.pts 04379243/expert_verified/points_label/7eeceefed2b3aa2794f3bda96cf548cc.seg 04379243 03001627/points/5eaa2730f10054d0f6cabe1df6f4c9d9.pts 03001627/expert_verified/points_label/5eaa2730f10054d0f6cabe1df6f4c9d9.seg 03001627 03001627/points/92f79b8e45269847f0efa341b439d741.pts 03001627/expert_verified/points_label/92f79b8e45269847f0efa341b439d741.seg 03001627 03001627/points/cbaca6a6edfa2d512b520984c067934c.pts 03001627/expert_verified/points_label/cbaca6a6edfa2d512b520984c067934c.seg 03001627 04379243/points/390e0db80fe12ef65fa6da97b9eb4a2f.pts 04379243/expert_verified/points_label/390e0db80fe12ef65fa6da97b9eb4a2f.seg 04379243 04379243/points/2ec33e8b457ac0fa278d386bfa54545.pts 04379243/expert_verified/points_label/2ec33e8b457ac0fa278d386bfa54545.seg 04379243 04225987/points/ac2b6924a60a7a87aa4f69d519551495.pts 04225987/expert_verified/points_label/ac2b6924a60a7a87aa4f69d519551495.seg 04225987 02958343/points/468780ef4ace9a422e877e82c90c24d.pts 02958343/expert_verified/points_label/468780ef4ace9a422e877e82c90c24d.seg 02958343 03001627/points/78c9204b2eac432b65b77a565916c7f.pts 03001627/expert_verified/points_label/78c9204b2eac432b65b77a565916c7f.seg 03001627 04379243/points/b278b58e294a7d2bac242c3aebc81b2f.pts 04379243/expert_verified/points_label/b278b58e294a7d2bac242c3aebc81b2f.seg 04379243 04379243/points/fc95d34ab1afb92b9118eee0b123125f.pts 04379243/expert_verified/points_label/fc95d34ab1afb92b9118eee0b123125f.seg 04379243 03790512/points/54f016b47a5864cd5dde04c96fd8146.pts 03790512/expert_verified/points_label/54f016b47a5864cd5dde04c96fd8146.seg 03790512 04379243/points/9afa121e3aec8bd7c387f328a37d8ece.pts 04379243/expert_verified/points_label/9afa121e3aec8bd7c387f328a37d8ece.seg 04379243 04379243/points/382889dbc86b5dd919fb4103277a6b93.pts 04379243/expert_verified/points_label/382889dbc86b5dd919fb4103277a6b93.seg 04379243 03467517/points/b83a81b2476ec59e59610f6f40382499.pts 03467517/expert_verified/points_label/b83a81b2476ec59e59610f6f40382499.seg 03467517 03001627/points/5d959b0f79a22e8c67c9124d122355ab.pts 03001627/expert_verified/points_label/5d959b0f79a22e8c67c9124d122355ab.seg 03001627 02691156/points/c4111dbb21e1f17043afdb9c81ff2967.pts 02691156/expert_verified/points_label/c4111dbb21e1f17043afdb9c81ff2967.seg 02691156 02691156/points/46829981c5c25285bfc0a2c490b4c222.pts 02691156/expert_verified/points_label/46829981c5c25285bfc0a2c490b4c222.seg 02691156 04379243/points/497659c4723fbc4fe90ff84c89de437.pts 04379243/expert_verified/points_label/497659c4723fbc4fe90ff84c89de437.seg 04379243 02691156/points/a805c30d4b09f11f62347b4731688b0f.pts 02691156/expert_verified/points_label/a805c30d4b09f11f62347b4731688b0f.seg 02691156 03636649/points/e485053f3e0d18252cd2160e449d45ae.pts 03636649/expert_verified/points_label/e485053f3e0d18252cd2160e449d45ae.seg 03636649 02958343/points/2fb5fe84c28b8b35cc02882a83047172.pts 02958343/expert_verified/points_label/2fb5fe84c28b8b35cc02882a83047172.seg 02958343 03636649/points/f7a4590c54e2ac7ce62fad6b4f42c880.pts 03636649/expert_verified/points_label/f7a4590c54e2ac7ce62fad6b4f42c880.seg 03636649 03642806/points/9fc5b76d363ca64ed03066fc8168e9c6.pts 03642806/expert_verified/points_label/9fc5b76d363ca64ed03066fc8168e9c6.seg 03642806 02691156/points/be080a797406422843afdb9c81ff2967.pts 02691156/expert_verified/points_label/be080a797406422843afdb9c81ff2967.seg 02691156 04379243/points/81a84fcb2b247a3348eaa510713cb074.pts 04379243/expert_verified/points_label/81a84fcb2b247a3348eaa510713cb074.seg 04379243 03001627/points/47c540c2e9c3483ce79a6b87656a120a.pts 03001627/expert_verified/points_label/47c540c2e9c3483ce79a6b87656a120a.seg 03001627 03001627/points/5073d7a546b9a4d0e810eba61b778ebb.pts 03001627/expert_verified/points_label/5073d7a546b9a4d0e810eba61b778ebb.seg 03001627 03001627/points/e4a890f2330ebd7e4a11872aa986426d.pts 03001627/expert_verified/points_label/e4a890f2330ebd7e4a11872aa986426d.seg 03001627 03001627/points/a7200578bd7bea065dc3653f8341633a.pts 03001627/expert_verified/points_label/a7200578bd7bea065dc3653f8341633a.seg 03001627 03467517/points/b004331ee5cc39caa24eeca91f583600.pts 03467517/expert_verified/points_label/b004331ee5cc39caa24eeca91f583600.seg 03467517 04379243/points/f01768b8b8ba025ee45ef4135c266a12.pts 04379243/expert_verified/points_label/f01768b8b8ba025ee45ef4135c266a12.seg 04379243 03642806/points/5173aa7f75ff3cf1b55fde51a411949f.pts 03642806/expert_verified/points_label/5173aa7f75ff3cf1b55fde51a411949f.seg 03642806 03636649/points/e7e45a8f0b0ab311c754474f0ac106.pts 03636649/expert_verified/points_label/e7e45a8f0b0ab311c754474f0ac106.seg 03636649 03642806/points/1b67b4bfed6688ba5b22feddf58c05e1.pts 03642806/expert_verified/points_label/1b67b4bfed6688ba5b22feddf58c05e1.seg 03642806 03797390/points/f1e439307b834015770a0ff1161fa15a.pts 03797390/expert_verified/points_label/f1e439307b834015770a0ff1161fa15a.seg 03797390 03001627/points/b6c9495629c00419940806ade53ef2f.pts 03001627/expert_verified/points_label/b6c9495629c00419940806ade53ef2f.seg 03001627 03001627/points/8e19d2ec95c45186a6fd617b2ff5d2d.pts 03001627/expert_verified/points_label/8e19d2ec95c45186a6fd617b2ff5d2d.seg 03001627 03001627/points/d7b8189fe69cebedc41b07b1627c4b43.pts 03001627/expert_verified/points_label/d7b8189fe69cebedc41b07b1627c4b43.seg 03001627 02691156/points/a7a0e7eddf4ffb8c19378fd691582500.pts 02691156/expert_verified/points_label/a7a0e7eddf4ffb8c19378fd691582500.seg 02691156 03001627/points/2b6cbad4ba1e9a0645881d7eab1353ba.pts 03001627/expert_verified/points_label/2b6cbad4ba1e9a0645881d7eab1353ba.seg 03001627 04379243/points/dade0594e68e2250be6c545952e7fa4a.pts 04379243/expert_verified/points_label/dade0594e68e2250be6c545952e7fa4a.seg 04379243 03001627/points/9850d225049f987e9b9f2eb77f5e247e.pts 03001627/expert_verified/points_label/9850d225049f987e9b9f2eb77f5e247e.seg 03001627 03948459/points/e9e6426605eb6d5952d52701459b1f0.pts 03948459/expert_verified/points_label/e9e6426605eb6d5952d52701459b1f0.seg 03948459 03636649/points/e507bc77c03a1b3afcb8d8c6d4df8143.pts 03636649/expert_verified/points_label/e507bc77c03a1b3afcb8d8c6d4df8143.seg 03636649 03797390/points/a6d9f9ae39728831808951ff5fb582ac.pts 03797390/expert_verified/points_label/a6d9f9ae39728831808951ff5fb582ac.seg 03797390 04379243/points/3144ba0c286cc61f490ad276cd2af3a4.pts 04379243/expert_verified/points_label/3144ba0c286cc61f490ad276cd2af3a4.seg 04379243 04379243/points/9be565678aab11cba0ab1d82ef09f78f.pts 04379243/expert_verified/points_label/9be565678aab11cba0ab1d82ef09f78f.seg 04379243 04379243/points/a4b2870ce7a54b8eec11c6b035aac769.pts 04379243/expert_verified/points_label/a4b2870ce7a54b8eec11c6b035aac769.seg 04379243 03636649/points/78b95abd1d1158ffef3a2c64cef919d0.pts 03636649/expert_verified/points_label/78b95abd1d1158ffef3a2c64cef919d0.seg 03636649 04379243/points/2182028f013e7eb530bbd4cddd04c77b.pts 04379243/expert_verified/points_label/2182028f013e7eb530bbd4cddd04c77b.seg 04379243 02691156/points/e00b89bc338348caa42c49797afd1f5c.pts 02691156/expert_verified/points_label/e00b89bc338348caa42c49797afd1f5c.seg 02691156 03001627/points/9d28a066df22319cca2e16d6cd76503c.pts 03001627/expert_verified/points_label/9d28a066df22319cca2e16d6cd76503c.seg 03001627 03636649/points/3c4d8c4ebe9dedbc2cd2160e449d45ae.pts 03636649/expert_verified/points_label/3c4d8c4ebe9dedbc2cd2160e449d45ae.seg 03636649 02691156/points/97d662e5e6345b46bd46d022fd7d80aa.pts 02691156/expert_verified/points_label/97d662e5e6345b46bd46d022fd7d80aa.seg 02691156 03001627/points/9dac39c51680daa2f71e06115e9c3b3e.pts 03001627/expert_verified/points_label/9dac39c51680daa2f71e06115e9c3b3e.seg 03001627 03624134/points/1ecb37ea8f0c4abc20fc54d2500eb7f1.pts 03624134/expert_verified/points_label/1ecb37ea8f0c4abc20fc54d2500eb7f1.seg 03624134 03624134/points/3a0f48139bfd3a4ea152d2e823b9fe06.pts 03624134/expert_verified/points_label/3a0f48139bfd3a4ea152d2e823b9fe06.seg 03624134 04379243/points/1264d88ae599df3fbeedb4c8fd29e2d1.pts 04379243/expert_verified/points_label/1264d88ae599df3fbeedb4c8fd29e2d1.seg 04379243 03001627/points/97bbc8970b05c4a3fcde6bcb709edd9a.pts 03001627/expert_verified/points_label/97bbc8970b05c4a3fcde6bcb709edd9a.seg 03001627 03636649/points/1f58b59a1b6b06df766fc93a239bada0.pts 03636649/expert_verified/points_label/1f58b59a1b6b06df766fc93a239bada0.seg 03636649 03001627/points/eb51e814c3f44a07914ced7dab3536b9.pts 03001627/expert_verified/points_label/eb51e814c3f44a07914ced7dab3536b9.seg 03001627 03636649/points/a138582b1d0b9cbb137af984a9f45d65.pts 03636649/expert_verified/points_label/a138582b1d0b9cbb137af984a9f45d65.seg 03636649 03790512/points/9f9de88a95b56660b37378f3c85478b4.pts 03790512/expert_verified/points_label/9f9de88a95b56660b37378f3c85478b4.seg 03790512 03001627/points/a521fba02ca7f9aa822215026d1e8d82.pts 03001627/expert_verified/points_label/a521fba02ca7f9aa822215026d1e8d82.seg 03001627 04225987/points/d303055e96cd59949da15808191f1405.pts 04225987/expert_verified/points_label/d303055e96cd59949da15808191f1405.seg 04225987 04379243/points/7e3022a7bd00eb4195b8ea6a366e14d.pts 04379243/expert_verified/points_label/7e3022a7bd00eb4195b8ea6a366e14d.seg 04379243 02691156/points/d83300deab42c100eb9db4e832a6dd82.pts 02691156/expert_verified/points_label/d83300deab42c100eb9db4e832a6dd82.seg 02691156 03642806/points/a4b410734514306ac401e233323032d6.pts 03642806/expert_verified/points_label/a4b410734514306ac401e233323032d6.seg 03642806 03790512/points/532e6f88a9975a27b37378f3c85478b4.pts 03790512/expert_verified/points_label/532e6f88a9975a27b37378f3c85478b4.seg 03790512 03642806/points/cc691d9e8e189ce47a381a112bfd785.pts 03642806/expert_verified/points_label/cc691d9e8e189ce47a381a112bfd785.seg 03642806 02691156/points/aa07239e9397cf189601fb40d0d298b9.pts 02691156/expert_verified/points_label/aa07239e9397cf189601fb40d0d298b9.seg 02691156 03642806/points/cc0535a34cdc7d676bf98d15712168f.pts 03642806/expert_verified/points_label/cc0535a34cdc7d676bf98d15712168f.seg 03642806 02691156/points/ddec69970cbc4d29112a90660b187a10.pts 02691156/expert_verified/points_label/ddec69970cbc4d29112a90660b187a10.seg 02691156 04379243/points/268e68f1819a225c1b4b790955c17432.pts 04379243/expert_verified/points_label/268e68f1819a225c1b4b790955c17432.seg 04379243 03624134/points/1943c87f92ac76e112cad8be168fe72d.pts 03624134/expert_verified/points_label/1943c87f92ac76e112cad8be168fe72d.seg 03624134 04379243/points/b9fc2f624533bb8119fb4103277a6b93.pts 04379243/expert_verified/points_label/b9fc2f624533bb8119fb4103277a6b93.seg 04379243 03001627/points/1c45b266d3c879dab36dcc661f3905d.pts 03001627/expert_verified/points_label/1c45b266d3c879dab36dcc661f3905d.seg 03001627 03948459/points/1660ef4b3f20b1e2a94b922b533051b7.pts 03948459/expert_verified/points_label/1660ef4b3f20b1e2a94b922b533051b7.seg 03948459 02691156/points/167250e2014c72dbb87697d3904b168b.pts 02691156/expert_verified/points_label/167250e2014c72dbb87697d3904b168b.seg 02691156 02691156/points/dfe65f8a20df11c5d1df55cbe0874aa.pts 02691156/expert_verified/points_label/dfe65f8a20df11c5d1df55cbe0874aa.seg 02691156 03001627/points/44a2a3952ea2315ff51f77a6d7299806.pts 03001627/expert_verified/points_label/44a2a3952ea2315ff51f77a6d7299806.seg 03001627 04379243/points/a1896691fe875eccb9968f25875bdef4.pts 04379243/expert_verified/points_label/a1896691fe875eccb9968f25875bdef4.seg 04379243 04379243/points/6f3506c9c5202101c4e8952b27b5f370.pts 04379243/expert_verified/points_label/6f3506c9c5202101c4e8952b27b5f370.seg 04379243 04379243/points/fead7e0c30a347b1710801cae5dc529.pts 04379243/expert_verified/points_label/fead7e0c30a347b1710801cae5dc529.seg 04379243 04379243/points/384bf53e12744e2019fb4103277a6b93.pts 04379243/expert_verified/points_label/384bf53e12744e2019fb4103277a6b93.seg 04379243 03001627/points/30378faa6bf5b245fdef1c01cbd4ae0c.pts 03001627/expert_verified/points_label/30378faa6bf5b245fdef1c01cbd4ae0c.seg 03001627 04379243/points/5690d17b330f73adfeb8ceb93793cb5.pts 04379243/expert_verified/points_label/5690d17b330f73adfeb8ceb93793cb5.seg 04379243 03467517/points/2e4ec0874ea34a50812ca0ac90db1c07.pts 03467517/expert_verified/points_label/2e4ec0874ea34a50812ca0ac90db1c07.seg 03467517 03001627/points/a007a3cd5b8ca7fb19fb4103277a6b93.pts 03001627/expert_verified/points_label/a007a3cd5b8ca7fb19fb4103277a6b93.seg 03001627 03001627/points/bc21c95f766502a78b03575bb54dfd4.pts 03001627/expert_verified/points_label/bc21c95f766502a78b03575bb54dfd4.seg 03001627 04379243/points/6a3ee73d42228f8581654cb17c02fd.pts 04379243/expert_verified/points_label/6a3ee73d42228f8581654cb17c02fd.seg 04379243 04379243/points/4b399cdce8337c29285e0e27752e54a8.pts 04379243/expert_verified/points_label/4b399cdce8337c29285e0e27752e54a8.seg 04379243 04379243/points/7f9d2da43d6aba67afb6676a5cd782b6.pts 04379243/expert_verified/points_label/7f9d2da43d6aba67afb6676a5cd782b6.seg 04379243 03001627/points/72669be1815b2bb81e4fe86c4ad3ec90.pts 03001627/expert_verified/points_label/72669be1815b2bb81e4fe86c4ad3ec90.seg 03001627 04379243/points/223fbcc813831d8c6e526771d2f7444e.pts 04379243/expert_verified/points_label/223fbcc813831d8c6e526771d2f7444e.seg 04379243 02691156/points/adeb5d68e8d65cc419ba010ddb4974fe.pts 02691156/expert_verified/points_label/adeb5d68e8d65cc419ba010ddb4974fe.seg 02691156 03001627/points/8a9d8dad6800d55ff37af16b2893f1d4.pts 03001627/expert_verified/points_label/8a9d8dad6800d55ff37af16b2893f1d4.seg 03001627 04379243/points/db406d9b2a94bce5622d7484764b58f.pts 04379243/expert_verified/points_label/db406d9b2a94bce5622d7484764b58f.seg 04379243 03001627/points/68b88c0be088c21d5e0096fb2d3266a.pts 03001627/expert_verified/points_label/68b88c0be088c21d5e0096fb2d3266a.seg 03001627 03790512/points/973d75ed9c12836f3d033e6cf82ec72c.pts 03790512/expert_verified/points_label/973d75ed9c12836f3d033e6cf82ec72c.seg 03790512 04379243/points/20292fba71362950c59c53f7df509858.pts 04379243/expert_verified/points_label/20292fba71362950c59c53f7df509858.seg 04379243 03001627/points/21fb308ca737174e22f2f93459bd863e.pts 03001627/expert_verified/points_label/21fb308ca737174e22f2f93459bd863e.seg 03001627 03001627/points/be9d5105e48ae27e713decb1a0563b12.pts 03001627/expert_verified/points_label/be9d5105e48ae27e713decb1a0563b12.seg 03001627 02958343/points/c6441f127d51e478f0fb72d24c42a39.pts 02958343/expert_verified/points_label/c6441f127d51e478f0fb72d24c42a39.seg 02958343 03001627/points/f29cbdb2c7bb10f9953d950bcd7de7a.pts 03001627/expert_verified/points_label/f29cbdb2c7bb10f9953d950bcd7de7a.seg 03001627 02691156/points/65654b5c4e488e0c961fa14fc879444e.pts 02691156/expert_verified/points_label/65654b5c4e488e0c961fa14fc879444e.seg 02691156 04379243/points/8654b644c766dd23d1dcc55e36186e4e.pts 04379243/expert_verified/points_label/8654b644c766dd23d1dcc55e36186e4e.seg 04379243 04379243/points/56bb7376dfa9cb5c8cf069d506f8b5ac.pts 04379243/expert_verified/points_label/56bb7376dfa9cb5c8cf069d506f8b5ac.seg 04379243 04379243/points/d291243cfb51ea7dcb25d116843b43a4.pts 04379243/expert_verified/points_label/d291243cfb51ea7dcb25d116843b43a4.seg 04379243 03790512/points/49edb54e97458de8d373c34785838ee4.pts 03790512/expert_verified/points_label/49edb54e97458de8d373c34785838ee4.seg 03790512 04379243/points/216da8313bc7b192ab610b0c94236463.pts 04379243/expert_verified/points_label/216da8313bc7b192ab610b0c94236463.seg 04379243 03001627/points/5ac8b44ff77e5490c8687ff9b0b4e4ac.pts 03001627/expert_verified/points_label/5ac8b44ff77e5490c8687ff9b0b4e4ac.seg 03001627 03001627/points/956063d67b939431f56aa11cd5e0c3e.pts 03001627/expert_verified/points_label/956063d67b939431f56aa11cd5e0c3e.seg 03001627 04379243/points/8dd8370dcaa8d770ea5682a3b818969a.pts 04379243/expert_verified/points_label/8dd8370dcaa8d770ea5682a3b818969a.seg 04379243 03636649/points/3b64d5033c580d2ef76898f881b76a.pts 03636649/expert_verified/points_label/3b64d5033c580d2ef76898f881b76a.seg 03636649 03001627/points/3d9dce1953180fe6f9c9f9697d1ec60.pts 03001627/expert_verified/points_label/3d9dce1953180fe6f9c9f9697d1ec60.seg 03001627 03001627/points/d1b03eeb33fd441d8189e5e3786f2290.pts 03001627/expert_verified/points_label/d1b03eeb33fd441d8189e5e3786f2290.seg 03001627 02691156/points/5294c39d2a57bd7e5cad6226edb8e82.pts 02691156/expert_verified/points_label/5294c39d2a57bd7e5cad6226edb8e82.seg 02691156 04379243/points/7bc93a4cc26fab5c8c12b667670a35f2.pts 04379243/expert_verified/points_label/7bc93a4cc26fab5c8c12b667670a35f2.seg 04379243 04379243/points/813d34995b5c4406b65b71636c46ae49.pts 04379243/expert_verified/points_label/813d34995b5c4406b65b71636c46ae49.seg 04379243 03001627/points/6782b941de7b2199a344c33f76676fbd.pts 03001627/expert_verified/points_label/6782b941de7b2199a344c33f76676fbd.seg 03001627 03636649/points/ea5ae3cfd142c3b923f93f957094a824.pts 03636649/expert_verified/points_label/ea5ae3cfd142c3b923f93f957094a824.seg 03636649 03001627/points/47caca00f993bc4e4b3c42e318f3affc.pts 03001627/expert_verified/points_label/47caca00f993bc4e4b3c42e318f3affc.seg 03001627 02691156/points/b702e35f4a59e81f64801ad2940cdd5.pts 02691156/expert_verified/points_label/b702e35f4a59e81f64801ad2940cdd5.seg 02691156 03636649/points/3b5f0c01c2b914fc6f16f167d27a7dab.pts 03636649/expert_verified/points_label/3b5f0c01c2b914fc6f16f167d27a7dab.seg 03636649 04379243/points/ad63116007d98a6d19758238d4c7aff2.pts 04379243/expert_verified/points_label/ad63116007d98a6d19758238d4c7aff2.seg 04379243 03797390/points/8f6c86feaa74698d5c91ee20ade72edc.pts 03797390/expert_verified/points_label/8f6c86feaa74698d5c91ee20ade72edc.seg 03797390 04379243/points/48baef3ab18d2d43d2afe8d5254a0d04.pts 04379243/expert_verified/points_label/48baef3ab18d2d43d2afe8d5254a0d04.seg 04379243 03001627/points/fe5310a3457bf0e5c4e8952b27b5f370.pts 03001627/expert_verified/points_label/fe5310a3457bf0e5c4e8952b27b5f370.seg 03001627 04379243/points/d4c330d27bbef3808f6610bf672cd686.pts 04379243/expert_verified/points_label/d4c330d27bbef3808f6610bf672cd686.seg 04379243 04379243/points/adcb67b58024afb99910b7ec4c4e599b.pts 04379243/expert_verified/points_label/adcb67b58024afb99910b7ec4c4e599b.seg 04379243 02958343/points/65d6433043c40046b82c0841410a924f.pts 02958343/expert_verified/points_label/65d6433043c40046b82c0841410a924f.seg 02958343 04379243/points/1a00aa6b75362cc5b324368d54a7416f.pts 04379243/expert_verified/points_label/1a00aa6b75362cc5b324368d54a7416f.seg 04379243 04379243/points/7982e2f2984978c6f4b6538438a0b930.pts 04379243/expert_verified/points_label/7982e2f2984978c6f4b6538438a0b930.seg 04379243 03467517/points/26e1801ea747f72f14fe0da28e4f8384.pts 03467517/expert_verified/points_label/26e1801ea747f72f14fe0da28e4f8384.seg 03467517 04379243/points/c8ee4a8b703180992985858e6f5832da.pts 04379243/expert_verified/points_label/c8ee4a8b703180992985858e6f5832da.seg 04379243 02691156/points/f24daae76836e249f0878b58b4e887bf.pts 02691156/expert_verified/points_label/f24daae76836e249f0878b58b4e887bf.seg 02691156 04379243/points/f29863d2fe8863d4195b8ea6a366e14d.pts 04379243/expert_verified/points_label/f29863d2fe8863d4195b8ea6a366e14d.seg 04379243 04379243/points/babb0963a0e17bb59cd0aef0207ac8c6.pts 04379243/expert_verified/points_label/babb0963a0e17bb59cd0aef0207ac8c6.seg 04379243 03001627/points/39911f927331db1c8687ff9b0b4e4ac.pts 03001627/expert_verified/points_label/39911f927331db1c8687ff9b0b4e4ac.seg 03001627 03001627/points/4a9d3ce54c09a2da696b74614952b2d0.pts 03001627/expert_verified/points_label/4a9d3ce54c09a2da696b74614952b2d0.seg 03001627 03642806/points/caa4afd404f24d21275c1147a304ed86.pts 03642806/expert_verified/points_label/caa4afd404f24d21275c1147a304ed86.seg 03642806 02691156/points/ff6e377e8e5b3757cc34b900bb2492e.pts 02691156/expert_verified/points_label/ff6e377e8e5b3757cc34b900bb2492e.seg 02691156 03001627/points/483cfed0659965ed73c478529c40c4e6.pts 03001627/expert_verified/points_label/483cfed0659965ed73c478529c40c4e6.seg 03001627 03797390/points/4b7888feea81219ab5f4a9188bfa0ef6.pts 03797390/expert_verified/points_label/4b7888feea81219ab5f4a9188bfa0ef6.seg 03797390 03790512/points/40d84e407c46e8d8b31e74d456742c7.pts 03790512/expert_verified/points_label/40d84e407c46e8d8b31e74d456742c7.seg 03790512 04379243/points/176e3b32d749ac94d79f2fc0b8d8ffad.pts 04379243/expert_verified/points_label/176e3b32d749ac94d79f2fc0b8d8ffad.seg 04379243 03001627/points/657790bc7fd16326c132086242d50af2.pts 03001627/expert_verified/points_label/657790bc7fd16326c132086242d50af2.seg 03001627 04379243/points/94c0ab5650ea392ddcfcef693e7ec696.pts 04379243/expert_verified/points_label/94c0ab5650ea392ddcfcef693e7ec696.seg 04379243 03624134/points/bf5cae3922d3cb2bca7250d90eb506cf.pts 03624134/expert_verified/points_label/bf5cae3922d3cb2bca7250d90eb506cf.seg 03624134 03001627/points/49a3b0242c13f92da6fee8e2140acec9.pts 03001627/expert_verified/points_label/49a3b0242c13f92da6fee8e2140acec9.seg 03001627 03636649/points/e4c9bb21fe5bfeb3e21f078602e2eda8.pts 03636649/expert_verified/points_label/e4c9bb21fe5bfeb3e21f078602e2eda8.seg 03636649 03636649/points/6595ee36783d261ed3281970e2c44dbe.pts 03636649/expert_verified/points_label/6595ee36783d261ed3281970e2c44dbe.seg 03636649 02958343/points/9a152b11907b11074549b3c52ae0632e.pts 02958343/expert_verified/points_label/9a152b11907b11074549b3c52ae0632e.seg 02958343 04379243/points/68a7bad2b06bc1a9d93768e7b9b1eabf.pts 04379243/expert_verified/points_label/68a7bad2b06bc1a9d93768e7b9b1eabf.seg 04379243 04379243/points/b9c756b2ff5d66ddfebad4f49b26ec52.pts 04379243/expert_verified/points_label/b9c756b2ff5d66ddfebad4f49b26ec52.seg 04379243 03797390/points/2d10421716b16580e45ef4135c266a12.pts 03797390/expert_verified/points_label/2d10421716b16580e45ef4135c266a12.seg 03797390 03001627/points/2c76aaa00e55c26836c07750784b6bc6.pts 03001627/expert_verified/points_label/2c76aaa00e55c26836c07750784b6bc6.seg 03001627 03636649/points/5cca570916f420e64b3c42e318f3affc.pts 03636649/expert_verified/points_label/5cca570916f420e64b3c42e318f3affc.seg 03636649 03001627/points/9225e57e34334ee019cb07ecb5b4102.pts 03001627/expert_verified/points_label/9225e57e34334ee019cb07ecb5b4102.seg 03001627 03001627/points/17aeeadccf0e560e274b862d3a151946.pts 03001627/expert_verified/points_label/17aeeadccf0e560e274b862d3a151946.seg 03001627 03636649/points/427806f30c61059c22e05b5d2ce39e3b.pts 03636649/expert_verified/points_label/427806f30c61059c22e05b5d2ce39e3b.seg 03636649 03636649/points/17349d6d35aac0685ed28d6c8a1bdfe5.pts 03636649/expert_verified/points_label/17349d6d35aac0685ed28d6c8a1bdfe5.seg 03636649 04379243/points/5ee4cbe45bdc4cd571a782a4379556c7.pts 04379243/expert_verified/points_label/5ee4cbe45bdc4cd571a782a4379556c7.seg 04379243 03636649/points/5eda619e5f36499fc1537287b5c50d9d.pts 03636649/expert_verified/points_label/5eda619e5f36499fc1537287b5c50d9d.seg 03636649 02691156/points/f57c74e194cd2b2bc8727b27ee96a4b7.pts 02691156/expert_verified/points_label/f57c74e194cd2b2bc8727b27ee96a4b7.seg 02691156 02958343/points/27d42437168ccd7ddd75f724c0ccbe00.pts 02958343/expert_verified/points_label/27d42437168ccd7ddd75f724c0ccbe00.seg 02958343 04379243/points/c8cf1c77bbb79d214719088c8e42c6ab.pts 04379243/expert_verified/points_label/c8cf1c77bbb79d214719088c8e42c6ab.seg 04379243 04379243/points/40b48121d1879be2ee0605a41c3320d6.pts 04379243/expert_verified/points_label/40b48121d1879be2ee0605a41c3320d6.seg 04379243 02691156/points/4f9b12d07dce21ac9d93a50cb0355558.pts 02691156/expert_verified/points_label/4f9b12d07dce21ac9d93a50cb0355558.seg 02691156 02691156/points/25bd1569261bc545e8323edc0fe816a8.pts 02691156/expert_verified/points_label/25bd1569261bc545e8323edc0fe816a8.seg 02691156 02691156/points/fbc429365ab7136be1a9c234926c21e2.pts 02691156/expert_verified/points_label/fbc429365ab7136be1a9c234926c21e2.seg 02691156 04379243/points/798c315f86d8f02f931e98da3a93e73e.pts 04379243/expert_verified/points_label/798c315f86d8f02f931e98da3a93e73e.seg 04379243 03790512/points/a0a40a9d5aabd6a7d5dde04c96fd8146.pts 03790512/expert_verified/points_label/a0a40a9d5aabd6a7d5dde04c96fd8146.seg 03790512 04379243/points/884f15cfc6a3eea3dcfcef693e7ec696.pts 04379243/expert_verified/points_label/884f15cfc6a3eea3dcfcef693e7ec696.seg 04379243 04379243/points/f16f939baeb7722e664b3b9b23ddfcbc.pts 04379243/expert_verified/points_label/f16f939baeb7722e664b3b9b23ddfcbc.seg 04379243 03001627/points/1e0580f443a9e6d2593ebeeedbff73b.pts 03001627/expert_verified/points_label/1e0580f443a9e6d2593ebeeedbff73b.seg 03001627 03636649/points/927e0654427c4d0b82241d99b4e87f38.pts 03636649/expert_verified/points_label/927e0654427c4d0b82241d99b4e87f38.seg 03636649 03001627/points/bdd29e651e5f6fb2b079317292bdc5d4.pts 03001627/expert_verified/points_label/bdd29e651e5f6fb2b079317292bdc5d4.seg 03001627 03642806/points/cb1e3a990782678b4b6682da890df381.pts 03642806/expert_verified/points_label/cb1e3a990782678b4b6682da890df381.seg 03642806 03001627/points/fd5ac9b342fe518b9d3ea1c6b57a0095.pts 03001627/expert_verified/points_label/fd5ac9b342fe518b9d3ea1c6b57a0095.seg 03001627 02958343/points/6bbcd5608ddf871a4cdd04162f008888.pts 02958343/expert_verified/points_label/6bbcd5608ddf871a4cdd04162f008888.seg 02958343 04379243/points/76338ed3326689b249524cfd5973a145.pts 04379243/expert_verified/points_label/76338ed3326689b249524cfd5973a145.seg 04379243 03001627/points/9a0571ae6169a6ebfebad4f49b26ec52.pts 03001627/expert_verified/points_label/9a0571ae6169a6ebfebad4f49b26ec52.seg 03001627 03948459/points/49429e1d1e90c1ca202be79d8b285c1e.pts 03948459/expert_verified/points_label/49429e1d1e90c1ca202be79d8b285c1e.seg 03948459 02691156/points/45a4ec99ed13ed773c2498c4c2f13ca.pts 02691156/expert_verified/points_label/45a4ec99ed13ed773c2498c4c2f13ca.seg 02691156 04379243/points/70995336d06fc07ae9f3e9c758fef992.pts 04379243/expert_verified/points_label/70995336d06fc07ae9f3e9c758fef992.seg 04379243 03001627/points/6fd76577d0df60669b9f2eb77f5e247e.pts 03001627/expert_verified/points_label/6fd76577d0df60669b9f2eb77f5e247e.seg 03001627 03001627/points/66f18d05d960ffe0bcd12732b5a4b789.pts 03001627/expert_verified/points_label/66f18d05d960ffe0bcd12732b5a4b789.seg 03001627 03001627/points/e401be99c5a51d8bef8e9284f76f3024.pts 03001627/expert_verified/points_label/e401be99c5a51d8bef8e9284f76f3024.seg 03001627 03001627/points/4a0b61d33846824ab1f04c301b6ccc90.pts 03001627/expert_verified/points_label/4a0b61d33846824ab1f04c301b6ccc90.seg 03001627 04379243/points/9a5cb4122d518111b339f790b1757e92.pts 04379243/expert_verified/points_label/9a5cb4122d518111b339f790b1757e92.seg 04379243 04379243/points/6281381ce38aa988de98d10ab5975b59.pts 04379243/expert_verified/points_label/6281381ce38aa988de98d10ab5975b59.seg 04379243 04379243/points/d382d9e34f365544278d386bfa54545.pts 04379243/expert_verified/points_label/d382d9e34f365544278d386bfa54545.seg 04379243 03948459/points/6de6e56c6f7d43692866658c90231a1a.pts 03948459/expert_verified/points_label/6de6e56c6f7d43692866658c90231a1a.seg 03948459 02691156/points/494a1698eb82572c3df325aac2f73830.pts 02691156/expert_verified/points_label/494a1698eb82572c3df325aac2f73830.seg 02691156 02691156/points/c581942f40cbb60819ba010ddb4974fe.pts 02691156/expert_verified/points_label/c581942f40cbb60819ba010ddb4974fe.seg 02691156 04379243/points/e9038664b7d35e6b436e6787c76ef3f0.pts 04379243/expert_verified/points_label/e9038664b7d35e6b436e6787c76ef3f0.seg 04379243 04099429/points/56c13d294f8afb1ffb88d148e845f82e.pts 04099429/expert_verified/points_label/56c13d294f8afb1ffb88d148e845f82e.seg 04099429 02958343/points/86fa16c6da908e6b44221994b043fd86.pts 02958343/expert_verified/points_label/86fa16c6da908e6b44221994b043fd86.seg 02958343 04379243/points/3249c3ad90085a9e98d5fc0473d00a1c.pts 04379243/expert_verified/points_label/3249c3ad90085a9e98d5fc0473d00a1c.seg 04379243 03636649/points/8581a3ae1f77319ac066b9622c005c53.pts 03636649/expert_verified/points_label/8581a3ae1f77319ac066b9622c005c53.seg 03636649 03790512/points/6e1397773a4d15db429f1c522640e6f0.pts 03790512/expert_verified/points_label/6e1397773a4d15db429f1c522640e6f0.seg 03790512 03624134/points/c1ab7029de67351cf97a65c35ea619f0.pts 03624134/expert_verified/points_label/c1ab7029de67351cf97a65c35ea619f0.seg 03624134 04379243/points/16e874e6165e836b30bbd4cddd04c77b.pts 04379243/expert_verified/points_label/16e874e6165e836b30bbd4cddd04c77b.seg 04379243 03636649/points/ff08713d837d87edf2098a9f7fc86999.pts 03636649/expert_verified/points_label/ff08713d837d87edf2098a9f7fc86999.seg 03636649 03790512/points/b649be9c09e2b332429f1c522640e6f0.pts 03790512/expert_verified/points_label/b649be9c09e2b332429f1c522640e6f0.seg 03790512 03001627/points/85b16941984902f8facfa12c7d71c89f.pts 03001627/expert_verified/points_label/85b16941984902f8facfa12c7d71c89f.seg 03001627 04379243/points/cf1a7653c10aaa0eab610b0c94236463.pts 04379243/expert_verified/points_label/cf1a7653c10aaa0eab610b0c94236463.seg 04379243 03001627/points/a42aa59fa23b4a4d9c0ca344f487323e.pts 03001627/expert_verified/points_label/a42aa59fa23b4a4d9c0ca344f487323e.seg 03001627 03001627/points/3f4f1d18c61a07f134b707eb14b2a4a5.pts 03001627/expert_verified/points_label/3f4f1d18c61a07f134b707eb14b2a4a5.seg 03001627 03001627/points/d2b9e98373e96afec8d65ca96e6b18ef.pts 03001627/expert_verified/points_label/d2b9e98373e96afec8d65ca96e6b18ef.seg 03001627 03636649/points/71dffdee89efe07cdff00b2637ddcbde.pts 03636649/expert_verified/points_label/71dffdee89efe07cdff00b2637ddcbde.seg 03636649 02691156/points/5ac0cd21410b2a6a341877ff7a6c751f.pts 02691156/expert_verified/points_label/5ac0cd21410b2a6a341877ff7a6c751f.seg 02691156 03636649/points/76eb7436c40e083384d184bdc625781a.pts 03636649/expert_verified/points_label/76eb7436c40e083384d184bdc625781a.seg 03636649 03642806/points/13330d1e7b199dd82530b9c2b65d3f86.pts 03642806/expert_verified/points_label/13330d1e7b199dd82530b9c2b65d3f86.seg 03642806 02691156/points/e726c8e6897130439a6e43b878d5b335.pts 02691156/expert_verified/points_label/e726c8e6897130439a6e43b878d5b335.seg 02691156 04379243/points/40a402e1d949364a104ceb84075e40d6.pts 04379243/expert_verified/points_label/40a402e1d949364a104ceb84075e40d6.seg 04379243 03001627/points/42140baad25c8598baa1a4ff2c45ffc9.pts 03001627/expert_verified/points_label/42140baad25c8598baa1a4ff2c45ffc9.seg 03001627 03001627/points/5283a98b5c693e64ebefe6b1d594ad2e.pts 03001627/expert_verified/points_label/5283a98b5c693e64ebefe6b1d594ad2e.seg 03001627 02691156/points/15898fef6fec88c53ada73811bb576de.pts 02691156/expert_verified/points_label/15898fef6fec88c53ada73811bb576de.seg 02691156 03001627/points/3f8d0d53e2bd74124b3c42e318f3affc.pts 03001627/expert_verified/points_label/3f8d0d53e2bd74124b3c42e318f3affc.seg 03001627 04379243/points/cd106955d3bdf8e751c4deb11af7079e.pts 04379243/expert_verified/points_label/cd106955d3bdf8e751c4deb11af7079e.seg 04379243 03001627/points/11506b96d41f7d3dd7c4a943f33e0384.pts 03001627/expert_verified/points_label/11506b96d41f7d3dd7c4a943f33e0384.seg 03001627 03001627/points/f51ab8433184dfd2c8687ff9b0b4e4ac.pts 03001627/expert_verified/points_label/f51ab8433184dfd2c8687ff9b0b4e4ac.seg 03001627 02691156/points/c9a6dcf87d1f15bca8607f540cc62ba.pts 02691156/expert_verified/points_label/c9a6dcf87d1f15bca8607f540cc62ba.seg 02691156 04379243/points/d9c75799ff9ff74664b3b9b23ddfcbc.pts 04379243/expert_verified/points_label/d9c75799ff9ff74664b3b9b23ddfcbc.seg 04379243 04379243/points/93e81005c19a74b8664b3b9b23ddfcbc.pts 04379243/expert_verified/points_label/93e81005c19a74b8664b3b9b23ddfcbc.seg 04379243 02958343/points/5057c9dbf72e0352728fa2df514c65d4.pts 02958343/expert_verified/points_label/5057c9dbf72e0352728fa2df514c65d4.seg 02958343 04379243/points/8ad88ee4442fd0fd8a6ba7ebad3985bb.pts 04379243/expert_verified/points_label/8ad88ee4442fd0fd8a6ba7ebad3985bb.seg 04379243 04379243/points/a2554ec7e2331a8fab610b0c94236463.pts 04379243/expert_verified/points_label/a2554ec7e2331a8fab610b0c94236463.seg 04379243 04379243/points/482a76d14781e55e25374da32e705c.pts 04379243/expert_verified/points_label/482a76d14781e55e25374da32e705c.seg 04379243 02691156/points/d06105ee2a2ae27c51008e496c6cfd2e.pts 02691156/expert_verified/points_label/d06105ee2a2ae27c51008e496c6cfd2e.seg 02691156 04379243/points/45a09b1ce3111e4f22f4fabdf1ee0670.pts 04379243/expert_verified/points_label/45a09b1ce3111e4f22f4fabdf1ee0670.seg 04379243 03467517/points/9aaad035af7e6ab1ed724609df3eb104.pts 03467517/expert_verified/points_label/9aaad035af7e6ab1ed724609df3eb104.seg 03467517 02691156/points/cf0cdaa94220ee3f4c3a35cee92bb95b.pts 02691156/expert_verified/points_label/cf0cdaa94220ee3f4c3a35cee92bb95b.seg 02691156 02691156/points/48cb2de06f46cde25ed29e0a9f14425.pts 02691156/expert_verified/points_label/48cb2de06f46cde25ed29e0a9f14425.seg 02691156 03001627/points/2f0a94efe6d1da7f8616812464c86290.pts 03001627/expert_verified/points_label/2f0a94efe6d1da7f8616812464c86290.seg 03001627 02691156/points/e0385af10bddc6a0ca8607f540cc62ba.pts 02691156/expert_verified/points_label/e0385af10bddc6a0ca8607f540cc62ba.seg 02691156 03467517/points/71139bd2ff6c4257280ec2e5049bb369.pts 03467517/expert_verified/points_label/71139bd2ff6c4257280ec2e5049bb369.seg 03467517 03001627/points/6251b398004a02fffebad4f49b26ec52.pts 03001627/expert_verified/points_label/6251b398004a02fffebad4f49b26ec52.seg 03001627 03467517/points/7eba657565cc69e913f86abea5e4b9e0.pts 03467517/expert_verified/points_label/7eba657565cc69e913f86abea5e4b9e0.seg 03467517 03001627/points/8d2fd4b9c583e1e6a12cdfe22cdc2f5d.pts 03001627/expert_verified/points_label/8d2fd4b9c583e1e6a12cdfe22cdc2f5d.seg 03001627 03001627/points/ffa1e25f499e586694e98ee4fdfd7464.pts 03001627/expert_verified/points_label/ffa1e25f499e586694e98ee4fdfd7464.seg 03001627 03797390/points/9af98540f45411467246665d3d3724c.pts 03797390/expert_verified/points_label/9af98540f45411467246665d3d3724c.seg 03797390 02691156/points/b9fabfa6d5fedbc3a8e091cb544689d5.pts 02691156/expert_verified/points_label/b9fabfa6d5fedbc3a8e091cb544689d5.seg 02691156 04379243/points/a2561614d015f2fdfebad4f49b26ec52.pts 04379243/expert_verified/points_label/a2561614d015f2fdfebad4f49b26ec52.seg 04379243 03642806/points/2134ad3fc25a6284193a4c984002ed32.pts 03642806/expert_verified/points_label/2134ad3fc25a6284193a4c984002ed32.seg 03642806 03001627/points/d3302b7fa6504cab1a461b43b8f257f.pts 03001627/expert_verified/points_label/d3302b7fa6504cab1a461b43b8f257f.seg 03001627 03467517/points/bf7026f9814230414269db3f92b7aa5e.pts 03467517/expert_verified/points_label/bf7026f9814230414269db3f92b7aa5e.seg 03467517 03636649/points/9aff9fdad0e3555c7eecb4e0df212ad9.pts 03636649/expert_verified/points_label/9aff9fdad0e3555c7eecb4e0df212ad9.seg 03636649 03797390/points/a3cd44bbd3ba5b019a4cbf5d3b79df06.pts 03797390/expert_verified/points_label/a3cd44bbd3ba5b019a4cbf5d3b79df06.seg 03797390 04099429/points/eff3a27a085e02e5146be45f8a3c1ff8.pts 04099429/expert_verified/points_label/eff3a27a085e02e5146be45f8a3c1ff8.seg 04099429 02958343/points/1e3f494626a24badf35b4953d8add91f.pts 02958343/expert_verified/points_label/1e3f494626a24badf35b4953d8add91f.seg 02958343 04379243/points/1f3e217cbc871152d7465eca206fda6f.pts 04379243/expert_verified/points_label/1f3e217cbc871152d7465eca206fda6f.seg 04379243 03636649/points/cef6757831b4d9738c8f019f17f4687c.pts 03636649/expert_verified/points_label/cef6757831b4d9738c8f019f17f4687c.seg 03636649 04379243/points/e8689b8b1610bf2841bb8a7ba579a58.pts 04379243/expert_verified/points_label/e8689b8b1610bf2841bb8a7ba579a58.seg 04379243 03001627/points/40168f46019eb867be7e1d42d63ca9f0.pts 03001627/expert_verified/points_label/40168f46019eb867be7e1d42d63ca9f0.seg 03001627 03624134/points/7aed22a7074f16431cf05d6e4dbb95af.pts 03624134/expert_verified/points_label/7aed22a7074f16431cf05d6e4dbb95af.seg 03624134 04379243/points/5d53ed3005f4dc6856786b90799c4fdb.pts 04379243/expert_verified/points_label/5d53ed3005f4dc6856786b90799c4fdb.seg 04379243 04379243/points/beebc267ea0c16a5c7f6a57f6f73d8a6.pts 04379243/expert_verified/points_label/beebc267ea0c16a5c7f6a57f6f73d8a6.seg 04379243 04379243/points/943d786e2df9251ec76aead7da70af41.pts 04379243/expert_verified/points_label/943d786e2df9251ec76aead7da70af41.seg 04379243 04379243/points/90d87b4d9a5a1e78f4b6538438a0b930.pts 04379243/expert_verified/points_label/90d87b4d9a5a1e78f4b6538438a0b930.seg 04379243 02958343/points/d47353fc60390df85d918097f81825e3.pts 02958343/expert_verified/points_label/d47353fc60390df85d918097f81825e3.seg 02958343 03624134/points/90021da7c71f6bcbf02ee453ff283e26.pts 03624134/expert_verified/points_label/90021da7c71f6bcbf02ee453ff283e26.seg 03624134 02958343/points/d1acd4916d3d3b57c48db2ed8f5e994c.pts 02958343/expert_verified/points_label/d1acd4916d3d3b57c48db2ed8f5e994c.seg 02958343 03001627/points/1d1c829a54f0ae426cdb122727dd360f.pts 03001627/expert_verified/points_label/1d1c829a54f0ae426cdb122727dd360f.seg 03001627 04379243/points/c35a14f84985f92a9856fa70a578baeb.pts 04379243/expert_verified/points_label/c35a14f84985f92a9856fa70a578baeb.seg 04379243 03636649/points/5c5119a226e1ce9934804d261199e1bf.pts 03636649/expert_verified/points_label/5c5119a226e1ce9934804d261199e1bf.seg 03636649 03636649/points/6bb8020fa82b27dde11a3e838aa2c287.pts 03636649/expert_verified/points_label/6bb8020fa82b27dde11a3e838aa2c287.seg 03636649 03797390/points/fad118b32085f3f2c2c72e575af174cd.pts 03797390/expert_verified/points_label/fad118b32085f3f2c2c72e575af174cd.seg 03797390 04379243/points/a82387cf9d9d253aa06f94abffad1304.pts 04379243/expert_verified/points_label/a82387cf9d9d253aa06f94abffad1304.seg 04379243 03948459/points/a7a340a901d63486260a770f90456bf7.pts 03948459/expert_verified/points_label/a7a340a901d63486260a770f90456bf7.seg 03948459 03624134/points/60e7b05ddeeb48eb37fa2c3ecb75f337.pts 03624134/expert_verified/points_label/60e7b05ddeeb48eb37fa2c3ecb75f337.seg 03624134 02958343/points/3e2c3cb4f4c65b9cde9d4070fcdfa604.pts 02958343/expert_verified/points_label/3e2c3cb4f4c65b9cde9d4070fcdfa604.seg 02958343 03001627/points/d58df0968070bf3b4b3c42e318f3affc.pts 03001627/expert_verified/points_label/d58df0968070bf3b4b3c42e318f3affc.seg 03001627 04379243/points/4a3641784a9ecca04fa8d6439169bda4.pts 04379243/expert_verified/points_label/4a3641784a9ecca04fa8d6439169bda4.seg 04379243 04225987/points/d31aaca67fd8ef1827d17dabad15093.pts 04225987/expert_verified/points_label/d31aaca67fd8ef1827d17dabad15093.seg 04225987 03001627/points/c51937167dd0db45f7628281ecb18112.pts 03001627/expert_verified/points_label/c51937167dd0db45f7628281ecb18112.seg 03001627 04379243/points/768cb2332a16fd63855931d119219022.pts 04379243/expert_verified/points_label/768cb2332a16fd63855931d119219022.seg 04379243 03001627/points/8c76176c82e3e42d283b00891f680579.pts 03001627/expert_verified/points_label/8c76176c82e3e42d283b00891f680579.seg 03001627 03001627/points/d4d9b991ff7d31e8c8687ff9b0b4e4ac.pts 03001627/expert_verified/points_label/d4d9b991ff7d31e8c8687ff9b0b4e4ac.seg 03001627 03797390/points/162201dfe14b73f0281365259d1cf342.pts 03797390/expert_verified/points_label/162201dfe14b73f0281365259d1cf342.seg 03797390 04379243/points/ed1e06e886b5514fe8f49d7c9e73ab9.pts 04379243/expert_verified/points_label/ed1e06e886b5514fe8f49d7c9e73ab9.seg 04379243 03636649/points/90651b3febfc3afe15226aa76eb7c3e.pts 03636649/expert_verified/points_label/90651b3febfc3afe15226aa76eb7c3e.seg 03636649 04379243/points/24b208dd138d8af36210db75a4cd581b.pts 04379243/expert_verified/points_label/24b208dd138d8af36210db75a4cd581b.seg 04379243 03001627/points/439418b35f600f4bb10dc0fca58d0b2c.pts 03001627/expert_verified/points_label/439418b35f600f4bb10dc0fca58d0b2c.seg 03001627 03636649/points/88257c5a48d94b1e2b151d8b52c53b90.pts 03636649/expert_verified/points_label/88257c5a48d94b1e2b151d8b52c53b90.seg 03636649 02691156/points/ad546b049b2246bd609e2d916fa0da27.pts 02691156/expert_verified/points_label/ad546b049b2246bd609e2d916fa0da27.seg 02691156 03001627/points/7efeece3b5cf2853d706779c93538ee1.pts 03001627/expert_verified/points_label/7efeece3b5cf2853d706779c93538ee1.seg 03001627 04379243/points/30dd74f09af6b1c2fe5c8ffd0f5eba47.pts 04379243/expert_verified/points_label/30dd74f09af6b1c2fe5c8ffd0f5eba47.seg 04379243 02691156/points/752d9a010346862551cfdb4c9f126c12.pts 02691156/expert_verified/points_label/752d9a010346862551cfdb4c9f126c12.seg 02691156 03001627/points/d1237422881f4d22ff25b0c2db862d19.pts 03001627/expert_verified/points_label/d1237422881f4d22ff25b0c2db862d19.seg 03001627 04379243/points/95af60aa8cb9be066a76e23e6f966dea.pts 04379243/expert_verified/points_label/95af60aa8cb9be066a76e23e6f966dea.seg 04379243 02691156/points/556d2b99469e62e623a346a784afd6ba.pts 02691156/expert_verified/points_label/556d2b99469e62e623a346a784afd6ba.seg 02691156 04379243/points/6e23179a3559775a65eacc25f128a1c5.pts 04379243/expert_verified/points_label/6e23179a3559775a65eacc25f128a1c5.seg 04379243 02691156/points/3b82e575165383903c83f6e156ad107a.pts 02691156/expert_verified/points_label/3b82e575165383903c83f6e156ad107a.seg 02691156 02773838/points/71ead7f072106c63ed13f430b2941481.pts 02773838/expert_verified/points_label/71ead7f072106c63ed13f430b2941481.seg 02773838 03001627/points/c9d68e1e5309ac25ac57e7d566628472.pts 03001627/expert_verified/points_label/c9d68e1e5309ac25ac57e7d566628472.seg 03001627 02691156/points/b3a59a941500e76535592b447835a16e.pts 02691156/expert_verified/points_label/b3a59a941500e76535592b447835a16e.seg 02691156 03797390/points/4d9764afa3fbeb1b6c69dceb67157a66.pts 03797390/expert_verified/points_label/4d9764afa3fbeb1b6c69dceb67157a66.seg 03797390 04379243/points/68ea1f319a9d724ec3bd24f986301745.pts 04379243/expert_verified/points_label/68ea1f319a9d724ec3bd24f986301745.seg 04379243 03001627/points/30363681727c804095937f6e581cbd41.pts 03001627/expert_verified/points_label/30363681727c804095937f6e581cbd41.seg 03001627 03001627/points/f4f1aba65ebe48eb70930286c914896b.pts 03001627/expert_verified/points_label/f4f1aba65ebe48eb70930286c914896b.seg 03001627 02691156/points/a3fc9ef9f611a783525e60273896d30a.pts 02691156/expert_verified/points_label/a3fc9ef9f611a783525e60273896d30a.seg 02691156 03636649/points/b0871c4ac8505d9c3d39d8012919dd25.pts 03636649/expert_verified/points_label/b0871c4ac8505d9c3d39d8012919dd25.seg 03636649 03001627/points/d7e26a070ee3b35cdf6cfab91d65bb91.pts 03001627/expert_verified/points_label/d7e26a070ee3b35cdf6cfab91d65bb91.seg 03001627 04379243/points/9012c6ca245c1bf4e6c5cd45aa112726.pts 04379243/expert_verified/points_label/9012c6ca245c1bf4e6c5cd45aa112726.seg 04379243 03636649/points/3ab9e4300cee0259f72e8839e840c146.pts 03636649/expert_verified/points_label/3ab9e4300cee0259f72e8839e840c146.seg 03636649 04379243/points/6e0fed54fcae8a62edccc47bf0dcf5d3.pts 04379243/expert_verified/points_label/6e0fed54fcae8a62edccc47bf0dcf5d3.seg 04379243 04379243/points/aafc579804cc095cbababe11fcea8796.pts 04379243/expert_verified/points_label/aafc579804cc095cbababe11fcea8796.seg 04379243 03636649/points/9adee08c737c7c134c6deb9ede0648df.pts 03636649/expert_verified/points_label/9adee08c737c7c134c6deb9ede0648df.seg 03636649 02691156/points/f39985959d394f8c863ab010b80d9ed.pts 02691156/expert_verified/points_label/f39985959d394f8c863ab010b80d9ed.seg 02691156 04379243/points/23d4170c7a0a2a014b3c42e318f3affc.pts 04379243/expert_verified/points_label/23d4170c7a0a2a014b3c42e318f3affc.seg 04379243 04379243/points/a1593fbe3a78c7858795000a72749c36.pts 04379243/expert_verified/points_label/a1593fbe3a78c7858795000a72749c36.seg 04379243 03001627/points/4b2ede169dcc83ce4591019e9d133858.pts 03001627/expert_verified/points_label/4b2ede169dcc83ce4591019e9d133858.seg 03001627 03001627/points/3fa1eeed2e8e2534febad4f49b26ec52.pts 03001627/expert_verified/points_label/3fa1eeed2e8e2534febad4f49b26ec52.seg 03001627 04379243/points/e8ba9621aef9395a3019620286259e2c.pts 04379243/expert_verified/points_label/e8ba9621aef9395a3019620286259e2c.seg 04379243 03001627/points/875925d42780159ffebad4f49b26ec52.pts 03001627/expert_verified/points_label/875925d42780159ffebad4f49b26ec52.seg 03001627 03001627/points/548ab6b6e8b2dc505ff61a3a2a0e2484.pts 03001627/expert_verified/points_label/548ab6b6e8b2dc505ff61a3a2a0e2484.seg 03001627 03467517/points/4f401d78068a9d348ee96618ee16ca27.pts 03467517/expert_verified/points_label/4f401d78068a9d348ee96618ee16ca27.seg 03467517 04379243/points/f7600660924857c0d31d0d81bfe9c743.pts 04379243/expert_verified/points_label/f7600660924857c0d31d0d81bfe9c743.seg 04379243 04379243/points/edba7eb533ae3578ece232edf44331c7.pts 04379243/expert_verified/points_label/edba7eb533ae3578ece232edf44331c7.seg 04379243 03001627/points/8b8fa92f9c677b0713decb1a0563b12.pts 03001627/expert_verified/points_label/8b8fa92f9c677b0713decb1a0563b12.seg 03001627 02691156/points/81e6b629264dad5daf2c6c19cc41708a.pts 02691156/expert_verified/points_label/81e6b629264dad5daf2c6c19cc41708a.seg 02691156 02691156/points/a0a7e673a1e1bca78699933784576e73.pts 02691156/expert_verified/points_label/a0a7e673a1e1bca78699933784576e73.seg 02691156 03636649/points/f01358d4f45cae23ce670f026edf07e5.pts 03636649/expert_verified/points_label/f01358d4f45cae23ce670f026edf07e5.seg 03636649 03001627/points/808fa82fe9ad86d9f1cc184b6fa3e1f9.pts 03001627/expert_verified/points_label/808fa82fe9ad86d9f1cc184b6fa3e1f9.seg 03001627 02691156/points/57937c7ab42260ebf119374ee5d5f944.pts 02691156/expert_verified/points_label/57937c7ab42260ebf119374ee5d5f944.seg 02691156 03001627/points/fbddac94cfa74a7b5c0228148b88226c.pts 03001627/expert_verified/points_label/fbddac94cfa74a7b5c0228148b88226c.seg 03001627 04379243/points/ad92bfc65465091c48d90eef8384210.pts 04379243/expert_verified/points_label/ad92bfc65465091c48d90eef8384210.seg 04379243 03467517/points/6ce23c82af30b629e8f705eb96ba3376.pts 03467517/expert_verified/points_label/6ce23c82af30b629e8f705eb96ba3376.seg 03467517 03001627/points/bd1787066323c7a64424fc4d3c9cb157.pts 03001627/expert_verified/points_label/bd1787066323c7a64424fc4d3c9cb157.seg 03001627 03001627/points/uca24feec-f0c0-454c-baaf-561530686f40.pts 03001627/expert_verified/points_label/uca24feec-f0c0-454c-baaf-561530686f40.seg 03001627 03001627/points/226704c72560008421ceb39dc3069834.pts 03001627/expert_verified/points_label/226704c72560008421ceb39dc3069834.seg 03001627 02691156/points/2c49289098e4492bca8607f540cc62ba.pts 02691156/expert_verified/points_label/2c49289098e4492bca8607f540cc62ba.seg 02691156 03001627/points/cff9a523a9e20eaeb40f0ac0fb9a650d.pts 03001627/expert_verified/points_label/cff9a523a9e20eaeb40f0ac0fb9a650d.seg 03001627 04379243/points/38e90183c838f443b43753a53e4593db.pts 04379243/expert_verified/points_label/38e90183c838f443b43753a53e4593db.seg 04379243 04379243/points/8b4ec70a3c1283b1fb5f8baea920e189.pts 04379243/expert_verified/points_label/8b4ec70a3c1283b1fb5f8baea920e189.seg 04379243 04379243/points/59a1703cb9320c018f49a52c8d710d0f.pts 04379243/expert_verified/points_label/59a1703cb9320c018f49a52c8d710d0f.seg 04379243 03636649/points/4ba237c2c40313f373b3ec02b97cb0f.pts 03636649/expert_verified/points_label/4ba237c2c40313f373b3ec02b97cb0f.seg 03636649 04379243/points/bb027ed892722b1f3399de188dc5ee56.pts 04379243/expert_verified/points_label/bb027ed892722b1f3399de188dc5ee56.seg 04379243 03467517/points/8b1d0f73e54ef59c93f0194265a9746c.pts 03467517/expert_verified/points_label/8b1d0f73e54ef59c93f0194265a9746c.seg 03467517 03467517/points/1300e8bafb819c8e1887f40a4f62df44.pts 03467517/expert_verified/points_label/1300e8bafb819c8e1887f40a4f62df44.seg 03467517 03642806/points/9fa387d7f442b96e75e60c00fabe2744.pts 03642806/expert_verified/points_label/9fa387d7f442b96e75e60c00fabe2744.seg 03642806 04379243/points/e153f757330a4ea3cdd1f51ef2b8f2ed.pts 04379243/expert_verified/points_label/e153f757330a4ea3cdd1f51ef2b8f2ed.seg 04379243 03636649/points/d00157a022079bdef3655a2ce983ab1f.pts 03636649/expert_verified/points_label/d00157a022079bdef3655a2ce983ab1f.seg 03636649 04379243/points/9eeea5f7b030ff6ac155f88004a92bc8.pts 04379243/expert_verified/points_label/9eeea5f7b030ff6ac155f88004a92bc8.seg 04379243 04379243/points/10ed64b4c7eb6d9311ee7ca4f000feba.pts 04379243/expert_verified/points_label/10ed64b4c7eb6d9311ee7ca4f000feba.seg 04379243 03001627/points/6db2255a51caf84e823e7e244bf84209.pts 03001627/expert_verified/points_label/6db2255a51caf84e823e7e244bf84209.seg 03001627 03001627/points/8ddaa112e6ba36b5b1e23c7675c49239.pts 03001627/expert_verified/points_label/8ddaa112e6ba36b5b1e23c7675c49239.seg 03001627 04379243/points/7813f4e4c0a58118cbb8bac2032149c.pts 04379243/expert_verified/points_label/7813f4e4c0a58118cbb8bac2032149c.seg 04379243 03797390/points/336122c3105440d193e42e2720468bf0.pts 03797390/expert_verified/points_label/336122c3105440d193e42e2720468bf0.seg 03797390 03001627/points/f2e2993abf4c952b2e69a7e134f91051.pts 03001627/expert_verified/points_label/f2e2993abf4c952b2e69a7e134f91051.seg 03001627 04379243/points/627248fa64c1db5fab610b0c94236463.pts 04379243/expert_verified/points_label/627248fa64c1db5fab610b0c94236463.seg 04379243 04379243/points/3b465822b34ed20ca05d3424fd8d541a.pts 04379243/expert_verified/points_label/3b465822b34ed20ca05d3424fd8d541a.seg 04379243 03467517/points/a7ddf2e5b9dc278293f0194265a9746c.pts 03467517/expert_verified/points_label/a7ddf2e5b9dc278293f0194265a9746c.seg 03467517 03636649/points/b36bfbbc98cb45431735ea0e092a805a.pts 03636649/expert_verified/points_label/b36bfbbc98cb45431735ea0e092a805a.seg 03636649 04379243/points/7d14ae7d0b7338bda0ab1d82ef09f78f.pts 04379243/expert_verified/points_label/7d14ae7d0b7338bda0ab1d82ef09f78f.seg 04379243 03467517/points/f7645b3c690d954682c2412261cb8600.pts 03467517/expert_verified/points_label/f7645b3c690d954682c2412261cb8600.seg 03467517 02958343/points/41a6deadd39b4c754d0f9a1ef5f184fe.pts 02958343/expert_verified/points_label/41a6deadd39b4c754d0f9a1ef5f184fe.seg 02958343 02691156/points/f74cbd91e6fb40dfce5965228d7e8c9f.pts 02691156/expert_verified/points_label/f74cbd91e6fb40dfce5965228d7e8c9f.seg 02691156 04379243/points/6c4c3bfe275e66b1b75e606711562bfc.pts 04379243/expert_verified/points_label/6c4c3bfe275e66b1b75e606711562bfc.seg 04379243 04379243/points/7d358a01c9467815a9505c473725122e.pts 04379243/expert_verified/points_label/7d358a01c9467815a9505c473725122e.seg 04379243 04379243/points/5fe3476df92392e1397aad305ec14786.pts 04379243/expert_verified/points_label/5fe3476df92392e1397aad305ec14786.seg 04379243 03001627/points/34d3960d35d8d5219b9f2eb77f5e247e.pts 03001627/expert_verified/points_label/34d3960d35d8d5219b9f2eb77f5e247e.seg 03001627 03001627/points/1b67a3a1101a9acb905477d2a8504646.pts 03001627/expert_verified/points_label/1b67a3a1101a9acb905477d2a8504646.seg 03001627 03001627/points/ee4858f78dc33591100e9bd5c4b0af54.pts 03001627/expert_verified/points_label/ee4858f78dc33591100e9bd5c4b0af54.seg 03001627 03001627/points/a578b0027e7d9ec7b2ca3ea77e53abe.pts 03001627/expert_verified/points_label/a578b0027e7d9ec7b2ca3ea77e53abe.seg 03001627 02691156/points/916950e40ca7aabc8b96ae1a0a8b84ec.pts 02691156/expert_verified/points_label/916950e40ca7aabc8b96ae1a0a8b84ec.seg 02691156 04379243/points/1abfb0c03c81fc2219fb4103277a6b93.pts 04379243/expert_verified/points_label/1abfb0c03c81fc2219fb4103277a6b93.seg 04379243 02691156/points/a702da03d770f5096e2738fc9da60e6f.pts 02691156/expert_verified/points_label/a702da03d770f5096e2738fc9da60e6f.seg 02691156 04379243/points/2e2894138df855b26f88aa1b7f7cc6c6.pts 04379243/expert_verified/points_label/2e2894138df855b26f88aa1b7f7cc6c6.seg 04379243 03001627/points/589cd6a1f4367fd834b707eb14b2a4a5.pts 03001627/expert_verified/points_label/589cd6a1f4367fd834b707eb14b2a4a5.seg 03001627 03636649/points/f8534299ecce5c16eaf14273fa406ffc.pts 03636649/expert_verified/points_label/f8534299ecce5c16eaf14273fa406ffc.seg 03636649 04379243/points/ea96b8a866121d1abed1bd9593e318c.pts 04379243/expert_verified/points_label/ea96b8a866121d1abed1bd9593e318c.seg 04379243 03624134/points/9746101f20473d346bbd83c2bc4c3b2e.pts 03624134/expert_verified/points_label/9746101f20473d346bbd83c2bc4c3b2e.seg 03624134 02958343/points/9c4a3879c71df693af0f25977186b501.pts 02958343/expert_verified/points_label/9c4a3879c71df693af0f25977186b501.seg 02958343 03001627/points/6621723f7af35f2dcd344c2b2cefcda6.pts 03001627/expert_verified/points_label/6621723f7af35f2dcd344c2b2cefcda6.seg 03001627 03948459/points/8c9e592c95f95e7c9a6e43b878d5b335.pts 03948459/expert_verified/points_label/8c9e592c95f95e7c9a6e43b878d5b335.seg 03948459 04379243/points/36a6d851dbe02410ad16260d4d73b56.pts 04379243/expert_verified/points_label/36a6d851dbe02410ad16260d4d73b56.seg 04379243 04379243/points/b1ca280d9567270ade98d10ab5975b59.pts 04379243/expert_verified/points_label/b1ca280d9567270ade98d10ab5975b59.seg 04379243 03467517/points/5ed99a0b793e1f5ee52744498b9b3051.pts 03467517/expert_verified/points_label/5ed99a0b793e1f5ee52744498b9b3051.seg 03467517 03001627/points/18fd8342fa5d1d4f5268b70948af88b2.pts 03001627/expert_verified/points_label/18fd8342fa5d1d4f5268b70948af88b2.seg 03001627 02691156/points/cc60baa1a796f5c14c3a35cee92bb95b.pts 02691156/expert_verified/points_label/cc60baa1a796f5c14c3a35cee92bb95b.seg 02691156 03642806/points/3237f5cd4bca555955357c338ec9641.pts 03642806/expert_verified/points_label/3237f5cd4bca555955357c338ec9641.seg 03642806 03001627/points/fee248777c9c4807f8bc1f8036e08e44.pts 03001627/expert_verified/points_label/fee248777c9c4807f8bc1f8036e08e44.seg 03001627 04379243/points/2d90a1998eca8778dcfcef693e7ec696.pts 04379243/expert_verified/points_label/2d90a1998eca8778dcfcef693e7ec696.seg 04379243 02958343/points/3ef7cfbc172840b2393bf61b30c528bb.pts 02958343/expert_verified/points_label/3ef7cfbc172840b2393bf61b30c528bb.seg 02958343 02691156/points/240fd3c1fd804ec1b8cf782e8c539948.pts 02691156/expert_verified/points_label/240fd3c1fd804ec1b8cf782e8c539948.seg 02691156 04379243/points/60c931dcc6d0982944bda2555d37e46.pts 04379243/expert_verified/points_label/60c931dcc6d0982944bda2555d37e46.seg 04379243 04379243/points/93040a14fad5588ed889130b88839a0c.pts 04379243/expert_verified/points_label/93040a14fad5588ed889130b88839a0c.seg 04379243 02958343/points/a75ff576da012340468bac13e007a6e9.pts 02958343/expert_verified/points_label/a75ff576da012340468bac13e007a6e9.seg 02958343 03467517/points/57286d92604c9ebea3d3eb77b119df6d.pts 03467517/expert_verified/points_label/57286d92604c9ebea3d3eb77b119df6d.seg 03467517 03636649/points/913ba6b6ac6aea3356c82fefb25b338b.pts 03636649/expert_verified/points_label/913ba6b6ac6aea3356c82fefb25b338b.seg 03636649 03001627/points/cce9ffdcc7ca8ddea300840c9d7bfa74.pts 03001627/expert_verified/points_label/cce9ffdcc7ca8ddea300840c9d7bfa74.seg 03001627 04379243/points/913c0ff011ad0658dcfcef693e7ec696.pts 04379243/expert_verified/points_label/913c0ff011ad0658dcfcef693e7ec696.seg 04379243 03001627/points/9d0b25421c13008e35836c728d324152.pts 03001627/expert_verified/points_label/9d0b25421c13008e35836c728d324152.seg 03001627 03797390/points/a8f7a0edd3edc3299e54b4084dc33544.pts 03797390/expert_verified/points_label/a8f7a0edd3edc3299e54b4084dc33544.seg 03797390 04379243/points/5b9a7b7952996844d802aa676be38da2.pts 04379243/expert_verified/points_label/5b9a7b7952996844d802aa676be38da2.seg 04379243 02954340/points/4bd0b6df02772d8f59c9250a427b57f.pts 02954340/expert_verified/points_label/4bd0b6df02772d8f59c9250a427b57f.seg 02954340 02958343/points/a72134cd499fd1c4f79e091fa09130a.pts 02958343/expert_verified/points_label/a72134cd499fd1c4f79e091fa09130a.seg 02958343 04379243/points/cc6fbdc6f2aa5ea3d889130b88839a0c.pts 04379243/expert_verified/points_label/cc6fbdc6f2aa5ea3d889130b88839a0c.seg 04379243 03624134/points/85ced924eedc6ff566b5b592ed1ddee0.pts 03624134/expert_verified/points_label/85ced924eedc6ff566b5b592ed1ddee0.seg 03624134 03001627/points/60622d74c0712934a5817f81a1efa3cc.pts 03001627/expert_verified/points_label/60622d74c0712934a5817f81a1efa3cc.seg 03001627 04379243/points/2633f011b236a8979070b65ce7b4b532.pts 04379243/expert_verified/points_label/2633f011b236a8979070b65ce7b4b532.seg 04379243 03001627/points/9d9d69e5f2bc80a867903707764646db.pts 03001627/expert_verified/points_label/9d9d69e5f2bc80a867903707764646db.seg 03001627 03001627/points/ce463d63d8771c5ccf19858fd1963d10.pts 03001627/expert_verified/points_label/ce463d63d8771c5ccf19858fd1963d10.seg 03001627 04379243/points/ad17445446e4fd3adcfcef693e7ec696.pts 04379243/expert_verified/points_label/ad17445446e4fd3adcfcef693e7ec696.seg 04379243 03001627/points/71372c1f20b6a04c43c40c5aa3d5c5b7.pts 03001627/expert_verified/points_label/71372c1f20b6a04c43c40c5aa3d5c5b7.seg 03001627 02691156/points/9436273fc1a5e3ca7af159eaf7625abf.pts 02691156/expert_verified/points_label/9436273fc1a5e3ca7af159eaf7625abf.seg 02691156 03797390/points/b98fa11a567f644344b25d683fe71de.pts 03797390/expert_verified/points_label/b98fa11a567f644344b25d683fe71de.seg 03797390 02691156/points/53eee66291c47a91bc0909d98a1ff2b4.pts 02691156/expert_verified/points_label/53eee66291c47a91bc0909d98a1ff2b4.seg 02691156 03642806/points/e55ececde88255b93e73f3893a7337bb.pts 03642806/expert_verified/points_label/e55ececde88255b93e73f3893a7337bb.seg 03642806 02958343/points/1079efee042629d4ce28f0f1b509eda.pts 02958343/expert_verified/points_label/1079efee042629d4ce28f0f1b509eda.seg 02958343 03001627/points/c826c65111c867ab45a1df43bcd9e471.pts 03001627/expert_verified/points_label/c826c65111c867ab45a1df43bcd9e471.seg 03001627 02958343/points/39201299cf83ec2577763486d77d1cb.pts 02958343/expert_verified/points_label/39201299cf83ec2577763486d77d1cb.seg 02958343 04379243/points/e8c01f71fd941af11190e285a2cbc9c.pts 04379243/expert_verified/points_label/e8c01f71fd941af11190e285a2cbc9c.seg 04379243 03001627/points/948f1555282e27da190c615a2115d2f7.pts 03001627/expert_verified/points_label/948f1555282e27da190c615a2115d2f7.seg 03001627 02691156/points/ca4ec545363b3b8e8c2814a4ead9cb90.pts 02691156/expert_verified/points_label/ca4ec545363b3b8e8c2814a4ead9cb90.seg 02691156 03001627/points/b8f4ce34b44620cc9b9f2eb77f5e247e.pts 03001627/expert_verified/points_label/b8f4ce34b44620cc9b9f2eb77f5e247e.seg 03001627 02958343/points/188621bbfc7d9477ce27281f3b76d1f5.pts 02958343/expert_verified/points_label/188621bbfc7d9477ce27281f3b76d1f5.seg 02958343 04379243/points/9a71b92445cd3f023a9bc242c86fb7a0.pts 04379243/expert_verified/points_label/9a71b92445cd3f023a9bc242c86fb7a0.seg 04379243 03001627/points/4372b33dfc84c2f56a9ab6fc87e1604e.pts 03001627/expert_verified/points_label/4372b33dfc84c2f56a9ab6fc87e1604e.seg 03001627 03001627/points/b16f1858c1a7c0a65001cb19c4a0eee4.pts 03001627/expert_verified/points_label/b16f1858c1a7c0a65001cb19c4a0eee4.seg 03001627 03467517/points/5238adec0790595930c206f77b5cb4d0.pts 03467517/expert_verified/points_label/5238adec0790595930c206f77b5cb4d0.seg 03467517 02958343/points/3ec7f0347638f7a891eea2fc80d4a25f.pts 02958343/expert_verified/points_label/3ec7f0347638f7a891eea2fc80d4a25f.seg 02958343 02691156/points/32e7224d196e5866bd564bd76cf3cbec.pts 02691156/expert_verified/points_label/32e7224d196e5866bd564bd76cf3cbec.seg 02691156 04379243/points/f9beeefdebf70350f4b6538438a0b930.pts 04379243/expert_verified/points_label/f9beeefdebf70350f4b6538438a0b930.seg 04379243 04379243/points/acbc99e153b9d4d419fb4103277a6b93.pts 04379243/expert_verified/points_label/acbc99e153b9d4d419fb4103277a6b93.seg 04379243 03467517/points/8ebc3d48afeceec752561cc0fb924c36.pts 03467517/expert_verified/points_label/8ebc3d48afeceec752561cc0fb924c36.seg 03467517 04379243/points/966cef675324e416cd415550f639925.pts 04379243/expert_verified/points_label/966cef675324e416cd415550f639925.seg 04379243 03636649/points/85f71a4724fa37c33d39d8012919dd25.pts 03636649/expert_verified/points_label/85f71a4724fa37c33d39d8012919dd25.seg 03636649 03636649/points/370623095c9773e42ce7d46577f8a9bd.pts 03636649/expert_verified/points_label/370623095c9773e42ce7d46577f8a9bd.seg 03636649 03624134/points/bbe934c9cdca9c1839ec49305bb07d3d.pts 03624134/expert_verified/points_label/bbe934c9cdca9c1839ec49305bb07d3d.seg 03624134 02958343/points/d22a2d20acbdca70c972ff3f74d38438.pts 02958343/expert_verified/points_label/d22a2d20acbdca70c972ff3f74d38438.seg 02958343 02958343/points/ff3c8e21a48ed17cc1bcae9def1986da.pts 02958343/expert_verified/points_label/ff3c8e21a48ed17cc1bcae9def1986da.seg 02958343 03001627/points/fd5ca05b59b30241d838ae16242881dc.pts 03001627/expert_verified/points_label/fd5ca05b59b30241d838ae16242881dc.seg 03001627 02691156/points/e3aff5ae3e8f2a7c4c2c88971423d0be.pts 02691156/expert_verified/points_label/e3aff5ae3e8f2a7c4c2c88971423d0be.seg 02691156 02691156/points/b4575e5e6161fd497b164268a44f7712.pts 02691156/expert_verified/points_label/b4575e5e6161fd497b164268a44f7712.seg 02691156 03467517/points/153e7883f6cf0e66d57700c05b1862d8.pts 03467517/expert_verified/points_label/153e7883f6cf0e66d57700c05b1862d8.seg 03467517 03642806/points/4fc3d56243d2d8801ef1ccfaf50f2048.pts 03642806/expert_verified/points_label/4fc3d56243d2d8801ef1ccfaf50f2048.seg 03642806 04379243/points/ec9861c234daf6bc915f51b5f5e95ffa.pts 04379243/expert_verified/points_label/ec9861c234daf6bc915f51b5f5e95ffa.seg 04379243 03001627/points/7114ef00fe68d053cccbd142483bf2e7.pts 03001627/expert_verified/points_label/7114ef00fe68d053cccbd142483bf2e7.seg 03001627 02691156/points/e812f54386acd072d44f37c9e0fb10d0.pts 02691156/expert_verified/points_label/e812f54386acd072d44f37c9e0fb10d0.seg 02691156 03001627/points/5490efbdadce792f524f4eb395a8604.pts 03001627/expert_verified/points_label/5490efbdadce792f524f4eb395a8604.seg 03001627 03948459/points/42740af029297f1d9874fa4c7b1a4298.pts 03948459/expert_verified/points_label/42740af029297f1d9874fa4c7b1a4298.seg 03948459 03001627/points/d1ec6e9b8063b7efd7f7a4c4609b0913.pts 03001627/expert_verified/points_label/d1ec6e9b8063b7efd7f7a4c4609b0913.seg 03001627 04379243/points/4b11be42b0c0482dd94faaee2b20e2bf.pts 04379243/expert_verified/points_label/4b11be42b0c0482dd94faaee2b20e2bf.seg 04379243 03001627/points/d29971cef754cc91cd8c5d1ba690a2c3.pts 03001627/expert_verified/points_label/d29971cef754cc91cd8c5d1ba690a2c3.seg 03001627 04379243/points/8cc8485f249a37f595b25bd3accf45b5.pts 04379243/expert_verified/points_label/8cc8485f249a37f595b25bd3accf45b5.seg 04379243 04379243/points/bb5dbf708d5eb7f82099f9e22ca45b04.pts 04379243/expert_verified/points_label/bb5dbf708d5eb7f82099f9e22ca45b04.seg 04379243 03001627/points/c1b64fef5f3efa0a129905ebfd12d5cd.pts 03001627/expert_verified/points_label/c1b64fef5f3efa0a129905ebfd12d5cd.seg 03001627 04379243/points/e58e958428584b2b79972b30518c97e2.pts 04379243/expert_verified/points_label/e58e958428584b2b79972b30518c97e2.seg 04379243 03790512/points/90a521e0def2631fd5dde04c96fd8146.pts 03790512/expert_verified/points_label/90a521e0def2631fd5dde04c96fd8146.seg 03790512 03467517/points/fcab134da044e5fc77f469126771fc30.pts 03467517/expert_verified/points_label/fcab134da044e5fc77f469126771fc30.seg 03467517 03001627/points/1d6faeb6d77d1f2cf95cd8df6bebbc3a.pts 03001627/expert_verified/points_label/1d6faeb6d77d1f2cf95cd8df6bebbc3a.seg 03001627 04379243/points/e993ddaf6d03003071a782a4379556c7.pts 04379243/expert_verified/points_label/e993ddaf6d03003071a782a4379556c7.seg 04379243 03001627/points/702cebffa33a19f019f079d1b712f46f.pts 03001627/expert_verified/points_label/702cebffa33a19f019f079d1b712f46f.seg 03001627 03790512/points/7b4eb8cbc470d0d6d5dde04c96fd8146.pts 03790512/expert_verified/points_label/7b4eb8cbc470d0d6d5dde04c96fd8146.seg 03790512 03001627/points/9515e377c1ec86529b9f2eb77f5e247e.pts 03001627/expert_verified/points_label/9515e377c1ec86529b9f2eb77f5e247e.seg 03001627 03001627/points/9c3d7b65c739a618285330f26226f8fb.pts 03001627/expert_verified/points_label/9c3d7b65c739a618285330f26226f8fb.seg 03001627 03790512/points/8ed4bdaf0c8b88ea8b31e74d456742c7.pts 03790512/expert_verified/points_label/8ed4bdaf0c8b88ea8b31e74d456742c7.seg 03790512 02958343/points/6ed2957beeb7940a9fbaa69916aaebda.pts 02958343/expert_verified/points_label/6ed2957beeb7940a9fbaa69916aaebda.seg 02958343 03001627/points/37e2b82d5e9dde21cbde89e0c48a01bf.pts 03001627/expert_verified/points_label/37e2b82d5e9dde21cbde89e0c48a01bf.seg 03001627 04379243/points/1b6bd64fda74bdc4d6983f351200ac6a.pts 04379243/expert_verified/points_label/1b6bd64fda74bdc4d6983f351200ac6a.seg 04379243 04379243/points/531381f5bbc69e485769b3af36a2ff9f.pts 04379243/expert_verified/points_label/531381f5bbc69e485769b3af36a2ff9f.seg 04379243 03790512/points/992fbae5178edcbc4e31d0cb4d7568.pts 03790512/expert_verified/points_label/992fbae5178edcbc4e31d0cb4d7568.seg 03790512 04379243/points/65e7fd8d158658106a76e23e6f966dea.pts 04379243/expert_verified/points_label/65e7fd8d158658106a76e23e6f966dea.seg 04379243 02691156/points/2229bc4e646f506679f56e78e8640bfb.pts 02691156/expert_verified/points_label/2229bc4e646f506679f56e78e8640bfb.seg 02691156 02954340/points/f40b47fcbf83b962f0d11ae402ef940e.pts 02954340/expert_verified/points_label/f40b47fcbf83b962f0d11ae402ef940e.seg 02954340 02773838/points/cbc2328cadf8dc573394926146371698.pts 02773838/expert_verified/points_label/cbc2328cadf8dc573394926146371698.seg 02773838 02958343/points/3c6d7c6ce950917b3a93df79ef2b80ef.pts 02958343/expert_verified/points_label/3c6d7c6ce950917b3a93df79ef2b80ef.seg 02958343 02958343/points/2ccaaa66525d7f095473e57e894e0ef5.pts 02958343/expert_verified/points_label/2ccaaa66525d7f095473e57e894e0ef5.seg 02958343 02691156/points/70d9304de59792a9515d73fcb34092fc.pts 02691156/expert_verified/points_label/70d9304de59792a9515d73fcb34092fc.seg 02691156 03001627/points/2ed8d45343a442097869557127addfc0.pts 03001627/expert_verified/points_label/2ed8d45343a442097869557127addfc0.seg 03001627 04379243/points/84f5e52756fc84f86df14337f24e49f4.pts 04379243/expert_verified/points_label/84f5e52756fc84f86df14337f24e49f4.seg 04379243 03001627/points/b33a3b1627ad61eb8ca4809dcf42fe1.pts 03001627/expert_verified/points_label/b33a3b1627ad61eb8ca4809dcf42fe1.seg 03001627 04379243/points/369c19c0971221f3664b3b9b23ddfcbc.pts 04379243/expert_verified/points_label/369c19c0971221f3664b3b9b23ddfcbc.seg 04379243 03642806/points/5a13f7551c20eb29f3ebfe51dc60263e.pts 03642806/expert_verified/points_label/5a13f7551c20eb29f3ebfe51dc60263e.seg 03642806 04379243/points/1b01ef65920c342323bdffac38e6b250.pts 04379243/expert_verified/points_label/1b01ef65920c342323bdffac38e6b250.seg 04379243 02691156/points/9b687f9cff46d43d89c2da356f872ebc.pts 02691156/expert_verified/points_label/9b687f9cff46d43d89c2da356f872ebc.seg 02691156 04379243/points/746ceaf694d85eb5d5192f88466da1dc.pts 04379243/expert_verified/points_label/746ceaf694d85eb5d5192f88466da1dc.seg 04379243 04379243/points/9f4eb0d734a2b7a4ab610b0c94236463.pts 04379243/expert_verified/points_label/9f4eb0d734a2b7a4ab610b0c94236463.seg 04379243 03001627/points/a1213da0e7efffcafebad4f49b26ec52.pts 03001627/expert_verified/points_label/a1213da0e7efffcafebad4f49b26ec52.seg 03001627 02958343/points/71b00ea32b1810ac373af83f3f2fe606.pts 02958343/expert_verified/points_label/71b00ea32b1810ac373af83f3f2fe606.seg 02958343 02691156/points/52a84fea7c314f4c3dfc741b4df74043.pts 02691156/expert_verified/points_label/52a84fea7c314f4c3dfc741b4df74043.seg 02691156 02958343/points/9f3c463272d13d39eb7780cdb3ece367.pts 02958343/expert_verified/points_label/9f3c463272d13d39eb7780cdb3ece367.seg 02958343 03001627/points/def03f645b3fbd665bb93149cc0adf0.pts 03001627/expert_verified/points_label/def03f645b3fbd665bb93149cc0adf0.seg 03001627 03001627/points/f9e386d968653602d68fb8f5d99affa0.pts 03001627/expert_verified/points_label/f9e386d968653602d68fb8f5d99affa0.seg 03001627 03467517/points/9c399ebc617349dcd016bd20f13ab302.pts 03467517/expert_verified/points_label/9c399ebc617349dcd016bd20f13ab302.seg 03467517 04379243/points/aaaba1bbe037d3b1e406974af41e8842.pts 04379243/expert_verified/points_label/aaaba1bbe037d3b1e406974af41e8842.seg 04379243 03001627/points/4030ea84b560b857febad4f49b26ec52.pts 03001627/expert_verified/points_label/4030ea84b560b857febad4f49b26ec52.seg 03001627 04379243/points/a38405108fb416d8356ca1f9220b9968.pts 04379243/expert_verified/points_label/a38405108fb416d8356ca1f9220b9968.seg 04379243 04379243/points/f864677894410315ab610b0c94236463.pts 04379243/expert_verified/points_label/f864677894410315ab610b0c94236463.seg 04379243 02954340/points/da5e5ec4c486d6c03baa6271927f050e.pts 02954340/expert_verified/points_label/da5e5ec4c486d6c03baa6271927f050e.seg 02954340 02691156/points/eed299b690be51ffbd931fcaa69140.pts 02691156/expert_verified/points_label/eed299b690be51ffbd931fcaa69140.seg 02691156 03797390/points/b4ae56d6638d5338de671f28c83d2dcb.pts 03797390/expert_verified/points_label/b4ae56d6638d5338de671f28c83d2dcb.seg 03797390 04379243/points/10cc8c941fc8aeaa71a782a4379556c7.pts 04379243/expert_verified/points_label/10cc8c941fc8aeaa71a782a4379556c7.seg 04379243 03636649/points/61b57e8b5da8fb13d527a9a6f5a872b9.pts 03636649/expert_verified/points_label/61b57e8b5da8fb13d527a9a6f5a872b9.seg 03636649 02691156/points/ae4a9574248395b671d03b466c72ce41.pts 02691156/expert_verified/points_label/ae4a9574248395b671d03b466c72ce41.seg 02691156 04379243/points/8cfe3ff92244310534506cc3910614fe.pts 04379243/expert_verified/points_label/8cfe3ff92244310534506cc3910614fe.seg 04379243 03001627/points/597cb92a5bfb580eed98cca8f0ccd5f7.pts 03001627/expert_verified/points_label/597cb92a5bfb580eed98cca8f0ccd5f7.seg 03001627 03001627/points/4231883e92a3c1a21c62d11641ffbd35.pts 03001627/expert_verified/points_label/4231883e92a3c1a21c62d11641ffbd35.seg 03001627 03636649/points/28793511c46b4fa030f6e0ede20c4525.pts 03636649/expert_verified/points_label/28793511c46b4fa030f6e0ede20c4525.seg 03636649 02958343/points/4c60f32b6efdc7217dfb1ee6a4b12bf8.pts 02958343/expert_verified/points_label/4c60f32b6efdc7217dfb1ee6a4b12bf8.seg 02958343 04379243/points/397c56f15e547fad1bb088904f7cb154.pts 04379243/expert_verified/points_label/397c56f15e547fad1bb088904f7cb154.seg 04379243 04379243/points/9bb816d6a3517a5ca74c2333655a11dd.pts 04379243/expert_verified/points_label/9bb816d6a3517a5ca74c2333655a11dd.seg 04379243 03790512/points/bae59e64a50d3aa2f68f798d07e007b6.pts 03790512/expert_verified/points_label/bae59e64a50d3aa2f68f798d07e007b6.seg 03790512 04379243/points/8b094873d775f6e21130871dbfe24c18.pts 04379243/expert_verified/points_label/8b094873d775f6e21130871dbfe24c18.seg 04379243 04379243/points/4d2f7c689e77df6b6dc1766995c17a41.pts 04379243/expert_verified/points_label/4d2f7c689e77df6b6dc1766995c17a41.seg 04379243 03467517/points/16916a50a064304bf6ed0b697979412e.pts 03467517/expert_verified/points_label/16916a50a064304bf6ed0b697979412e.seg 03467517 03636649/points/c802fa4c82498450af6016f34c89d087.pts 03636649/expert_verified/points_label/c802fa4c82498450af6016f34c89d087.seg 03636649 03001627/points/1ec5a88141aefca9cf6e4dd7ee69d71f.pts 03001627/expert_verified/points_label/1ec5a88141aefca9cf6e4dd7ee69d71f.seg 03001627 04379243/points/bdefbb1f281434e39961e1085a81acc5.pts 04379243/expert_verified/points_label/bdefbb1f281434e39961e1085a81acc5.seg 04379243 04379243/points/acf57dbafe8966f577fb15a8d7923976.pts 04379243/expert_verified/points_label/acf57dbafe8966f577fb15a8d7923976.seg 04379243 03642806/points/cc67f6608c41743ec1830f8ca7a3cbed.pts 03642806/expert_verified/points_label/cc67f6608c41743ec1830f8ca7a3cbed.seg 03642806 03001627/points/95e1571acdd75922afdb9a672b7d3b8a.pts 03001627/expert_verified/points_label/95e1571acdd75922afdb9a672b7d3b8a.seg 03001627 04379243/points/2ebe5dfb7bd9a50c6effbd64ad6b71b8.pts 04379243/expert_verified/points_label/2ebe5dfb7bd9a50c6effbd64ad6b71b8.seg 04379243 03001627/points/a6420c4ed13cf628945a77b945b7b70f.pts 03001627/expert_verified/points_label/a6420c4ed13cf628945a77b945b7b70f.seg 03001627 04379243/points/1de679dd26d8c69cae44c65a6d0f0732.pts 04379243/expert_verified/points_label/1de679dd26d8c69cae44c65a6d0f0732.seg 04379243 03001627/points/271012d5de261d08101accd22c701b9.pts 03001627/expert_verified/points_label/271012d5de261d08101accd22c701b9.seg 03001627 04379243/points/5e409a2627f7cd7d63ecd64ef0e6814c.pts 04379243/expert_verified/points_label/5e409a2627f7cd7d63ecd64ef0e6814c.seg 04379243 02691156/points/c9aeb20d7cd1b3b45e9e2656aff7dd5b.pts 02691156/expert_verified/points_label/c9aeb20d7cd1b3b45e9e2656aff7dd5b.seg 02691156 04379243/points/45b23ac79688170893ba1eeaf62819a2.pts 04379243/expert_verified/points_label/45b23ac79688170893ba1eeaf62819a2.seg 04379243 02691156/points/9ac292686a2fcebbe719b5362fe06bbb.pts 02691156/expert_verified/points_label/9ac292686a2fcebbe719b5362fe06bbb.seg 02691156 04379243/points/3b0c62bde7b24de85ce578b5b4bfae3c.pts 04379243/expert_verified/points_label/3b0c62bde7b24de85ce578b5b4bfae3c.seg 04379243 02958343/points/c487e9850891e1ec2d15396b7bcc6366.pts 02958343/expert_verified/points_label/c487e9850891e1ec2d15396b7bcc6366.seg 02958343 03636649/points/b8e25e0825cb5db7765609a3f435fe9d.pts 03636649/expert_verified/points_label/b8e25e0825cb5db7765609a3f435fe9d.seg 03636649 03001627/points/9fd6bb18dc21c70766ef9dd2f3ef27d3.pts 03001627/expert_verified/points_label/9fd6bb18dc21c70766ef9dd2f3ef27d3.seg 03001627 02958343/points/bf37249fc8e16fd8f9a88cc63b910f3.pts 02958343/expert_verified/points_label/bf37249fc8e16fd8f9a88cc63b910f3.seg 02958343 04225987/points/58ae991bd0350810b9ac379f661f5c75.pts 04225987/expert_verified/points_label/58ae991bd0350810b9ac379f661f5c75.seg 04225987 03001627/points/508306f8ddf1b54c41cc9e8c39b4e399.pts 03001627/expert_verified/points_label/508306f8ddf1b54c41cc9e8c39b4e399.seg 03001627 03642806/points/ef5b312fc20f1b20aab089a6db538ba7.pts 03642806/expert_verified/points_label/ef5b312fc20f1b20aab089a6db538ba7.seg 03642806 03001627/points/d97c5945e9449a58737e4e0df09d751.pts 03001627/expert_verified/points_label/d97c5945e9449a58737e4e0df09d751.seg 03001627 03001627/points/e1897a4391784bc2e8b2b8dc0c816caf.pts 03001627/expert_verified/points_label/e1897a4391784bc2e8b2b8dc0c816caf.seg 03001627 04379243/points/a624ebf0bf0451a8d93768e7b9b1eabf.pts 04379243/expert_verified/points_label/a624ebf0bf0451a8d93768e7b9b1eabf.seg 04379243 03636649/points/1e5e1ff56c27c0d2adc5f5aafedb1c38.pts 03636649/expert_verified/points_label/1e5e1ff56c27c0d2adc5f5aafedb1c38.seg 03636649 03642806/points/2ce3a50ca6087f30d8e007cc6755cce9.pts 03642806/expert_verified/points_label/2ce3a50ca6087f30d8e007cc6755cce9.seg 03642806 02691156/points/d615a8217b70af06bc0909d98a1ff2b4.pts 02691156/expert_verified/points_label/d615a8217b70af06bc0909d98a1ff2b4.seg 02691156 02691156/points/6f72a0d86494b551a834b9c8bfc8647a.pts 02691156/expert_verified/points_label/6f72a0d86494b551a834b9c8bfc8647a.seg 02691156 03001627/points/20fbab2b8770a1cbf51f77a6d7299806.pts 03001627/expert_verified/points_label/20fbab2b8770a1cbf51f77a6d7299806.seg 03001627 03001627/points/d239d38424429a9a4626612b5d655dc.pts 03001627/expert_verified/points_label/d239d38424429a9a4626612b5d655dc.seg 03001627 03001627/points/4c97f421c4ea4396d8ac5d7ad0953104.pts 03001627/expert_verified/points_label/4c97f421c4ea4396d8ac5d7ad0953104.seg 03001627 03001627/points/5b68a6c2baf0ad61d0de9c949c366777.pts 03001627/expert_verified/points_label/5b68a6c2baf0ad61d0de9c949c366777.seg 03001627 04379243/points/9bd1c242bd66d2fbb63c01786992bd2f.pts 04379243/expert_verified/points_label/9bd1c242bd66d2fbb63c01786992bd2f.seg 04379243 03001627/points/e2dbe84030167f1ca5aad165050e534c.pts 03001627/expert_verified/points_label/e2dbe84030167f1ca5aad165050e534c.seg 03001627 03001627/points/1c17cc67b8c747c3febad4f49b26ec52.pts 03001627/expert_verified/points_label/1c17cc67b8c747c3febad4f49b26ec52.seg 03001627 04379243/points/2766a883126503cac3bd24f986301745.pts 04379243/expert_verified/points_label/2766a883126503cac3bd24f986301745.seg 04379243 04225987/points/755dc44dae7791761082f2ea630bf69e.pts 04225987/expert_verified/points_label/755dc44dae7791761082f2ea630bf69e.seg 04225987 04379243/points/c38ba6c06d2b813230c589758b4b5646.pts 04379243/expert_verified/points_label/c38ba6c06d2b813230c589758b4b5646.seg 04379243 02691156/points/44c0cb6571f6f000ca8607f540cc62ba.pts 02691156/expert_verified/points_label/44c0cb6571f6f000ca8607f540cc62ba.seg 02691156 03636649/points/522bc10920249e67141c66e2b49d221.pts 03636649/expert_verified/points_label/522bc10920249e67141c66e2b49d221.seg 03636649 03790512/points/4548d86cf7f1c11ad373c34785838ee4.pts 03790512/expert_verified/points_label/4548d86cf7f1c11ad373c34785838ee4.seg 03790512 02958343/points/37c5ac3d5b34761add75f724c0ccbe00.pts 02958343/expert_verified/points_label/37c5ac3d5b34761add75f724c0ccbe00.seg 02958343 04379243/points/a15f31e2302f6ae5d67a73ffd62ba73f.pts 04379243/expert_verified/points_label/a15f31e2302f6ae5d67a73ffd62ba73f.seg 04379243 02958343/points/6d714f7b7170a581da8e502a3c6cb4fb.pts 02958343/expert_verified/points_label/6d714f7b7170a581da8e502a3c6cb4fb.seg 02958343 03624134/points/17c4163247e9237d4b7644126b1d71e0.pts 03624134/expert_verified/points_label/17c4163247e9237d4b7644126b1d71e0.seg 03624134 03636649/points/7972fd0fe5755b4ad42b9650f19dd425.pts 03636649/expert_verified/points_label/7972fd0fe5755b4ad42b9650f19dd425.seg 03636649 03001627/points/8ff4ba87d700054546992ce9fde1b2c2.pts 03001627/expert_verified/points_label/8ff4ba87d700054546992ce9fde1b2c2.seg 03001627 03636649/points/a654df55875a2104d663817442d5278.pts 03636649/expert_verified/points_label/a654df55875a2104d663817442d5278.seg 03636649 04379243/points/9c12fada31224bdf58c4e7e56d799d97.pts 04379243/expert_verified/points_label/9c12fada31224bdf58c4e7e56d799d97.seg 04379243 03636649/points/9dad7ce60aa168d72cd2160e449d45ae.pts 03636649/expert_verified/points_label/9dad7ce60aa168d72cd2160e449d45ae.seg 03636649 02691156/points/cfb555a4d82a600aca8607f540cc62ba.pts 02691156/expert_verified/points_label/cfb555a4d82a600aca8607f540cc62ba.seg 02691156 04379243/points/415c174ecdc612fb6f5c30e29039b12d.pts 04379243/expert_verified/points_label/415c174ecdc612fb6f5c30e29039b12d.seg 04379243 03467517/points/a5e2f05386e4ba55a894e1aba5d3799a.pts 03467517/expert_verified/points_label/a5e2f05386e4ba55a894e1aba5d3799a.seg 03467517 03001627/points/a91b2c89e543a4b3aa3d970c5602cd4a.pts 03001627/expert_verified/points_label/a91b2c89e543a4b3aa3d970c5602cd4a.seg 03001627 03624134/points/97ed13011e2d85e16029317225a75a9f.pts 03624134/expert_verified/points_label/97ed13011e2d85e16029317225a75a9f.seg 03624134 04379243/points/388ea3f8ba27da8b777b6246417c94ff.pts 04379243/expert_verified/points_label/388ea3f8ba27da8b777b6246417c94ff.seg 04379243 04379243/points/983cd9caf65adf1ddf6cfab91d65bb91.pts 04379243/expert_verified/points_label/983cd9caf65adf1ddf6cfab91d65bb91.seg 04379243 03001627/points/e65d2f0ed75a786a37b2bb75885cfc44.pts 03001627/expert_verified/points_label/e65d2f0ed75a786a37b2bb75885cfc44.seg 03001627 03624134/points/dce941899bcb752dfe474f09e3f3ac9a.pts 03624134/expert_verified/points_label/dce941899bcb752dfe474f09e3f3ac9a.seg 03624134 04379243/points/ea3bcd9e6c4205031964126395b17c2a.pts 04379243/expert_verified/points_label/ea3bcd9e6c4205031964126395b17c2a.seg 04379243 02691156/points/d13d131a649c5df38b96ae1a0a8b84ec.pts 02691156/expert_verified/points_label/d13d131a649c5df38b96ae1a0a8b84ec.seg 02691156 04379243/points/f917474a20558aa33bbab77a66bc3671.pts 04379243/expert_verified/points_label/f917474a20558aa33bbab77a66bc3671.seg 04379243 03001627/points/4a24652fbf2bed7e93583c67df8faf1.pts 03001627/expert_verified/points_label/4a24652fbf2bed7e93583c67df8faf1.seg 03001627 02691156/points/5dd2324cd6ebf52e293fdbda4e7beec9.pts 02691156/expert_verified/points_label/5dd2324cd6ebf52e293fdbda4e7beec9.seg 02691156 03642806/points/a59d3d87068d313c2656684d670220c2.pts 03642806/expert_verified/points_label/a59d3d87068d313c2656684d670220c2.seg 03642806 04379243/points/5354ecb0e3aa1da074a16879fb3ac81f.pts 04379243/expert_verified/points_label/5354ecb0e3aa1da074a16879fb3ac81f.seg 04379243 03642806/points/6c6a96e4486cc02cda66ecbb2c411f37.pts 03642806/expert_verified/points_label/6c6a96e4486cc02cda66ecbb2c411f37.seg 03642806 04225987/points/fd3627deb2476b0f1f942c57ac0e8959.pts 04225987/expert_verified/points_label/fd3627deb2476b0f1f942c57ac0e8959.seg 04225987 04379243/points/91bf48934d3b52ea36658c6705d0c08.pts 04379243/expert_verified/points_label/91bf48934d3b52ea36658c6705d0c08.seg 04379243 04379243/points/18be1556eb4da5af7ccf848ce05c84be.pts 04379243/expert_verified/points_label/18be1556eb4da5af7ccf848ce05c84be.seg 04379243 02958343/points/33211aabfefa14603b05c2ad25b4380f.pts 02958343/expert_verified/points_label/33211aabfefa14603b05c2ad25b4380f.seg 02958343 04379243/points/3243ddb2aa4d1659beb83c64f2162734.pts 04379243/expert_verified/points_label/3243ddb2aa4d1659beb83c64f2162734.seg 04379243 04379243/points/4ce90fe70faf4c3e255bc16374754e69.pts 04379243/expert_verified/points_label/4ce90fe70faf4c3e255bc16374754e69.seg 04379243 04379243/points/15be511a2433482aa192483aa282f8e5.pts 04379243/expert_verified/points_label/15be511a2433482aa192483aa282f8e5.seg 04379243 03624134/points/70b6b3ba6a27fd6f782db73f915dfbb8.pts 03624134/expert_verified/points_label/70b6b3ba6a27fd6f782db73f915dfbb8.seg 03624134 03001627/points/519d19f3adebd20aba49014d9a3afe99.pts 03001627/expert_verified/points_label/519d19f3adebd20aba49014d9a3afe99.seg 03001627 03467517/points/ca9720d793355dd693f0194265a9746c.pts 03467517/expert_verified/points_label/ca9720d793355dd693f0194265a9746c.seg 03467517 03001627/points/e19214cabca496a3f7b54e04c7238d7.pts 03001627/expert_verified/points_label/e19214cabca496a3f7b54e04c7238d7.seg 03001627 03001627/points/ea1bfe81b88395fcaa29e9f0529e8ef7.pts 03001627/expert_verified/points_label/ea1bfe81b88395fcaa29e9f0529e8ef7.seg 03001627 03001627/points/2b110b833111b38c420adf24e49f74c8.pts 03001627/expert_verified/points_label/2b110b833111b38c420adf24e49f74c8.seg 03001627 03001627/points/7b405c1d6d2dbea9f91663a74ccd2338.pts 03001627/expert_verified/points_label/7b405c1d6d2dbea9f91663a74ccd2338.seg 03001627 02691156/points/489d3e4cc3d790a0ca8607f540cc62ba.pts 02691156/expert_verified/points_label/489d3e4cc3d790a0ca8607f540cc62ba.seg 02691156 04379243/points/79eeee790ed5a5aac242632b2a8c3129.pts 04379243/expert_verified/points_label/79eeee790ed5a5aac242632b2a8c3129.seg 04379243 03624134/points/665bf5d30d342d64adee73efb2c043f8.pts 03624134/expert_verified/points_label/665bf5d30d342d64adee73efb2c043f8.seg 03624134 03467517/points/7f3f5c9953fb7e0a6cbec6f3d994a573.pts 03467517/expert_verified/points_label/7f3f5c9953fb7e0a6cbec6f3d994a573.seg 03467517 03001627/points/d2597d18fdc3594e1dc59d2adbe5297d.pts 03001627/expert_verified/points_label/d2597d18fdc3594e1dc59d2adbe5297d.seg 03001627 03001627/points/a9a1147eae9936f76f1e07a56c129dfc.pts 03001627/expert_verified/points_label/a9a1147eae9936f76f1e07a56c129dfc.seg 03001627 02691156/points/64cb683afd5e9e559db1d21b460eacef.pts 02691156/expert_verified/points_label/64cb683afd5e9e559db1d21b460eacef.seg 02691156 03624134/points/e0a78d771cfde145a5cea7e40e4d21ff.pts 03624134/expert_verified/points_label/e0a78d771cfde145a5cea7e40e4d21ff.seg 03624134 02691156/points/e59c4f290d8585a862b600da24e0965.pts 02691156/expert_verified/points_label/e59c4f290d8585a862b600da24e0965.seg 02691156 04379243/points/523ac3575244c7f3a130bbab7337a0cf.pts 04379243/expert_verified/points_label/523ac3575244c7f3a130bbab7337a0cf.seg 04379243 03001627/points/96e83c79e8d76d4519fb4103277a6b93.pts 03001627/expert_verified/points_label/96e83c79e8d76d4519fb4103277a6b93.seg 03001627 04379243/points/a2781622b5941ff2a886fe6408aa7382.pts 04379243/expert_verified/points_label/a2781622b5941ff2a886fe6408aa7382.seg 04379243 04379243/points/5d24567426a614ecfd726e98b98fb36f.pts 04379243/expert_verified/points_label/5d24567426a614ecfd726e98b98fb36f.seg 04379243 03001627/points/a5a2d09e5384237869513d0907f19c8f.pts 03001627/expert_verified/points_label/a5a2d09e5384237869513d0907f19c8f.seg 03001627 02691156/points/e02485f093835f45c1b64d86df61366a.pts 02691156/expert_verified/points_label/e02485f093835f45c1b64d86df61366a.seg 02691156 04379243/points/58f8fd169c9578e62f81cb887dc35578.pts 04379243/expert_verified/points_label/58f8fd169c9578e62f81cb887dc35578.seg 04379243 04379243/points/c755eeaa4a588fcba9126dd5adc92c1e.pts 04379243/expert_verified/points_label/c755eeaa4a588fcba9126dd5adc92c1e.seg 04379243 03001627/points/704179dd47a2282e676de9b6e111da8b.pts 03001627/expert_verified/points_label/704179dd47a2282e676de9b6e111da8b.seg 03001627 03001627/points/9253f198c06794cdc7689830acac6e59.pts 03001627/expert_verified/points_label/9253f198c06794cdc7689830acac6e59.seg 03001627 04379243/points/2ba8eb5ec0a05694593ebeeedbff73b.pts 04379243/expert_verified/points_label/2ba8eb5ec0a05694593ebeeedbff73b.seg 04379243 03467517/points/133ebdf2ca7bf4b81d4e8021f58beea0.pts 03467517/expert_verified/points_label/133ebdf2ca7bf4b81d4e8021f58beea0.seg 03467517 03467517/points/ba6d3dcff42ea7bba32c4b8efb0131e.pts 03467517/expert_verified/points_label/ba6d3dcff42ea7bba32c4b8efb0131e.seg 03467517 03467517/points/222b705a80d75a4343b0b12983b9982.pts 03467517/expert_verified/points_label/222b705a80d75a4343b0b12983b9982.seg 03467517 04379243/points/47317755c82114d5c3bd24f986301745.pts 04379243/expert_verified/points_label/47317755c82114d5c3bd24f986301745.seg 04379243 04379243/points/175c0be26d0f2e916cb0bd372b0960ba.pts 04379243/expert_verified/points_label/175c0be26d0f2e916cb0bd372b0960ba.seg 04379243 03636649/points/19388898dd69dd9fddc8e6d1ec6242c3.pts 03636649/expert_verified/points_label/19388898dd69dd9fddc8e6d1ec6242c3.seg 03636649 04379243/points/3cec584145ee513d635418e95eea8a17.pts 04379243/expert_verified/points_label/3cec584145ee513d635418e95eea8a17.seg 04379243 03001627/points/3a5c8d46fdc6793b956abdbfba57903a.pts 03001627/expert_verified/points_label/3a5c8d46fdc6793b956abdbfba57903a.seg 03001627 03001627/points/3d32d89db2286377e63c6421b71f17c8.pts 03001627/expert_verified/points_label/3d32d89db2286377e63c6421b71f17c8.seg 03001627 03001627/points/47a45ce9fb219083411e8b42940aba04.pts 03001627/expert_verified/points_label/47a45ce9fb219083411e8b42940aba04.seg 03001627 03467517/points/214f6a08b78670de2cb522418d5742a0.pts 03467517/expert_verified/points_label/214f6a08b78670de2cb522418d5742a0.seg 03467517 04379243/points/1b4bc147baf68d4ff008d8a3590fb522.pts 04379243/expert_verified/points_label/1b4bc147baf68d4ff008d8a3590fb522.seg 04379243 03467517/points/83b2ecf5caced214e313875ff213ee10.pts 03467517/expert_verified/points_label/83b2ecf5caced214e313875ff213ee10.seg 03467517 02691156/points/57fe8ad460bcb4929a4a28ef635593ce.pts 02691156/expert_verified/points_label/57fe8ad460bcb4929a4a28ef635593ce.seg 02691156 03624134/points/e8a6915bd0bcf1bebaa284808a1567a8.pts 03624134/expert_verified/points_label/e8a6915bd0bcf1bebaa284808a1567a8.seg 03624134 03001627/points/1da29597f89c2b004b3c42e318f3affc.pts 03001627/expert_verified/points_label/1da29597f89c2b004b3c42e318f3affc.seg 03001627 04379243/points/2ef899e67eecef65190a91fd9a6f7d55.pts 04379243/expert_verified/points_label/2ef899e67eecef65190a91fd9a6f7d55.seg 04379243 04379243/points/811a7be3be14bd2b62103e4bff47b4cd.pts 04379243/expert_verified/points_label/811a7be3be14bd2b62103e4bff47b4cd.seg 04379243 03948459/points/592017db407391c68e7e947594effe19.pts 03948459/expert_verified/points_label/592017db407391c68e7e947594effe19.seg 03948459 03636649/points/eb311e6232cb7011bb5bd941c6665c21.pts 03636649/expert_verified/points_label/eb311e6232cb7011bb5bd941c6665c21.seg 03636649 02691156/points/caa7e70beee4543f42c20743f866e1a6.pts 02691156/expert_verified/points_label/caa7e70beee4543f42c20743f866e1a6.seg 02691156 03001627/points/3aaa59b19eebcb5f41552c6ecbda964b.pts 03001627/expert_verified/points_label/3aaa59b19eebcb5f41552c6ecbda964b.seg 03001627 03001627/points/a93aac9ad86008e69fc01fb65ca37d30.pts 03001627/expert_verified/points_label/a93aac9ad86008e69fc01fb65ca37d30.seg 03001627 03624134/points/ceeb38ab7929361e76ec14627bf6bbcb.pts 03624134/expert_verified/points_label/ceeb38ab7929361e76ec14627bf6bbcb.seg 03624134 03001627/points/93dc91115a9002e1663fcfd6703c85f3.pts 03001627/expert_verified/points_label/93dc91115a9002e1663fcfd6703c85f3.seg 03001627 04379243/points/b08310a1d75702eda09ce9c1262c7237.pts 04379243/expert_verified/points_label/b08310a1d75702eda09ce9c1262c7237.seg 04379243 03797390/points/e9bd4ee553eb35c1d5ccc40b510e4bd.pts 03797390/expert_verified/points_label/e9bd4ee553eb35c1d5ccc40b510e4bd.seg 03797390 03001627/points/bdd57499bf64fab6bf80985a99195eb8.pts 03001627/expert_verified/points_label/bdd57499bf64fab6bf80985a99195eb8.seg 03001627 04379243/points/48af84a5600ad5bc19fb4103277a6b93.pts 04379243/expert_verified/points_label/48af84a5600ad5bc19fb4103277a6b93.seg 04379243 03001627/points/738395f54b301d80b1f5d603f931c1aa.pts 03001627/expert_verified/points_label/738395f54b301d80b1f5d603f931c1aa.seg 03001627 03790512/points/6819949f5625ca12d0f568c31c1cd62a.pts 03790512/expert_verified/points_label/6819949f5625ca12d0f568c31c1cd62a.seg 03790512 03467517/points/70d9a5d0330abd9df4b498e11fb60a4b.pts 03467517/expert_verified/points_label/70d9a5d0330abd9df4b498e11fb60a4b.seg 03467517 02958343/points/174f1a421f652029d577c0ac53e96823.pts 02958343/expert_verified/points_label/174f1a421f652029d577c0ac53e96823.seg 02958343 03001627/points/d764960666572084b1ea4e06e88051f3.pts 03001627/expert_verified/points_label/d764960666572084b1ea4e06e88051f3.seg 03001627 02691156/points/ba662ec78231c493252b4f9439ef95a6.pts 02691156/expert_verified/points_label/ba662ec78231c493252b4f9439ef95a6.seg 02691156 03636649/points/8a9f2e5b726ea37f60ad823977adaa23.pts 03636649/expert_verified/points_label/8a9f2e5b726ea37f60ad823977adaa23.seg 03636649 04379243/points/80af0f92ecf69f69f5ff054d67d5fe35.pts 04379243/expert_verified/points_label/80af0f92ecf69f69f5ff054d67d5fe35.seg 04379243 04379243/points/ce4e075487aa05ecdcfcef693e7ec696.pts 04379243/expert_verified/points_label/ce4e075487aa05ecdcfcef693e7ec696.seg 04379243 03001627/points/564f5f96bc718194166420d06689fcf.pts 03001627/expert_verified/points_label/564f5f96bc718194166420d06689fcf.seg 03001627 03636649/points/88d29e1350eda810c066b9622c005c53.pts 03636649/expert_verified/points_label/88d29e1350eda810c066b9622c005c53.seg 03636649 04379243/points/346db24c1279e8d273fdbe4b39ff4036.pts 04379243/expert_verified/points_label/346db24c1279e8d273fdbe4b39ff4036.seg 04379243 04379243/points/7062f5b229674ab7b0b54dd2cf2a35d4.pts 04379243/expert_verified/points_label/7062f5b229674ab7b0b54dd2cf2a35d4.seg 04379243 03636649/points/923097cec128ae77469cbaa3d6420fb4.pts 03636649/expert_verified/points_label/923097cec128ae77469cbaa3d6420fb4.seg 03636649 04379243/points/3fb5033b5ddaaf365f7afad12924b3b5.pts 04379243/expert_verified/points_label/3fb5033b5ddaaf365f7afad12924b3b5.seg 04379243 03636649/points/32e9d8a4b5a141a2615efc34c3b36ef0.pts 03636649/expert_verified/points_label/32e9d8a4b5a141a2615efc34c3b36ef0.seg 03636649 02691156/points/997cb29f544d6f2726360e1e29a956c7.pts 02691156/expert_verified/points_label/997cb29f544d6f2726360e1e29a956c7.seg 02691156 04379243/points/7df9115b511668bdde98d10ab5975b59.pts 04379243/expert_verified/points_label/7df9115b511668bdde98d10ab5975b59.seg 04379243 03636649/points/5580b95ab8e7806c6c5b8009db95f66f.pts 03636649/expert_verified/points_label/5580b95ab8e7806c6c5b8009db95f66f.seg 03636649 04379243/points/6862bebc1f59a5caac7bed72580dc30f.pts 04379243/expert_verified/points_label/6862bebc1f59a5caac7bed72580dc30f.seg 04379243 02691156/points/56ba815f883279b462b600da24e0965.pts 02691156/expert_verified/points_label/56ba815f883279b462b600da24e0965.seg 02691156 03797390/points/5c48d471200d2bf16e8a121e6886e18d.pts 03797390/expert_verified/points_label/5c48d471200d2bf16e8a121e6886e18d.seg 03797390 04379243/points/b48d04600e7cf2bebeedb4c8fd29e2d1.pts 04379243/expert_verified/points_label/b48d04600e7cf2bebeedb4c8fd29e2d1.seg 04379243 02958343/points/323c9dc2a8911e146f2f07de403e98d8.pts 02958343/expert_verified/points_label/323c9dc2a8911e146f2f07de403e98d8.seg 02958343 04225987/points/d3ff56062272f3e6346e65609be6d72f.pts 04225987/expert_verified/points_label/d3ff56062272f3e6346e65609be6d72f.seg 04225987 03001627/points/af28dbdce6ed8cea19fb4103277a6b93.pts 03001627/expert_verified/points_label/af28dbdce6ed8cea19fb4103277a6b93.seg 03001627 02958343/points/dfa6c32dec07727ee9d8921ebe6d5b8e.pts 02958343/expert_verified/points_label/dfa6c32dec07727ee9d8921ebe6d5b8e.seg 02958343 03001627/points/c2b898dd5601454d626d7e3d07da8352.pts 03001627/expert_verified/points_label/c2b898dd5601454d626d7e3d07da8352.seg 03001627 04379243/points/a7ef45d86ae5b496a97f238e46bc2221.pts 04379243/expert_verified/points_label/a7ef45d86ae5b496a97f238e46bc2221.seg 04379243 04379243/points/1bd138c3e54a75d32f38c0d2792fb5e.pts 04379243/expert_verified/points_label/1bd138c3e54a75d32f38c0d2792fb5e.seg 04379243 02958343/points/cd67376cac9f989151008e496c6cfd2e.pts 02958343/expert_verified/points_label/cd67376cac9f989151008e496c6cfd2e.seg 02958343 03948459/points/af9eaed1d9574387ab2c2809513f396e.pts 03948459/expert_verified/points_label/af9eaed1d9574387ab2c2809513f396e.seg 03948459 04379243/points/c418195771c7625945821c000807c3b1.pts 04379243/expert_verified/points_label/c418195771c7625945821c000807c3b1.seg 04379243 04379243/points/88b227c5fb3906ce47c638c0eee4a2b3.pts 04379243/expert_verified/points_label/88b227c5fb3906ce47c638c0eee4a2b3.seg 04379243 03467517/points/81bd0c7a35a147988cc3ae4061da3bb0.pts 03467517/expert_verified/points_label/81bd0c7a35a147988cc3ae4061da3bb0.seg 03467517 04379243/points/5292f2930f188e0a7ff6ace05b36a5.pts 04379243/expert_verified/points_label/5292f2930f188e0a7ff6ace05b36a5.seg 04379243 03636649/points/5f0a23ce527d0be52f38c0d2792fb5e.pts 03636649/expert_verified/points_label/5f0a23ce527d0be52f38c0d2792fb5e.seg 03636649 03636649/points/98cdb45ca9925feb194eb328dc97c7e2.pts 03636649/expert_verified/points_label/98cdb45ca9925feb194eb328dc97c7e2.seg 03636649 03790512/points/47054c1839830834a88e8cb97b773125.pts 03790512/expert_verified/points_label/47054c1839830834a88e8cb97b773125.seg 03790512 03001627/points/b058cc77e628ac01c433ba3e0e025e8c.pts 03001627/expert_verified/points_label/b058cc77e628ac01c433ba3e0e025e8c.seg 03001627 04225987/points/f74a5dfc0094e2d5561dce3fe08634b7.pts 04225987/expert_verified/points_label/f74a5dfc0094e2d5561dce3fe08634b7.seg 04225987 02958343/points/e20b8a9c388eeb012c8b6ee41d7d5d62.pts 02958343/expert_verified/points_label/e20b8a9c388eeb012c8b6ee41d7d5d62.seg 02958343 02958343/points/7203130a35ab20a4b1bb46d2556ba67d.pts 02958343/expert_verified/points_label/7203130a35ab20a4b1bb46d2556ba67d.seg 02958343 03261776/points/2c6f04001afcce7ded85c3dc02bada79.pts 03261776/expert_verified/points_label/2c6f04001afcce7ded85c3dc02bada79.seg 03261776 03001627/points/951fb0d7ad8ab2bec5b5bea66ef4576d.pts 03001627/expert_verified/points_label/951fb0d7ad8ab2bec5b5bea66ef4576d.seg 03001627 02691156/points/54e926e12382808b66cf1b4a8fc3914e.pts 02691156/expert_verified/points_label/54e926e12382808b66cf1b4a8fc3914e.seg 02691156 03001627/points/4c513ea0804fc008c8687ff9b0b4e4ac.pts 03001627/expert_verified/points_label/4c513ea0804fc008c8687ff9b0b4e4ac.seg 03001627 03001627/points/748957972cae6b03c56be62b05937331.pts 03001627/expert_verified/points_label/748957972cae6b03c56be62b05937331.seg 03001627 03001627/points/cc2639f8c584001a922dfe32810651d0.pts 03001627/expert_verified/points_label/cc2639f8c584001a922dfe32810651d0.seg 03001627 04379243/points/d2f811bc37858425a63ceecddc308b25.pts 04379243/expert_verified/points_label/d2f811bc37858425a63ceecddc308b25.seg 04379243 03001627/points/d48dac046436a29ec3bd24f986301745.pts 03001627/expert_verified/points_label/d48dac046436a29ec3bd24f986301745.seg 03001627 03001627/points/30fafef5c734f926781ba0fdb47276df.pts 03001627/expert_verified/points_label/30fafef5c734f926781ba0fdb47276df.seg 03001627 03001627/points/7293291b3fe8233fdef1c01cbd4ae0c.pts 03001627/expert_verified/points_label/7293291b3fe8233fdef1c01cbd4ae0c.seg 03001627 03636649/points/3deedc86a83bbf23f647dc544bb0ab61.pts 03636649/expert_verified/points_label/3deedc86a83bbf23f647dc544bb0ab61.seg 03636649 03467517/points/bb4a5712da8f63330d758421dd01f45.pts 03467517/expert_verified/points_label/bb4a5712da8f63330d758421dd01f45.seg 03467517 03636649/points/39af776c1435a3374b59758e9336ca87.pts 03636649/expert_verified/points_label/39af776c1435a3374b59758e9336ca87.seg 03636649 04379243/points/ef9f3af9b8453613febad4f49b26ec52.pts 04379243/expert_verified/points_label/ef9f3af9b8453613febad4f49b26ec52.seg 04379243 02691156/points/29192f8c96264e3435fc197bbabcd5bd.pts 02691156/expert_verified/points_label/29192f8c96264e3435fc197bbabcd5bd.seg 02691156 02691156/points/75d162523d703917b87697d3904b168b.pts 02691156/expert_verified/points_label/75d162523d703917b87697d3904b168b.seg 02691156 04379243/points/3c04f4e0d183976a7e7cb173e141227.pts 04379243/expert_verified/points_label/3c04f4e0d183976a7e7cb173e141227.seg 04379243 03790512/points/80011e85cd42668ad373c34785838ee4.pts 03790512/expert_verified/points_label/80011e85cd42668ad373c34785838ee4.seg 03790512 04379243/points/994e524d70043c3496e349c87c588bf2.pts 04379243/expert_verified/points_label/994e524d70043c3496e349c87c588bf2.seg 04379243 02691156/points/b1f08c51a098c43696d224195a988f09.pts 02691156/expert_verified/points_label/b1f08c51a098c43696d224195a988f09.seg 02691156 04379243/points/cb31b6293506eb639a3528690d225ee1.pts 04379243/expert_verified/points_label/cb31b6293506eb639a3528690d225ee1.seg 04379243 02691156/points/d70d648947c65b1eca8607f540cc62ba.pts 02691156/expert_verified/points_label/d70d648947c65b1eca8607f540cc62ba.seg 02691156 03636649/points/7bebdd742342ba93febad4f49b26ec52.pts 03636649/expert_verified/points_label/7bebdd742342ba93febad4f49b26ec52.seg 03636649 02691156/points/2a2caad9e540dcc687bf26680c510802.pts 02691156/expert_verified/points_label/2a2caad9e540dcc687bf26680c510802.seg 02691156 03790512/points/73fd19410ce60b83d5dde04c96fd8146.pts 03790512/expert_verified/points_label/73fd19410ce60b83d5dde04c96fd8146.seg 03790512 04379243/points/ccb8c52ff9e7a01819fb4103277a6b93.pts 04379243/expert_verified/points_label/ccb8c52ff9e7a01819fb4103277a6b93.seg 04379243 03467517/points/cc9e9ef3e1326c5363e148e250c0340d.pts 03467517/expert_verified/points_label/cc9e9ef3e1326c5363e148e250c0340d.seg 03467517 03001627/points/d5360f2b0b0299c29b9f2eb77f5e247e.pts 03001627/expert_verified/points_label/d5360f2b0b0299c29b9f2eb77f5e247e.seg 03001627 02691156/points/6b69e4c1cceb6e0681fa1ee3c368532e.pts 02691156/expert_verified/points_label/6b69e4c1cceb6e0681fa1ee3c368532e.seg 02691156 02691156/points/3ae96a1e1bb488942296d88107d065f6.pts 02691156/expert_verified/points_label/3ae96a1e1bb488942296d88107d065f6.seg 02691156 04379243/points/5e4351c4525fae6d6fa63795f94c4d8c.pts 04379243/expert_verified/points_label/5e4351c4525fae6d6fa63795f94c4d8c.seg 04379243 04225987/points/5c55e6b6708f730d758f6def7204bd6b.pts 04225987/expert_verified/points_label/5c55e6b6708f730d758f6def7204bd6b.seg 04225987 03001627/points/a48e359faed3da88d3519c62a8100783.pts 03001627/expert_verified/points_label/a48e359faed3da88d3519c62a8100783.seg 03001627 03467517/points/a4170135b1055cb8982c503992eaf09.pts 03467517/expert_verified/points_label/a4170135b1055cb8982c503992eaf09.seg 03467517 02958343/points/b3f1ad55fa401c35e8c505ac322336cc.pts 02958343/expert_verified/points_label/b3f1ad55fa401c35e8c505ac322336cc.seg 02958343 02691156/points/c7c5bb658cafcc7c67711f7c205c5b63.pts 02691156/expert_verified/points_label/c7c5bb658cafcc7c67711f7c205c5b63.seg 02691156 02691156/points/914c308ac4a9156842c20743f866e1a6.pts 02691156/expert_verified/points_label/914c308ac4a9156842c20743f866e1a6.seg 02691156 04379243/points/23acbe1f91d445f91ca1c7e576bee6b9.pts 04379243/expert_verified/points_label/23acbe1f91d445f91ca1c7e576bee6b9.seg 04379243 04379243/points/8eb366f4f602219b490ad276cd2af3a4.pts 04379243/expert_verified/points_label/8eb366f4f602219b490ad276cd2af3a4.seg 04379243 03624134/points/508ca8fa00e0cbb3e168961dc7b88f65.pts 03624134/expert_verified/points_label/508ca8fa00e0cbb3e168961dc7b88f65.seg 03624134 04379243/points/be045fca16562f6764c85287e21825c4.pts 04379243/expert_verified/points_label/be045fca16562f6764c85287e21825c4.seg 04379243 03001627/points/70f57047512c2eb84104b1c5cb7f9280.pts 03001627/expert_verified/points_label/70f57047512c2eb84104b1c5cb7f9280.seg 03001627 03001627/points/975ea4be01c7488611bc8e8361bc5303.pts 03001627/expert_verified/points_label/975ea4be01c7488611bc8e8361bc5303.seg 03001627 04379243/points/3c7cf00cd78adaef4b3c42e318f3affc.pts 04379243/expert_verified/points_label/3c7cf00cd78adaef4b3c42e318f3affc.seg 04379243 02773838/points/220f08ff0c1d2a4542282fc88db7886b.pts 02773838/expert_verified/points_label/220f08ff0c1d2a4542282fc88db7886b.seg 02773838 03636649/points/e35c4fadbf8d0426c26e81144f3196d5.pts 03636649/expert_verified/points_label/e35c4fadbf8d0426c26e81144f3196d5.seg 03636649 03642806/points/93958423b98be8b538ff1b6d120c56aa.pts 03642806/expert_verified/points_label/93958423b98be8b538ff1b6d120c56aa.seg 03642806 04379243/points/cf24f0128755080569080f7eaa8f3e1d.pts 04379243/expert_verified/points_label/cf24f0128755080569080f7eaa8f3e1d.seg 04379243 04379243/points/f5cbbe04afdc4697562b835b63cfd09c.pts 04379243/expert_verified/points_label/f5cbbe04afdc4697562b835b63cfd09c.seg 04379243 04379243/points/7a7590d19cf8274dab610b0c94236463.pts 04379243/expert_verified/points_label/7a7590d19cf8274dab610b0c94236463.seg 04379243 03001627/points/bdfc3a43eccaac7e908cb3a44391b80.pts 03001627/expert_verified/points_label/bdfc3a43eccaac7e908cb3a44391b80.seg 03001627 03636649/points/90d70f0a6b1cf72d79f0be73913de469.pts 03636649/expert_verified/points_label/90d70f0a6b1cf72d79f0be73913de469.seg 03636649 03642806/points/17069b6604fc28bfa2f5beb253216d5b.pts 03642806/expert_verified/points_label/17069b6604fc28bfa2f5beb253216d5b.seg 03642806 04379243/points/3b0625a3d623a7decfbec6fc6446a041.pts 04379243/expert_verified/points_label/3b0625a3d623a7decfbec6fc6446a041.seg 04379243 04379243/points/9482c5f0a38a73c0fa16d3c3138134ae.pts 04379243/expert_verified/points_label/9482c5f0a38a73c0fa16d3c3138134ae.seg 04379243 04379243/points/ed73c41dcfe9170119cc3eaf35cd388f.pts 04379243/expert_verified/points_label/ed73c41dcfe9170119cc3eaf35cd388f.seg 04379243 04379243/points/1abed35643d34f60afed86cbd9fd5335.pts 04379243/expert_verified/points_label/1abed35643d34f60afed86cbd9fd5335.seg 04379243 03001627/points/98e1936d3f25389bc3c6a889ee0bd115.pts 03001627/expert_verified/points_label/98e1936d3f25389bc3c6a889ee0bd115.seg 03001627 03797390/points/ef24c302911bcde6ea6ff2182dd34668.pts 03797390/expert_verified/points_label/ef24c302911bcde6ea6ff2182dd34668.seg 03797390 02773838/points/22b7d6fa819d62aefc69b7db9c6d5ad9.pts 02773838/expert_verified/points_label/22b7d6fa819d62aefc69b7db9c6d5ad9.seg 02773838 03001627/points/19666f52289092a3394a3bbfc81460.pts 03001627/expert_verified/points_label/19666f52289092a3394a3bbfc81460.seg 03001627 03001627/points/49b38e22f104005ecbde89e0c48a01bf.pts 03001627/expert_verified/points_label/49b38e22f104005ecbde89e0c48a01bf.seg 03001627 04379243/points/de077e0bd6932baef12d7184a2ad3430.pts 04379243/expert_verified/points_label/de077e0bd6932baef12d7184a2ad3430.seg 04379243 03001627/points/fe99f16c2532cdd07ba99ad16fdc05cd.pts 03001627/expert_verified/points_label/fe99f16c2532cdd07ba99ad16fdc05cd.seg 03001627 03642806/points/a17cf326705a6443a09a37cf78d1b866.pts 03642806/expert_verified/points_label/a17cf326705a6443a09a37cf78d1b866.seg 03642806 04379243/points/890940359fdfa036569c11df1aea8ca4.pts 04379243/expert_verified/points_label/890940359fdfa036569c11df1aea8ca4.seg 04379243 03642806/points/7f75b94bd59d649958dd315c54df0c15.pts 03642806/expert_verified/points_label/7f75b94bd59d649958dd315c54df0c15.seg 03642806 04379243/points/d0ef9d431a16e70de6c5cd45aa112726.pts 04379243/expert_verified/points_label/d0ef9d431a16e70de6c5cd45aa112726.seg 04379243 03001627/points/2dc5055b8d900ec7db4b0ee93cf61ed1.pts 03001627/expert_verified/points_label/2dc5055b8d900ec7db4b0ee93cf61ed1.seg 03001627 03001627/points/9e6b834449ed2db86199d6fe090be061.pts 03001627/expert_verified/points_label/9e6b834449ed2db86199d6fe090be061.seg 03001627 04379243/points/9e3f1901ea14aca753315facdf531a34.pts 04379243/expert_verified/points_label/9e3f1901ea14aca753315facdf531a34.seg 04379243 03001627/points/c4ebef05a72fc4f39d62eb3fdc2d3f8a.pts 03001627/expert_verified/points_label/c4ebef05a72fc4f39d62eb3fdc2d3f8a.seg 03001627 03001627/points/428b77d0ffe6ab456e06155d245f15d6.pts 03001627/expert_verified/points_label/428b77d0ffe6ab456e06155d245f15d6.seg 03001627 04225987/points/591971ce679ca4b93ad38b993d9e745f.pts 04225987/expert_verified/points_label/591971ce679ca4b93ad38b993d9e745f.seg 04225987 03790512/points/bcabe20e46e5126ed5dde04c96fd8146.pts 03790512/expert_verified/points_label/bcabe20e46e5126ed5dde04c96fd8146.seg 03790512 04379243/points/3ed500a12dfa511ba6040757a0125a99.pts 04379243/expert_verified/points_label/3ed500a12dfa511ba6040757a0125a99.seg 04379243 04379243/points/1581d2682187764730bbd4cddd04c77b.pts 04379243/expert_verified/points_label/1581d2682187764730bbd4cddd04c77b.seg 04379243 02691156/points/bb7d526405e9347b8f6810e1a2b6aa04.pts 02691156/expert_verified/points_label/bb7d526405e9347b8f6810e1a2b6aa04.seg 02691156 02691156/points/fb9deec3a422b06b609e2d916fa0da27.pts 02691156/expert_verified/points_label/fb9deec3a422b06b609e2d916fa0da27.seg 02691156 03636649/points/5e6abfc7d93fa5f1dc0efee4b442070.pts 03636649/expert_verified/points_label/5e6abfc7d93fa5f1dc0efee4b442070.seg 03636649 03467517/points/2dbc73ad4ce7950163e148e250c0340d.pts 03467517/expert_verified/points_label/2dbc73ad4ce7950163e148e250c0340d.seg 03467517 02958343/points/eea7f5d02088d49dfdb3c05088c091ae.pts 02958343/expert_verified/points_label/eea7f5d02088d49dfdb3c05088c091ae.seg 02958343 04379243/points/83c24aad3914e61a73376642dd664bfd.pts 04379243/expert_verified/points_label/83c24aad3914e61a73376642dd664bfd.seg 04379243 04379243/points/51874066ba946c58aaf15b62af6b513f.pts 04379243/expert_verified/points_label/51874066ba946c58aaf15b62af6b513f.seg 04379243 03636649/points/5be8cdad3b218e373d39d8012919dd25.pts 03636649/expert_verified/points_label/5be8cdad3b218e373d39d8012919dd25.seg 03636649 03636649/points/49cd0dd4d1c008edbbc7a6acbd8f058b.pts 03636649/expert_verified/points_label/49cd0dd4d1c008edbbc7a6acbd8f058b.seg 03636649 03642806/points/d7e7e6651a23afc68ba4e518219eb66a.pts 03642806/expert_verified/points_label/d7e7e6651a23afc68ba4e518219eb66a.seg 03642806 02958343/points/6026684ab31d567328044fe9244db50a.pts 02958343/expert_verified/points_label/6026684ab31d567328044fe9244db50a.seg 02958343 04379243/points/c177762c0445d57ab20aa91e9e90c311.pts 04379243/expert_verified/points_label/c177762c0445d57ab20aa91e9e90c311.seg 04379243 02691156/points/7bad9d15c0f0d3c03554ccf8c30febe7.pts 02691156/expert_verified/points_label/7bad9d15c0f0d3c03554ccf8c30febe7.seg 02691156 03636649/points/dd818b0269b1aa15fcb8d8c6d4df8143.pts 03636649/expert_verified/points_label/dd818b0269b1aa15fcb8d8c6d4df8143.seg 03636649 03624134/points/c4851aee1af7d874cc34b900bb2492e.pts 03624134/expert_verified/points_label/c4851aee1af7d874cc34b900bb2492e.seg 03624134 03001627/points/e2ced471afce616454bfa32aa0766acb.pts 03001627/expert_verified/points_label/e2ced471afce616454bfa32aa0766acb.seg 03001627 03797390/points/896f1d494bac0ebcdec712af445786fe.pts 03797390/expert_verified/points_label/896f1d494bac0ebcdec712af445786fe.seg 03797390 04379243/points/481e00e4559705c616a2b5862518c93.pts 04379243/expert_verified/points_label/481e00e4559705c616a2b5862518c93.seg 04379243 04379243/points/2ca883ba6a9dc6f68985be89a0ee21a.pts 04379243/expert_verified/points_label/2ca883ba6a9dc6f68985be89a0ee21a.seg 04379243 04379243/points/ebc82e7df36f6e9a33963916b86d221f.pts 04379243/expert_verified/points_label/ebc82e7df36f6e9a33963916b86d221f.seg 04379243 03001627/points/cdea84a63ad8c44febad4f49b26ec52.pts 03001627/expert_verified/points_label/cdea84a63ad8c44febad4f49b26ec52.seg 03001627 03624134/points/c71280ea272fbfed4b7644126b1d71e0.pts 03624134/expert_verified/points_label/c71280ea272fbfed4b7644126b1d71e0.seg 03624134 02958343/points/974c3d82f8726f086b418c7d9fedcaa9.pts 02958343/expert_verified/points_label/974c3d82f8726f086b418c7d9fedcaa9.seg 02958343 02958343/points/4dbf4e0654d0c234e811106a82796d20.pts 02958343/expert_verified/points_label/4dbf4e0654d0c234e811106a82796d20.seg 02958343 03467517/points/de9ca0c3e32f907dcb61cf5d9c47c2c7.pts 03467517/expert_verified/points_label/de9ca0c3e32f907dcb61cf5d9c47c2c7.seg 03467517 02958343/points/9f4bbcf9f51fe1e42957c02bdefc95c8.pts 02958343/expert_verified/points_label/9f4bbcf9f51fe1e42957c02bdefc95c8.seg 02958343 03467517/points/173e4f1824f7b9fa93f0194265a9746c.pts 03467517/expert_verified/points_label/173e4f1824f7b9fa93f0194265a9746c.seg 03467517 03636649/points/b4f166440439171741657e31b569b105.pts 03636649/expert_verified/points_label/b4f166440439171741657e31b569b105.seg 03636649 03948459/points/d1ba405fef56efa0fa29682ba98e856d.pts 03948459/expert_verified/points_label/d1ba405fef56efa0fa29682ba98e856d.seg 03948459 03467517/points/a39dcefa599a76dd93f0194265a9746c.pts 03467517/expert_verified/points_label/a39dcefa599a76dd93f0194265a9746c.seg 03467517 02958343/points/e213d976734431773a3afd30f2e86bd7.pts 02958343/expert_verified/points_label/e213d976734431773a3afd30f2e86bd7.seg 02958343 04379243/points/b1335d826d7d60726e066e11deddab75.pts 04379243/expert_verified/points_label/b1335d826d7d60726e066e11deddab75.seg 04379243 04379243/points/e37262abd76852ac00ee852f6d8aa3c.pts 04379243/expert_verified/points_label/e37262abd76852ac00ee852f6d8aa3c.seg 04379243 03001627/points/5d346bdb7db27accf3588493d5c284.pts 03001627/expert_verified/points_label/5d346bdb7db27accf3588493d5c284.seg 03001627 04379243/points/198ff59a42a147eb8ac5948d70801389.pts 04379243/expert_verified/points_label/198ff59a42a147eb8ac5948d70801389.seg 04379243 03001627/points/b3fd987b330d0d2acda56795a6fbde1f.pts 03001627/expert_verified/points_label/b3fd987b330d0d2acda56795a6fbde1f.seg 03001627 02691156/points/1cb757280b862ae52c7575c9089791ff.pts 02691156/expert_verified/points_label/1cb757280b862ae52c7575c9089791ff.seg 02691156 03636649/points/4631e756666a8a208ca4aeb5e3b33af7.pts 03636649/expert_verified/points_label/4631e756666a8a208ca4aeb5e3b33af7.seg 03636649 04379243/points/b82c6769c98e877d24d29f1dedd03a57.pts 04379243/expert_verified/points_label/b82c6769c98e877d24d29f1dedd03a57.seg 04379243 03636649/points/2b194d6bed8daa82c0b2dda5ff15ea28.pts 03636649/expert_verified/points_label/2b194d6bed8daa82c0b2dda5ff15ea28.seg 03636649 03001627/points/7e6b4a7b4dd60c40cc8bd7a04c9659f1.pts 03001627/expert_verified/points_label/7e6b4a7b4dd60c40cc8bd7a04c9659f1.seg 03001627 03948459/points/d1cc54762432fd058a2c998c0df41abe.pts 03948459/expert_verified/points_label/d1cc54762432fd058a2c998c0df41abe.seg 03948459 04225987/points/776eaffd7cbe7bc6b9e8bdc9c4a49aa2.pts 04225987/expert_verified/points_label/776eaffd7cbe7bc6b9e8bdc9c4a49aa2.seg 04225987 04379243/points/6ce30b0327db26f340b4c5428883e585.pts 04379243/expert_verified/points_label/6ce30b0327db26f340b4c5428883e585.seg 04379243 04379243/points/c5230678204a1bb8dcfcef693e7ec696.pts 04379243/expert_verified/points_label/c5230678204a1bb8dcfcef693e7ec696.seg 04379243 02691156/points/563cef4df464ddb1e153dd90dac45a6d.pts 02691156/expert_verified/points_label/563cef4df464ddb1e153dd90dac45a6d.seg 02691156 02958343/points/42e6ce03b361102ab86e0633bb69faea.pts 02958343/expert_verified/points_label/42e6ce03b361102ab86e0633bb69faea.seg 02958343 03001627/points/26e8033e59a3adf6bb53a6a5f5051240.pts 03001627/expert_verified/points_label/26e8033e59a3adf6bb53a6a5f5051240.seg 03001627 04379243/points/731b983cb313634fd018082a1777a5f8.pts 04379243/expert_verified/points_label/731b983cb313634fd018082a1777a5f8.seg 04379243 02691156/points/10aa040f470500c6a66ef8df4909ded9.pts 02691156/expert_verified/points_label/10aa040f470500c6a66ef8df4909ded9.seg 02691156 03467517/points/bb895a87931f51c893f0194265a9746c.pts 03467517/expert_verified/points_label/bb895a87931f51c893f0194265a9746c.seg 03467517 03624134/points/a105080ce4564145aeb54153795ede63.pts 03624134/expert_verified/points_label/a105080ce4564145aeb54153795ede63.seg 03624134 04379243/points/c12147db9b29ef9ee0480c954dcd56d1.pts 04379243/expert_verified/points_label/c12147db9b29ef9ee0480c954dcd56d1.seg 04379243 04379243/points/21cdc417e398378e40f3ac0af6b7e700.pts 04379243/expert_verified/points_label/21cdc417e398378e40f3ac0af6b7e700.seg 04379243 04379243/points/b11e0feb428f61edf008d8a3590fb522.pts 04379243/expert_verified/points_label/b11e0feb428f61edf008d8a3590fb522.seg 04379243 04379243/points/2700f6693447c32d66c64744a4252d3.pts 04379243/expert_verified/points_label/2700f6693447c32d66c64744a4252d3.seg 04379243 03467517/points/b6d0cf333c7e013993f0194265a9746c.pts 03467517/expert_verified/points_label/b6d0cf333c7e013993f0194265a9746c.seg 03467517 03001627/points/ece627bd883d9bbfb0eb7e753c06942.pts 03001627/expert_verified/points_label/ece627bd883d9bbfb0eb7e753c06942.seg 03001627 03636649/points/26f0f37f0f2623c4a3fa46ae73c48b4.pts 03636649/expert_verified/points_label/26f0f37f0f2623c4a3fa46ae73c48b4.seg 03636649 04379243/points/8b07d458499d63f36d96c6cb347d6a90.pts 04379243/expert_verified/points_label/8b07d458499d63f36d96c6cb347d6a90.seg 04379243 04379243/points/eb363770ee36b0309a79b01b89f55c86.pts 04379243/expert_verified/points_label/eb363770ee36b0309a79b01b89f55c86.seg 04379243 04379243/points/ccf36a20b7ef3bd128071d61462a212d.pts 04379243/expert_verified/points_label/ccf36a20b7ef3bd128071d61462a212d.seg 04379243 03001627/points/cf24fc2d10f8da31283b00891f680579.pts 03001627/expert_verified/points_label/cf24fc2d10f8da31283b00891f680579.seg 03001627 02958343/points/8b4879617bd256391738f25e3015f92e.pts 02958343/expert_verified/points_label/8b4879617bd256391738f25e3015f92e.seg 02958343 03001627/points/55e1cde05a99f6c7d1d34366ca81fb3b.pts 03001627/expert_verified/points_label/55e1cde05a99f6c7d1d34366ca81fb3b.seg 03001627 03001627/points/6c25ec1178e9bab6e545858398955dd1.pts 03001627/expert_verified/points_label/6c25ec1178e9bab6e545858398955dd1.seg 03001627 03001627/points/862f70e73fa70c9b1a719e2a845bdada.pts 03001627/expert_verified/points_label/862f70e73fa70c9b1a719e2a845bdada.seg 03001627 04379243/points/fa5dce1043f44c06ab88e3acae6e8bc5.pts 04379243/expert_verified/points_label/fa5dce1043f44c06ab88e3acae6e8bc5.seg 04379243 03467517/points/6f9d1467eb39f8abfae47f572c17b9cb.pts 03467517/expert_verified/points_label/6f9d1467eb39f8abfae47f572c17b9cb.seg 03467517 04379243/points/60ef2830979fd08ec72d4ae978770752.pts 04379243/expert_verified/points_label/60ef2830979fd08ec72d4ae978770752.seg 04379243 03624134/points/d69e028056c9291069654277b747a908.pts 03624134/expert_verified/points_label/d69e028056c9291069654277b747a908.seg 03624134 04379243/points/8e7c894039ae2cfe99e8bf807e902261.pts 04379243/expert_verified/points_label/8e7c894039ae2cfe99e8bf807e902261.seg 04379243 02958343/points/4e2ca20091449636599389919f6522e6.pts 02958343/expert_verified/points_label/4e2ca20091449636599389919f6522e6.seg 02958343 04379243/points/b10d84b3a04085b17618b16b281bdf56.pts 04379243/expert_verified/points_label/b10d84b3a04085b17618b16b281bdf56.seg 04379243 03948459/points/d13986cc2403a2034b4b3d2a28039009.pts 03948459/expert_verified/points_label/d13986cc2403a2034b4b3d2a28039009.seg 03948459 03636649/points/d97a86cea650ae0baf5b49ad7809302.pts 03636649/expert_verified/points_label/d97a86cea650ae0baf5b49ad7809302.seg 03636649 03797390/points/ca198dc3f7dc0cacec6338171298c66b.pts 03797390/expert_verified/points_label/ca198dc3f7dc0cacec6338171298c66b.seg 03797390 03636649/points/3f968096c74ee3a3b04a2e6a78ff6c49.pts 03636649/expert_verified/points_label/3f968096c74ee3a3b04a2e6a78ff6c49.seg 03636649 02691156/points/4d6ec762d1583ded46555ee25941a22e.pts 02691156/expert_verified/points_label/4d6ec762d1583ded46555ee25941a22e.seg 02691156 03467517/points/401ff6021157dee293f0194265a9746c.pts 03467517/expert_verified/points_label/401ff6021157dee293f0194265a9746c.seg 03467517 04379243/points/c1d808c75cc5e7ab4da5bb83ec125010.pts 04379243/expert_verified/points_label/c1d808c75cc5e7ab4da5bb83ec125010.seg 04379243 03790512/points/3d37db1d974499287395d58407f193ba.pts 03790512/expert_verified/points_label/3d37db1d974499287395d58407f193ba.seg 03790512 03624134/points/65892e0f7f93129d14cb807a24b99e1e.pts 03624134/expert_verified/points_label/65892e0f7f93129d14cb807a24b99e1e.seg 03624134 03624134/points/854e7bb73afaff7591ea3afb2749822f.pts 03624134/expert_verified/points_label/854e7bb73afaff7591ea3afb2749822f.seg 03624134 03624134/points/7b492f2baa1dc710cc34b900bb2492e.pts 03624134/expert_verified/points_label/7b492f2baa1dc710cc34b900bb2492e.seg 03624134 03636649/points/b4b15a84b9067f94a75d03186a0409e2.pts 03636649/expert_verified/points_label/b4b15a84b9067f94a75d03186a0409e2.seg 03636649 03636649/points/9db87bf898efd448cbde89e0c48a01bf.pts 03636649/expert_verified/points_label/9db87bf898efd448cbde89e0c48a01bf.seg 03636649 02954340/points/9bd54e0123d3cd70a52821bf1aa3b19a.pts 02954340/expert_verified/points_label/9bd54e0123d3cd70a52821bf1aa3b19a.seg 02954340 ================================================ FILE: dgcnn/tensorflow/part_seg/train_multi_gpu.py ================================================ import argparse import subprocess import tensorflow as tf import numpy as np from datetime import datetime import json import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) sys.path.append(os.path.dirname(BASE_DIR)) import provider import part_seg_model as model TOWER_NAME = 'tower' # DEFAULT SETTINGS parser = argparse.ArgumentParser() parser.add_argument('--num_gpu', type=int, default=2, help='The number of GPUs to use [default: 2]') parser.add_argument('--batch', type=int, default=16, help='Batch Size per GPU during training [default: 32]') parser.add_argument('--epoch', type=int, default=201, help='Epoch to run [default: 50]') parser.add_argument('--point_num', type=int, default=2048, help='Point Number [256/512/1024/2048]') parser.add_argument('--output_dir', type=str, default='train_results', help='Directory that stores all training logs and trained models') parser.add_argument('--wd', type=float, default=0, help='Weight Decay [Default: 0.0]') FLAGS = parser.parse_args() hdf5_data_dir = os.path.join(BASE_DIR, './hdf5_data') # MAIN SCRIPT point_num = FLAGS.point_num batch_size = FLAGS.batch output_dir = FLAGS.output_dir if not os.path.exists(output_dir): os.mkdir(output_dir) # color_map_file = os.path.join(hdf5_data_dir, 'part_color_mapping.json') # color_map = json.load(open(color_map_file, 'r')) all_obj_cats_file = os.path.join(hdf5_data_dir, 'all_object_categories.txt') fin = open(all_obj_cats_file, 'r') lines = [line.rstrip() for line in fin.readlines()] all_obj_cats = [(line.split()[0], line.split()[1]) for line in lines] fin.close() all_cats = json.load(open(os.path.join(hdf5_data_dir, 'overallid_to_catid_partid.json'), 'r')) NUM_CATEGORIES = 16 NUM_PART_CATS = len(all_cats) print('#### Batch Size Per GPU: {0}'.format(batch_size)) print('#### Point Number: {0}'.format(point_num)) print('#### Using GPUs: {0}'.format(FLAGS.num_gpu)) DECAY_STEP = 16881 * 20 DECAY_RATE = 0.5 LEARNING_RATE_CLIP = 1e-5 BN_INIT_DECAY = 0.5 BN_DECAY_DECAY_RATE = 0.5 BN_DECAY_DECAY_STEP = float(DECAY_STEP * 2) BN_DECAY_CLIP = 0.99 BASE_LEARNING_RATE = 0.003 MOMENTUM = 0.9 TRAINING_EPOCHES = FLAGS.epoch print('### Training epoch: {0}'.format(TRAINING_EPOCHES)) TRAINING_FILE_LIST = os.path.join(hdf5_data_dir, 'train_hdf5_file_list.txt') TESTING_FILE_LIST = os.path.join(hdf5_data_dir, 'val_hdf5_file_list.txt') MODEL_STORAGE_PATH = os.path.join(output_dir, 'trained_models') if not os.path.exists(MODEL_STORAGE_PATH): os.mkdir(MODEL_STORAGE_PATH) LOG_STORAGE_PATH = os.path.join(output_dir, 'logs') if not os.path.exists(LOG_STORAGE_PATH): os.mkdir(LOG_STORAGE_PATH) SUMMARIES_FOLDER = os.path.join(output_dir, 'summaries') if not os.path.exists(SUMMARIES_FOLDER): os.mkdir(SUMMARIES_FOLDER) def printout(flog, data): print(data) flog.write(data + '\n') def convert_label_to_one_hot(labels): label_one_hot = np.zeros((labels.shape[0], NUM_CATEGORIES)) for idx in range(labels.shape[0]): label_one_hot[idx, labels[idx]] = 1 return label_one_hot def average_gradients(tower_grads): """Calculate average gradient for each shared variable across all towers. Note that this function provides a synchronization point across all towers. Args: tower_grads: List of lists of (gradient, variable) tuples. The outer list is over individual gradients. The inner list is over the gradient calculation for each tower. Returns: List of pairs of (gradient, variable) where the gradient has been averaged across all towers. """ average_grads = [] for grad_and_vars in zip(*tower_grads): # Note that each grad_and_vars looks like the following: # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) grads = [] for g, _ in grad_and_vars: if g is None: continue expanded_g = tf.expand_dims(g, 0) grads.append(expanded_g) # Average over the 'tower' dimension. grad = tf.concat(grads, 0) grad = tf.reduce_mean(grad, 0) # Keep in mind that the Variables are redundant because they are shared # across towers. So .. we will just return the first tower's pointer to # the Variable. v = grad_and_vars[0][1] grad_and_var = (grad, v) average_grads.append(grad_and_var) return average_grads def train(): with tf.Graph().as_default(), tf.device('/cpu:0'): batch = tf.Variable(0, trainable=False) learning_rate = tf.train.exponential_decay( BASE_LEARNING_RATE, # base learning rate batch * batch_size, # global_var indicating the number of steps DECAY_STEP, # step size DECAY_RATE, # decay rate staircase=True # Stair-case or continuous decreasing ) learning_rate = tf.maximum(learning_rate, LEARNING_RATE_CLIP) bn_momentum = tf.train.exponential_decay( BN_INIT_DECAY, batch*batch_size, BN_DECAY_DECAY_STEP, BN_DECAY_DECAY_RATE, staircase=True) bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum) lr_op = tf.summary.scalar('learning_rate', learning_rate) batch_op = tf.summary.scalar('batch_number', batch) bn_decay_op = tf.summary.scalar('bn_decay', bn_decay) trainer = tf.train.AdamOptimizer(learning_rate) # store tensors for different gpus tower_grads = [] pointclouds_phs = [] input_label_phs = [] seg_phs =[] is_training_phs =[] with tf.variable_scope(tf.get_variable_scope()): for i in xrange(FLAGS.num_gpu): with tf.device('/gpu:%d' % i): with tf.name_scope('%s_%d' % (TOWER_NAME, i)) as scope: pointclouds_phs.append(tf.placeholder(tf.float32, shape=(batch_size, point_num, 3))) # for points input_label_phs.append(tf.placeholder(tf.float32, shape=(batch_size, NUM_CATEGORIES))) # for one-hot category label seg_phs.append(tf.placeholder(tf.int32, shape=(batch_size, point_num))) # for part labels is_training_phs.append(tf.placeholder(tf.bool, shape=())) seg_pred = model.get_model(pointclouds_phs[-1], input_label_phs[-1], \ is_training=is_training_phs[-1], bn_decay=bn_decay, cat_num=NUM_CATEGORIES, \ part_num=NUM_PART_CATS, batch_size=batch_size, num_point=point_num, weight_decay=FLAGS.wd) loss, per_instance_seg_loss, per_instance_seg_pred_res \ = model.get_loss(seg_pred, seg_phs[-1]) total_training_loss_ph = tf.placeholder(tf.float32, shape=()) total_testing_loss_ph = tf.placeholder(tf.float32, shape=()) seg_training_acc_ph = tf.placeholder(tf.float32, shape=()) seg_testing_acc_ph = tf.placeholder(tf.float32, shape=()) seg_testing_acc_avg_cat_ph = tf.placeholder(tf.float32, shape=()) total_train_loss_sum_op = tf.summary.scalar('total_training_loss', total_training_loss_ph) total_test_loss_sum_op = tf.summary.scalar('total_testing_loss', total_testing_loss_ph) seg_train_acc_sum_op = tf.summary.scalar('seg_training_acc', seg_training_acc_ph) seg_test_acc_sum_op = tf.summary.scalar('seg_testing_acc', seg_testing_acc_ph) seg_test_acc_avg_cat_op = tf.summary.scalar('seg_testing_acc_avg_cat', seg_testing_acc_avg_cat_ph) tf.get_variable_scope().reuse_variables() grads = trainer.compute_gradients(loss) tower_grads.append(grads) grads = average_gradients(tower_grads) train_op = trainer.apply_gradients(grads, global_step=batch) saver = tf.train.Saver(tf.global_variables(), sharded=True, max_to_keep=20) config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True sess = tf.Session(config=config) init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) sess.run(init) train_writer = tf.summary.FileWriter(SUMMARIES_FOLDER + '/train', sess.graph) test_writer = tf.summary.FileWriter(SUMMARIES_FOLDER + '/test') train_file_list = provider.getDataFiles(TRAINING_FILE_LIST) num_train_file = len(train_file_list) test_file_list = provider.getDataFiles(TESTING_FILE_LIST) num_test_file = len(test_file_list) fcmd = open(os.path.join(LOG_STORAGE_PATH, 'cmd.txt'), 'w') fcmd.write(str(FLAGS)) fcmd.close() # write logs to the disk flog = open(os.path.join(LOG_STORAGE_PATH, 'log.txt'), 'w') def train_one_epoch(train_file_idx, epoch_num): is_training = True for i in range(num_train_file): cur_train_filename = os.path.join(hdf5_data_dir, train_file_list[train_file_idx[i]]) printout(flog, 'Loading train file ' + cur_train_filename) cur_data, cur_labels, cur_seg = provider.load_h5_data_label_seg(cur_train_filename) cur_data, cur_labels, order = provider.shuffle_data(cur_data, np.squeeze(cur_labels)) cur_seg = cur_seg[order, ...] cur_labels_one_hot = convert_label_to_one_hot(cur_labels) num_data = len(cur_labels) num_batch = num_data // (FLAGS.num_gpu * batch_size) # For all working gpus total_loss = 0.0 total_seg_acc = 0.0 for j in range(num_batch): begidx_0 = j * batch_size endidx_0 = (j + 1) * batch_size begidx_1 = (j + 1) * batch_size endidx_1 = (j + 2) * batch_size feed_dict = { # For the first gpu pointclouds_phs[0]: cur_data[begidx_0: endidx_0, ...], input_label_phs[0]: cur_labels_one_hot[begidx_0: endidx_0, ...], seg_phs[0]: cur_seg[begidx_0: endidx_0, ...], is_training_phs[0]: is_training, # For the second gpu pointclouds_phs[1]: cur_data[begidx_1: endidx_1, ...], input_label_phs[1]: cur_labels_one_hot[begidx_1: endidx_1, ...], seg_phs[1]: cur_seg[begidx_1: endidx_1, ...], is_training_phs[1]: is_training, } # train_op is for both gpus, and the others are for gpu_1 _, loss_val, per_instance_seg_loss_val, seg_pred_val, pred_seg_res \ = sess.run([train_op, loss, per_instance_seg_loss, seg_pred, per_instance_seg_pred_res], \ feed_dict=feed_dict) per_instance_part_acc = np.mean(pred_seg_res == cur_seg[begidx_1: endidx_1, ...], axis=1) average_part_acc = np.mean(per_instance_part_acc) total_loss += loss_val total_seg_acc += average_part_acc total_loss = total_loss * 1.0 / num_batch total_seg_acc = total_seg_acc * 1.0 / num_batch lr_sum, bn_decay_sum, batch_sum, train_loss_sum, train_seg_acc_sum = sess.run(\ [lr_op, bn_decay_op, batch_op, total_train_loss_sum_op, seg_train_acc_sum_op], \ feed_dict={total_training_loss_ph: total_loss, seg_training_acc_ph: total_seg_acc}) train_writer.add_summary(train_loss_sum, i + epoch_num * num_train_file) train_writer.add_summary(lr_sum, i + epoch_num * num_train_file) train_writer.add_summary(bn_decay_sum, i + epoch_num * num_train_file) train_writer.add_summary(train_seg_acc_sum, i + epoch_num * num_train_file) train_writer.add_summary(batch_sum, i + epoch_num * num_train_file) printout(flog, '\tTraining Total Mean_loss: %f' % total_loss) printout(flog, '\t\tTraining Seg Accuracy: %f' % total_seg_acc) def eval_one_epoch(epoch_num): is_training = False total_loss = 0.0 total_seg_acc = 0.0 total_seen = 0 total_seg_acc_per_cat = np.zeros((NUM_CATEGORIES)).astype(np.float32) total_seen_per_cat = np.zeros((NUM_CATEGORIES)).astype(np.int32) for i in range(num_test_file): cur_test_filename = os.path.join(hdf5_data_dir, test_file_list[i]) printout(flog, 'Loading test file ' + cur_test_filename) cur_data, cur_labels, cur_seg = provider.load_h5_data_label_seg(cur_test_filename) cur_labels = np.squeeze(cur_labels) cur_labels_one_hot = convert_label_to_one_hot(cur_labels) num_data = len(cur_labels) num_batch = num_data // batch_size # Run on gpu_1, since the tensors used for evaluation are defined on gpu_1 for j in range(num_batch): begidx = j * batch_size endidx = (j + 1) * batch_size feed_dict = { pointclouds_phs[1]: cur_data[begidx: endidx, ...], input_label_phs[1]: cur_labels_one_hot[begidx: endidx, ...], seg_phs[1]: cur_seg[begidx: endidx, ...], is_training_phs[1]: is_training} loss_val, per_instance_seg_loss_val, seg_pred_val, pred_seg_res \ = sess.run([loss, per_instance_seg_loss, seg_pred, per_instance_seg_pred_res], \ feed_dict=feed_dict) per_instance_part_acc = np.mean(pred_seg_res == cur_seg[begidx: endidx, ...], axis=1) average_part_acc = np.mean(per_instance_part_acc) total_seen += 1 total_loss += loss_val total_seg_acc += average_part_acc for shape_idx in range(begidx, endidx): total_seen_per_cat[cur_labels[shape_idx]] += 1 total_seg_acc_per_cat[cur_labels[shape_idx]] += per_instance_part_acc[shape_idx - begidx] total_loss = total_loss * 1.0 / total_seen total_seg_acc = total_seg_acc * 1.0 / total_seen test_loss_sum, test_seg_acc_sum = sess.run(\ [total_test_loss_sum_op, seg_test_acc_sum_op], \ feed_dict={total_testing_loss_ph: total_loss, \ seg_testing_acc_ph: total_seg_acc}) test_writer.add_summary(test_loss_sum, (epoch_num+1) * num_train_file-1) test_writer.add_summary(test_seg_acc_sum, (epoch_num+1) * num_train_file-1) printout(flog, '\tTesting Total Mean_loss: %f' % total_loss) printout(flog, '\t\tTesting Seg Accuracy: %f' % total_seg_acc) for cat_idx in range(NUM_CATEGORIES): if total_seen_per_cat[cat_idx] > 0: printout(flog, '\n\t\tCategory %s Object Number: %d' % (all_obj_cats[cat_idx][0], total_seen_per_cat[cat_idx])) printout(flog, '\t\tCategory %s Seg Accuracy: %f' % (all_obj_cats[cat_idx][0], total_seg_acc_per_cat[cat_idx]/total_seen_per_cat[cat_idx])) if not os.path.exists(MODEL_STORAGE_PATH): os.mkdir(MODEL_STORAGE_PATH) for epoch in range(TRAINING_EPOCHES): printout(flog, '\n<<< Testing on the test dataset ...') eval_one_epoch(epoch) printout(flog, '\n>>> Training for the epoch %d/%d ...' % (epoch, TRAINING_EPOCHES)) train_file_idx = np.arange(0, len(train_file_list)) np.random.shuffle(train_file_idx) train_one_epoch(train_file_idx, epoch) if epoch % 5 == 0: cp_filename = saver.save(sess, os.path.join(MODEL_STORAGE_PATH, 'epoch_' + str(epoch)+'.ckpt')) printout(flog, 'Successfully store the checkpoint model into ' + cp_filename) flog.flush() flog.close() if __name__=='__main__': train() ================================================ FILE: dgcnn/tensorflow/provider.py ================================================ import os import sys import numpy as np import h5py BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) # Download dataset for point cloud classification DATA_DIR = os.path.join(BASE_DIR, 'data') if not os.path.exists(DATA_DIR): os.mkdir(DATA_DIR) if not os.path.exists(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048')): www = 'https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip' zipfile = os.path.basename(www) os.system('wget %s; unzip %s' % (www, zipfile)) os.system('mv %s %s' % (zipfile[:-4], DATA_DIR)) os.system('rm %s' % (zipfile)) def shuffle_data(data, labels): """ Shuffle data and labels. Input: data: B,N,... numpy array label: B,... numpy array Return: shuffled data, label and shuffle indices """ idx = np.arange(len(labels)) np.random.shuffle(idx) return data[idx, ...], labels[idx], idx def rotate_point_cloud(batch_data): """ Randomly rotate the point clouds to augument the dataset rotation is per shape based along up direction Input: BxNx3 array, original batch of point clouds Return: BxNx3 array, rotated batch of point clouds """ rotated_data = np.zeros(batch_data.shape, dtype=np.float32) for k in xrange(batch_data.shape[0]): rotation_angle = np.random.uniform() * 2 * np.pi cosval = np.cos(rotation_angle) sinval = np.sin(rotation_angle) rotation_matrix = np.array([[cosval, 0, sinval], [0, 1, 0], [-sinval, 0, cosval]]) shape_pc = batch_data[k, ...] rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) return rotated_data def rotate_point_cloud_by_angle(batch_data, rotation_angle): """ Rotate the point cloud along up direction with certain angle. Input: BxNx3 array, original batch of point clouds Return: BxNx3 array, rotated batch of point clouds """ rotated_data = np.zeros(batch_data.shape, dtype=np.float32) for k in xrange(batch_data.shape[0]): #rotation_angle = np.random.uniform() * 2 * np.pi cosval = np.cos(rotation_angle) sinval = np.sin(rotation_angle) rotation_matrix = np.array([[cosval, 0, sinval], [0, 1, 0], [-sinval, 0, cosval]]) shape_pc = batch_data[k, ...] rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) return rotated_data def rotate_perturbation_point_cloud(batch_data, angle_sigma=0.06, angle_clip=0.18): """ Randomly perturb the point clouds by small rotations Input: BxNx3 array, original batch of point clouds Return: BxNx3 array, rotated batch of point clouds """ rotated_data = np.zeros(batch_data.shape, dtype=np.float32) for k in xrange(batch_data.shape[0]): angles = np.clip(angle_sigma*np.random.randn(3), -angle_clip, angle_clip) Rx = np.array([[1,0,0], [0,np.cos(angles[0]),-np.sin(angles[0])], [0,np.sin(angles[0]),np.cos(angles[0])]]) Ry = np.array([[np.cos(angles[1]),0,np.sin(angles[1])], [0,1,0], [-np.sin(angles[1]),0,np.cos(angles[1])]]) Rz = np.array([[np.cos(angles[2]),-np.sin(angles[2]),0], [np.sin(angles[2]),np.cos(angles[2]),0], [0,0,1]]) R = np.dot(Rz, np.dot(Ry,Rx)) shape_pc = batch_data[k, ...] rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), R) return rotated_data def jitter_point_cloud(batch_data, sigma=0.01, clip=0.05): """ Randomly jitter points. jittering is per point. Input: BxNx3 array, original batch of point clouds Return: BxNx3 array, jittered batch of point clouds """ B, N, C = batch_data.shape assert(clip > 0) jittered_data = np.clip(sigma * np.random.randn(B, N, C), -1*clip, clip) jittered_data += batch_data return jittered_data def shift_point_cloud(batch_data, shift_range=0.1): """ Randomly shift point cloud. Shift is per point cloud. Input: BxNx3 array, original batch of point clouds Return: BxNx3 array, shifted batch of point clouds """ B, N, C = batch_data.shape shifts = np.random.uniform(-shift_range, shift_range, (B,3)) for batch_index in range(B): batch_data[batch_index,:,:] += shifts[batch_index,:] return batch_data def random_scale_point_cloud(batch_data, scale_low=0.8, scale_high=1.25): """ Randomly scale the point cloud. Scale is per point cloud. Input: BxNx3 array, original batch of point clouds Return: BxNx3 array, scaled batch of point clouds """ B, N, C = batch_data.shape scales = np.random.uniform(scale_low, scale_high, B) for batch_index in range(B): batch_data[batch_index,:,:] *= scales[batch_index] return batch_data def getDataFiles(list_filename): return [line.rstrip() for line in open(list_filename)] def load_h5(h5_filename): f = h5py.File(h5_filename) data = f['data'][:] label = f['label'][:] return (data, label) def loadDataFile(filename): return load_h5(filename) def load_h5_data_label_seg(h5_filename): f = h5py.File(h5_filename) data = f['data'][:] # (2048, 2048, 3) label = f['label'][:] # (2048, 1) seg = f['pid'][:] # (2048, 2048) return (data, label, seg) ================================================ FILE: dgcnn/tensorflow/sem_seg/README.md ================================================ ## Semantic segmentation of indoor scenes ### Dataset 1. Donwload prepared HDF5 data for training: ``` sh +x download_data.sh ``` 2. Download 3D indoor parsing dataset (S3DIS Dataset) for testing and visualization. "Stanford3dDataset_v1.2_Aligned_Version.zip" of the dataset is used. Unzip the downloaded file into "dgcnn/data/", and then run ``` python collect_indoor3d_data.py ``` to generate "dgcnn/data/stanford_indoor3d" ### Train We use 6-fold training, such that 6 models are trained leaving 1 of 6 areas as the testing area for each model. We keep using 2 GPUs for distributed training. To train 6 models sequentially, run ``` sh +x train_job.sh ``` ### Evaluation 1. To generate predicted results for all 6 areas, run ``` sh +x test_job.sh ``` The model parameters are saved every 10 epochs, the saved model used to generate predited results can be changed by setting "--model_path" in "test_job.sh". For example, if you want to use the model saved after 70 epochs, you can set "--model_path" to "log*n*/epoch_70.ckpt" for *n* = 1, 2, ..., 6. To visualize the results, you can add "--visu" flag in the end of each line in "test_job.sh". 2. To obtain overall quantitative evaluation results, run ``` python eval_iou_accuracy.py ``` ================================================ FILE: dgcnn/tensorflow/sem_seg/batch_inference.py ================================================ import argparse import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = os.path.dirname(BASE_DIR) sys.path.append(BASE_DIR) from model import * import indoor3d_util parser = argparse.ArgumentParser() parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]') parser.add_argument('--batch_size', type=int, default=1, help='Batch Size during training [default: 1]') parser.add_argument('--num_point', type=int, default=4096, help='Point number [default: 4096]') parser.add_argument('--model_path', required=True, help='model checkpoint file path') parser.add_argument('--dump_dir', required=True, help='dump folder path') parser.add_argument('--output_filelist', required=True, help='TXT filename, filelist, each line is an output for a room') parser.add_argument('--room_data_filelist', required=True, help='TXT filename, filelist, each line is a test room data label file.') parser.add_argument('--no_clutter', action='store_true', help='If true, donot count the clutter class') parser.add_argument('--visu', action='store_true', help='Whether to output OBJ file for prediction visualization.') FLAGS = parser.parse_args() BATCH_SIZE = FLAGS.batch_size NUM_POINT = FLAGS.num_point MODEL_PATH = FLAGS.model_path GPU_INDEX = FLAGS.gpu DUMP_DIR = FLAGS.dump_dir if not os.path.exists(DUMP_DIR): os.mkdir(DUMP_DIR) LOG_FOUT = open(os.path.join(DUMP_DIR, 'log_evaluate.txt'), 'w') LOG_FOUT.write(str(FLAGS)+'\n') ROOM_PATH_LIST = [os.path.join(ROOT_DIR,line.rstrip()) for line in open(FLAGS.room_data_filelist)] NUM_CLASSES = 13 def log_string(out_str): LOG_FOUT.write(out_str+'\n') LOG_FOUT.flush() print(out_str) def evaluate(): is_training = False with tf.device('/gpu:'+str(GPU_INDEX)): pointclouds_pl, labels_pl = placeholder_inputs(BATCH_SIZE, NUM_POINT) is_training_pl = tf.placeholder(tf.bool, shape=()) pred = get_model(pointclouds_pl, is_training_pl) loss = get_loss(pred, labels_pl) pred_softmax = tf.nn.softmax(pred) saver = tf.train.Saver() config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True sess = tf.Session(config=config) saver.restore(sess, MODEL_PATH) log_string("Model restored.") ops = {'pointclouds_pl': pointclouds_pl, 'labels_pl': labels_pl, 'is_training_pl': is_training_pl, 'pred': pred, 'pred_softmax': pred_softmax, 'loss': loss} total_correct = 0 total_seen = 0 fout_out_filelist = open(FLAGS.output_filelist, 'w') for room_path in ROOM_PATH_LIST: out_data_label_filename = os.path.basename(room_path)[:-4] + '_pred.txt' out_data_label_filename = os.path.join(DUMP_DIR, out_data_label_filename) out_gt_label_filename = os.path.basename(room_path)[:-4] + '_gt.txt' out_gt_label_filename = os.path.join(DUMP_DIR, out_gt_label_filename) print(room_path, out_data_label_filename) # Evaluate room one by one. a, b = eval_one_epoch(sess, ops, room_path, out_data_label_filename, out_gt_label_filename) total_correct += a total_seen += b fout_out_filelist.write(out_data_label_filename+'\n') fout_out_filelist.close() log_string('all room eval accuracy: %f'% (total_correct / float(total_seen))) def eval_one_epoch(sess, ops, room_path, out_data_label_filename, out_gt_label_filename): error_cnt = 0 is_training = False total_correct = 0 total_seen = 0 loss_sum = 0 total_seen_class = [0 for _ in range(NUM_CLASSES)] total_correct_class = [0 for _ in range(NUM_CLASSES)] if FLAGS.visu: fout = open(os.path.join(DUMP_DIR, os.path.basename(room_path)[:-4]+'_pred.obj'), 'w') fout_gt = open(os.path.join(DUMP_DIR, os.path.basename(room_path)[:-4]+'_gt.obj'), 'w') fout_real_color = open(os.path.join(DUMP_DIR, os.path.basename(room_path)[:-4]+'_real_color.obj'), 'w') fout_data_label = open(out_data_label_filename, 'w') fout_gt_label = open(out_gt_label_filename, 'w') current_data, current_label = indoor3d_util.room2blocks_wrapper_normalized(room_path, NUM_POINT) current_data = current_data[:,0:NUM_POINT,:] current_label = np.squeeze(current_label) # Get room dimension.. data_label = np.load(room_path) data = data_label[:,0:6] max_room_x = max(data[:,0]) max_room_y = max(data[:,1]) max_room_z = max(data[:,2]) file_size = current_data.shape[0] num_batches = file_size // BATCH_SIZE print(file_size) for batch_idx in range(num_batches): start_idx = batch_idx * BATCH_SIZE end_idx = (batch_idx+1) * BATCH_SIZE cur_batch_size = end_idx - start_idx feed_dict = {ops['pointclouds_pl']: current_data[start_idx:end_idx, :, :], ops['labels_pl']: current_label[start_idx:end_idx], ops['is_training_pl']: is_training} loss_val, pred_val = sess.run([ops['loss'], ops['pred_softmax']], feed_dict=feed_dict) if FLAGS.no_clutter: pred_label = np.argmax(pred_val[:,:,0:12], 2) # BxN else: pred_label = np.argmax(pred_val, 2) # BxN # Save prediction labels to OBJ file for b in range(BATCH_SIZE): pts = current_data[start_idx+b, :, :] l = current_label[start_idx+b,:] pts[:,6] *= max_room_x pts[:,7] *= max_room_y pts[:,8] *= max_room_z pts[:,3:6] *= 255.0 pred = pred_label[b, :] for i in range(NUM_POINT): color = indoor3d_util.g_label2color[pred[i]] color_gt = indoor3d_util.g_label2color[current_label[start_idx+b, i]] if FLAGS.visu: fout.write('v %f %f %f %d %d %d\n' % (pts[i,6], pts[i,7], pts[i,8], color[0], color[1], color[2])) fout_gt.write('v %f %f %f %d %d %d\n' % (pts[i,6], pts[i,7], pts[i,8], color_gt[0], color_gt[1], color_gt[2])) fout_data_label.write('%f %f %f %d %d %d %f %d\n' % (pts[i,6], pts[i,7], pts[i,8], pts[i,3], pts[i,4], pts[i,5], pred_val[b,i,pred[i]], pred[i])) fout_gt_label.write('%d\n' % (l[i])) correct = np.sum(pred_label == current_label[start_idx:end_idx,:]) total_correct += correct total_seen += (cur_batch_size*NUM_POINT) loss_sum += (loss_val*BATCH_SIZE) for i in range(start_idx, end_idx): for j in range(NUM_POINT): l = current_label[i, j] total_seen_class[l] += 1 total_correct_class[l] += (pred_label[i-start_idx, j] == l) log_string('eval mean loss: %f' % (loss_sum / float(total_seen/NUM_POINT))) log_string('eval accuracy: %f'% (total_correct / float(total_seen))) fout_data_label.close() fout_gt_label.close() if FLAGS.visu: fout.close() fout_gt.close() return total_correct, total_seen if __name__=='__main__': with tf.Graph().as_default(): evaluate() LOG_FOUT.close() ================================================ FILE: dgcnn/tensorflow/sem_seg/collect_indoor3d_data.py ================================================ import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = os.path.dirname(BASE_DIR) sys.path.append(BASE_DIR) import indoor3d_util anno_paths = [line.rstrip() for line in open(os.path.join(BASE_DIR, 'meta/anno_paths.txt'))] anno_paths = [os.path.join(indoor3d_util.DATA_PATH, p) for p in anno_paths] output_folder = os.path.join(ROOT_DIR, 'data/stanford_indoor3d') if not os.path.exists(output_folder): os.mkdir(output_folder) # Note: there is an extra character in the v1.2 data in Area_5/hallway_6. It's fixed manually. for anno_path in anno_paths: print(anno_path) try: elements = anno_path.split('/') out_filename = elements[-3]+'_'+elements[-2]+'.npy' indoor3d_util.collect_point_label(anno_path, os.path.join(output_folder, out_filename), 'numpy') except: print(anno_path, 'ERROR!!') ================================================ FILE: dgcnn/tensorflow/sem_seg/download_data.sh ================================================ #!/bin/bash # Download HDF5 for indoor 3d semantic segmentation (around 1.6GB) -> 'indoor3d_sem_seg_hdf5_data' wget https://shapenet.cs.stanford.edu/media/indoor3d_sem_seg_hdf5_data.zip unzip indoor3d_sem_seg_hdf5_data.zip rm indoor3d_sem_seg_hdf5_data.zip ================================================ FILE: dgcnn/tensorflow/sem_seg/eval_iou_accuracy.py ================================================ import numpy as np pred_data_label_filenames = [] for i in range(1,7): file_name = 'log{}/output_filelist.txt'.format(i) pred_data_label_filenames += [line.rstrip() for line in open(file_name)] gt_label_filenames = [f.rstrip('_pred\.txt') + '_gt.txt' for f in pred_data_label_filenames] num_room = len(gt_label_filenames) gt_classes = [0 for _ in range(13)] positive_classes = [0 for _ in range(13)] true_positive_classes = [0 for _ in range(13)] for i in range(num_room): print(i) data_label = np.loadtxt(pred_data_label_filenames[i]) pred_label = data_label[:,-1] gt_label = np.loadtxt(gt_label_filenames[i]) print(gt_label.shape) for j in xrange(gt_label.shape[0]): gt_l = int(gt_label[j]) pred_l = int(pred_label[j]) gt_classes[gt_l] += 1 positive_classes[pred_l] += 1 true_positive_classes[gt_l] += int(gt_l==pred_l) print(gt_classes) print(positive_classes) print(true_positive_classes) print('Overall accuracy: {0}'.format(sum(true_positive_classes)/float(sum(positive_classes)))) print 'IoU:' iou_list = [] for i in range(13): iou = true_positive_classes[i]/float(gt_classes[i]+positive_classes[i]-true_positive_classes[i]) print(iou) iou_list.append(iou) print 'avg IoU:' print(sum(iou_list)/13.0) ================================================ FILE: dgcnn/tensorflow/sem_seg/indoor3d_util.py ================================================ import numpy as np import glob import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = os.path.dirname(BASE_DIR) sys.path.append(BASE_DIR) # ----------------------------------------------------------------------------- # CONSTANTS # ----------------------------------------------------------------------------- DATA_PATH = os.path.join(ROOT_DIR, 'data', 'Stanford3dDataset_v1.2_Aligned_Version') g_classes = [x.rstrip() for x in open(os.path.join(BASE_DIR, 'meta/class_names.txt'))] g_class2label = {cls: i for i,cls in enumerate(g_classes)} g_class2color = {'ceiling': [0,255,0], 'floor': [0,0,255], 'wall': [0,255,255], 'beam': [255,255,0], 'column': [255,0,255], 'window': [100,100,255], 'door': [200,200,100], 'table': [170,120,200], 'chair': [255,0,0], 'sofa': [200,100,100], 'bookcase': [10,200,100], 'board': [200,200,200], 'clutter': [50,50,50]} g_easy_view_labels = [7,8,9,10,11,1] g_label2color = {g_classes.index(cls): g_class2color[cls] for cls in g_classes} # ----------------------------------------------------------------------------- # CONVERT ORIGINAL DATA TO OUR DATA_LABEL FILES # ----------------------------------------------------------------------------- def collect_point_label(anno_path, out_filename, file_format='txt'): """ Convert original dataset files to data_label file (each line is XYZRGBL). We aggregated all the points from each instance in the room. Args: anno_path: path to annotations. e.g. Area_1/office_2/Annotations/ out_filename: path to save collected points and labels (each line is XYZRGBL) file_format: txt or numpy, determines what file format to save. Returns: None Note: the points are shifted before save, the most negative point is now at origin. """ points_list = [] for f in glob.glob(os.path.join(anno_path, '*.txt')): cls = os.path.basename(f).split('_')[0] if cls not in g_classes: # note: in some room there is 'staris' class.. cls = 'clutter' points = np.loadtxt(f) labels = np.ones((points.shape[0],1)) * g_class2label[cls] points_list.append(np.concatenate([points, labels], 1)) # Nx7 data_label = np.concatenate(points_list, 0) xyz_min = np.amin(data_label, axis=0)[0:3] data_label[:, 0:3] -= xyz_min if file_format=='txt': fout = open(out_filename, 'w') for i in range(data_label.shape[0]): fout.write('%f %f %f %d %d %d %d\n' % \ (data_label[i,0], data_label[i,1], data_label[i,2], data_label[i,3], data_label[i,4], data_label[i,5], data_label[i,6])) fout.close() elif file_format=='numpy': np.save(out_filename, data_label) else: print('ERROR!! Unknown file format: %s, please use txt or numpy.' % \ (file_format)) exit() def point_label_to_obj(input_filename, out_filename, label_color=True, easy_view=False, no_wall=False): """ For visualization of a room from data_label file, input_filename: each line is X Y Z R G B L out_filename: OBJ filename, visualize input file by coloring point with label color easy_view: only visualize furnitures and floor """ data_label = np.loadtxt(input_filename) data = data_label[:, 0:6] label = data_label[:, -1].astype(int) fout = open(out_filename, 'w') for i in range(data.shape[0]): color = g_label2color[label[i]] if easy_view and (label[i] not in g_easy_view_labels): continue if no_wall and ((label[i] == 2) or (label[i]==0)): continue if label_color: fout.write('v %f %f %f %d %d %d\n' % \ (data[i,0], data[i,1], data[i,2], color[0], color[1], color[2])) else: fout.write('v %f %f %f %d %d %d\n' % \ (data[i,0], data[i,1], data[i,2], data[i,3], data[i,4], data[i,5])) fout.close() # ----------------------------------------------------------------------------- # PREPARE BLOCK DATA FOR DEEPNETS TRAINING/TESTING # ----------------------------------------------------------------------------- def sample_data(data, num_sample): """ data is in N x ... we want to keep num_samplexC of them. if N > num_sample, we will randomly keep num_sample of them. if N < num_sample, we will randomly duplicate samples. """ N = data.shape[0] if (N == num_sample): return data, range(N) elif (N > num_sample): sample = np.random.choice(N, num_sample) return data[sample, ...], sample else: sample = np.random.choice(N, num_sample-N) dup_data = data[sample, ...] return np.concatenate([data, dup_data], 0), range(N)+list(sample) def sample_data_label(data, label, num_sample): new_data, sample_indices = sample_data(data, num_sample) new_label = label[sample_indices] return new_data, new_label def room2blocks(data, label, num_point, block_size=1.0, stride=1.0, random_sample=False, sample_num=None, sample_aug=1): """ Prepare block training data. Args: data: N x 6 numpy array, 012 are XYZ in meters, 345 are RGB in [0,1] assumes the data is shifted (min point is origin) and aligned (aligned with XYZ axis) label: N size uint8 numpy array from 0-12 num_point: int, how many points to sample in each block block_size: float, physical size of the block in meters stride: float, stride for block sweeping random_sample: bool, if True, we will randomly sample blocks in the room sample_num: int, if random sample, how many blocks to sample [default: room area] sample_aug: if random sample, how much aug Returns: block_datas: K x num_point x 6 np array of XYZRGB, RGB is in [0,1] block_labels: K x num_point x 1 np array of uint8 labels TODO: for this version, blocking is in fixed, non-overlapping pattern. """ assert(stride<=block_size) limit = np.amax(data, 0)[0:3] # Get the corner location for our sampling blocks xbeg_list = [] ybeg_list = [] if not random_sample: num_block_x = int(np.ceil((limit[0] - block_size) / stride)) + 1 num_block_y = int(np.ceil((limit[1] - block_size) / stride)) + 1 for i in range(num_block_x): for j in range(num_block_y): xbeg_list.append(i*stride) ybeg_list.append(j*stride) else: num_block_x = int(np.ceil(limit[0] / block_size)) num_block_y = int(np.ceil(limit[1] / block_size)) if sample_num is None: sample_num = num_block_x * num_block_y * sample_aug for _ in range(sample_num): xbeg = np.random.uniform(-block_size, limit[0]) ybeg = np.random.uniform(-block_size, limit[1]) xbeg_list.append(xbeg) ybeg_list.append(ybeg) # Collect blocks block_data_list = [] block_label_list = [] idx = 0 for idx in range(len(xbeg_list)): xbeg = xbeg_list[idx] ybeg = ybeg_list[idx] xcond = (data[:,0]<=xbeg+block_size) & (data[:,0]>=xbeg) ycond = (data[:,1]<=ybeg+block_size) & (data[:,1]>=ybeg) cond = xcond & ycond if np.sum(cond) < 100: # discard block if there are less than 100 pts. continue block_data = data[cond, :] block_label = label[cond] # randomly subsample data block_data_sampled, block_label_sampled = \ sample_data_label(block_data, block_label, num_point) block_data_list.append(np.expand_dims(block_data_sampled, 0)) block_label_list.append(np.expand_dims(block_label_sampled, 0)) return np.concatenate(block_data_list, 0), \ np.concatenate(block_label_list, 0) def room2blocks_plus(data_label, num_point, block_size, stride, random_sample, sample_num, sample_aug): """ room2block with input filename and RGB preprocessing. """ data = data_label[:,0:6] data[:,3:6] /= 255.0 label = data_label[:,-1].astype(np.uint8) return room2blocks(data, label, num_point, block_size, stride, random_sample, sample_num, sample_aug) def room2blocks_wrapper(data_label_filename, num_point, block_size=1.0, stride=1.0, random_sample=False, sample_num=None, sample_aug=1): if data_label_filename[-3:] == 'txt': data_label = np.loadtxt(data_label_filename) elif data_label_filename[-3:] == 'npy': data_label = np.load(data_label_filename) else: print('Unknown file type! exiting.') exit() return room2blocks_plus(data_label, num_point, block_size, stride, random_sample, sample_num, sample_aug) def room2blocks_plus_normalized(data_label, num_point, block_size, stride, random_sample, sample_num, sample_aug): """ room2block, with input filename and RGB preprocessing. for each block centralize XYZ, add normalized XYZ as 678 channels """ data = data_label[:,0:6] data[:,3:6] /= 255.0 label = data_label[:,-1].astype(np.uint8) max_room_x = max(data[:,0]) max_room_y = max(data[:,1]) max_room_z = max(data[:,2]) data_batch, label_batch = room2blocks(data, label, num_point, block_size, stride, random_sample, sample_num, sample_aug) new_data_batch = np.zeros((data_batch.shape[0], num_point, 9)) for b in range(data_batch.shape[0]): new_data_batch[b, :, 6] = data_batch[b, :, 0]/max_room_x new_data_batch[b, :, 7] = data_batch[b, :, 1]/max_room_y new_data_batch[b, :, 8] = data_batch[b, :, 2]/max_room_z minx = min(data_batch[b, :, 0]) miny = min(data_batch[b, :, 1]) data_batch[b, :, 0] -= (minx+block_size/2) data_batch[b, :, 1] -= (miny+block_size/2) new_data_batch[:, :, 0:6] = data_batch return new_data_batch, label_batch def room2blocks_wrapper_normalized(data_label_filename, num_point, block_size=1.0, stride=1.0, random_sample=False, sample_num=None, sample_aug=1): if data_label_filename[-3:] == 'txt': data_label = np.loadtxt(data_label_filename) elif data_label_filename[-3:] == 'npy': data_label = np.load(data_label_filename) else: print('Unknown file type! exiting.') exit() return room2blocks_plus_normalized(data_label, num_point, block_size, stride, random_sample, sample_num, sample_aug) def room2samples(data, label, sample_num_point): """ Prepare whole room samples. Args: data: N x 6 numpy array, 012 are XYZ in meters, 345 are RGB in [0,1] assumes the data is shifted (min point is origin) and aligned (aligned with XYZ axis) label: N size uint8 numpy array from 0-12 sample_num_point: int, how many points to sample in each sample Returns: sample_datas: K x sample_num_point x 9 numpy array of XYZRGBX'Y'Z', RGB is in [0,1] sample_labels: K x sample_num_point x 1 np array of uint8 labels """ N = data.shape[0] order = np.arange(N) np.random.shuffle(order) data = data[order, :] label = label[order] batch_num = int(np.ceil(N / float(sample_num_point))) sample_datas = np.zeros((batch_num, sample_num_point, 6)) sample_labels = np.zeros((batch_num, sample_num_point, 1)) for i in range(batch_num): beg_idx = i*sample_num_point end_idx = min((i+1)*sample_num_point, N) num = end_idx - beg_idx sample_datas[i,0:num,:] = data[beg_idx:end_idx, :] sample_labels[i,0:num,0] = label[beg_idx:end_idx] if num < sample_num_point: makeup_indices = np.random.choice(N, sample_num_point - num) sample_datas[i,num:,:] = data[makeup_indices, :] sample_labels[i,num:,0] = label[makeup_indices] return sample_datas, sample_labels def room2samples_plus_normalized(data_label, num_point): """ room2sample, with input filename and RGB preprocessing. for each block centralize XYZ, add normalized XYZ as 678 channels """ data = data_label[:,0:6] data[:,3:6] /= 255.0 label = data_label[:,-1].astype(np.uint8) max_room_x = max(data[:,0]) max_room_y = max(data[:,1]) max_room_z = max(data[:,2]) #print(max_room_x, max_room_y, max_room_z) data_batch, label_batch = room2samples(data, label, num_point) new_data_batch = np.zeros((data_batch.shape[0], num_point, 9)) for b in range(data_batch.shape[0]): new_data_batch[b, :, 6] = data_batch[b, :, 0]/max_room_x new_data_batch[b, :, 7] = data_batch[b, :, 1]/max_room_y new_data_batch[b, :, 8] = data_batch[b, :, 2]/max_room_z #minx = min(data_batch[b, :, 0]) #miny = min(data_batch[b, :, 1]) #data_batch[b, :, 0] -= (minx+block_size/2) #data_batch[b, :, 1] -= (miny+block_size/2) new_data_batch[:, :, 0:6] = data_batch return new_data_batch, label_batch def room2samples_wrapper_normalized(data_label_filename, num_point): if data_label_filename[-3:] == 'txt': data_label = np.loadtxt(data_label_filename) elif data_label_filename[-3:] == 'npy': data_label = np.load(data_label_filename) else: print('Unknown file type! exiting.') exit() return room2samples_plus_normalized(data_label, num_point) # ----------------------------------------------------------------------------- # EXTRACT INSTANCE BBOX FROM ORIGINAL DATA (for detection evaluation) # ----------------------------------------------------------------------------- def collect_bounding_box(anno_path, out_filename): """ Compute bounding boxes from each instance in original dataset files on one room. **We assume the bbox is aligned with XYZ coordinate.** Args: anno_path: path to annotations. e.g. Area_1/office_2/Annotations/ out_filename: path to save instance bounding boxes for that room. each line is x1 y1 z1 x2 y2 z2 label, where (x1,y1,z1) is the point on the diagonal closer to origin Returns: None Note: room points are shifted, the most negative point is now at origin. """ bbox_label_list = [] for f in glob.glob(os.path.join(anno_path, '*.txt')): cls = os.path.basename(f).split('_')[0] if cls not in g_classes: # note: in some room there is 'staris' class.. cls = 'clutter' points = np.loadtxt(f) label = g_class2label[cls] # Compute tightest axis aligned bounding box xyz_min = np.amin(points[:, 0:3], axis=0) xyz_max = np.amax(points[:, 0:3], axis=0) ins_bbox_label = np.expand_dims( np.concatenate([xyz_min, xyz_max, np.array([label])], 0), 0) bbox_label_list.append(ins_bbox_label) bbox_label = np.concatenate(bbox_label_list, 0) room_xyz_min = np.amin(bbox_label[:, 0:3], axis=0) bbox_label[:, 0:3] -= room_xyz_min bbox_label[:, 3:6] -= room_xyz_min fout = open(out_filename, 'w') for i in range(bbox_label.shape[0]): fout.write('%f %f %f %f %f %f %d\n' % \ (bbox_label[i,0], bbox_label[i,1], bbox_label[i,2], bbox_label[i,3], bbox_label[i,4], bbox_label[i,5], bbox_label[i,6])) fout.close() def bbox_label_to_obj(input_filename, out_filename_prefix, easy_view=False): """ Visualization of bounding boxes. Args: input_filename: each line is x1 y1 z1 x2 y2 z2 label out_filename_prefix: OBJ filename prefix, visualize object by g_label2color easy_view: if True, only visualize furniture and floor Returns: output a list of OBJ file and MTL files with the same prefix """ bbox_label = np.loadtxt(input_filename) bbox = bbox_label[:, 0:6] label = bbox_label[:, -1].astype(int) v_cnt = 0 # count vertex ins_cnt = 0 # count instance for i in range(bbox.shape[0]): if easy_view and (label[i] not in g_easy_view_labels): continue obj_filename = out_filename_prefix+'_'+g_classes[label[i]]+'_'+str(ins_cnt)+'.obj' mtl_filename = out_filename_prefix+'_'+g_classes[label[i]]+'_'+str(ins_cnt)+'.mtl' fout_obj = open(obj_filename, 'w') fout_mtl = open(mtl_filename, 'w') fout_obj.write('mtllib %s\n' % (os.path.basename(mtl_filename))) length = bbox[i, 3:6] - bbox[i, 0:3] a = length[0] b = length[1] c = length[2] x = bbox[i, 0] y = bbox[i, 1] z = bbox[i, 2] color = np.array(g_label2color[label[i]], dtype=float) / 255.0 material = 'material%d' % (ins_cnt) fout_obj.write('usemtl %s\n' % (material)) fout_obj.write('v %f %f %f\n' % (x,y,z+c)) fout_obj.write('v %f %f %f\n' % (x,y+b,z+c)) fout_obj.write('v %f %f %f\n' % (x+a,y+b,z+c)) fout_obj.write('v %f %f %f\n' % (x+a,y,z+c)) fout_obj.write('v %f %f %f\n' % (x,y,z)) fout_obj.write('v %f %f %f\n' % (x,y+b,z)) fout_obj.write('v %f %f %f\n' % (x+a,y+b,z)) fout_obj.write('v %f %f %f\n' % (x+a,y,z)) fout_obj.write('g default\n') v_cnt = 0 # for individual box fout_obj.write('f %d %d %d %d\n' % (4+v_cnt, 3+v_cnt, 2+v_cnt, 1+v_cnt)) fout_obj.write('f %d %d %d %d\n' % (1+v_cnt, 2+v_cnt, 6+v_cnt, 5+v_cnt)) fout_obj.write('f %d %d %d %d\n' % (7+v_cnt, 6+v_cnt, 2+v_cnt, 3+v_cnt)) fout_obj.write('f %d %d %d %d\n' % (4+v_cnt, 8+v_cnt, 7+v_cnt, 3+v_cnt)) fout_obj.write('f %d %d %d %d\n' % (5+v_cnt, 8+v_cnt, 4+v_cnt, 1+v_cnt)) fout_obj.write('f %d %d %d %d\n' % (5+v_cnt, 6+v_cnt, 7+v_cnt, 8+v_cnt)) fout_obj.write('\n') fout_mtl.write('newmtl %s\n' % (material)) fout_mtl.write('Kd %f %f %f\n' % (color[0], color[1], color[2])) fout_mtl.write('\n') fout_obj.close() fout_mtl.close() v_cnt += 8 ins_cnt += 1 def bbox_label_to_obj_room(input_filename, out_filename_prefix, easy_view=False, permute=None, center=False, exclude_table=False): """ Visualization of bounding boxes. Args: input_filename: each line is x1 y1 z1 x2 y2 z2 label out_filename_prefix: OBJ filename prefix, visualize object by g_label2color easy_view: if True, only visualize furniture and floor permute: if not None, permute XYZ for rendering, e.g. [0 2 1] center: if True, move obj to have zero origin Returns: output a list of OBJ file and MTL files with the same prefix """ bbox_label = np.loadtxt(input_filename) bbox = bbox_label[:, 0:6] if permute is not None: assert(len(permute)==3) permute = np.array(permute) bbox[:,0:3] = bbox[:,permute] bbox[:,3:6] = bbox[:,permute+3] if center: xyz_max = np.amax(bbox[:,3:6], 0) bbox[:,0:3] -= (xyz_max/2.0) bbox[:,3:6] -= (xyz_max/2.0) bbox /= np.max(xyz_max/2.0) label = bbox_label[:, -1].astype(int) obj_filename = out_filename_prefix+'.obj' mtl_filename = out_filename_prefix+'.mtl' fout_obj = open(obj_filename, 'w') fout_mtl = open(mtl_filename, 'w') fout_obj.write('mtllib %s\n' % (os.path.basename(mtl_filename))) v_cnt = 0 # count vertex ins_cnt = 0 # count instance for i in range(bbox.shape[0]): if easy_view and (label[i] not in g_easy_view_labels): continue if exclude_table and label[i] == g_classes.index('table'): continue length = bbox[i, 3:6] - bbox[i, 0:3] a = length[0] b = length[1] c = length[2] x = bbox[i, 0] y = bbox[i, 1] z = bbox[i, 2] color = np.array(g_label2color[label[i]], dtype=float) / 255.0 material = 'material%d' % (ins_cnt) fout_obj.write('usemtl %s\n' % (material)) fout_obj.write('v %f %f %f\n' % (x,y,z+c)) fout_obj.write('v %f %f %f\n' % (x,y+b,z+c)) fout_obj.write('v %f %f %f\n' % (x+a,y+b,z+c)) fout_obj.write('v %f %f %f\n' % (x+a,y,z+c)) fout_obj.write('v %f %f %f\n' % (x,y,z)) fout_obj.write('v %f %f %f\n' % (x,y+b,z)) fout_obj.write('v %f %f %f\n' % (x+a,y+b,z)) fout_obj.write('v %f %f %f\n' % (x+a,y,z)) fout_obj.write('g default\n') fout_obj.write('f %d %d %d %d\n' % (4+v_cnt, 3+v_cnt, 2+v_cnt, 1+v_cnt)) fout_obj.write('f %d %d %d %d\n' % (1+v_cnt, 2+v_cnt, 6+v_cnt, 5+v_cnt)) fout_obj.write('f %d %d %d %d\n' % (7+v_cnt, 6+v_cnt, 2+v_cnt, 3+v_cnt)) fout_obj.write('f %d %d %d %d\n' % (4+v_cnt, 8+v_cnt, 7+v_cnt, 3+v_cnt)) fout_obj.write('f %d %d %d %d\n' % (5+v_cnt, 8+v_cnt, 4+v_cnt, 1+v_cnt)) fout_obj.write('f %d %d %d %d\n' % (5+v_cnt, 6+v_cnt, 7+v_cnt, 8+v_cnt)) fout_obj.write('\n') fout_mtl.write('newmtl %s\n' % (material)) fout_mtl.write('Kd %f %f %f\n' % (color[0], color[1], color[2])) fout_mtl.write('\n') v_cnt += 8 ins_cnt += 1 fout_obj.close() fout_mtl.close() def collect_point_bounding_box(anno_path, out_filename, file_format): """ Compute bounding boxes from each instance in original dataset files on one room. **We assume the bbox is aligned with XYZ coordinate.** Save both the point XYZRGB and the bounding box for the point's parent element. Args: anno_path: path to annotations. e.g. Area_1/office_2/Annotations/ out_filename: path to save instance bounding boxes for each point, plus the point's XYZRGBL each line is XYZRGBL offsetX offsetY offsetZ a b c, where cx = X+offsetX, cy=X+offsetY, cz=Z+offsetZ where (cx,cy,cz) is center of the box, a,b,c are distances from center to the surfaces of the box, i.e. x1 = cx-a, x2 = cx+a, y1=cy-b etc. file_format: output file format, txt or numpy Returns: None Note: room points are shifted, the most negative point is now at origin. """ point_bbox_list = [] for f in glob.glob(os.path.join(anno_path, '*.txt')): cls = os.path.basename(f).split('_')[0] if cls not in g_classes: # note: in some room there is 'staris' class.. cls = 'clutter' points = np.loadtxt(f) # Nx6 label = g_class2label[cls] # N, # Compute tightest axis aligned bounding box xyz_min = np.amin(points[:, 0:3], axis=0) # 3, xyz_max = np.amax(points[:, 0:3], axis=0) # 3, xyz_center = (xyz_min + xyz_max) / 2 dimension = (xyz_max - xyz_min) / 2 xyz_offsets = xyz_center - points[:,0:3] # Nx3 dimensions = np.ones((points.shape[0],3)) * dimension # Nx3 labels = np.ones((points.shape[0],1)) * label # N point_bbox_list.append(np.concatenate([points, labels, xyz_offsets, dimensions], 1)) # Nx13 point_bbox = np.concatenate(point_bbox_list, 0) # KxNx13 room_xyz_min = np.amin(point_bbox[:, 0:3], axis=0) point_bbox[:, 0:3] -= room_xyz_min if file_format == 'txt': fout = open(out_filename, 'w') for i in range(point_bbox.shape[0]): fout.write('%f %f %f %d %d %d %d %f %f %f %f %f %f\n' % \ (point_bbox[i,0], point_bbox[i,1], point_bbox[i,2], point_bbox[i,3], point_bbox[i,4], point_bbox[i,5], point_bbox[i,6], point_bbox[i,7], point_bbox[i,8], point_bbox[i,9], point_bbox[i,10], point_bbox[i,11], point_bbox[i,12])) fout.close() elif file_format == 'numpy': np.save(out_filename, point_bbox) else: print('ERROR!! Unknown file format: %s, please use txt or numpy.' % \ (file_format)) exit() ================================================ FILE: dgcnn/tensorflow/sem_seg/meta/all_data_label.txt ================================================ Area_1_conferenceRoom_1.npy Area_1_conferenceRoom_2.npy Area_1_copyRoom_1.npy Area_1_hallway_1.npy Area_1_hallway_2.npy Area_1_hallway_3.npy Area_1_hallway_4.npy Area_1_hallway_5.npy Area_1_hallway_6.npy Area_1_hallway_7.npy Area_1_hallway_8.npy Area_1_office_10.npy Area_1_office_11.npy Area_1_office_12.npy Area_1_office_13.npy Area_1_office_14.npy Area_1_office_15.npy Area_1_office_16.npy Area_1_office_17.npy Area_1_office_18.npy Area_1_office_19.npy Area_1_office_1.npy Area_1_office_20.npy Area_1_office_21.npy Area_1_office_22.npy Area_1_office_23.npy Area_1_office_24.npy Area_1_office_25.npy Area_1_office_26.npy Area_1_office_27.npy Area_1_office_28.npy Area_1_office_29.npy Area_1_office_2.npy Area_1_office_30.npy Area_1_office_31.npy Area_1_office_3.npy Area_1_office_4.npy Area_1_office_5.npy Area_1_office_6.npy Area_1_office_7.npy Area_1_office_8.npy Area_1_office_9.npy Area_1_pantry_1.npy Area_1_WC_1.npy Area_2_auditorium_1.npy Area_2_auditorium_2.npy Area_2_conferenceRoom_1.npy Area_2_hallway_10.npy Area_2_hallway_11.npy Area_2_hallway_12.npy Area_2_hallway_1.npy Area_2_hallway_2.npy Area_2_hallway_3.npy Area_2_hallway_4.npy Area_2_hallway_5.npy Area_2_hallway_6.npy Area_2_hallway_7.npy Area_2_hallway_8.npy Area_2_hallway_9.npy Area_2_office_10.npy Area_2_office_11.npy Area_2_office_12.npy Area_2_office_13.npy Area_2_office_14.npy Area_2_office_1.npy Area_2_office_2.npy Area_2_office_3.npy Area_2_office_4.npy Area_2_office_5.npy Area_2_office_6.npy Area_2_office_7.npy Area_2_office_8.npy Area_2_office_9.npy Area_2_storage_1.npy Area_2_storage_2.npy Area_2_storage_3.npy Area_2_storage_4.npy Area_2_storage_5.npy Area_2_storage_6.npy Area_2_storage_7.npy Area_2_storage_8.npy Area_2_storage_9.npy Area_2_WC_1.npy Area_2_WC_2.npy Area_3_conferenceRoom_1.npy Area_3_hallway_1.npy Area_3_hallway_2.npy Area_3_hallway_3.npy Area_3_hallway_4.npy Area_3_hallway_5.npy Area_3_hallway_6.npy Area_3_lounge_1.npy Area_3_lounge_2.npy Area_3_office_10.npy Area_3_office_1.npy Area_3_office_2.npy Area_3_office_3.npy Area_3_office_4.npy Area_3_office_5.npy Area_3_office_6.npy Area_3_office_7.npy Area_3_office_8.npy Area_3_office_9.npy Area_3_storage_1.npy Area_3_storage_2.npy Area_3_WC_1.npy Area_3_WC_2.npy Area_4_conferenceRoom_1.npy Area_4_conferenceRoom_2.npy Area_4_conferenceRoom_3.npy Area_4_hallway_10.npy Area_4_hallway_11.npy Area_4_hallway_12.npy Area_4_hallway_13.npy Area_4_hallway_14.npy Area_4_hallway_1.npy Area_4_hallway_2.npy Area_4_hallway_3.npy Area_4_hallway_4.npy Area_4_hallway_5.npy Area_4_hallway_6.npy Area_4_hallway_7.npy Area_4_hallway_8.npy Area_4_hallway_9.npy Area_4_lobby_1.npy Area_4_lobby_2.npy Area_4_office_10.npy Area_4_office_11.npy Area_4_office_12.npy Area_4_office_13.npy Area_4_office_14.npy Area_4_office_15.npy Area_4_office_16.npy Area_4_office_17.npy Area_4_office_18.npy Area_4_office_19.npy Area_4_office_1.npy Area_4_office_20.npy Area_4_office_21.npy Area_4_office_22.npy Area_4_office_2.npy Area_4_office_3.npy Area_4_office_4.npy Area_4_office_5.npy Area_4_office_6.npy Area_4_office_7.npy Area_4_office_8.npy Area_4_office_9.npy Area_4_storage_1.npy Area_4_storage_2.npy Area_4_storage_3.npy Area_4_storage_4.npy Area_4_WC_1.npy Area_4_WC_2.npy Area_4_WC_3.npy Area_4_WC_4.npy Area_5_conferenceRoom_1.npy Area_5_conferenceRoom_2.npy Area_5_conferenceRoom_3.npy Area_5_hallway_10.npy Area_5_hallway_11.npy Area_5_hallway_12.npy Area_5_hallway_13.npy Area_5_hallway_14.npy Area_5_hallway_15.npy Area_5_hallway_1.npy Area_5_hallway_2.npy Area_5_hallway_3.npy Area_5_hallway_4.npy Area_5_hallway_5.npy Area_5_hallway_6.npy Area_5_hallway_7.npy Area_5_hallway_8.npy Area_5_hallway_9.npy Area_5_lobby_1.npy Area_5_office_10.npy Area_5_office_11.npy Area_5_office_12.npy Area_5_office_13.npy Area_5_office_14.npy Area_5_office_15.npy Area_5_office_16.npy Area_5_office_17.npy Area_5_office_18.npy Area_5_office_19.npy Area_5_office_1.npy Area_5_office_20.npy Area_5_office_21.npy Area_5_office_22.npy Area_5_office_23.npy Area_5_office_24.npy Area_5_office_25.npy Area_5_office_26.npy Area_5_office_27.npy Area_5_office_28.npy Area_5_office_29.npy Area_5_office_2.npy Area_5_office_30.npy Area_5_office_31.npy Area_5_office_32.npy Area_5_office_33.npy Area_5_office_34.npy Area_5_office_35.npy Area_5_office_36.npy Area_5_office_37.npy Area_5_office_38.npy Area_5_office_39.npy Area_5_office_3.npy Area_5_office_40.npy Area_5_office_41.npy Area_5_office_42.npy Area_5_office_4.npy Area_5_office_5.npy Area_5_office_6.npy Area_5_office_7.npy Area_5_office_8.npy Area_5_office_9.npy Area_5_pantry_1.npy Area_5_storage_1.npy Area_5_storage_2.npy Area_5_storage_3.npy Area_5_storage_4.npy Area_5_WC_1.npy Area_5_WC_2.npy Area_6_conferenceRoom_1.npy Area_6_copyRoom_1.npy Area_6_hallway_1.npy Area_6_hallway_2.npy Area_6_hallway_3.npy Area_6_hallway_4.npy Area_6_hallway_5.npy Area_6_hallway_6.npy Area_6_lounge_1.npy Area_6_office_10.npy Area_6_office_11.npy Area_6_office_12.npy Area_6_office_13.npy Area_6_office_14.npy Area_6_office_15.npy Area_6_office_16.npy Area_6_office_17.npy Area_6_office_18.npy Area_6_office_19.npy Area_6_office_1.npy Area_6_office_20.npy Area_6_office_21.npy Area_6_office_22.npy Area_6_office_23.npy Area_6_office_24.npy Area_6_office_25.npy Area_6_office_26.npy Area_6_office_27.npy Area_6_office_28.npy Area_6_office_29.npy Area_6_office_2.npy Area_6_office_30.npy Area_6_office_31.npy Area_6_office_32.npy Area_6_office_33.npy Area_6_office_34.npy Area_6_office_35.npy Area_6_office_36.npy Area_6_office_37.npy Area_6_office_3.npy Area_6_office_4.npy Area_6_office_5.npy Area_6_office_6.npy Area_6_office_7.npy Area_6_office_8.npy Area_6_office_9.npy Area_6_openspace_1.npy Area_6_pantry_1.npy ================================================ FILE: dgcnn/tensorflow/sem_seg/meta/anno_paths.txt ================================================ Area_1/conferenceRoom_1/Annotations Area_1/conferenceRoom_2/Annotations Area_1/copyRoom_1/Annotations Area_1/hallway_1/Annotations Area_1/hallway_2/Annotations Area_1/hallway_3/Annotations Area_1/hallway_4/Annotations Area_1/hallway_5/Annotations Area_1/hallway_6/Annotations Area_1/hallway_7/Annotations Area_1/hallway_8/Annotations Area_1/office_10/Annotations Area_1/office_11/Annotations Area_1/office_12/Annotations Area_1/office_13/Annotations Area_1/office_14/Annotations Area_1/office_15/Annotations Area_1/office_16/Annotations Area_1/office_17/Annotations Area_1/office_18/Annotations Area_1/office_19/Annotations Area_1/office_1/Annotations Area_1/office_20/Annotations Area_1/office_21/Annotations Area_1/office_22/Annotations Area_1/office_23/Annotations Area_1/office_24/Annotations Area_1/office_25/Annotations Area_1/office_26/Annotations Area_1/office_27/Annotations Area_1/office_28/Annotations Area_1/office_29/Annotations Area_1/office_2/Annotations Area_1/office_30/Annotations Area_1/office_31/Annotations Area_1/office_3/Annotations Area_1/office_4/Annotations Area_1/office_5/Annotations Area_1/office_6/Annotations Area_1/office_7/Annotations Area_1/office_8/Annotations Area_1/office_9/Annotations Area_1/pantry_1/Annotations Area_1/WC_1/Annotations Area_2/auditorium_1/Annotations Area_2/auditorium_2/Annotations Area_2/conferenceRoom_1/Annotations Area_2/hallway_10/Annotations Area_2/hallway_11/Annotations Area_2/hallway_12/Annotations Area_2/hallway_1/Annotations Area_2/hallway_2/Annotations Area_2/hallway_3/Annotations Area_2/hallway_4/Annotations Area_2/hallway_5/Annotations Area_2/hallway_6/Annotations Area_2/hallway_7/Annotations Area_2/hallway_8/Annotations Area_2/hallway_9/Annotations Area_2/office_10/Annotations Area_2/office_11/Annotations Area_2/office_12/Annotations Area_2/office_13/Annotations Area_2/office_14/Annotations Area_2/office_1/Annotations Area_2/office_2/Annotations Area_2/office_3/Annotations Area_2/office_4/Annotations Area_2/office_5/Annotations Area_2/office_6/Annotations Area_2/office_7/Annotations Area_2/office_8/Annotations Area_2/office_9/Annotations Area_2/storage_1/Annotations Area_2/storage_2/Annotations Area_2/storage_3/Annotations Area_2/storage_4/Annotations Area_2/storage_5/Annotations Area_2/storage_6/Annotations Area_2/storage_7/Annotations Area_2/storage_8/Annotations Area_2/storage_9/Annotations Area_2/WC_1/Annotations Area_2/WC_2/Annotations Area_3/conferenceRoom_1/Annotations Area_3/hallway_1/Annotations Area_3/hallway_2/Annotations Area_3/hallway_3/Annotations Area_3/hallway_4/Annotations Area_3/hallway_5/Annotations Area_3/hallway_6/Annotations Area_3/lounge_1/Annotations Area_3/lounge_2/Annotations Area_3/office_10/Annotations Area_3/office_1/Annotations Area_3/office_2/Annotations Area_3/office_3/Annotations Area_3/office_4/Annotations Area_3/office_5/Annotations Area_3/office_6/Annotations Area_3/office_7/Annotations Area_3/office_8/Annotations Area_3/office_9/Annotations Area_3/storage_1/Annotations Area_3/storage_2/Annotations Area_3/WC_1/Annotations Area_3/WC_2/Annotations Area_4/conferenceRoom_1/Annotations Area_4/conferenceRoom_2/Annotations Area_4/conferenceRoom_3/Annotations Area_4/hallway_10/Annotations Area_4/hallway_11/Annotations Area_4/hallway_12/Annotations Area_4/hallway_13/Annotations Area_4/hallway_14/Annotations Area_4/hallway_1/Annotations Area_4/hallway_2/Annotations Area_4/hallway_3/Annotations Area_4/hallway_4/Annotations Area_4/hallway_5/Annotations Area_4/hallway_6/Annotations Area_4/hallway_7/Annotations Area_4/hallway_8/Annotations Area_4/hallway_9/Annotations Area_4/lobby_1/Annotations Area_4/lobby_2/Annotations Area_4/office_10/Annotations Area_4/office_11/Annotations Area_4/office_12/Annotations Area_4/office_13/Annotations Area_4/office_14/Annotations Area_4/office_15/Annotations Area_4/office_16/Annotations Area_4/office_17/Annotations Area_4/office_18/Annotations Area_4/office_19/Annotations Area_4/office_1/Annotations Area_4/office_20/Annotations Area_4/office_21/Annotations Area_4/office_22/Annotations Area_4/office_2/Annotations Area_4/office_3/Annotations Area_4/office_4/Annotations Area_4/office_5/Annotations Area_4/office_6/Annotations Area_4/office_7/Annotations Area_4/office_8/Annotations Area_4/office_9/Annotations Area_4/storage_1/Annotations Area_4/storage_2/Annotations Area_4/storage_3/Annotations Area_4/storage_4/Annotations Area_4/WC_1/Annotations Area_4/WC_2/Annotations Area_4/WC_3/Annotations Area_4/WC_4/Annotations Area_5/conferenceRoom_1/Annotations Area_5/conferenceRoom_2/Annotations Area_5/conferenceRoom_3/Annotations Area_5/hallway_10/Annotations Area_5/hallway_11/Annotations Area_5/hallway_12/Annotations Area_5/hallway_13/Annotations Area_5/hallway_14/Annotations Area_5/hallway_15/Annotations Area_5/hallway_1/Annotations Area_5/hallway_2/Annotations Area_5/hallway_3/Annotations Area_5/hallway_4/Annotations Area_5/hallway_5/Annotations Area_5/hallway_6/Annotations Area_5/hallway_7/Annotations Area_5/hallway_8/Annotations Area_5/hallway_9/Annotations Area_5/lobby_1/Annotations Area_5/office_10/Annotations Area_5/office_11/Annotations Area_5/office_12/Annotations Area_5/office_13/Annotations Area_5/office_14/Annotations Area_5/office_15/Annotations Area_5/office_16/Annotations Area_5/office_17/Annotations Area_5/office_18/Annotations Area_5/office_19/Annotations Area_5/office_1/Annotations Area_5/office_20/Annotations Area_5/office_21/Annotations Area_5/office_22/Annotations Area_5/office_23/Annotations Area_5/office_24/Annotations Area_5/office_25/Annotations Area_5/office_26/Annotations Area_5/office_27/Annotations Area_5/office_28/Annotations Area_5/office_29/Annotations Area_5/office_2/Annotations Area_5/office_30/Annotations Area_5/office_31/Annotations Area_5/office_32/Annotations Area_5/office_33/Annotations Area_5/office_34/Annotations Area_5/office_35/Annotations Area_5/office_36/Annotations Area_5/office_37/Annotations Area_5/office_38/Annotations Area_5/office_39/Annotations Area_5/office_3/Annotations Area_5/office_40/Annotations Area_5/office_41/Annotations Area_5/office_42/Annotations Area_5/office_4/Annotations Area_5/office_5/Annotations Area_5/office_6/Annotations Area_5/office_7/Annotations Area_5/office_8/Annotations Area_5/office_9/Annotations Area_5/pantry_1/Annotations Area_5/storage_1/Annotations Area_5/storage_2/Annotations Area_5/storage_3/Annotations Area_5/storage_4/Annotations Area_5/WC_1/Annotations Area_5/WC_2/Annotations Area_6/conferenceRoom_1/Annotations Area_6/copyRoom_1/Annotations Area_6/hallway_1/Annotations Area_6/hallway_2/Annotations Area_6/hallway_3/Annotations Area_6/hallway_4/Annotations Area_6/hallway_5/Annotations Area_6/hallway_6/Annotations Area_6/lounge_1/Annotations Area_6/office_10/Annotations Area_6/office_11/Annotations Area_6/office_12/Annotations Area_6/office_13/Annotations Area_6/office_14/Annotations Area_6/office_15/Annotations Area_6/office_16/Annotations Area_6/office_17/Annotations Area_6/office_18/Annotations Area_6/office_19/Annotations Area_6/office_1/Annotations Area_6/office_20/Annotations Area_6/office_21/Annotations Area_6/office_22/Annotations Area_6/office_23/Annotations Area_6/office_24/Annotations Area_6/office_25/Annotations Area_6/office_26/Annotations Area_6/office_27/Annotations Area_6/office_28/Annotations Area_6/office_29/Annotations Area_6/office_2/Annotations Area_6/office_30/Annotations Area_6/office_31/Annotations Area_6/office_32/Annotations Area_6/office_33/Annotations Area_6/office_34/Annotations Area_6/office_35/Annotations Area_6/office_36/Annotations Area_6/office_37/Annotations Area_6/office_3/Annotations Area_6/office_4/Annotations Area_6/office_5/Annotations Area_6/office_6/Annotations Area_6/office_7/Annotations Area_6/office_8/Annotations Area_6/office_9/Annotations Area_6/openspace_1/Annotations Area_6/pantry_1/Annotations ================================================ FILE: dgcnn/tensorflow/sem_seg/meta/area1_data_label.txt ================================================ data/stanford_indoor3d/Area_1_conferenceRoom_1.npy data/stanford_indoor3d/Area_1_conferenceRoom_2.npy data/stanford_indoor3d/Area_1_copyRoom_1.npy data/stanford_indoor3d/Area_1_hallway_1.npy data/stanford_indoor3d/Area_1_hallway_2.npy data/stanford_indoor3d/Area_1_hallway_3.npy data/stanford_indoor3d/Area_1_hallway_4.npy data/stanford_indoor3d/Area_1_hallway_5.npy data/stanford_indoor3d/Area_1_hallway_6.npy data/stanford_indoor3d/Area_1_hallway_7.npy data/stanford_indoor3d/Area_1_hallway_8.npy data/stanford_indoor3d/Area_1_office_10.npy data/stanford_indoor3d/Area_1_office_11.npy data/stanford_indoor3d/Area_1_office_12.npy data/stanford_indoor3d/Area_1_office_13.npy data/stanford_indoor3d/Area_1_office_14.npy data/stanford_indoor3d/Area_1_office_15.npy data/stanford_indoor3d/Area_1_office_16.npy data/stanford_indoor3d/Area_1_office_17.npy data/stanford_indoor3d/Area_1_office_18.npy data/stanford_indoor3d/Area_1_office_19.npy data/stanford_indoor3d/Area_1_office_1.npy data/stanford_indoor3d/Area_1_office_20.npy data/stanford_indoor3d/Area_1_office_21.npy data/stanford_indoor3d/Area_1_office_22.npy data/stanford_indoor3d/Area_1_office_23.npy data/stanford_indoor3d/Area_1_office_24.npy data/stanford_indoor3d/Area_1_office_25.npy data/stanford_indoor3d/Area_1_office_26.npy data/stanford_indoor3d/Area_1_office_27.npy data/stanford_indoor3d/Area_1_office_28.npy data/stanford_indoor3d/Area_1_office_29.npy data/stanford_indoor3d/Area_1_office_2.npy data/stanford_indoor3d/Area_1_office_30.npy data/stanford_indoor3d/Area_1_office_31.npy data/stanford_indoor3d/Area_1_office_3.npy data/stanford_indoor3d/Area_1_office_4.npy data/stanford_indoor3d/Area_1_office_5.npy data/stanford_indoor3d/Area_1_office_6.npy data/stanford_indoor3d/Area_1_office_7.npy data/stanford_indoor3d/Area_1_office_8.npy data/stanford_indoor3d/Area_1_office_9.npy data/stanford_indoor3d/Area_1_pantry_1.npy data/stanford_indoor3d/Area_1_WC_1.npy ================================================ FILE: dgcnn/tensorflow/sem_seg/meta/area2_data_label.txt ================================================ data/stanford_indoor3d/Area_2_auditorium_1.npy data/stanford_indoor3d/Area_2_auditorium_2.npy data/stanford_indoor3d/Area_2_conferenceRoom_1.npy data/stanford_indoor3d/Area_2_hallway_10.npy data/stanford_indoor3d/Area_2_hallway_11.npy data/stanford_indoor3d/Area_2_hallway_12.npy data/stanford_indoor3d/Area_2_hallway_1.npy data/stanford_indoor3d/Area_2_hallway_2.npy data/stanford_indoor3d/Area_2_hallway_3.npy data/stanford_indoor3d/Area_2_hallway_4.npy data/stanford_indoor3d/Area_2_hallway_5.npy data/stanford_indoor3d/Area_2_hallway_6.npy data/stanford_indoor3d/Area_2_hallway_7.npy data/stanford_indoor3d/Area_2_hallway_8.npy data/stanford_indoor3d/Area_2_hallway_9.npy data/stanford_indoor3d/Area_2_office_10.npy data/stanford_indoor3d/Area_2_office_11.npy data/stanford_indoor3d/Area_2_office_12.npy data/stanford_indoor3d/Area_2_office_13.npy data/stanford_indoor3d/Area_2_office_14.npy data/stanford_indoor3d/Area_2_office_1.npy data/stanford_indoor3d/Area_2_office_2.npy data/stanford_indoor3d/Area_2_office_3.npy data/stanford_indoor3d/Area_2_office_4.npy data/stanford_indoor3d/Area_2_office_5.npy data/stanford_indoor3d/Area_2_office_6.npy data/stanford_indoor3d/Area_2_office_7.npy data/stanford_indoor3d/Area_2_office_8.npy data/stanford_indoor3d/Area_2_office_9.npy data/stanford_indoor3d/Area_2_storage_1.npy data/stanford_indoor3d/Area_2_storage_2.npy data/stanford_indoor3d/Area_2_storage_3.npy data/stanford_indoor3d/Area_2_storage_4.npy data/stanford_indoor3d/Area_2_storage_5.npy data/stanford_indoor3d/Area_2_storage_6.npy data/stanford_indoor3d/Area_2_storage_7.npy data/stanford_indoor3d/Area_2_storage_8.npy data/stanford_indoor3d/Area_2_storage_9.npy data/stanford_indoor3d/Area_2_WC_1.npy data/stanford_indoor3d/Area_2_WC_2.npy ================================================ FILE: dgcnn/tensorflow/sem_seg/meta/area3_data_label.txt ================================================ data/stanford_indoor3d/Area_3_conferenceRoom_1.npy data/stanford_indoor3d/Area_3_hallway_1.npy data/stanford_indoor3d/Area_3_hallway_2.npy data/stanford_indoor3d/Area_3_hallway_3.npy data/stanford_indoor3d/Area_3_hallway_4.npy data/stanford_indoor3d/Area_3_hallway_5.npy data/stanford_indoor3d/Area_3_hallway_6.npy data/stanford_indoor3d/Area_3_lounge_1.npy data/stanford_indoor3d/Area_3_lounge_2.npy data/stanford_indoor3d/Area_3_office_10.npy data/stanford_indoor3d/Area_3_office_1.npy data/stanford_indoor3d/Area_3_office_2.npy data/stanford_indoor3d/Area_3_office_3.npy data/stanford_indoor3d/Area_3_office_4.npy data/stanford_indoor3d/Area_3_office_5.npy data/stanford_indoor3d/Area_3_office_6.npy data/stanford_indoor3d/Area_3_office_7.npy data/stanford_indoor3d/Area_3_office_8.npy data/stanford_indoor3d/Area_3_office_9.npy data/stanford_indoor3d/Area_3_storage_1.npy data/stanford_indoor3d/Area_3_storage_2.npy data/stanford_indoor3d/Area_3_WC_1.npy data/stanford_indoor3d/Area_3_WC_2.npy ================================================ FILE: dgcnn/tensorflow/sem_seg/meta/area4_data_label.txt ================================================ data/stanford_indoor3d/Area_4_conferenceRoom_1.npy data/stanford_indoor3d/Area_4_conferenceRoom_2.npy data/stanford_indoor3d/Area_4_conferenceRoom_3.npy data/stanford_indoor3d/Area_4_hallway_10.npy data/stanford_indoor3d/Area_4_hallway_11.npy data/stanford_indoor3d/Area_4_hallway_12.npy data/stanford_indoor3d/Area_4_hallway_13.npy data/stanford_indoor3d/Area_4_hallway_14.npy data/stanford_indoor3d/Area_4_hallway_1.npy data/stanford_indoor3d/Area_4_hallway_2.npy data/stanford_indoor3d/Area_4_hallway_3.npy data/stanford_indoor3d/Area_4_hallway_4.npy data/stanford_indoor3d/Area_4_hallway_5.npy data/stanford_indoor3d/Area_4_hallway_6.npy data/stanford_indoor3d/Area_4_hallway_7.npy data/stanford_indoor3d/Area_4_hallway_8.npy data/stanford_indoor3d/Area_4_hallway_9.npy data/stanford_indoor3d/Area_4_lobby_1.npy data/stanford_indoor3d/Area_4_lobby_2.npy data/stanford_indoor3d/Area_4_office_10.npy data/stanford_indoor3d/Area_4_office_11.npy data/stanford_indoor3d/Area_4_office_12.npy data/stanford_indoor3d/Area_4_office_13.npy data/stanford_indoor3d/Area_4_office_14.npy data/stanford_indoor3d/Area_4_office_15.npy data/stanford_indoor3d/Area_4_office_16.npy data/stanford_indoor3d/Area_4_office_17.npy data/stanford_indoor3d/Area_4_office_18.npy data/stanford_indoor3d/Area_4_office_19.npy data/stanford_indoor3d/Area_4_office_1.npy data/stanford_indoor3d/Area_4_office_20.npy data/stanford_indoor3d/Area_4_office_21.npy data/stanford_indoor3d/Area_4_office_22.npy data/stanford_indoor3d/Area_4_office_2.npy data/stanford_indoor3d/Area_4_office_3.npy data/stanford_indoor3d/Area_4_office_4.npy data/stanford_indoor3d/Area_4_office_5.npy data/stanford_indoor3d/Area_4_office_6.npy data/stanford_indoor3d/Area_4_office_7.npy data/stanford_indoor3d/Area_4_office_8.npy data/stanford_indoor3d/Area_4_office_9.npy data/stanford_indoor3d/Area_4_storage_1.npy data/stanford_indoor3d/Area_4_storage_2.npy data/stanford_indoor3d/Area_4_storage_3.npy data/stanford_indoor3d/Area_4_storage_4.npy data/stanford_indoor3d/Area_4_WC_1.npy data/stanford_indoor3d/Area_4_WC_2.npy data/stanford_indoor3d/Area_4_WC_3.npy data/stanford_indoor3d/Area_4_WC_4.npy ================================================ FILE: dgcnn/tensorflow/sem_seg/meta/area5_data_label.txt ================================================ data/stanford_indoor3d/Area_5_conferenceRoom_1.npy data/stanford_indoor3d/Area_5_conferenceRoom_2.npy data/stanford_indoor3d/Area_5_conferenceRoom_3.npy data/stanford_indoor3d/Area_5_hallway_10.npy data/stanford_indoor3d/Area_5_hallway_11.npy data/stanford_indoor3d/Area_5_hallway_12.npy data/stanford_indoor3d/Area_5_hallway_13.npy data/stanford_indoor3d/Area_5_hallway_14.npy data/stanford_indoor3d/Area_5_hallway_15.npy data/stanford_indoor3d/Area_5_hallway_1.npy data/stanford_indoor3d/Area_5_hallway_2.npy data/stanford_indoor3d/Area_5_hallway_3.npy data/stanford_indoor3d/Area_5_hallway_4.npy data/stanford_indoor3d/Area_5_hallway_5.npy data/stanford_indoor3d/Area_5_hallway_6.npy data/stanford_indoor3d/Area_5_hallway_7.npy data/stanford_indoor3d/Area_5_hallway_8.npy data/stanford_indoor3d/Area_5_hallway_9.npy data/stanford_indoor3d/Area_5_lobby_1.npy data/stanford_indoor3d/Area_5_office_10.npy data/stanford_indoor3d/Area_5_office_11.npy data/stanford_indoor3d/Area_5_office_12.npy data/stanford_indoor3d/Area_5_office_13.npy data/stanford_indoor3d/Area_5_office_14.npy data/stanford_indoor3d/Area_5_office_15.npy data/stanford_indoor3d/Area_5_office_16.npy data/stanford_indoor3d/Area_5_office_17.npy data/stanford_indoor3d/Area_5_office_18.npy data/stanford_indoor3d/Area_5_office_19.npy data/stanford_indoor3d/Area_5_office_1.npy data/stanford_indoor3d/Area_5_office_20.npy data/stanford_indoor3d/Area_5_office_21.npy data/stanford_indoor3d/Area_5_office_22.npy data/stanford_indoor3d/Area_5_office_23.npy data/stanford_indoor3d/Area_5_office_24.npy data/stanford_indoor3d/Area_5_office_25.npy data/stanford_indoor3d/Area_5_office_26.npy data/stanford_indoor3d/Area_5_office_27.npy data/stanford_indoor3d/Area_5_office_28.npy data/stanford_indoor3d/Area_5_office_29.npy data/stanford_indoor3d/Area_5_office_2.npy data/stanford_indoor3d/Area_5_office_30.npy data/stanford_indoor3d/Area_5_office_31.npy data/stanford_indoor3d/Area_5_office_32.npy data/stanford_indoor3d/Area_5_office_33.npy data/stanford_indoor3d/Area_5_office_34.npy data/stanford_indoor3d/Area_5_office_35.npy data/stanford_indoor3d/Area_5_office_36.npy data/stanford_indoor3d/Area_5_office_37.npy data/stanford_indoor3d/Area_5_office_38.npy data/stanford_indoor3d/Area_5_office_39.npy data/stanford_indoor3d/Area_5_office_3.npy data/stanford_indoor3d/Area_5_office_40.npy data/stanford_indoor3d/Area_5_office_41.npy data/stanford_indoor3d/Area_5_office_42.npy data/stanford_indoor3d/Area_5_office_4.npy data/stanford_indoor3d/Area_5_office_5.npy data/stanford_indoor3d/Area_5_office_6.npy data/stanford_indoor3d/Area_5_office_7.npy data/stanford_indoor3d/Area_5_office_8.npy data/stanford_indoor3d/Area_5_office_9.npy data/stanford_indoor3d/Area_5_pantry_1.npy data/stanford_indoor3d/Area_5_storage_1.npy data/stanford_indoor3d/Area_5_storage_2.npy data/stanford_indoor3d/Area_5_storage_3.npy data/stanford_indoor3d/Area_5_storage_4.npy data/stanford_indoor3d/Area_5_WC_1.npy data/stanford_indoor3d/Area_5_WC_2.npy ================================================ FILE: dgcnn/tensorflow/sem_seg/meta/area6_data_label.txt ================================================ data/stanford_indoor3d/Area_6_conferenceRoom_1.npy data/stanford_indoor3d/Area_6_copyRoom_1.npy data/stanford_indoor3d/Area_6_hallway_1.npy data/stanford_indoor3d/Area_6_hallway_2.npy data/stanford_indoor3d/Area_6_hallway_3.npy data/stanford_indoor3d/Area_6_hallway_4.npy data/stanford_indoor3d/Area_6_hallway_5.npy data/stanford_indoor3d/Area_6_hallway_6.npy data/stanford_indoor3d/Area_6_lounge_1.npy data/stanford_indoor3d/Area_6_office_10.npy data/stanford_indoor3d/Area_6_office_11.npy data/stanford_indoor3d/Area_6_office_12.npy data/stanford_indoor3d/Area_6_office_13.npy data/stanford_indoor3d/Area_6_office_14.npy data/stanford_indoor3d/Area_6_office_15.npy data/stanford_indoor3d/Area_6_office_16.npy data/stanford_indoor3d/Area_6_office_17.npy data/stanford_indoor3d/Area_6_office_18.npy data/stanford_indoor3d/Area_6_office_19.npy data/stanford_indoor3d/Area_6_office_1.npy data/stanford_indoor3d/Area_6_office_20.npy data/stanford_indoor3d/Area_6_office_21.npy data/stanford_indoor3d/Area_6_office_22.npy data/stanford_indoor3d/Area_6_office_23.npy data/stanford_indoor3d/Area_6_office_24.npy data/stanford_indoor3d/Area_6_office_25.npy data/stanford_indoor3d/Area_6_office_26.npy data/stanford_indoor3d/Area_6_office_27.npy data/stanford_indoor3d/Area_6_office_28.npy data/stanford_indoor3d/Area_6_office_29.npy data/stanford_indoor3d/Area_6_office_2.npy data/stanford_indoor3d/Area_6_office_30.npy data/stanford_indoor3d/Area_6_office_31.npy data/stanford_indoor3d/Area_6_office_32.npy data/stanford_indoor3d/Area_6_office_33.npy data/stanford_indoor3d/Area_6_office_34.npy data/stanford_indoor3d/Area_6_office_35.npy data/stanford_indoor3d/Area_6_office_36.npy data/stanford_indoor3d/Area_6_office_37.npy data/stanford_indoor3d/Area_6_office_3.npy data/stanford_indoor3d/Area_6_office_4.npy data/stanford_indoor3d/Area_6_office_5.npy data/stanford_indoor3d/Area_6_office_6.npy data/stanford_indoor3d/Area_6_office_7.npy data/stanford_indoor3d/Area_6_office_8.npy data/stanford_indoor3d/Area_6_office_9.npy data/stanford_indoor3d/Area_6_openspace_1.npy data/stanford_indoor3d/Area_6_pantry_1.npy ================================================ FILE: dgcnn/tensorflow/sem_seg/meta/class_names.txt ================================================ ceiling floor wall beam column window door table chair sofa bookcase board clutter ================================================ FILE: dgcnn/tensorflow/sem_seg/model.py ================================================ import tensorflow as tf import math import time import numpy as np import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = os.path.dirname(BASE_DIR) sys.path.append(os.path.join(ROOT_DIR, 'utils')) sys.path.append(os.path.join(BASE_DIR, '../models')) import tf_util def placeholder_inputs(batch_size, num_point): pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 9)) labels_pl = tf.placeholder(tf.int32, shape=(batch_size, num_point)) return pointclouds_pl, labels_pl def get_model(point_cloud, is_training, bn_decay=None): """ ConvNet baseline, input is BxNx9 gray image """ batch_size = point_cloud.get_shape()[0].value num_point = point_cloud.get_shape()[1].value input_image = tf.expand_dims(point_cloud, -1) k = 20 adj = tf_util.pairwise_distance(point_cloud[:, :, 6:]) nn_idx = tf_util.knn(adj, k=k) # (batch, num_points, k) edge_feature = tf_util.get_edge_feature(input_image, nn_idx=nn_idx, k=k) out1 = tf_util.conv2d(edge_feature, 64, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, weight_decay=weight_decay, scope='adj_conv1', bn_decay=bn_decay, is_dist=True) out2 = tf_util.conv2d(out1, 64, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, weight_decay=weight_decay, scope='adj_conv2', bn_decay=bn_decay, is_dist=True) net_1 = tf.reduce_max(out2, axis=-2, keep_dims=True) adj = tf_util.pairwise_distance(net_1) nn_idx = tf_util.knn(adj, k=k) edge_feature = tf_util.get_edge_feature(net_1, nn_idx=nn_idx, k=k) out3 = tf_util.conv2d(edge_feature, 64, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, weight_decay=weight_decay, scope='adj_conv3', bn_decay=bn_decay, is_dist=True) out4 = tf_util.conv2d(out3, 64, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, weight_decay=weight_decay, scope='adj_conv4', bn_decay=bn_decay, is_dist=True) net_2 = tf.reduce_max(out4, axis=-2, keep_dims=True) adj = tf_util.pairwise_distance(net_2) nn_idx = tf_util.knn(adj, k=k) edge_feature = tf_util.get_edge_feature(net_2, nn_idx=nn_idx, k=k) out5 = tf_util.conv2d(edge_feature, 64, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, weight_decay=weight_decay, scope='adj_conv5', bn_decay=bn_decay, is_dist=True) # out6 = tf_util.conv2d(out5, 64, [1,1], # padding='VALID', stride=[1,1], # bn=True, is_training=is_training, weight_decay=weight_decay, # scope='adj_conv6', bn_decay=bn_decay, is_dist=True) net_3 = tf.reduce_max(out5, axis=-2, keep_dims=True) out7 = tf_util.conv2d(tf.concat([net_1, net_2, net_3], axis=-1), 1024, [1, 1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='adj_conv7', bn_decay=bn_decay, is_dist=True) out_max = tf_util.max_pool2d(out7, [num_point, 1], padding='VALID', scope='maxpool') expand = tf.tile(out_max, [1, num_point, 1, 1]) concat = tf.concat(axis=3, values=[expand, net_1, net_2, net_3]) # CONV net = tf_util.conv2d(concat, 512, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='seg/conv1', is_dist=True) net = tf_util.conv2d(net, 256, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='seg/conv2', is_dist=True) net = tf_util.dropout(net, keep_prob=0.7, is_training=is_training, scope='dp1') net = tf_util.conv2d(net, 13, [1,1], padding='VALID', stride=[1,1], activation_fn=None, scope='seg/conv3', is_dist=True) net = tf.squeeze(net, [2]) return net def get_loss(pred, label): """ pred: B,N,13; label: B,N """ loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label) return tf.reduce_mean(loss) ================================================ FILE: dgcnn/tensorflow/sem_seg/test_job.sh ================================================ python batch_inference.py --model_path log1/epoch_60.ckpt --dump_dir log1/dump --output_filelist log1/output_filelist.txt --room_data_filelist meta/area1_data_label.txt python batch_inference.py --model_path log2/epoch_60.ckpt --dump_dir log2/dump --output_filelist log2/output_filelist.txt --room_data_filelist meta/area2_data_label.txt python batch_inference.py --model_path log3/epoch_60.ckpt --dump_dir log3/dump --output_filelist log3/output_filelist.txt --room_data_filelist meta/area3_data_label.txt python batch_inference.py --model_path log4/epoch_60.ckpt --dump_dir log4/dump --output_filelist log4/output_filelist.txt --room_data_filelist meta/area4_data_label.txt python batch_inference.py --model_path log5/epoch_60.ckpt --dump_dir log5/dump --output_filelist log5/output_filelist.txt --room_data_filelist meta/area5_data_label.txt python batch_inference.py --model_path log6/epoch_60.ckpt --dump_dir log6/dump --output_filelist log6/output_filelist.txt --room_data_filelist meta/area6_data_label.txt ================================================ FILE: dgcnn/tensorflow/sem_seg/train.py ================================================ import argparse import math import h5py import numpy as np import tensorflow as tf import socket import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = os.path.dirname(BASE_DIR) sys.path.append(BASE_DIR) sys.path.append(ROOT_DIR) sys.path.append(os.path.join(ROOT_DIR, 'utils')) import provider import tf_util from model import * parser = argparse.ArgumentParser() parser.add_argument('--num_gpu', type=int, default=2, help='the number of GPUs to use [default: 2]') parser.add_argument('--log_dir', default='log', help='Log dir [default: log]') parser.add_argument('--num_point', type=int, default=4096, help='Point number [default: 4096]') parser.add_argument('--max_epoch', type=int, default=101, help='Epoch to run [default: 50]') parser.add_argument('--batch_size', type=int, default=12, help='Batch Size during training for each GPU [default: 24]') parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]') parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]') parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]') parser.add_argument('--decay_step', type=int, default=300000, help='Decay step for lr decay [default: 300000]') parser.add_argument('--decay_rate', type=float, default=0.5, help='Decay rate for lr decay [default: 0.5]') parser.add_argument('--test_area', type=int, default=6, help='Which area to use for test, option: 1-6 [default: 6]') FLAGS = parser.parse_args() TOWER_NAME = 'tower' BATCH_SIZE = FLAGS.batch_size NUM_POINT = FLAGS.num_point MAX_EPOCH = FLAGS.max_epoch NUM_POINT = FLAGS.num_point BASE_LEARNING_RATE = FLAGS.learning_rate MOMENTUM = FLAGS.momentum OPTIMIZER = FLAGS.optimizer DECAY_STEP = FLAGS.decay_step DECAY_RATE = FLAGS.decay_rate LOG_DIR = FLAGS.log_dir if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR) os.system('cp model.py %s' % (LOG_DIR)) os.system('cp train.py %s' % (LOG_DIR)) LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w') LOG_FOUT.write(str(FLAGS)+'\n') MAX_NUM_POINT = 4096 NUM_CLASSES = 13 BN_INIT_DECAY = 0.5 BN_DECAY_DECAY_RATE = 0.5 BN_DECAY_DECAY_STEP = float(DECAY_STEP) BN_DECAY_CLIP = 0.99 HOSTNAME = socket.gethostname() ALL_FILES = provider.getDataFiles('indoor3d_sem_seg_hdf5_data/all_files.txt') room_filelist = [line.rstrip() for line in open('indoor3d_sem_seg_hdf5_data/room_filelist.txt')] print len(room_filelist) # Load ALL data data_batch_list = [] label_batch_list = [] for h5_filename in ALL_FILES: data_batch, label_batch = provider.loadDataFile(h5_filename) data_batch_list.append(data_batch) label_batch_list.append(label_batch) data_batches = np.concatenate(data_batch_list, 0) label_batches = np.concatenate(label_batch_list, 0) print(data_batches.shape) print(label_batches.shape) test_area = 'Area_'+str(FLAGS.test_area) train_idxs = [] test_idxs = [] for i,room_name in enumerate(room_filelist): if test_area in room_name: test_idxs.append(i) else: train_idxs.append(i) train_data = data_batches[train_idxs,...] train_label = label_batches[train_idxs] test_data = data_batches[test_idxs,...] test_label = label_batches[test_idxs] print(train_data.shape, train_label.shape) print(test_data.shape, test_label.shape) def log_string(out_str): LOG_FOUT.write(out_str+'\n') LOG_FOUT.flush() print(out_str) def get_learning_rate(batch): learning_rate = tf.train.exponential_decay( BASE_LEARNING_RATE, # Base learning rate. batch * BATCH_SIZE, # Current index into the dataset. DECAY_STEP, # Decay step. DECAY_RATE, # Decay rate. staircase=True) learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE!! return learning_rate def get_bn_decay(batch): bn_momentum = tf.train.exponential_decay( BN_INIT_DECAY, batch*BATCH_SIZE, BN_DECAY_DECAY_STEP, BN_DECAY_DECAY_RATE, staircase=True) bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum) return bn_decay def average_gradients(tower_grads): """Calculate average gradient for each shared variable across all towers. Note that this function provides a synchronization point across all towers. Args: tower_grads: List of lists of (gradient, variable) tuples. The outer list is over individual gradients. The inner list is over the gradient calculation for each tower. Returns: List of pairs of (gradient, variable) where the gradient has been averaged across all towers. """ average_grads = [] for grad_and_vars in zip(*tower_grads): # Note that each grad_and_vars looks like the following: # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) grads = [] for g, _ in grad_and_vars: expanded_g = tf.expand_dims(g, 0) grads.append(expanded_g) # Average over the 'tower' dimension. grad = tf.concat(grads, 0) grad = tf.reduce_mean(grad, 0) # Keep in mind that the Variables are redundant because they are shared # across towers. So .. we will just return the first tower's pointer to # the Variable. v = grad_and_vars[0][1] grad_and_var = (grad, v) average_grads.append(grad_and_var) return average_grads def train(): with tf.Graph().as_default(), tf.device('/cpu:0'): batch = tf.Variable(0, trainable=False) bn_decay = get_bn_decay(batch) tf.summary.scalar('bn_decay', bn_decay) learning_rate = get_learning_rate(batch) tf.summary.scalar('learning_rate', learning_rate) trainer = tf.train.AdamOptimizer(learning_rate) tower_grads = [] pointclouds_phs = [] labels_phs = [] is_training_phs =[] with tf.variable_scope(tf.get_variable_scope()): for i in xrange(FLAGS.num_gpu): with tf.device('/gpu:%d' % i): with tf.name_scope('%s_%d' % (TOWER_NAME, i)) as scope: pointclouds_pl, labels_pl = placeholder_inputs(BATCH_SIZE, NUM_POINT) is_training_pl = tf.placeholder(tf.bool, shape=()) pointclouds_phs.append(pointclouds_pl) labels_phs.append(labels_pl) is_training_phs.append(is_training_pl) pred = get_model(pointclouds_phs[-1], is_training_phs[-1], bn_decay=bn_decay) loss = get_loss(pred, labels_phs[-1]) tf.summary.scalar('loss', loss) correct = tf.equal(tf.argmax(pred, 2), tf.to_int64(labels_phs[-1])) accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE*NUM_POINT) tf.summary.scalar('accuracy', accuracy) tf.get_variable_scope().reuse_variables() grads = trainer.compute_gradients(loss) tower_grads.append(grads) grads = average_gradients(tower_grads) train_op = trainer.apply_gradients(grads, global_step=batch) saver = tf.train.Saver(tf.global_variables(), sharded=True, max_to_keep=10) # Create a session config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True sess = tf.Session(config=config) # Add summary writers merged = tf.summary.merge_all() train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'), sess.graph) test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test')) # Init variables for two GPUs init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) sess.run(init) ops = {'pointclouds_phs': pointclouds_phs, 'labels_phs': labels_phs, 'is_training_phs': is_training_phs, 'pred': pred, 'loss': loss, 'train_op': train_op, 'merged': merged, 'step': batch} for epoch in range(MAX_EPOCH): log_string('**** EPOCH %03d ****' % (epoch)) sys.stdout.flush() train_one_epoch(sess, ops, train_writer) # Save the variables to disk. if epoch % 10 == 0: save_path = saver.save(sess, os.path.join(LOG_DIR,'epoch_' + str(epoch)+'.ckpt')) log_string("Model saved in file: %s" % save_path) def train_one_epoch(sess, ops, train_writer): """ ops: dict mapping from string to tf ops """ is_training = True log_string('----') current_data, current_label, _ = provider.shuffle_data(train_data[:,0:NUM_POINT,:], train_label) file_size = current_data.shape[0] num_batches = file_size // (FLAGS.num_gpu * BATCH_SIZE) total_correct = 0 total_seen = 0 loss_sum = 0 for batch_idx in range(num_batches): if batch_idx % 100 == 0: print('Current batch/total batch num: %d/%d'%(batch_idx,num_batches)) start_idx_0 = batch_idx * BATCH_SIZE end_idx_0 = (batch_idx+1) * BATCH_SIZE start_idx_1 = (batch_idx+1) * BATCH_SIZE end_idx_1 = (batch_idx+2) * BATCH_SIZE feed_dict = {ops['pointclouds_phs'][0]: current_data[start_idx_0:end_idx_0, :, :], ops['pointclouds_phs'][1]: current_data[start_idx_1:end_idx_1, :, :], ops['labels_phs'][0]: current_label[start_idx_0:end_idx_0], ops['labels_phs'][1]: current_label[start_idx_1:end_idx_1], ops['is_training_phs'][0]: is_training, ops['is_training_phs'][1]: is_training} summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'], ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict) train_writer.add_summary(summary, step) pred_val = np.argmax(pred_val, 2) correct = np.sum(pred_val == current_label[start_idx_1:end_idx_1]) total_correct += correct total_seen += (BATCH_SIZE*NUM_POINT) loss_sum += loss_val log_string('mean loss: %f' % (loss_sum / float(num_batches))) log_string('accuracy: %f' % (total_correct / float(total_seen))) if __name__ == "__main__": train() LOG_FOUT.close() ================================================ FILE: dgcnn/tensorflow/sem_seg/train_job.sh ================================================ python train.py --log_dir log1 --test_area 1 python train.py --log_dir log2 --test_area 2 python train.py --log_dir log3 --test_area 3 python train.py --log_dir log4 --test_area 4 python train.py --log_dir log5 --test_area 5 python train.py --log_dir log6 --test_area 6 ================================================ FILE: dgcnn/tensorflow/train.py ================================================ import argparse import math import h5py import numpy as np import tensorflow as tf import socket import importlib import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) sys.path.append(os.path.join(BASE_DIR, 'models')) sys.path.append(os.path.join(BASE_DIR, 'utils')) import provider import tf_util parser = argparse.ArgumentParser() parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]') parser.add_argument('--model', default='dgcnn', help='Model name: dgcnn') parser.add_argument('--log_dir', default='log', help='Log dir [default: log]') parser.add_argument('--num_point', type=int, default=1024, help='Point Number [256/512/1024/2048] [default: 1024]') parser.add_argument('--max_epoch', type=int, default=250, help='Epoch to run [default: 250]') parser.add_argument('--batch_size', type=int, default=32, help='Batch Size during training [default: 32]') parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]') parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]') parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]') parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]') parser.add_argument('--decay_rate', type=float, default=0.7, help='Decay rate for lr decay [default: 0.8]') FLAGS = parser.parse_args() BATCH_SIZE = FLAGS.batch_size NUM_POINT = FLAGS.num_point MAX_EPOCH = FLAGS.max_epoch BASE_LEARNING_RATE = FLAGS.learning_rate GPU_INDEX = FLAGS.gpu MOMENTUM = FLAGS.momentum OPTIMIZER = FLAGS.optimizer DECAY_STEP = FLAGS.decay_step DECAY_RATE = FLAGS.decay_rate MODEL = importlib.import_module(FLAGS.model) # import network module MODEL_FILE = os.path.join(BASE_DIR, 'models', FLAGS.model+'.py') LOG_DIR = FLAGS.log_dir if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR) os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w') LOG_FOUT.write(str(FLAGS)+'\n') MAX_NUM_POINT = 2048 NUM_CLASSES = 40 BN_INIT_DECAY = 0.5 BN_DECAY_DECAY_RATE = 0.5 BN_DECAY_DECAY_STEP = float(DECAY_STEP) BN_DECAY_CLIP = 0.99 HOSTNAME = socket.gethostname() # ModelNet40 official train/test split TRAIN_FILES = provider.getDataFiles( \ os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/train_files.txt')) TEST_FILES = provider.getDataFiles(\ os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/test_files.txt')) def log_string(out_str): LOG_FOUT.write(out_str+'\n') LOG_FOUT.flush() print(out_str) def get_learning_rate(batch): learning_rate = tf.train.exponential_decay( BASE_LEARNING_RATE, # Base learning rate. batch * BATCH_SIZE, # Current index into the dataset. DECAY_STEP, # Decay step. DECAY_RATE, # Decay rate. staircase=True) learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE! return learning_rate def get_bn_decay(batch): bn_momentum = tf.train.exponential_decay( BN_INIT_DECAY, batch*BATCH_SIZE, BN_DECAY_DECAY_STEP, BN_DECAY_DECAY_RATE, staircase=True) bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum) return bn_decay def train(): with tf.Graph().as_default(): with tf.device('/gpu:'+str(GPU_INDEX)): pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT) is_training_pl = tf.placeholder(tf.bool, shape=()) print(is_training_pl) # Note the global_step=batch parameter to minimize. # That tells the optimizer to helpfully increment the 'batch' parameter for you every time it trains. batch = tf.Variable(0) bn_decay = get_bn_decay(batch) tf.summary.scalar('bn_decay', bn_decay) # Get model and loss pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl, bn_decay=bn_decay) loss = MODEL.get_loss(pred, labels_pl, end_points) tf.summary.scalar('loss', loss) correct = tf.equal(tf.argmax(pred, 1), tf.to_int64(labels_pl)) accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE) tf.summary.scalar('accuracy', accuracy) # Get training operator learning_rate = get_learning_rate(batch) tf.summary.scalar('learning_rate', learning_rate) if OPTIMIZER == 'momentum': optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM) elif OPTIMIZER == 'adam': optimizer = tf.train.AdamOptimizer(learning_rate) train_op = optimizer.minimize(loss, global_step=batch) # Add ops to save and restore all the variables. saver = tf.train.Saver() # Create a session config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True config.log_device_placement = False sess = tf.Session(config=config) # Add summary writers #merged = tf.merge_all_summaries() merged = tf.summary.merge_all() train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'), sess.graph) test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test')) # Init variables init = tf.global_variables_initializer() # To fix the bug introduced in TF 0.12.1 as in # http://stackoverflow.com/questions/41543774/invalidargumenterror-for-tensor-bool-tensorflow-0-12-1 #sess.run(init) sess.run(init, {is_training_pl: True}) ops = {'pointclouds_pl': pointclouds_pl, 'labels_pl': labels_pl, 'is_training_pl': is_training_pl, 'pred': pred, 'loss': loss, 'train_op': train_op, 'merged': merged, 'step': batch} for epoch in range(MAX_EPOCH): log_string('**** EPOCH %03d ****' % (epoch)) sys.stdout.flush() train_one_epoch(sess, ops, train_writer) eval_one_epoch(sess, ops, test_writer) # Save the variables to disk. if epoch % 10 == 0: save_path = saver.save(sess, os.path.join(LOG_DIR, "model.ckpt")) log_string("Model saved in file: %s" % save_path) def train_one_epoch(sess, ops, train_writer): """ ops: dict mapping from string to tf ops """ is_training = True # Shuffle train files train_file_idxs = np.arange(0, len(TRAIN_FILES)) np.random.shuffle(train_file_idxs) for fn in range(len(TRAIN_FILES)): log_string('----' + str(fn) + '-----') current_data, current_label = provider.loadDataFile(TRAIN_FILES[train_file_idxs[fn]]) current_data = current_data[:,0:NUM_POINT,:] current_data, current_label, _ = provider.shuffle_data(current_data, np.squeeze(current_label)) current_label = np.squeeze(current_label) file_size = current_data.shape[0] num_batches = file_size // BATCH_SIZE total_correct = 0 total_seen = 0 loss_sum = 0 for batch_idx in range(num_batches): start_idx = batch_idx * BATCH_SIZE end_idx = (batch_idx+1) * BATCH_SIZE # Augment batched point clouds by rotation and jittering rotated_data = provider.rotate_point_cloud(current_data[start_idx:end_idx, :, :]) jittered_data = provider.jitter_point_cloud(rotated_data) jittered_data = provider.random_scale_point_cloud(jittered_data) jittered_data = provider.rotate_perturbation_point_cloud(jittered_data) jittered_data = provider.shift_point_cloud(jittered_data) feed_dict = {ops['pointclouds_pl']: jittered_data, ops['labels_pl']: current_label[start_idx:end_idx], ops['is_training_pl']: is_training,} summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'], ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict) train_writer.add_summary(summary, step) pred_val = np.argmax(pred_val, 1) correct = np.sum(pred_val == current_label[start_idx:end_idx]) total_correct += correct total_seen += BATCH_SIZE loss_sum += loss_val log_string('mean loss: %f' % (loss_sum / float(num_batches))) log_string('accuracy: %f' % (total_correct / float(total_seen))) def eval_one_epoch(sess, ops, test_writer): """ ops: dict mapping from string to tf ops """ is_training = False total_correct = 0 total_seen = 0 loss_sum = 0 total_seen_class = [0 for _ in range(NUM_CLASSES)] total_correct_class = [0 for _ in range(NUM_CLASSES)] for fn in range(len(TEST_FILES)): log_string('----' + str(fn) + '-----') current_data, current_label = provider.loadDataFile(TEST_FILES[fn]) current_data = current_data[:,0:NUM_POINT,:] current_label = np.squeeze(current_label) file_size = current_data.shape[0] num_batches = file_size // BATCH_SIZE for batch_idx in range(num_batches): start_idx = batch_idx * BATCH_SIZE end_idx = (batch_idx+1) * BATCH_SIZE feed_dict = {ops['pointclouds_pl']: current_data[start_idx:end_idx, :, :], ops['labels_pl']: current_label[start_idx:end_idx], ops['is_training_pl']: is_training} summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'], ops['loss'], ops['pred']], feed_dict=feed_dict) pred_val = np.argmax(pred_val, 1) correct = np.sum(pred_val == current_label[start_idx:end_idx]) total_correct += correct total_seen += BATCH_SIZE loss_sum += (loss_val*BATCH_SIZE) for i in range(start_idx, end_idx): l = current_label[i] total_seen_class[l] += 1 total_correct_class[l] += (pred_val[i-start_idx] == l) log_string('eval mean loss: %f' % (loss_sum / float(total_seen))) log_string('eval accuracy: %f'% (total_correct / float(total_seen))) log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float)))) if __name__ == "__main__": train() LOG_FOUT.close() ================================================ FILE: dgcnn/tensorflow/utils/data_prep_util.py ================================================ import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) from plyfile import (PlyData, PlyElement, make2d, PlyParseError, PlyProperty) import numpy as np import h5py SAMPLING_BIN = os.path.join(BASE_DIR, 'third_party/mesh_sampling/build/pcsample') SAMPLING_POINT_NUM = 2048 SAMPLING_LEAF_SIZE = 0.005 MODELNET40_PATH = '../datasets/modelnet40' def export_ply(pc, filename): vertex = np.zeros(pc.shape[0], dtype=[('x', 'f4'), ('y', 'f4'), ('z', 'f4')]) for i in range(pc.shape[0]): vertex[i] = (pc[i][0], pc[i][1], pc[i][2]) ply_out = PlyData([PlyElement.describe(vertex, 'vertex', comments=['vertices'])]) ply_out.write(filename) # Sample points on the obj shape def get_sampling_command(obj_filename, ply_filename): cmd = SAMPLING_BIN + ' ' + obj_filename cmd += ' ' + ply_filename cmd += ' -n_samples %d ' % SAMPLING_POINT_NUM cmd += ' -leaf_size %f ' % SAMPLING_LEAF_SIZE return cmd # -------------------------------------------------------------- # Following are the helper functions to load MODELNET40 shapes # -------------------------------------------------------------- # Read in the list of categories in MODELNET40 def get_category_names(): shape_names_file = os.path.join(MODELNET40_PATH, 'shape_names.txt') shape_names = [line.rstrip() for line in open(shape_names_file)] return shape_names # Return all the filepaths for the shapes in MODELNET40 def get_obj_filenames(): obj_filelist_file = os.path.join(MODELNET40_PATH, 'filelist.txt') obj_filenames = [os.path.join(MODELNET40_PATH, line.rstrip()) for line in open(obj_filelist_file)] print('Got %d obj files in modelnet40.' % len(obj_filenames)) return obj_filenames # Helper function to create the father folder and all subdir folders if not exist def batch_mkdir(output_folder, subdir_list): if not os.path.exists(output_folder): os.mkdir(output_folder) for subdir in subdir_list: if not os.path.exists(os.path.join(output_folder, subdir)): os.mkdir(os.path.join(output_folder, subdir)) # ---------------------------------------------------------------- # Following are the helper functions to load save/load HDF5 files # ---------------------------------------------------------------- # Write numpy array data and label to h5_filename def save_h5_data_label_normal(h5_filename, data, label, normal, data_dtype='float32', label_dtype='uint8', noral_dtype='float32'): h5_fout = h5py.File(h5_filename) h5_fout.create_dataset( 'data', data=data, compression='gzip', compression_opts=4, dtype=data_dtype) h5_fout.create_dataset( 'normal', data=normal, compression='gzip', compression_opts=4, dtype=normal_dtype) h5_fout.create_dataset( 'label', data=label, compression='gzip', compression_opts=1, dtype=label_dtype) h5_fout.close() # Write numpy array data and label to h5_filename def save_h5(h5_filename, data, label, data_dtype='uint8', label_dtype='uint8'): h5_fout = h5py.File(h5_filename) h5_fout.create_dataset( 'data', data=data, compression='gzip', compression_opts=4, dtype=data_dtype) h5_fout.create_dataset( 'label', data=label, compression='gzip', compression_opts=1, dtype=label_dtype) h5_fout.close() # Read numpy array data and label from h5_filename def load_h5_data_label_normal(h5_filename): f = h5py.File(h5_filename) data = f['data'][:] label = f['label'][:] normal = f['normal'][:] return (data, label, normal) # Read numpy array data and label from h5_filename def load_h5_data_label_seg(h5_filename): f = h5py.File(h5_filename) data = f['data'][:] label = f['label'][:] seg = f['pid'][:] return (data, label, seg) # Read numpy array data and label from h5_filename def load_h5(h5_filename): f = h5py.File(h5_filename) data = f['data'][:] label = f['label'][:] return (data, label) # ---------------------------------------------------------------- # Following are the helper functions to load save/load PLY files # ---------------------------------------------------------------- # Load PLY file def load_ply_data(filename, point_num): plydata = PlyData.read(filename) pc = plydata['vertex'].data[:point_num] pc_array = np.array([[x, y, z] for x,y,z in pc]) return pc_array # Load PLY file def load_ply_normal(filename, point_num): plydata = PlyData.read(filename) pc = plydata['normal'].data[:point_num] pc_array = np.array([[x, y, z] for x,y,z in pc]) return pc_array # Make up rows for Nxk array # Input Pad is 'edge' or 'constant' def pad_arr_rows(arr, row, pad='edge'): assert(len(arr.shape) == 2) assert(arr.shape[0] <= row) assert(pad == 'edge' or pad == 'constant') if arr.shape[0] == row: return arr if pad == 'edge': return np.lib.pad(arr, ((0, row-arr.shape[0]), (0, 0)), 'edge') if pad == 'constant': return np.lib.pad(arr, ((0, row-arr.shape[0]), (0, 0)), 'constant', (0, 0)) ================================================ FILE: dgcnn/tensorflow/utils/eulerangles.py ================================================ # emacs: -*- mode: python-mode; py-indent-offset: 4; indent-tabs-mode: nil -*- # vi: set ft=python sts=4 ts=4 sw=4 et: ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## # # See COPYING file distributed along with the NiBabel package for the # copyright and license terms. # ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## ''' Module implementing Euler angle rotations and their conversions See: * http://en.wikipedia.org/wiki/Rotation_matrix * http://en.wikipedia.org/wiki/Euler_angles * http://mathworld.wolfram.com/EulerAngles.html See also: *Representing Attitude with Euler Angles and Quaternions: A Reference* (2006) by James Diebel. A cached PDF link last found here: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.5134 Euler's rotation theorem tells us that any rotation in 3D can be described by 3 angles. Let's call the 3 angles the *Euler angle vector* and call the angles in the vector :math:`alpha`, :math:`beta` and :math:`gamma`. The vector is [ :math:`alpha`, :math:`beta`. :math:`gamma` ] and, in this description, the order of the parameters specifies the order in which the rotations occur (so the rotation corresponding to :math:`alpha` is applied first). In order to specify the meaning of an *Euler angle vector* we need to specify the axes around which each of the rotations corresponding to :math:`alpha`, :math:`beta` and :math:`gamma` will occur. There are therefore three axes for the rotations :math:`alpha`, :math:`beta` and :math:`gamma`; let's call them :math:`i` :math:`j`, :math:`k`. Let us express the rotation :math:`alpha` around axis `i` as a 3 by 3 rotation matrix `A`. Similarly :math:`beta` around `j` becomes 3 x 3 matrix `B` and :math:`gamma` around `k` becomes matrix `G`. Then the whole rotation expressed by the Euler angle vector [ :math:`alpha`, :math:`beta`. :math:`gamma` ], `R` is given by:: R = np.dot(G, np.dot(B, A)) See http://mathworld.wolfram.com/EulerAngles.html The order :math:`G B A` expresses the fact that the rotations are performed in the order of the vector (:math:`alpha` around axis `i` = `A` first). To convert a given Euler angle vector to a meaningful rotation, and a rotation matrix, we need to define: * the axes `i`, `j`, `k` * whether a rotation matrix should be applied on the left of a vector to be transformed (vectors are column vectors) or on the right (vectors are row vectors). * whether the rotations move the axes as they are applied (intrinsic rotations) - compared the situation where the axes stay fixed and the vectors move within the axis frame (extrinsic) * the handedness of the coordinate system See: http://en.wikipedia.org/wiki/Rotation_matrix#Ambiguities We are using the following conventions: * axes `i`, `j`, `k` are the `z`, `y`, and `x` axes respectively. Thus an Euler angle vector [ :math:`alpha`, :math:`beta`. :math:`gamma` ] in our convention implies a :math:`alpha` radian rotation around the `z` axis, followed by a :math:`beta` rotation around the `y` axis, followed by a :math:`gamma` rotation around the `x` axis. * the rotation matrix applies on the left, to column vectors on the right, so if `R` is the rotation matrix, and `v` is a 3 x N matrix with N column vectors, the transformed vector set `vdash` is given by ``vdash = np.dot(R, v)``. * extrinsic rotations - the axes are fixed, and do not move with the rotations. * a right-handed coordinate system The convention of rotation around ``z``, followed by rotation around ``y``, followed by rotation around ``x``, is known (confusingly) as "xyz", pitch-roll-yaw, Cardan angles, or Tait-Bryan angles. ''' import math import sys if sys.version_info >= (3,0): from functools import reduce import numpy as np _FLOAT_EPS_4 = np.finfo(float).eps * 4.0 def euler2mat(z=0, y=0, x=0): ''' Return matrix for rotations around z, y and x axes Uses the z, then y, then x convention above Parameters ---------- z : scalar Rotation angle in radians around z-axis (performed first) y : scalar Rotation angle in radians around y-axis x : scalar Rotation angle in radians around x-axis (performed last) Returns ------- M : array shape (3,3) Rotation matrix giving same rotation as for given angles Examples -------- >>> zrot = 1.3 # radians >>> yrot = -0.1 >>> xrot = 0.2 >>> M = euler2mat(zrot, yrot, xrot) >>> M.shape == (3, 3) True The output rotation matrix is equal to the composition of the individual rotations >>> M1 = euler2mat(zrot) >>> M2 = euler2mat(0, yrot) >>> M3 = euler2mat(0, 0, xrot) >>> composed_M = np.dot(M3, np.dot(M2, M1)) >>> np.allclose(M, composed_M) True You can specify rotations by named arguments >>> np.all(M3 == euler2mat(x=xrot)) True When applying M to a vector, the vector should column vector to the right of M. If the right hand side is a 2D array rather than a vector, then each column of the 2D array represents a vector. >>> vec = np.array([1, 0, 0]).reshape((3,1)) >>> v2 = np.dot(M, vec) >>> vecs = np.array([[1, 0, 0],[0, 1, 0]]).T # giving 3x2 array >>> vecs2 = np.dot(M, vecs) Rotations are counter-clockwise. >>> zred = np.dot(euler2mat(z=np.pi/2), np.eye(3)) >>> np.allclose(zred, [[0, -1, 0],[1, 0, 0], [0, 0, 1]]) True >>> yred = np.dot(euler2mat(y=np.pi/2), np.eye(3)) >>> np.allclose(yred, [[0, 0, 1],[0, 1, 0], [-1, 0, 0]]) True >>> xred = np.dot(euler2mat(x=np.pi/2), np.eye(3)) >>> np.allclose(xred, [[1, 0, 0],[0, 0, -1], [0, 1, 0]]) True Notes ----- The direction of rotation is given by the right-hand rule (orient the thumb of the right hand along the axis around which the rotation occurs, with the end of the thumb at the positive end of the axis; curl your fingers; the direction your fingers curl is the direction of rotation). Therefore, the rotations are counterclockwise if looking along the axis of rotation from positive to negative. ''' Ms = [] if z: cosz = math.cos(z) sinz = math.sin(z) Ms.append(np.array( [[cosz, -sinz, 0], [sinz, cosz, 0], [0, 0, 1]])) if y: cosy = math.cos(y) siny = math.sin(y) Ms.append(np.array( [[cosy, 0, siny], [0, 1, 0], [-siny, 0, cosy]])) if x: cosx = math.cos(x) sinx = math.sin(x) Ms.append(np.array( [[1, 0, 0], [0, cosx, -sinx], [0, sinx, cosx]])) if Ms: return reduce(np.dot, Ms[::-1]) return np.eye(3) def mat2euler(M, cy_thresh=None): ''' Discover Euler angle vector from 3x3 matrix Uses the conventions above. Parameters ---------- M : array-like, shape (3,3) cy_thresh : None or scalar, optional threshold below which to give up on straightforward arctan for estimating x rotation. If None (default), estimate from precision of input. Returns ------- z : scalar y : scalar x : scalar Rotations in radians around z, y, x axes, respectively Notes ----- If there was no numerical error, the routine could be derived using Sympy expression for z then y then x rotation matrix, which is:: [ cos(y)*cos(z), -cos(y)*sin(z), sin(y)], [cos(x)*sin(z) + cos(z)*sin(x)*sin(y), cos(x)*cos(z) - sin(x)*sin(y)*sin(z), -cos(y)*sin(x)], [sin(x)*sin(z) - cos(x)*cos(z)*sin(y), cos(z)*sin(x) + cos(x)*sin(y)*sin(z), cos(x)*cos(y)] with the obvious derivations for z, y, and x z = atan2(-r12, r11) y = asin(r13) x = atan2(-r23, r33) Problems arise when cos(y) is close to zero, because both of:: z = atan2(cos(y)*sin(z), cos(y)*cos(z)) x = atan2(cos(y)*sin(x), cos(x)*cos(y)) will be close to atan2(0, 0), and highly unstable. The ``cy`` fix for numerical instability below is from: *Graphics Gems IV*, Paul Heckbert (editor), Academic Press, 1994, ISBN: 0123361559. Specifically it comes from EulerAngles.c by Ken Shoemake, and deals with the case where cos(y) is close to zero: See: http://www.graphicsgems.org/ The code appears to be licensed (from the website) as "can be used without restrictions". ''' M = np.asarray(M) if cy_thresh is None: try: cy_thresh = np.finfo(M.dtype).eps * 4 except ValueError: cy_thresh = _FLOAT_EPS_4 r11, r12, r13, r21, r22, r23, r31, r32, r33 = M.flat # cy: sqrt((cos(y)*cos(z))**2 + (cos(x)*cos(y))**2) cy = math.sqrt(r33*r33 + r23*r23) if cy > cy_thresh: # cos(y) not close to zero, standard form z = math.atan2(-r12, r11) # atan2(cos(y)*sin(z), cos(y)*cos(z)) y = math.atan2(r13, cy) # atan2(sin(y), cy) x = math.atan2(-r23, r33) # atan2(cos(y)*sin(x), cos(x)*cos(y)) else: # cos(y) (close to) zero, so x -> 0.0 (see above) # so r21 -> sin(z), r22 -> cos(z) and z = math.atan2(r21, r22) y = math.atan2(r13, cy) # atan2(sin(y), cy) x = 0.0 return z, y, x def euler2quat(z=0, y=0, x=0): ''' Return quaternion corresponding to these Euler angles Uses the z, then y, then x convention above Parameters ---------- z : scalar Rotation angle in radians around z-axis (performed first) y : scalar Rotation angle in radians around y-axis x : scalar Rotation angle in radians around x-axis (performed last) Returns ------- quat : array shape (4,) Quaternion in w, x, y z (real, then vector) format Notes ----- We can derive this formula in Sympy using: 1. Formula giving quaternion corresponding to rotation of theta radians about arbitrary axis: http://mathworld.wolfram.com/EulerParameters.html 2. Generated formulae from 1.) for quaternions corresponding to theta radians rotations about ``x, y, z`` axes 3. Apply quaternion multiplication formula - http://en.wikipedia.org/wiki/Quaternions#Hamilton_product - to formulae from 2.) to give formula for combined rotations. ''' z = z/2.0 y = y/2.0 x = x/2.0 cz = math.cos(z) sz = math.sin(z) cy = math.cos(y) sy = math.sin(y) cx = math.cos(x) sx = math.sin(x) return np.array([ cx*cy*cz - sx*sy*sz, cx*sy*sz + cy*cz*sx, cx*cz*sy - sx*cy*sz, cx*cy*sz + sx*cz*sy]) def quat2euler(q): ''' Return Euler angles corresponding to quaternion `q` Parameters ---------- q : 4 element sequence w, x, y, z of quaternion Returns ------- z : scalar Rotation angle in radians around z-axis (performed first) y : scalar Rotation angle in radians around y-axis x : scalar Rotation angle in radians around x-axis (performed last) Notes ----- It's possible to reduce the amount of calculation a little, by combining parts of the ``quat2mat`` and ``mat2euler`` functions, but the reduction in computation is small, and the code repetition is large. ''' # delayed import to avoid cyclic dependencies import nibabel.quaternions as nq return mat2euler(nq.quat2mat(q)) def euler2angle_axis(z=0, y=0, x=0): ''' Return angle, axis corresponding to these Euler angles Uses the z, then y, then x convention above Parameters ---------- z : scalar Rotation angle in radians around z-axis (performed first) y : scalar Rotation angle in radians around y-axis x : scalar Rotation angle in radians around x-axis (performed last) Returns ------- theta : scalar angle of rotation vector : array shape (3,) axis around which rotation occurs Examples -------- >>> theta, vec = euler2angle_axis(0, 1.5, 0) >>> print(theta) 1.5 >>> np.allclose(vec, [0, 1, 0]) True ''' # delayed import to avoid cyclic dependencies import nibabel.quaternions as nq return nq.quat2angle_axis(euler2quat(z, y, x)) def angle_axis2euler(theta, vector, is_normalized=False): ''' Convert angle, axis pair to Euler angles Parameters ---------- theta : scalar angle of rotation vector : 3 element sequence vector specifying axis for rotation. is_normalized : bool, optional True if vector is already normalized (has norm of 1). Default False Returns ------- z : scalar y : scalar x : scalar Rotations in radians around z, y, x axes, respectively Examples -------- >>> z, y, x = angle_axis2euler(0, [1, 0, 0]) >>> np.allclose((z, y, x), 0) True Notes ----- It's possible to reduce the amount of calculation a little, by combining parts of the ``angle_axis2mat`` and ``mat2euler`` functions, but the reduction in computation is small, and the code repetition is large. ''' # delayed import to avoid cyclic dependencies import nibabel.quaternions as nq M = nq.angle_axis2mat(theta, vector, is_normalized) return mat2euler(M) ================================================ FILE: dgcnn/tensorflow/utils/pc_util.py ================================================ """ Utility functions for processing point clouds. Author: Charles R. Qi, Hao Su Date: November 2016 """ import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) # Draw point cloud from eulerangles import euler2mat # Point cloud IO import numpy as np from plyfile import PlyData, PlyElement # ---------------------------------------- # Point Cloud/Volume Conversions # ---------------------------------------- def point_cloud_to_volume_batch(point_clouds, vsize=12, radius=1.0, flatten=True): """ Input is BxNx3 batch of point cloud Output is Bx(vsize^3) """ vol_list = [] for b in range(point_clouds.shape[0]): vol = point_cloud_to_volume(np.squeeze(point_clouds[b,:,:]), vsize, radius) if flatten: vol_list.append(vol.flatten()) else: vol_list.append(np.expand_dims(np.expand_dims(vol, -1), 0)) if flatten: return np.vstack(vol_list) else: return np.concatenate(vol_list, 0) def point_cloud_to_volume(points, vsize, radius=1.0): """ input is Nx3 points. output is vsize*vsize*vsize assumes points are in range [-radius, radius] """ vol = np.zeros((vsize,vsize,vsize)) voxel = 2*radius/float(vsize) locations = (points + radius)/voxel locations = locations.astype(int) vol[locations[:,0],locations[:,1],locations[:,2]] = 1.0 return vol #a = np.zeros((16,1024,3)) #print point_cloud_to_volume_batch(a, 12, 1.0, False).shape def volume_to_point_cloud(vol): """ vol is occupancy grid (value = 0 or 1) of size vsize*vsize*vsize return Nx3 numpy array. """ vsize = vol.shape[0] assert(vol.shape[1] == vsize and vol.shape[1] == vsize) points = [] for a in range(vsize): for b in range(vsize): for c in range(vsize): if vol[a,b,c] == 1: points.append(np.array([a,b,c])) if len(points) == 0: return np.zeros((0,3)) points = np.vstack(points) return points # ---------------------------------------- # Point cloud IO # ---------------------------------------- def read_ply(filename): """ read XYZ point cloud from filename PLY file """ plydata = PlyData.read(filename) pc = plydata['vertex'].data pc_array = np.array([[x, y, z] for x,y,z in pc]) return pc_array def write_ply(points, filename, text=True): """ input: Nx3, write points to filename as PLY format. """ points = [(points[i,0], points[i,1], points[i,2]) for i in range(points.shape[0])] vertex = np.array(points, dtype=[('x', 'f4'), ('y', 'f4'),('z', 'f4')]) el = PlyElement.describe(vertex, 'vertex', comments=['vertices']) PlyData([el], text=text).write(filename) # ---------------------------------------- # Simple Point cloud and Volume Renderers # ---------------------------------------- def draw_point_cloud(input_points, canvasSize=500, space=200, diameter=25, xrot=0, yrot=0, zrot=0, switch_xyz=[0,1,2], normalize=True): """ Render point cloud to image with alpha channel. Input: points: Nx3 numpy array (+y is up direction) Output: gray image as numpy array of size canvasSizexcanvasSize """ image = np.zeros((canvasSize, canvasSize)) if input_points is None or input_points.shape[0] == 0: return image points = input_points[:, switch_xyz] M = euler2mat(zrot, yrot, xrot) points = (np.dot(M, points.transpose())).transpose() # Normalize the point cloud # We normalize scale to fit points in a unit sphere if normalize: centroid = np.mean(points, axis=0) points -= centroid furthest_distance = np.max(np.sqrt(np.sum(abs(points)**2,axis=-1))) points /= furthest_distance # Pre-compute the Gaussian disk radius = (diameter-1)/2.0 disk = np.zeros((diameter, diameter)) for i in range(diameter): for j in range(diameter): if (i - radius) * (i-radius) + (j-radius) * (j-radius) <= radius * radius: disk[i, j] = np.exp((-(i-radius)**2 - (j-radius)**2)/(radius**2)) mask = np.argwhere(disk > 0) dx = mask[:, 0] dy = mask[:, 1] dv = disk[disk > 0] # Order points by z-buffer zorder = np.argsort(points[:, 2]) points = points[zorder, :] points[:, 2] = (points[:, 2] - np.min(points[:, 2])) / (np.max(points[:, 2] - np.min(points[:, 2]))) max_depth = np.max(points[:, 2]) for i in range(points.shape[0]): j = points.shape[0] - i - 1 x = points[j, 0] y = points[j, 1] xc = canvasSize/2 + (x*space) yc = canvasSize/2 + (y*space) xc = int(np.round(xc)) yc = int(np.round(yc)) px = dx + xc py = dy + yc image[px, py] = image[px, py] * 0.7 + dv * (max_depth - points[j, 2]) * 0.3 image = image / np.max(image) return image def point_cloud_three_views(points): """ input points Nx3 numpy array (+y is up direction). return an numpy array gray image of size 500x1500. """ # +y is up direction # xrot is azimuth # yrot is in-plane # zrot is elevation img1 = draw_point_cloud(points, zrot=110/180.0*np.pi, xrot=45/180.0*np.pi, yrot=0/180.0*np.pi) img2 = draw_point_cloud(points, zrot=70/180.0*np.pi, xrot=135/180.0*np.pi, yrot=0/180.0*np.pi) img3 = draw_point_cloud(points, zrot=180.0/180.0*np.pi, xrot=90/180.0*np.pi, yrot=0/180.0*np.pi) image_large = np.concatenate([img1, img2, img3], 1) return image_large from PIL import Image def point_cloud_three_views_demo(): """ Demo for draw_point_cloud function """ points = read_ply('../third_party/mesh_sampling/piano.ply') im_array = point_cloud_three_views(points) img = Image.fromarray(np.uint8(im_array*255.0)) img.save('piano.jpg') if __name__=="__main__": point_cloud_three_views_demo() import matplotlib.pyplot as plt def pyplot_draw_point_cloud(points, output_filename): """ points is a Nx3 numpy array """ fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.scatter(points[:,0], points[:,1], points[:,2]) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') #savefig(output_filename) def pyplot_draw_volume(vol, output_filename): """ vol is of size vsize*vsize*vsize output an image to output_filename """ points = volume_to_point_cloud(vol) pyplot_draw_point_cloud(points, output_filename) ================================================ FILE: dgcnn/tensorflow/utils/plyfile.py ================================================ # Copyright 2014 Darsh Ranjan # # This file is part of python-plyfile. # # python-plyfile is free software: you can redistribute it and/or # modify it under the terms of the GNU General Public License as # published by the Free Software Foundation, either version 3 of the # License, or (at your option) any later version. # # python-plyfile 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 # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with python-plyfile. If not, see # . from itertools import islice as _islice import numpy as _np from sys import byteorder as _byteorder try: _range = xrange except NameError: _range = range # Many-many relation _data_type_relation = [ ('int8', 'i1'), ('char', 'i1'), ('uint8', 'u1'), ('uchar', 'b1'), ('uchar', 'u1'), ('int16', 'i2'), ('short', 'i2'), ('uint16', 'u2'), ('ushort', 'u2'), ('int32', 'i4'), ('int', 'i4'), ('uint32', 'u4'), ('uint', 'u4'), ('float32', 'f4'), ('float', 'f4'), ('float64', 'f8'), ('double', 'f8') ] _data_types = dict(_data_type_relation) _data_type_reverse = dict((b, a) for (a, b) in _data_type_relation) _types_list = [] _types_set = set() for (_a, _b) in _data_type_relation: if _a not in _types_set: _types_list.append(_a) _types_set.add(_a) if _b not in _types_set: _types_list.append(_b) _types_set.add(_b) _byte_order_map = { 'ascii': '=', 'binary_little_endian': '<', 'binary_big_endian': '>' } _byte_order_reverse = { '<': 'binary_little_endian', '>': 'binary_big_endian' } _native_byte_order = {'little': '<', 'big': '>'}[_byteorder] def _lookup_type(type_str): if type_str not in _data_type_reverse: try: type_str = _data_types[type_str] except KeyError: raise ValueError("field type %r not in %r" % (type_str, _types_list)) return _data_type_reverse[type_str] def _split_line(line, n): fields = line.split(None, n) if len(fields) == n: fields.append('') assert len(fields) == n + 1 return fields def make2d(array, cols=None, dtype=None): ''' Make a 2D array from an array of arrays. The `cols' and `dtype' arguments can be omitted if the array is not empty. ''' if (cols is None or dtype is None) and not len(array): raise RuntimeError("cols and dtype must be specified for empty " "array") if cols is None: cols = len(array[0]) if dtype is None: dtype = array[0].dtype return _np.fromiter(array, [('_', dtype, (cols,))], count=len(array))['_'] class PlyParseError(Exception): ''' Raised when a PLY file cannot be parsed. The attributes `element', `row', `property', and `message' give additional information. ''' def __init__(self, message, element=None, row=None, prop=None): self.message = message self.element = element self.row = row self.prop = prop s = '' if self.element: s += 'element %r: ' % self.element.name if self.row is not None: s += 'row %d: ' % self.row if self.prop: s += 'property %r: ' % self.prop.name s += self.message Exception.__init__(self, s) def __repr__(self): return ('PlyParseError(%r, element=%r, row=%r, prop=%r)' % self.message, self.element, self.row, self.prop) class PlyData(object): ''' PLY file header and data. A PlyData instance is created in one of two ways: by the static method PlyData.read (to read a PLY file), or directly from __init__ given a sequence of elements (which can then be written to a PLY file). ''' def __init__(self, elements=[], text=False, byte_order='=', comments=[], obj_info=[]): ''' elements: sequence of PlyElement instances. text: whether the resulting PLY file will be text (True) or binary (False). byte_order: '<' for little-endian, '>' for big-endian, or '=' for native. This is only relevant if `text' is False. comments: sequence of strings that will be placed in the header between the 'ply' and 'format ...' lines. obj_info: like comments, but will be placed in the header with "obj_info ..." instead of "comment ...". ''' if byte_order == '=' and not text: byte_order = _native_byte_order self.byte_order = byte_order self.text = text self.comments = list(comments) self.obj_info = list(obj_info) self.elements = elements def _get_elements(self): return self._elements def _set_elements(self, elements): self._elements = tuple(elements) self._index() elements = property(_get_elements, _set_elements) def _get_byte_order(self): return self._byte_order def _set_byte_order(self, byte_order): if byte_order not in ['<', '>', '=']: raise ValueError("byte order must be '<', '>', or '='") self._byte_order = byte_order byte_order = property(_get_byte_order, _set_byte_order) def _index(self): self._element_lookup = dict((elt.name, elt) for elt in self._elements) if len(self._element_lookup) != len(self._elements): raise ValueError("two elements with same name") @staticmethod def _parse_header(stream): ''' Parse a PLY header from a readable file-like stream. ''' lines = [] comments = {'comment': [], 'obj_info': []} while True: line = stream.readline().decode('ascii').strip() fields = _split_line(line, 1) if fields[0] == 'end_header': break elif fields[0] in comments.keys(): lines.append(fields) else: lines.append(line.split()) a = 0 if lines[a] != ['ply']: raise PlyParseError("expected 'ply'") a += 1 while lines[a][0] in comments.keys(): comments[lines[a][0]].append(lines[a][1]) a += 1 if lines[a][0] != 'format': raise PlyParseError("expected 'format'") if lines[a][2] != '1.0': raise PlyParseError("expected version '1.0'") if len(lines[a]) != 3: raise PlyParseError("too many fields after 'format'") fmt = lines[a][1] if fmt not in _byte_order_map: raise PlyParseError("don't understand format %r" % fmt) byte_order = _byte_order_map[fmt] text = fmt == 'ascii' a += 1 while a < len(lines) and lines[a][0] in comments.keys(): comments[lines[a][0]].append(lines[a][1]) a += 1 return PlyData(PlyElement._parse_multi(lines[a:]), text, byte_order, comments['comment'], comments['obj_info']) @staticmethod def read(stream): ''' Read PLY data from a readable file-like object or filename. ''' (must_close, stream) = _open_stream(stream, 'read') try: data = PlyData._parse_header(stream) for elt in data: elt._read(stream, data.text, data.byte_order) finally: if must_close: stream.close() return data def write(self, stream): ''' Write PLY data to a writeable file-like object or filename. ''' (must_close, stream) = _open_stream(stream, 'write') try: stream.write(self.header.encode('ascii')) stream.write(b'\r\n') for elt in self: elt._write(stream, self.text, self.byte_order) finally: if must_close: stream.close() @property def header(self): ''' Provide PLY-formatted metadata for the instance. ''' lines = ['ply'] if self.text: lines.append('format ascii 1.0') else: lines.append('format ' + _byte_order_reverse[self.byte_order] + ' 1.0') # Some information is lost here, since all comments are placed # between the 'format' line and the first element. for c in self.comments: lines.append('comment ' + c) for c in self.obj_info: lines.append('obj_info ' + c) lines.extend(elt.header for elt in self.elements) lines.append('end_header') return '\r\n'.join(lines) def __iter__(self): return iter(self.elements) def __len__(self): return len(self.elements) def __contains__(self, name): return name in self._element_lookup def __getitem__(self, name): return self._element_lookup[name] def __str__(self): return self.header def __repr__(self): return ('PlyData(%r, text=%r, byte_order=%r, ' 'comments=%r, obj_info=%r)' % (self.elements, self.text, self.byte_order, self.comments, self.obj_info)) def _open_stream(stream, read_or_write): if hasattr(stream, read_or_write): return (False, stream) try: return (True, open(stream, read_or_write[0] + 'b')) except TypeError: raise RuntimeError("expected open file or filename") class PlyElement(object): ''' PLY file element. A client of this library doesn't normally need to instantiate this directly, so the following is only for the sake of documenting the internals. Creating a PlyElement instance is generally done in one of two ways: as a byproduct of PlyData.read (when reading a PLY file) and by PlyElement.describe (before writing a PLY file). ''' def __init__(self, name, properties, count, comments=[]): ''' This is not part of the public interface. The preferred methods of obtaining PlyElement instances are PlyData.read (to read from a file) and PlyElement.describe (to construct from a numpy array). ''' self._name = str(name) self._check_name() self._count = count self._properties = tuple(properties) self._index() self.comments = list(comments) self._have_list = any(isinstance(p, PlyListProperty) for p in self.properties) @property def count(self): return self._count def _get_data(self): return self._data def _set_data(self, data): self._data = data self._count = len(data) self._check_sanity() data = property(_get_data, _set_data) def _check_sanity(self): for prop in self.properties: if prop.name not in self._data.dtype.fields: raise ValueError("dangling property %r" % prop.name) def _get_properties(self): return self._properties def _set_properties(self, properties): self._properties = tuple(properties) self._check_sanity() self._index() properties = property(_get_properties, _set_properties) def _index(self): self._property_lookup = dict((prop.name, prop) for prop in self._properties) if len(self._property_lookup) != len(self._properties): raise ValueError("two properties with same name") def ply_property(self, name): return self._property_lookup[name] @property def name(self): return self._name def _check_name(self): if any(c.isspace() for c in self._name): msg = "element name %r contains spaces" % self._name raise ValueError(msg) def dtype(self, byte_order='='): ''' Return the numpy dtype of the in-memory representation of the data. (If there are no list properties, and the PLY format is binary, then this also accurately describes the on-disk representation of the element.) ''' return [(prop.name, prop.dtype(byte_order)) for prop in self.properties] @staticmethod def _parse_multi(header_lines): ''' Parse a list of PLY element definitions. ''' elements = [] while header_lines: (elt, header_lines) = PlyElement._parse_one(header_lines) elements.append(elt) return elements @staticmethod def _parse_one(lines): ''' Consume one element definition. The unconsumed input is returned along with a PlyElement instance. ''' a = 0 line = lines[a] if line[0] != 'element': raise PlyParseError("expected 'element'") if len(line) > 3: raise PlyParseError("too many fields after 'element'") if len(line) < 3: raise PlyParseError("too few fields after 'element'") (name, count) = (line[1], int(line[2])) comments = [] properties = [] while True: a += 1 if a >= len(lines): break if lines[a][0] == 'comment': comments.append(lines[a][1]) elif lines[a][0] == 'property': properties.append(PlyProperty._parse_one(lines[a])) else: break return (PlyElement(name, properties, count, comments), lines[a:]) @staticmethod def describe(data, name, len_types={}, val_types={}, comments=[]): ''' Construct a PlyElement from an array's metadata. len_types and val_types can be given as mappings from list property names to type strings (like 'u1', 'f4', etc., or 'int8', 'float32', etc.). These can be used to define the length and value types of list properties. List property lengths always default to type 'u1' (8-bit unsigned integer), and value types default to 'i4' (32-bit integer). ''' if not isinstance(data, _np.ndarray): raise TypeError("only numpy arrays are supported") if len(data.shape) != 1: raise ValueError("only one-dimensional arrays are " "supported") count = len(data) properties = [] descr = data.dtype.descr for t in descr: if not isinstance(t[1], str): raise ValueError("nested records not supported") if not t[0]: raise ValueError("field with empty name") if len(t) != 2 or t[1][1] == 'O': # non-scalar field, which corresponds to a list # property in PLY. if t[1][1] == 'O': if len(t) != 2: raise ValueError("non-scalar object fields not " "supported") len_str = _data_type_reverse[len_types.get(t[0], 'u1')] if t[1][1] == 'O': val_type = val_types.get(t[0], 'i4') val_str = _lookup_type(val_type) else: val_str = _lookup_type(t[1][1:]) prop = PlyListProperty(t[0], len_str, val_str) else: val_str = _lookup_type(t[1][1:]) prop = PlyProperty(t[0], val_str) properties.append(prop) elt = PlyElement(name, properties, count, comments) elt.data = data return elt def _read(self, stream, text, byte_order): ''' Read the actual data from a PLY file. ''' if text: self._read_txt(stream) else: if self._have_list: # There are list properties, so a simple load is # impossible. self._read_bin(stream, byte_order) else: # There are no list properties, so loading the data is # much more straightforward. self._data = _np.fromfile(stream, self.dtype(byte_order), self.count) if len(self._data) < self.count: k = len(self._data) del self._data raise PlyParseError("early end-of-file", self, k) self._check_sanity() def _write(self, stream, text, byte_order): ''' Write the data to a PLY file. ''' if text: self._write_txt(stream) else: if self._have_list: # There are list properties, so serialization is # slightly complicated. self._write_bin(stream, byte_order) else: # no list properties, so serialization is # straightforward. self.data.astype(self.dtype(byte_order), copy=False).tofile(stream) def _read_txt(self, stream): ''' Load a PLY element from an ASCII-format PLY file. The element may contain list properties. ''' self._data = _np.empty(self.count, dtype=self.dtype()) k = 0 for line in _islice(iter(stream.readline, b''), self.count): fields = iter(line.strip().split()) for prop in self.properties: try: self._data[prop.name][k] = prop._from_fields(fields) except StopIteration: raise PlyParseError("early end-of-line", self, k, prop) except ValueError: raise PlyParseError("malformed input", self, k, prop) try: next(fields) except StopIteration: pass else: raise PlyParseError("expected end-of-line", self, k) k += 1 if k < self.count: del self._data raise PlyParseError("early end-of-file", self, k) def _write_txt(self, stream): ''' Save a PLY element to an ASCII-format PLY file. The element may contain list properties. ''' for rec in self.data: fields = [] for prop in self.properties: fields.extend(prop._to_fields(rec[prop.name])) _np.savetxt(stream, [fields], '%.18g', newline='\r\n') def _read_bin(self, stream, byte_order): ''' Load a PLY element from a binary PLY file. The element may contain list properties. ''' self._data = _np.empty(self.count, dtype=self.dtype(byte_order)) for k in _range(self.count): for prop in self.properties: try: self._data[prop.name][k] = \ prop._read_bin(stream, byte_order) except StopIteration: raise PlyParseError("early end-of-file", self, k, prop) def _write_bin(self, stream, byte_order): ''' Save a PLY element to a binary PLY file. The element may contain list properties. ''' for rec in self.data: for prop in self.properties: prop._write_bin(rec[prop.name], stream, byte_order) @property def header(self): ''' Format this element's metadata as it would appear in a PLY header. ''' lines = ['element %s %d' % (self.name, self.count)] # Some information is lost here, since all comments are placed # between the 'element' line and the first property definition. for c in self.comments: lines.append('comment ' + c) lines.extend(list(map(str, self.properties))) return '\r\n'.join(lines) def __getitem__(self, key): return self.data[key] def __setitem__(self, key, value): self.data[key] = value def __str__(self): return self.header def __repr__(self): return ('PlyElement(%r, %r, count=%d, comments=%r)' % (self.name, self.properties, self.count, self.comments)) class PlyProperty(object): ''' PLY property description. This class is pure metadata; the data itself is contained in PlyElement instances. ''' def __init__(self, name, val_dtype): self._name = str(name) self._check_name() self.val_dtype = val_dtype def _get_val_dtype(self): return self._val_dtype def _set_val_dtype(self, val_dtype): self._val_dtype = _data_types[_lookup_type(val_dtype)] val_dtype = property(_get_val_dtype, _set_val_dtype) @property def name(self): return self._name def _check_name(self): if any(c.isspace() for c in self._name): msg = "Error: property name %r contains spaces" % self._name raise RuntimeError(msg) @staticmethod def _parse_one(line): assert line[0] == 'property' if line[1] == 'list': if len(line) > 5: raise PlyParseError("too many fields after " "'property list'") if len(line) < 5: raise PlyParseError("too few fields after " "'property list'") return PlyListProperty(line[4], line[2], line[3]) else: if len(line) > 3: raise PlyParseError("too many fields after " "'property'") if len(line) < 3: raise PlyParseError("too few fields after " "'property'") return PlyProperty(line[2], line[1]) def dtype(self, byte_order='='): ''' Return the numpy dtype description for this property (as a tuple of strings). ''' return byte_order + self.val_dtype def _from_fields(self, fields): ''' Parse from generator. Raise StopIteration if the property could not be read. ''' return _np.dtype(self.dtype()).type(next(fields)) def _to_fields(self, data): ''' Return generator over one item. ''' yield _np.dtype(self.dtype()).type(data) def _read_bin(self, stream, byte_order): ''' Read data from a binary stream. Raise StopIteration if the property could not be read. ''' try: return _np.fromfile(stream, self.dtype(byte_order), 1)[0] except IndexError: raise StopIteration def _write_bin(self, data, stream, byte_order): ''' Write data to a binary stream. ''' _np.dtype(self.dtype(byte_order)).type(data).tofile(stream) def __str__(self): val_str = _data_type_reverse[self.val_dtype] return 'property %s %s' % (val_str, self.name) def __repr__(self): return 'PlyProperty(%r, %r)' % (self.name, _lookup_type(self.val_dtype)) class PlyListProperty(PlyProperty): ''' PLY list property description. ''' def __init__(self, name, len_dtype, val_dtype): PlyProperty.__init__(self, name, val_dtype) self.len_dtype = len_dtype def _get_len_dtype(self): return self._len_dtype def _set_len_dtype(self, len_dtype): self._len_dtype = _data_types[_lookup_type(len_dtype)] len_dtype = property(_get_len_dtype, _set_len_dtype) def dtype(self, byte_order='='): ''' List properties always have a numpy dtype of "object". ''' return '|O' def list_dtype(self, byte_order='='): ''' Return the pair (len_dtype, val_dtype) (both numpy-friendly strings). ''' return (byte_order + self.len_dtype, byte_order + self.val_dtype) def _from_fields(self, fields): (len_t, val_t) = self.list_dtype() n = int(_np.dtype(len_t).type(next(fields))) data = _np.loadtxt(list(_islice(fields, n)), val_t, ndmin=1) if len(data) < n: raise StopIteration return data def _to_fields(self, data): ''' Return generator over the (numerical) PLY representation of the list data (length followed by actual data). ''' (len_t, val_t) = self.list_dtype() data = _np.asarray(data, dtype=val_t).ravel() yield _np.dtype(len_t).type(data.size) for x in data: yield x def _read_bin(self, stream, byte_order): (len_t, val_t) = self.list_dtype(byte_order) try: n = _np.fromfile(stream, len_t, 1)[0] except IndexError: raise StopIteration data = _np.fromfile(stream, val_t, n) if len(data) < n: raise StopIteration return data def _write_bin(self, data, stream, byte_order): ''' Write data to a binary stream. ''' (len_t, val_t) = self.list_dtype(byte_order) data = _np.asarray(data, dtype=val_t).ravel() _np.array(data.size, dtype=len_t).tofile(stream) data.tofile(stream) def __str__(self): len_str = _data_type_reverse[self.len_dtype] val_str = _data_type_reverse[self.val_dtype] return 'property list %s %s %s' % (len_str, val_str, self.name) def __repr__(self): return ('PlyListProperty(%r, %r, %r)' % (self.name, _lookup_type(self.len_dtype), _lookup_type(self.val_dtype))) ================================================ FILE: dgcnn/tensorflow/utils/tf_util.py ================================================ """ Wrapper functions for TensorFlow layers. Author: Charles R. Qi Date: November 2016 Upadted by Yue Wang and Yongbin Sun """ import numpy as np import tensorflow as tf def _variable_on_cpu(name, shape, initializer, use_fp16=False, trainable=True): """Helper to create a Variable stored on CPU memory. Args: name: name of the variable shape: list of ints initializer: initializer for Variable Returns: Variable Tensor """ with tf.device('/cpu:0'): dtype = tf.float16 if use_fp16 else tf.float32 var = tf.get_variable(name, shape, initializer=initializer, dtype=dtype, trainable=trainable) return var def _variable_with_weight_decay(name, shape, stddev, wd, use_xavier=True): """Helper to create an initialized Variable with weight decay. Note that the Variable is initialized with a truncated normal distribution. A weight decay is added only if one is specified. Args: name: name of the variable shape: list of ints stddev: standard deviation of a truncated Gaussian wd: add L2Loss weight decay multiplied by this float. If None, weight decay is not added for this Variable. use_xavier: bool, whether to use xavier initializer Returns: Variable Tensor """ if use_xavier: initializer = tf.contrib.layers.xavier_initializer() else: initializer = tf.truncated_normal_initializer(stddev=stddev) var = _variable_on_cpu(name, shape, initializer) if wd is not None: weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss') tf.add_to_collection('losses', weight_decay) return var def conv1d(inputs, num_output_channels, kernel_size, scope, stride=1, padding='SAME', use_xavier=True, stddev=1e-3, weight_decay=0.0, activation_fn=tf.nn.relu, bn=False, bn_decay=None, is_training=None, is_dist=False): """ 1D convolution with non-linear operation. Args: inputs: 3-D tensor variable BxLxC num_output_channels: int kernel_size: int scope: string stride: int padding: 'SAME' or 'VALID' use_xavier: bool, use xavier_initializer if true stddev: float, stddev for truncated_normal init weight_decay: float activation_fn: function bn: bool, whether to use batch norm bn_decay: float or float tensor variable in [0,1] is_training: bool Tensor variable Returns: Variable tensor """ with tf.variable_scope(scope) as sc: num_in_channels = inputs.get_shape()[-1].value kernel_shape = [kernel_size, num_in_channels, num_output_channels] kernel = _variable_with_weight_decay('weights', shape=kernel_shape, use_xavier=use_xavier, stddev=stddev, wd=weight_decay) outputs = tf.nn.conv1d(inputs, kernel, stride=stride, padding=padding) biases = _variable_on_cpu('biases', [num_output_channels], tf.constant_initializer(0.0)) outputs = tf.nn.bias_add(outputs, biases) if bn: outputs = batch_norm_for_conv1d(outputs, is_training, bn_decay=bn_decay, scope='bn', is_dist=is_dist) if activation_fn is not None: outputs = activation_fn(outputs) return outputs def conv2d(inputs, num_output_channels, kernel_size, scope, stride=[1, 1], padding='SAME', use_xavier=True, stddev=1e-3, weight_decay=0.0, activation_fn=tf.nn.relu, bn=False, bn_decay=None, is_training=None, is_dist=False): """ 2D convolution with non-linear operation. Args: inputs: 4-D tensor variable BxHxWxC num_output_channels: int kernel_size: a list of 2 ints scope: string stride: a list of 2 ints padding: 'SAME' or 'VALID' use_xavier: bool, use xavier_initializer if true stddev: float, stddev for truncated_normal init weight_decay: float activation_fn: function bn: bool, whether to use batch norm bn_decay: float or float tensor variable in [0,1] is_training: bool Tensor variable Returns: Variable tensor """ with tf.variable_scope(scope) as sc: kernel_h, kernel_w = kernel_size num_in_channels = inputs.get_shape()[-1].value kernel_shape = [kernel_h, kernel_w, num_in_channels, num_output_channels] kernel = _variable_with_weight_decay('weights', shape=kernel_shape, use_xavier=use_xavier, stddev=stddev, wd=weight_decay) stride_h, stride_w = stride outputs = tf.nn.conv2d(inputs, kernel, [1, stride_h, stride_w, 1], padding=padding) biases = _variable_on_cpu('biases', [num_output_channels], tf.constant_initializer(0.0)) outputs = tf.nn.bias_add(outputs, biases) if bn: outputs = batch_norm_for_conv2d(outputs, is_training, bn_decay=bn_decay, scope='bn', is_dist=is_dist) if activation_fn is not None: outputs = activation_fn(outputs) return outputs def conv2d_transpose(inputs, num_output_channels, kernel_size, scope, stride=[1, 1], padding='SAME', use_xavier=True, stddev=1e-3, weight_decay=0.0, activation_fn=tf.nn.relu, bn=False, bn_decay=None, is_training=None, is_dist=False): """ 2D convolution transpose with non-linear operation. Args: inputs: 4-D tensor variable BxHxWxC num_output_channels: int kernel_size: a list of 2 ints scope: string stride: a list of 2 ints padding: 'SAME' or 'VALID' use_xavier: bool, use xavier_initializer if true stddev: float, stddev for truncated_normal init weight_decay: float activation_fn: function bn: bool, whether to use batch norm bn_decay: float or float tensor variable in [0,1] is_training: bool Tensor variable Returns: Variable tensor Note: conv2d(conv2d_transpose(a, num_out, ksize, stride), a.shape[-1], ksize, stride) == a """ with tf.variable_scope(scope) as sc: kernel_h, kernel_w = kernel_size num_in_channels = inputs.get_shape()[-1].value kernel_shape = [kernel_h, kernel_w, num_output_channels, num_in_channels] # reversed to conv2d kernel = _variable_with_weight_decay('weights', shape=kernel_shape, use_xavier=use_xavier, stddev=stddev, wd=weight_decay) stride_h, stride_w = stride # from slim.convolution2d_transpose def get_deconv_dim(dim_size, stride_size, kernel_size, padding): dim_size *= stride_size if padding == 'VALID' and dim_size is not None: dim_size += max(kernel_size - stride_size, 0) return dim_size # caculate output shape batch_size = inputs.get_shape()[0].value height = inputs.get_shape()[1].value width = inputs.get_shape()[2].value out_height = get_deconv_dim(height, stride_h, kernel_h, padding) out_width = get_deconv_dim(width, stride_w, kernel_w, padding) output_shape = [batch_size, out_height, out_width, num_output_channels] outputs = tf.nn.conv2d_transpose(inputs, kernel, output_shape, [1, stride_h, stride_w, 1], padding=padding) biases = _variable_on_cpu('biases', [num_output_channels], tf.constant_initializer(0.0)) outputs = tf.nn.bias_add(outputs, biases) if bn: outputs = batch_norm_for_conv2d(outputs, is_training, bn_decay=bn_decay, scope='bn', is_dist=is_dist) if activation_fn is not None: outputs = activation_fn(outputs) return outputs def conv3d(inputs, num_output_channels, kernel_size, scope, stride=[1, 1, 1], padding='SAME', use_xavier=True, stddev=1e-3, weight_decay=0.0, activation_fn=tf.nn.relu, bn=False, bn_decay=None, is_training=None, is_dist=False): """ 3D convolution with non-linear operation. Args: inputs: 5-D tensor variable BxDxHxWxC num_output_channels: int kernel_size: a list of 3 ints scope: string stride: a list of 3 ints padding: 'SAME' or 'VALID' use_xavier: bool, use xavier_initializer if true stddev: float, stddev for truncated_normal init weight_decay: float activation_fn: function bn: bool, whether to use batch norm bn_decay: float or float tensor variable in [0,1] is_training: bool Tensor variable Returns: Variable tensor """ with tf.variable_scope(scope) as sc: kernel_d, kernel_h, kernel_w = kernel_size num_in_channels = inputs.get_shape()[-1].value kernel_shape = [kernel_d, kernel_h, kernel_w, num_in_channels, num_output_channels] kernel = _variable_with_weight_decay('weights', shape=kernel_shape, use_xavier=use_xavier, stddev=stddev, wd=weight_decay) stride_d, stride_h, stride_w = stride outputs = tf.nn.conv3d(inputs, kernel, [1, stride_d, stride_h, stride_w, 1], padding=padding) biases = _variable_on_cpu('biases', [num_output_channels], tf.constant_initializer(0.0)) outputs = tf.nn.bias_add(outputs, biases) if bn: outputs = batch_norm_for_conv3d(outputs, is_training, bn_decay=bn_decay, scope='bn', is_dist=is_dist) if activation_fn is not None: outputs = activation_fn(outputs) return outputs def fully_connected(inputs, num_outputs, scope, use_xavier=True, stddev=1e-3, weight_decay=0.0, activation_fn=tf.nn.relu, bn=False, bn_decay=None, is_training=None, is_dist=False): """ Fully connected layer with non-linear operation. Args: inputs: 2-D tensor BxN num_outputs: int Returns: Variable tensor of size B x num_outputs. """ with tf.variable_scope(scope) as sc: num_input_units = inputs.get_shape()[-1].value weights = _variable_with_weight_decay('weights', shape=[num_input_units, num_outputs], use_xavier=use_xavier, stddev=stddev, wd=weight_decay) outputs = tf.matmul(inputs, weights) biases = _variable_on_cpu('biases', [num_outputs], tf.constant_initializer(0.0)) outputs = tf.nn.bias_add(outputs, biases) if bn: outputs = batch_norm_for_fc(outputs, is_training, bn_decay, 'bn', is_dist=is_dist) if activation_fn is not None: outputs = activation_fn(outputs) return outputs def max_pool2d(inputs, kernel_size, scope, stride=[2, 2], padding='VALID'): """ 2D max pooling. Args: inputs: 4-D tensor BxHxWxC kernel_size: a list of 2 ints stride: a list of 2 ints Returns: Variable tensor """ with tf.variable_scope(scope) as sc: kernel_h, kernel_w = kernel_size stride_h, stride_w = stride outputs = tf.nn.max_pool(inputs, ksize=[1, kernel_h, kernel_w, 1], strides=[1, stride_h, stride_w, 1], padding=padding, name=sc.name) return outputs def avg_pool2d(inputs, kernel_size, scope, stride=[2, 2], padding='VALID'): """ 2D avg pooling. Args: inputs: 4-D tensor BxHxWxC kernel_size: a list of 2 ints stride: a list of 2 ints Returns: Variable tensor """ with tf.variable_scope(scope) as sc: kernel_h, kernel_w = kernel_size stride_h, stride_w = stride outputs = tf.nn.avg_pool(inputs, ksize=[1, kernel_h, kernel_w, 1], strides=[1, stride_h, stride_w, 1], padding=padding, name=sc.name) return outputs def max_pool3d(inputs, kernel_size, scope, stride=[2, 2, 2], padding='VALID'): """ 3D max pooling. Args: inputs: 5-D tensor BxDxHxWxC kernel_size: a list of 3 ints stride: a list of 3 ints Returns: Variable tensor """ with tf.variable_scope(scope) as sc: kernel_d, kernel_h, kernel_w = kernel_size stride_d, stride_h, stride_w = stride outputs = tf.nn.max_pool3d(inputs, ksize=[1, kernel_d, kernel_h, kernel_w, 1], strides=[1, stride_d, stride_h, stride_w, 1], padding=padding, name=sc.name) return outputs def avg_pool3d(inputs, kernel_size, scope, stride=[2, 2, 2], padding='VALID'): """ 3D avg pooling. Args: inputs: 5-D tensor BxDxHxWxC kernel_size: a list of 3 ints stride: a list of 3 ints Returns: Variable tensor """ with tf.variable_scope(scope) as sc: kernel_d, kernel_h, kernel_w = kernel_size stride_d, stride_h, stride_w = stride outputs = tf.nn.avg_pool3d(inputs, ksize=[1, kernel_d, kernel_h, kernel_w, 1], strides=[1, stride_d, stride_h, stride_w, 1], padding=padding, name=sc.name) return outputs def batch_norm_template(inputs, is_training, scope, moments_dims, bn_decay): """ Batch normalization on convolutional maps and beyond... Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow Args: inputs: Tensor, k-D input ... x C could be BC or BHWC or BDHWC is_training: boolean tf.Varialbe, true indicates training phase scope: string, variable scope moments_dims: a list of ints, indicating dimensions for moments calculation bn_decay: float or float tensor variable, controling moving average weight Return: normed: batch-normalized maps """ with tf.variable_scope(scope) as sc: num_channels = inputs.get_shape()[-1].value beta = tf.Variable(tf.constant(0.0, shape=[num_channels]), name='beta', trainable=True) gamma = tf.Variable(tf.constant(1.0, shape=[num_channels]), name='gamma', trainable=True) batch_mean, batch_var = tf.nn.moments(inputs, moments_dims, name='moments') decay = bn_decay if bn_decay is not None else 0.9 ema = tf.train.ExponentialMovingAverage(decay=decay) # Operator that maintains moving averages of variables. ema_apply_op = tf.cond(is_training, lambda: ema.apply([batch_mean, batch_var]), lambda: tf.no_op()) # Update moving average and return current batch's avg and var. def mean_var_with_update(): with tf.control_dependencies([ema_apply_op]): return tf.identity(batch_mean), tf.identity(batch_var) # ema.average returns the Variable holding the average of var. mean, var = tf.cond(is_training, mean_var_with_update, lambda: (ema.average(batch_mean), ema.average(batch_var))) normed = tf.nn.batch_normalization(inputs, mean, var, beta, gamma, 1e-3) return normed def batch_norm_dist_template(inputs, is_training, scope, moments_dims, bn_decay): """ The batch normalization for distributed training. Args: inputs: Tensor, k-D input ... x C could be BC or BHWC or BDHWC is_training: boolean tf.Varialbe, true indicates training phase scope: string, variable scope moments_dims: a list of ints, indicating dimensions for moments calculation bn_decay: float or float tensor variable, controling moving average weight Return: normed: batch-normalized maps """ with tf.variable_scope(scope) as sc: num_channels = inputs.get_shape()[-1].value beta = _variable_on_cpu('beta', [num_channels], initializer=tf.zeros_initializer()) gamma = _variable_on_cpu('gamma', [num_channels], initializer=tf.ones_initializer()) pop_mean = _variable_on_cpu('pop_mean', [num_channels], initializer=tf.zeros_initializer(), trainable=False) pop_var = _variable_on_cpu('pop_var', [num_channels], initializer=tf.ones_initializer(), trainable=False) def train_bn_op(): batch_mean, batch_var = tf.nn.moments(inputs, moments_dims, name='moments') decay = bn_decay if bn_decay is not None else 0.9 train_mean = tf.assign(pop_mean, pop_mean * decay + batch_mean * (1 - decay)) train_var = tf.assign(pop_var, pop_var * decay + batch_var * (1 - decay)) with tf.control_dependencies([train_mean, train_var]): return tf.nn.batch_normalization(inputs, batch_mean, batch_var, beta, gamma, 1e-3) def test_bn_op(): return tf.nn.batch_normalization(inputs, pop_mean, pop_var, beta, gamma, 1e-3) normed = tf.cond(is_training, train_bn_op, test_bn_op) return normed def batch_norm_for_fc(inputs, is_training, bn_decay, scope, is_dist=False): """ Batch normalization on FC data. Args: inputs: Tensor, 2D BxC input is_training: boolean tf.Varialbe, true indicates training phase bn_decay: float or float tensor variable, controling moving average weight scope: string, variable scope is_dist: true indicating distributed training scheme Return: normed: batch-normalized maps """ if is_dist: return batch_norm_dist_template(inputs, is_training, scope, [0,], bn_decay) else: return batch_norm_template(inputs, is_training, scope, [0,], bn_decay) def batch_norm_for_conv1d(inputs, is_training, bn_decay, scope, is_dist=False): """ Batch normalization on 1D convolutional maps. Args: inputs: Tensor, 3D BLC input maps is_training: boolean tf.Varialbe, true indicates training phase bn_decay: float or float tensor variable, controling moving average weight scope: string, variable scope is_dist: true indicating distributed training scheme Return: normed: batch-normalized maps """ if is_dist: return batch_norm_dist_template(inputs, is_training, scope, [0,1], bn_decay) else: return batch_norm_template(inputs, is_training, scope, [0,1], bn_decay) def batch_norm_for_conv2d(inputs, is_training, bn_decay, scope, is_dist=False): """ Batch normalization on 2D convolutional maps. Args: inputs: Tensor, 4D BHWC input maps is_training: boolean tf.Varialbe, true indicates training phase bn_decay: float or float tensor variable, controling moving average weight scope: string, variable scope is_dist: true indicating distributed training scheme Return: normed: batch-normalized maps """ if is_dist: return batch_norm_dist_template(inputs, is_training, scope, [0,1,2], bn_decay) else: return batch_norm_template(inputs, is_training, scope, [0,1,2], bn_decay) def batch_norm_for_conv3d(inputs, is_training, bn_decay, scope, is_dist=False): """ Batch normalization on 3D convolutional maps. Args: inputs: Tensor, 5D BDHWC input maps is_training: boolean tf.Varialbe, true indicates training phase bn_decay: float or float tensor variable, controling moving average weight scope: string, variable scope is_dist: true indicating distributed training scheme Return: normed: batch-normalized maps """ if is_dist: return batch_norm_dist_template(inputs, is_training, scope, [0,1,2,3], bn_decay) else: return batch_norm_template(inputs, is_training, scope, [0,1,2,3], bn_decay) def dropout(inputs, is_training, scope, keep_prob=0.5, noise_shape=None): """ Dropout layer. Args: inputs: tensor is_training: boolean tf.Variable scope: string keep_prob: float in [0,1] noise_shape: list of ints Returns: tensor variable """ with tf.variable_scope(scope) as sc: outputs = tf.cond(is_training, lambda: tf.nn.dropout(inputs, keep_prob, noise_shape), lambda: inputs) return outputs def pairwise_distance(point_cloud): """Compute pairwise distance of a point cloud. Args: point_cloud: tensor (batch_size, num_points, num_dims) Returns: pairwise distance: (batch_size, num_points, num_points) """ og_batch_size = point_cloud.get_shape().as_list()[0] point_cloud = tf.squeeze(point_cloud) if og_batch_size == 1: point_cloud = tf.expand_dims(point_cloud, 0) point_cloud_transpose = tf.transpose(point_cloud, perm=[0, 2, 1]) point_cloud_inner = tf.matmul(point_cloud, point_cloud_transpose) point_cloud_inner = -2*point_cloud_inner point_cloud_square = tf.reduce_sum(tf.square(point_cloud), axis=-1, keep_dims=True) point_cloud_square_tranpose = tf.transpose(point_cloud_square, perm=[0, 2, 1]) return point_cloud_square + point_cloud_inner + point_cloud_square_tranpose def knn(adj_matrix, k=20): """Get KNN based on the pairwise distance. Args: pairwise distance: (batch_size, num_points, num_points) k: int Returns: nearest neighbors: (batch_size, num_points, k) """ neg_adj = -adj_matrix _, nn_idx = tf.nn.top_k(neg_adj, k=k) return nn_idx def get_edge_feature(point_cloud, nn_idx, k=20): """Construct edge feature for each point Args: point_cloud: (batch_size, num_points, 1, num_dims) nn_idx: (batch_size, num_points, k) k: int Returns: edge features: (batch_size, num_points, k, num_dims) """ og_batch_size = point_cloud.get_shape().as_list()[0] point_cloud = tf.squeeze(point_cloud) if og_batch_size == 1: point_cloud = tf.expand_dims(point_cloud, 0) point_cloud_central = point_cloud point_cloud_shape = point_cloud.get_shape() batch_size = point_cloud_shape[0].value num_points = point_cloud_shape[1].value num_dims = point_cloud_shape[2].value idx_ = tf.range(batch_size) * num_points idx_ = tf.reshape(idx_, [batch_size, 1, 1]) point_cloud_flat = tf.reshape(point_cloud, [-1, num_dims]) point_cloud_neighbors = tf.gather(point_cloud_flat, nn_idx+idx_) point_cloud_central = tf.expand_dims(point_cloud_central, axis=-2) point_cloud_central = tf.tile(point_cloud_central, [1, 1, k, 1]) edge_feature = tf.concat([point_cloud_central, point_cloud_neighbors-point_cloud_central], axis=-1) return edge_feature ================================================ FILE: download.sh ================================================ ### # @Description: # @Autor: Jiachen Sun # @Date: 2022-02-16 22:23:16 # @LastEditors: Jiachen Sun # @LastEditTime: 2022-02-17 13:09:42 ### wgetgdrive(){ # $1 = file ID # $2 = file name URL="https://docs.google.com/uc?export=download&id=$1" wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate $URL -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=$1" -O $2 && rm -rf /tmp/cookies.txt } mkdir -p tmp key="$1" case $key in pretrained) wgetgdrive 1qSkMYYK1qkT4wMMeAXerSI2Q7AxWujsS tmp/pretrained.zip unzip -o tmp/pretrained.zip ;; runs) mkdir -p runs cd runs python ../gdrivedl.py https://drive.google.com/drive/folders/1UT-OfAsQ1OGSa6HSLZcK6YyJeIkaJUfF?usp=sharing cd .. ;; cor_exp) mkdir -p cor_exp cd cor_exp python ../gdrivedl.py https://drive.google.com/drive/folders/1iYcJwFCFm9JWSiL1puIVfjpEgNF2dSoy?usp=sharing cd .. ;; modelnet40_c) mkdir -p data/modelnet40_c cd data/modelnet40_c python ../../gdrivedl.py https://drive.google.com/drive/folders/10YeQRh92r_WdL-Dnog2zQfFr03UW4qXX?usp=sharing cd ../.. ;; modelnet40) wget --no-check-certificate https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip unzip modelnet40_ply_hdf5_2048.zip mv modelnet40_ply_hdf5_2048 data rm -r modelnet40_ply_hdf5_2048.zip wgetgdrive 1jXe7UR6He-pV3B7vIxMAjEt63Vhy1bV8 tmp/modelnet40_ply_hdf5_2048_valid_small.zip unzip -o tmp/modelnet40_ply_hdf5_2048_valid_small.zip mv modelnet40_ply_hdf5_2048_valid_small/* data/modelnet40_ply_hdf5_2048/ rm -r modelnet40_ply_hdf5_2048_valid_small wget http://modelnet.cs.princeton.edu/ModelNet40.zip unzip ModelNet40.zip mv ModelNet40 data rm -r ModelNet40.zip rm -rf modelnet40_ply_hdf5_2048 ;; mesh) wget --no-check-certificate http://modelnet.cs.princeton.edu/ModelNet40.zip unzip ModelNet40.zip mv ModelNet40 data rm -r ModelNet40.zip ;; *) echo "unknow argument $1" # unknown argument ;; esac rm -r tmp ================================================ FILE: emd/README.md ================================================ ## Earth Mover's Distance of point clouds ![](/emd/CDEMD.png) Compared to the Chamfer Distance (CD), the Earth Mover's Distance (EMD) is more reliable to distinguish the visual quality of the point clouds. See our [paper](http://cseweb.ucsd.edu/~mil070/projects/AAAI2020/paper.pdf) for more details. We provide an EMD implementation for point cloud comparison, which only needs $O(n)$ memory and thus enables dense point clouds (with 10,000 points or over) and large batch size. It is based on an approximated algorithm (auction algorithm) and cannot guarantee a (but near) bijection assignment. It employs a parameter $\epsilon$ to balance the error rate and the speed of convergence. Smaller $\epsilon$ achieves more accurate results, but needs a longer time for convergence. The time complexity is $O(n^2k)$, where $k$ is the number of iterations. We set a $\epsilon = 0.005, k = 50$ during training and a $\epsilon = 0.002, k = 10000$ during testing. ### Compile Run `python3 setup.py install` to compile. ### Example See `emd_module.py/test_emd()` for examples. ### Input - **xyz1, xyz2**: float tensors with shape `[#batch, #points, 3]`. xyz1 is the predicted point cloud and xyz2 is the ground truth point cloud. Two point clouds should have same size and be normalized to [0, 1]. The number of points should be a multiple of 1024. The batch size should be no greater than 512. Since we only calculate gradients for xyz1, please do not swap xyz1 and xyz2. - **eps**: a float tensor, the parameter balances the error rate and the speed of convergence. - **iters**: a int tensor, the number of iterations. ### Output - **dist**: a float tensor with shape `[#batch, #points]`. sqrt(dist) are the L2 distances between the pairs of points. - **assignment**: a int tensor with shape `[#batch, #points]`. The index of the matched point in the ground truth point cloud. ================================================ FILE: emd/emd.cpp ================================================ // EMD approximation module (based on auction algorithm) // author: Minghua Liu #include #include int emd_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist, at::Tensor assignment, at::Tensor price, at::Tensor assignment_inv, at::Tensor bid, at::Tensor bid_increments, at::Tensor max_increments, at::Tensor unass_idx, at::Tensor unass_cnt, at::Tensor unass_cnt_sum, at::Tensor cnt_tmp, at::Tensor max_idx, float eps, int iters); int emd_cuda_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz, at::Tensor graddist, at::Tensor idx); int emd_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist, at::Tensor assignment, at::Tensor price, at::Tensor assignment_inv, at::Tensor bid, at::Tensor bid_increments, at::Tensor max_increments, at::Tensor unass_idx, at::Tensor unass_cnt, at::Tensor unass_cnt_sum, at::Tensor cnt_tmp, at::Tensor max_idx, float eps, int iters) { return emd_cuda_forward(xyz1, xyz2, dist, assignment, price, assignment_inv, bid, bid_increments, max_increments, unass_idx, unass_cnt, unass_cnt_sum, cnt_tmp, max_idx, eps, iters); } int emd_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz, at::Tensor graddist, at::Tensor idx) { return emd_cuda_backward(xyz1, xyz2, gradxyz, graddist, idx); } PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("forward", &emd_forward, "emd forward (CUDA)"); m.def("backward", &emd_backward, "emd backward (CUDA)"); } ================================================ FILE: emd/emd_cuda.cu ================================================ // EMD approximation module (based on auction algorithm) // author: Minghua Liu #include #include #include #include #include __device__ __forceinline__ float atomicMax(float *address, float val) { int ret = __float_as_int(*address); while(val > __int_as_float(ret)) { int old = ret; if((ret = atomicCAS((int *)address, old, __float_as_int(val))) == old) break; } return __int_as_float(ret); } __global__ void clear(int b, int * cnt_tmp, int * unass_cnt) { for (int i = threadIdx.x; i < b; i += blockDim.x) { cnt_tmp[i] = 0; unass_cnt[i] = 0; } } __global__ void calc_unass_cnt(int b, int n, int * assignment, int * unass_cnt) { // count the number of unassigned points in each batch const int BLOCK_SIZE = 1024; __shared__ int scan_array[BLOCK_SIZE]; for (int i = blockIdx.x; i < b; i += gridDim.x) { scan_array[threadIdx.x] = assignment[i * n + blockIdx.y * BLOCK_SIZE + threadIdx.x] == -1 ? 1 : 0; __syncthreads(); int stride = 1; while(stride <= BLOCK_SIZE / 2) { int index = (threadIdx.x + 1) * stride * 2 - 1; if(index < BLOCK_SIZE) scan_array[index] += scan_array[index - stride]; stride = stride * 2; __syncthreads(); } __syncthreads(); if (threadIdx.x == BLOCK_SIZE - 1) { atomicAdd(&unass_cnt[i], scan_array[threadIdx.x]); } __syncthreads(); } } __global__ void calc_unass_cnt_sum(int b, int * unass_cnt, int * unass_cnt_sum) { // count the cumulative sum over over unass_cnt const int BLOCK_SIZE = 512; // batch_size <= 512 __shared__ int scan_array[BLOCK_SIZE]; scan_array[threadIdx.x] = unass_cnt[threadIdx.x]; __syncthreads(); int stride = 1; while(stride <= BLOCK_SIZE / 2) { int index = (threadIdx.x + 1) * stride * 2 - 1; if(index < BLOCK_SIZE) scan_array[index] += scan_array[index - stride]; stride = stride * 2; __syncthreads(); } __syncthreads(); stride = BLOCK_SIZE / 4; while(stride > 0) { int index = (threadIdx.x + 1) * stride * 2 - 1; if((index + stride) < BLOCK_SIZE) scan_array[index + stride] += scan_array[index]; stride = stride / 2; __syncthreads(); } __syncthreads(); //printf("%d\n", unass_cnt_sum[b - 1]); unass_cnt_sum[threadIdx.x] = scan_array[threadIdx.x]; } __global__ void calc_unass_idx(int b, int n, int * assignment, int * unass_idx, int * unass_cnt, int * unass_cnt_sum, int * cnt_tmp) { // list all the unassigned points for (int i = blockIdx.x; i < b; i += gridDim.x) { if (assignment[i * n + blockIdx.y * 1024 + threadIdx.x] == -1) { int idx = atomicAdd(&cnt_tmp[i], 1); unass_idx[unass_cnt_sum[i] - unass_cnt[i] + idx] = blockIdx.y * 1024 + threadIdx.x; } } } __global__ void Bid(int b, int n, const float * xyz1, const float * xyz2, float eps, int * assignment, int * assignment_inv, float * price, int * bid, float * bid_increments, float * max_increments, int * unass_cnt, int * unass_cnt_sum, int * unass_idx) { const int batch = 2048, block_size = 1024, block_cnt = n / 1024; __shared__ float xyz2_buf[batch * 3]; __shared__ float price_buf[batch]; __shared__ float best_buf[block_size]; __shared__ float better_buf[block_size]; __shared__ int best_i_buf[block_size]; for (int i = blockIdx.x; i < b; i += gridDim.x) { int _unass_cnt = unass_cnt[i]; if (_unass_cnt == 0) continue; int _unass_cnt_sum = unass_cnt_sum[i]; int unass_per_block = (_unass_cnt + block_cnt - 1) / block_cnt; int thread_per_unass = block_size / unass_per_block; int unass_this_block = max(min(_unass_cnt - (int) blockIdx.y * unass_per_block, unass_per_block), 0); float x1, y1, z1, best = -1e9, better = -1e9; int best_i = -1, _unass_id = -1, thread_in_unass; if (threadIdx.x < thread_per_unass * unass_this_block) { _unass_id = unass_per_block * blockIdx.y + threadIdx.x / thread_per_unass + _unass_cnt_sum - _unass_cnt; _unass_id = unass_idx[_unass_id]; thread_in_unass = threadIdx.x % thread_per_unass; x1 = xyz1[(i * n + _unass_id) * 3 + 0]; y1 = xyz1[(i * n + _unass_id) * 3 + 1]; z1 = xyz1[(i * n + _unass_id) * 3 + 2]; } for (int k2 = 0; k2 < n; k2 += batch) { int end_k = min(n, k2 + batch) - k2; for (int j = threadIdx.x; j < end_k * 3; j += blockDim.x) { xyz2_buf[j] = xyz2[(i * n + k2) * 3 + j]; } for (int j = threadIdx.x; j < end_k; j += blockDim.x) { price_buf[j] = price[i * n + k2 + j]; } __syncthreads(); if (_unass_id != -1) { int delta = (end_k + thread_per_unass - 1) / thread_per_unass; int l = thread_in_unass * delta; int r = min((thread_in_unass + 1) * delta, end_k); for (int k = l; k < r; k++) //if (!last || assignment_inv[i * n + k + k2] == -1) { float x2 = xyz2_buf[k * 3 + 0] - x1; float y2 = xyz2_buf[k * 3 + 1] - y1; float z2 = xyz2_buf[k * 3 + 2] - z1; // the coordinates of points should be normalized to [0, 1] float d = 3.0 - sqrtf(x2 * x2 + y2 * y2 + z2 * z2) - price_buf[k]; if (d > best) { better = best; best = d; best_i = k + k2; } else if (d > better) { better = d; } } } __syncthreads(); } best_buf[threadIdx.x] = best; better_buf[threadIdx.x] = better; best_i_buf[threadIdx.x] = best_i; __syncthreads(); if (_unass_id != -1 && thread_in_unass == 0) { for (int j = threadIdx.x + 1; j < threadIdx.x + thread_per_unass; j++) { if (best_buf[j] > best) { better = max(best, better_buf[j]); best = best_buf[j]; best_i = best_i_buf[j]; } else better = max(better, best_buf[j]); } bid[i * n + _unass_id] = best_i; bid_increments[i * n + _unass_id] = best - better + eps; atomicMax(&max_increments[i * n + best_i], best - better + eps); } } } __global__ void GetMax(int b, int n, int * assignment, int * bid, float * bid_increments, float * max_increments, int * max_idx) { for (int i = blockIdx.x; i < b; i += gridDim.x) { int j = threadIdx.x + blockIdx.y * blockDim.x; if (assignment[i * n + j] == -1) { int bid_id = bid[i * n + j]; float bid_inc = bid_increments[i * n + j]; float max_inc = max_increments[i * n + bid_id]; if (bid_inc - 1e-6 <= max_inc && max_inc <= bid_inc + 1e-6) { max_idx[i * n + bid_id] = j; } } } } __global__ void Assign(int b, int n, int * assignment, int * assignment_inv, float * price, int * bid, float * bid_increments, float * max_increments, int * max_idx, bool last) { for (int i = blockIdx.x; i < b; i += gridDim.x) { int j = threadIdx.x + blockIdx.y * blockDim.x; if (assignment[i * n + j] == -1) { int bid_id = bid[i * n + j]; if (last || max_idx[i * n + bid_id] == j) { float bid_inc = bid_increments[i * n + j]; int ass_inv = assignment_inv[i * n + bid_id]; if (!last && ass_inv != -1) { assignment[i * n + ass_inv] = -1; } assignment_inv[i * n + bid_id] = j; assignment[i * n + j] = bid_id; price[i * n + bid_id] += bid_inc; max_increments[i * n + bid_id] = -1e9; } } } } __global__ void CalcDist(int b, int n, float * xyz1, float * xyz2, float * dist, int * assignment) { for (int i = blockIdx.x; i < b; i += gridDim.x) { int j = threadIdx.x + blockIdx.y * blockDim.x; int k = assignment[i * n + j]; float deltax = xyz1[(i * n + j) * 3 + 0] - xyz2[(i * n + k) * 3 + 0]; float deltay = xyz1[(i * n + j) * 3 + 1] - xyz2[(i * n + k) * 3 + 1]; float deltaz = xyz1[(i * n + j) * 3 + 2] - xyz2[(i * n + k) * 3 + 2]; dist[i * n + j] = deltax * deltax + deltay * deltay + deltaz * deltaz; } } int emd_cuda_forward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor dist, at::Tensor assignment, at::Tensor price, at::Tensor assignment_inv, at::Tensor bid, at::Tensor bid_increments, at::Tensor max_increments, at::Tensor unass_idx, at::Tensor unass_cnt, at::Tensor unass_cnt_sum, at::Tensor cnt_tmp, at::Tensor max_idx, float eps, int iters) { const auto batch_size = xyz1.size(0); const auto n = xyz1.size(1); //num_points point cloud A const auto m = xyz2.size(1); //num_points point cloud B if (n != m) { printf("Input Error! The two point clouds should have the same size.\n"); return -1; } if (batch_size > 512) { printf("Input Error! The batch size should be less than 512.\n"); return -1; } if (n % 1024 != 0) { printf("Input Error! The size of the point clouds should be a multiple of 1024.\n"); return -1; } //cudaEvent_t start,stop; //cudaEventCreate(&start); //cudaEventCreate(&stop); //cudaEventRecord(start); //int iters = 50; for (int i = 0; i < iters; i++) { clear<<<1, batch_size>>>(batch_size, cnt_tmp.data(), unass_cnt.data()); calc_unass_cnt<<>>(batch_size, n, assignment.data(), unass_cnt.data()); calc_unass_cnt_sum<<<1, batch_size>>>(batch_size, unass_cnt.data(), unass_cnt_sum.data()); calc_unass_idx<<>>(batch_size, n, assignment.data(), unass_idx.data(), unass_cnt.data(), unass_cnt_sum.data(), cnt_tmp.data()); Bid<<>>(batch_size, n, xyz1.data(), xyz2.data(), eps, assignment.data(), assignment_inv.data(), price.data(), bid.data(), bid_increments.data(), max_increments.data(), unass_cnt.data(), unass_cnt_sum.data(), unass_idx.data()); GetMax<<>>(batch_size, n, assignment.data(), bid.data(), bid_increments.data(), max_increments.data(), max_idx.data()); Assign<<>>(batch_size, n, assignment.data(), assignment_inv.data(), price.data(), bid.data(), bid_increments.data(), max_increments.data(), max_idx.data(), i == iters - 1); } CalcDist<<>>(batch_size, n, xyz1.data(), xyz2.data(), dist.data(), assignment.data()); //cudaEventRecord(stop); //cudaEventSynchronize(stop); //float elapsedTime; //cudaEventElapsedTime(&elapsedTime,start,stop); //printf("%lf\n", elapsedTime); cudaError_t err = cudaGetLastError(); if (err != cudaSuccess) { printf("error in nnd Output: %s\n", cudaGetErrorString(err)); return 0; } return 1; } __global__ void NmDistanceGradKernel(int b, int n, const float * xyz1, const float * xyz2, const float * grad_dist, const int * idx, float * grad_xyz){ for (int i = blockIdx.x; i < b; i += gridDim.x) { for (int j = threadIdx.x + blockIdx.y * blockDim.x; j < n; j += blockDim.x * gridDim.y) { float x1 = xyz1[(i * n + j) * 3 + 0]; float y1 = xyz1[(i * n + j) * 3 + 1]; float z1 = xyz1[(i * n + j) * 3 + 2]; int j2 = idx[i * n + j]; float x2 = xyz2[(i * n + j2) * 3 + 0]; float y2 = xyz2[(i * n + j2) * 3 + 1]; float z2 = xyz2[(i * n + j2) * 3 + 2]; float g = grad_dist[i * n + j] * 2; atomicAdd(&(grad_xyz[(i * n + j) * 3 + 0]), g * (x1 - x2)); atomicAdd(&(grad_xyz[(i * n + j) * 3 + 1]), g * (y1 - y2)); atomicAdd(&(grad_xyz[(i * n + j) * 3 + 2]), g * (z1 - z2)); } } } int emd_cuda_backward(at::Tensor xyz1, at::Tensor xyz2, at::Tensor gradxyz, at::Tensor graddist, at::Tensor idx){ const auto batch_size = xyz1.size(0); const auto n = xyz1.size(1); const auto m = xyz2.size(1); NmDistanceGradKernel<<>>(batch_size, n, xyz1.data(), xyz2.data(), graddist.data(), idx.data(), gradxyz.data()); cudaError_t err = cudaGetLastError(); if (err != cudaSuccess) { printf("error in nnd get grad: %s\n", cudaGetErrorString(err)); return 0; } return 1; } ================================================ FILE: emd/emd_module.py ================================================ # EMD approximation module (based on auction algorithm) # memory complexity: O(n) # time complexity: O(n^2 * iter) # author: Minghua Liu # Input: # xyz1, xyz2: [#batch, #points, 3] # where xyz1 is the predicted point cloud and xyz2 is the ground truth point cloud # two point clouds should have same size and be normalized to [0, 1] # #points should be a multiple of 1024 # #batch should be no greater than 512 # eps is a parameter which balances the error rate and the speed of convergence # iters is the number of iteration # we only calculate gradient for xyz1 # Output: # dist: [#batch, #points], sqrt(dist) -> L2 distance # assignment: [#batch, #points], index of the matched point in the ground truth point cloud # the result is an approximation and the assignment is not guranteed to be a bijection import time import numpy as np import torch from torch import nn from torch.autograd import Function import emd class emdFunction(Function): @staticmethod def forward(ctx, xyz1, xyz2, eps, iters): batchsize, n, _ = xyz1.size() _, m, _ = xyz2.size() assert(n == m) assert(xyz1.size()[0] == xyz2.size()[0]) assert(n % 1024 == 0) assert(batchsize <= 512) xyz1 = xyz1.contiguous().float().cuda() xyz2 = xyz2.contiguous().float().cuda() dist = torch.zeros(batchsize, n, device='cuda').contiguous() assignment = torch.zeros(batchsize, n, device='cuda', dtype=torch.int32).contiguous() - 1 assignment_inv = torch.zeros(batchsize, m, device='cuda', dtype=torch.int32).contiguous() - 1 price = torch.zeros(batchsize, m, device='cuda').contiguous() bid = torch.zeros(batchsize, n, device='cuda', dtype=torch.int32).contiguous() bid_increments = torch.zeros(batchsize, n, device='cuda').contiguous() max_increments = torch.zeros(batchsize, m, device='cuda').contiguous() unass_idx = torch.zeros(batchsize * n, device='cuda', dtype=torch.int32).contiguous() max_idx = torch.zeros(batchsize * m, device='cuda', dtype=torch.int32).contiguous() unass_cnt = torch.zeros(512, dtype=torch.int32, device='cuda').contiguous() unass_cnt_sum = torch.zeros(512, dtype=torch.int32, device='cuda').contiguous() cnt_tmp = torch.zeros(512, dtype=torch.int32, device='cuda').contiguous() emd.forward(xyz1, xyz2, dist, assignment, price, assignment_inv, bid, bid_increments, max_increments, unass_idx, unass_cnt, unass_cnt_sum, cnt_tmp, max_idx, eps, iters) ctx.save_for_backward(xyz1, xyz2, assignment) return dist, assignment @staticmethod def backward(ctx, graddist, gradidx): xyz1, xyz2, assignment = ctx.saved_tensors graddist = graddist.contiguous() gradxyz1 = torch.zeros(xyz1.size(), device='cuda').contiguous() gradxyz2 = torch.zeros(xyz2.size(), device='cuda').contiguous() emd.backward(xyz1, xyz2, gradxyz1, graddist, assignment) return gradxyz1, gradxyz2, None, None class emdModule(nn.Module): def __init__(self): super(emdModule, self).__init__() def forward(self, input1, input2, eps, iters): return emdFunction.apply(input1, input2, eps, iters) def test_emd(): x1 = torch.rand(20, 8192, 3).cuda() x2 = torch.rand(20, 8192, 3).cuda() emd = emdModule() start_time = time.perf_counter() dis, assigment = emd(x1, x2, 0.05, 3000) print("Input_size: ", x1.shape) print("Runtime: %lfs" % (time.perf_counter() - start_time)) print("EMD: %lf" % np.sqrt(dis.cpu()).mean()) print("|set(assignment)|: %d" % assigment.unique().numel()) assigment = assigment.cpu().numpy() assigment = np.expand_dims(assigment, -1) x2 = np.take_along_axis(x2, assigment, axis = 1) d = (x1 - x2) * (x1 - x2) print("Verified EMD: %lf" % np.sqrt(d.cpu().sum(-1)).mean()) #test_emd() ================================================ FILE: emd/setup.py ================================================ from setuptools import setup from torch.utils.cpp_extension import BuildExtension, CUDAExtension setup( name='emd', ext_modules=[ CUDAExtension('emd', [ 'emd.cpp', 'emd_cuda.cu', ]), ], cmdclass={ 'build_ext': BuildExtension }) ================================================ FILE: eval_cor.sh ================================================ ### # @Description: # @Autor: Jiachen Sun # @Date: 2022-02-16 22:23:16 # @LastEditors: Jiachen Sun # @LastEditTime: 2022-02-23 17:20:27 ### if [ ! -d "output" ]; then mkdir "output" fi for model in 'gdanet'; do #'pointnet' 'pct' 'rscnn' 'pointnet2' 'simpleview' 'dgcnn' 'pointMLP' 'curvenet'; do for cor in 'uniform' 'gaussian' 'background' 'impulse' 'upsampling' 'distortion_rbf' 'distortion_rbf_inv' 'density' 'density_inc' 'shear' 'rotation' 'cutout' 'distortion' 'occlusion' 'lidar'; do for sev in 1 2 3 4 5; do # for aug in 'rsmix' 'cutmix_r' 'cutmix_k' 'mixup' 'pgd'; do # CUDA_VISIBLE_DEVICES=0 python main.py --entry test --model-path runs/${aug}_${model}_run_1/model_best_test.pth --exp-config configs/corruption/${model}.yaml --severity ${sev} --corruption ${cor} --output ./output/${model}_${aug}_${cor}_${sev}.txt # done # for adapt in 'tent' 'bn'; do # CUDA_VISIBLE_DEVICES=0 python main.py --entry test --model-path cor_exp/dgcnn_${model}_run_1/model_best_test.pth --exp-config configs/${adapt}/${model}.yaml --severity ${sev} --corruption ${cor} --output ./output/${model}_${adapt}_${cor}_${sev}.txt # done CUDA_VISIBLE_DEVICES=0 python main.py --entry test --model-path runs/dgcnn_${model}_run_1/model_best_test.pth --exp-config configs/corruption/${model}.yaml --severity ${sev} --corruption ${cor} --output ./output/${model}_none_${cor}_${sev}.txt done done done ================================================ FILE: eval_og.sh ================================================ if [ ! -d "output" ]; then mkdir "output" fi for model in 'gdanet'; do #'dgcnn' 'rscnn' 'pct' 'pointnet' 'pointnet2' 'simpleview' 'curvenet' 'pointMLP';; do # for aug in 'pgd'; do CUDA_VISIBLE_DEVICES=1 python main.py --entry test --model-path runs/dgcnn_${model}_run_1/model_best_test.pth --exp-config configs/dgcnn_${model}_run_1.yaml --output ./output/${model}_clean.txt done ================================================ FILE: eval_tent_cutmix.sh ================================================ if [ ! -d "output" ]; then mkdir "output" fi for model in 'rscnn' 'pct' 'pointnet' 'pointnet2' 'simpleview' 'dgcnn'; do for cor in 'uniform' 'gaussian' 'background' 'impulse' 'upsampling' 'distortion_rbf' 'distortion_rbf_inv' 'density' 'density_inc' 'shear' 'rotation' 'cutout' 'distortion' 'occlusion' 'lidar'; do for sev in 1 2 3 4 5; do CUDA_VISIBLE_DEVICES=0 python main.py --entry test --model-path runs/cutmix_r_${model}_run_1/model_best_test.pth --exp-config configs/tent_cutmix/${model}.yaml --severity ${sev} --corruption ${cor} --output ./output/${model}_megamerger_${cor}_${sev}.txt done done done ================================================ FILE: gdrivedl.py ================================================ #!/usr/bin/env python from __future__ import unicode_literals import json import os import re import sys import unicodedata import argparse import logging try: #Python3 from urllib.request import Request, urlopen, build_opener, HTTPCookieProcessor from http.cookiejar import CookieJar except ImportError: #Python2 from urllib2 import Request, urlopen, build_opener, HTTPCookieProcessor from cookielib import CookieJar ITEM_URL = 'https://drive.google.com/open?id={id}' FILE_URL = 'https://docs.google.com/uc?export=download&id={id}&confirm={confirm}' FOLDER_URL = 'https://drive.google.com/embeddedfolderview?id={id}#list' CHUNKSIZE = 4096 USER_AGENT = 'Mozilla/5.0' ID_PATTERNS = [ re.compile('/file/d/([0-9A-Za-z_-]{10,})(?:/|$)', re.IGNORECASE), re.compile('/folders/([0-9A-Za-z_-]{10,})(?:/|$)', re.IGNORECASE), re.compile('id=([0-9A-Za-z_-]{10,})(?:&|$)', re.IGNORECASE), re.compile('([0-9A-Za-z_-]{10,})', re.IGNORECASE) ] FOLDER_PATTERN = re.compile('(.*?)', re.DOTALL | re.IGNORECASE) CONFIRM_PATTERN = re.compile("download_warning[0-9A-Za-z_-]+=([0-9A-Za-z_-]+);", re.IGNORECASE) FILENAME_PATTERN = re.compile('attachment;filename="(.*?)"', re.IGNORECASE) def output(text): try: sys.stdout.write(text) except UnicodeEncodeError: sys.stdout.write(text.encode('utf8')) # Big thanks to leo_wallentin for below sanitize function (modified slightly for this script) # https://gitlab.com/jplusplus/sanitize-filename/-/blob/master/sanitize_filename/sanitize_filename.py def sanitize(filename): blacklist = ["\\", "/", ":", "*", "?", "\"", "<", ">", "|", "\0"] reserved = [ "CON", "PRN", "AUX", "NUL", "COM1", "COM2", "COM3", "COM4", "COM5", "COM6", "COM7", "COM8", "COM9", "LPT1", "LPT2", "LPT3", "LPT4", "LPT5", "LPT6", "LPT7", "LPT8", "LPT9", ] filename = "".join(c for c in filename if c not in blacklist) filename = "".join(c for c in filename if 31 < ord(c)) filename = unicodedata.normalize("NFKD", filename) filename = filename.rstrip(". ") filename = filename.strip() if all([x == "." for x in filename]): filename = "_" + filename if filename in reserved: filename = "_" + filename if len(filename) == 0: filename = "_" if len(filename) > 255: parts = re.split(r"/|\\", filename)[-1].split(".") if len(parts) > 1: ext = "." + parts.pop() filename = filename[:-len(ext)] else: ext = "" if filename == "": filename = "_" if len(ext) > 254: ext = ext[254:] maxl = 255 - len(ext) filename = filename[:maxl] filename = filename + ext filename = filename.rstrip(". ") if len(filename) == 0: filename = "_" return filename def url_to_id(url): for pattern in ID_PATTERNS: match = pattern.search(url) if match: return match.group(1) logging.error('Unable to get ID from {}'.format(url)) sys.exit(1) class GDriveDL(object): def __init__(self, quiet=False, overwrite=False): self._quiet = quiet self._overwrite = overwrite self._create_empty_dirs = True self._opener = build_opener(HTTPCookieProcessor(CookieJar())) def _request(self, url): logging.debug('Requesting: {}'.format(url)) req = Request(url, headers={'User-Agent': USER_AGENT}) return self._opener.open(req) def process_url(self, url, directory, filename=None): id = url_to_id(url) if '://' not in url: url = ITEM_URL.format(id=id) resp = self._request(url) url = resp.geturl() if '/file/' in url.lower(): self.process_file(id, directory, filename=filename) elif '/folders/' in url.lower(): if filename: logging.warn("Ignoring --output-document option for folder download") self.process_folder(id, directory) else: logging.error('That id {} returned an unknown url {}'.format(id, url)) sys.exit(1) def process_folder(self, id, directory): url = FOLDER_URL.format(id=id) resp = self._request(url) html = resp.read().decode('utf-8') matches = re.findall(FOLDER_PATTERN, html) if not matches and 'ServiceLogin' in html: logging.error('Folder: {} does not have link sharing enabled'.format(id)) sys.exit(1) for match in matches: url, item_name = match id = url_to_id(url) if '/file/' in url.lower(): self.process_file(id, directory, filename=sanitize(item_name)) elif '/folders/' in url.lower(): self.process_folder(id, os.path.join(directory, sanitize(item_name))) if self._create_empty_dirs and not os.path.exists(directory): os.makedirs(directory) logging.info('Directory: {directory} [Created]'.format(directory=directory)) def process_file(self, id, directory, filename=None, confirm=''): file_path = None if filename: file_path = filename if os.path.isabs(filename) else os.path.join(directory, filename) if not self._overwrite and os.path.exists(file_path): logging.info('{file_path} [Exists]'.format(file_path=file_path)) return url = FILE_URL.format(id=id, confirm=confirm) resp = self._request(url) if 'ServiceLogin' in resp.url: logging.error('File: {} does not have link sharing enabled'.format(id)) sys.exit(1) cookies = resp.headers.get('Set-Cookie') or '' if not confirm and 'download_warning' in cookies: confirm = CONFIRM_PATTERN.search(cookies) return self.process_file(id, directory, filename=filename, confirm=confirm.group(1)) if not file_path: filename = FILENAME_PATTERN.search(resp.headers.get('content-disposition')).group(1) file_path = os.path.join(directory, sanitize(filename)) if not self._overwrite and os.path.exists(file_path): logging.info('{file_path} [Exists]'.format(file_path=file_path)) return directory = os.path.dirname(file_path) if not os.path.exists(directory): os.makedirs(directory) logging.info('Directory: {directory} [Created]'.format(directory=directory)) try: with open(file_path, 'wb') as f: dl = 0 last_out = 0 while True: chunk = resp.read(CHUNKSIZE) if not chunk: break if b'Too many users have viewed or downloaded this file recently' in chunk: logging.error('Quota exceeded for this file') sys.exit(1) dl += len(chunk) f.write(chunk) if not self._quiet and (not last_out or dl-last_out > 1048576): output("\r{} {:.2f}MB".format( file_path, dl / 1024 / 1024, )) last_out = dl sys.stdout.flush() except: if os.path.exists(file_path): os.remove(file_path) raise if not self._quiet: output('\n') def main(args=None): parser = argparse.ArgumentParser(description='Download Google Drive files & folders') parser.add_argument("url", help="Shared Google Drive URL") parser.add_argument("-P", "--directory-prefix", default='.', help="Output directory (default is current directory)") parser.add_argument("-O", "--output-document", help="Output filename. Defaults to the GDrive filename. Not valid when downloading folders") parser.add_argument("-q", "--quiet", help="Disable console output", default=False, action="store_true") args = parser.parse_args(args) if args.quiet: logging.basicConfig(format='%(levelname)s: %(message)s', level=logging.WARN) else: logging.basicConfig(format='%(levelname)s: %(message)s', level=logging.INFO) url = args.url id = '' for pattern in ID_PATTERNS: match = pattern.search(url) if match: id = match.group(1) break if not id: logging.error('Unable to get ID from {}'.format(url)) sys.exit(1) gdrive = GDriveDL(quiet=args.quiet, overwrite=args.output_document is not None) gdrive.process_url(url, directory=args.directory_prefix, filename=args.output_document) if __name__ == "__main__": main() ================================================ FILE: main.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F import torch.optim.lr_scheduler as lr_scheduler import random from dataloader import create_dataloader from time import time from datetime import datetime from progressbar import ProgressBar import models from collections import defaultdict import os import numpy as np import argparse from all_utils import ( TensorboardManager, PerfTrackTrain, PerfTrackVal, TrackTrain, smooth_loss, DATASET_NUM_CLASS, rscnn_voting_evaluate_cls, pn2_vote_evaluate_cls) from configs import get_cfg_defaults import pprint from pointnet_pyt.pointnet.model import feature_transform_regularizer import sys import aug_utils from third_party import bn_helper, tent_helper DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if DEVICE.type == 'cpu': print('WARNING: Using CPU') def adapt_bn(data,model,cfg): model = bn_helper.configure_model(model,eps=1e-5, momentum=0.1,reset_stats=False,no_stats=False) for _ in range(cfg.ITER): model(**data) print("Adaptation Done ...") model.eval() return model def adapt_tent(data,model,cfg): model = tent_helper.configure_model(model,eps=1e-5, momentum=0.1) parameter,_ = tent_helper.collect_params(model) optimizer_tent = torch.optim.SGD(parameter, lr=0.001,momentum=0.9) for _ in range(cfg.ITER): # index = np.random.choice(args.number,args.batch_size,replace=False) tent_helper.forward_and_adapt(data,model,optimizer_tent) print("Adaptation Done ...") model.eval() return model def check_inp_fmt(task, data_batch, dataset_name): if task in ['cls', 'cls_trans']: # assert set(data_batch.keys()) == {'pc', 'label'} # print(data_batch['pc'],data_batch['label']) pc, label = data_batch['pc'], data_batch['label'] # special case made for modelnet40_dgcnn to match the # original implementation # dgcnn loads torch.DoubleTensor for the test dataset if dataset_name == 'modelnet40_dgcnn': assert isinstance(pc, torch.FloatTensor) or isinstance( pc, torch.DoubleTensor) else: assert isinstance(pc, torch.FloatTensor) assert isinstance(label, torch.LongTensor) assert len(pc.shape) == 3 assert len(label.shape) == 1 b1, _, y = pc.shape[0], pc.shape[1], pc.shape[2] b2 = label.shape[0] assert b1 == b2 assert y == 3 assert label.max().item() < DATASET_NUM_CLASS[dataset_name] assert label.min().item() >= 0 else: assert NotImplemented def check_out_fmt(task, out, dataset_name): if task == 'cls': assert set(out.keys()) == {'logit'} logit = out['logit'] assert isinstance(logit, torch.FloatTensor if DEVICE.type == 'cpu' else torch.cuda.FloatTensor) assert len(logit.shape) == 2 assert DATASET_NUM_CLASS[dataset_name] == logit.shape[1] elif task == 'cls_trans': assert set(out.keys()) == {'logit', 'trans_feat'} logit = out['logit'] trans_feat = out['trans_feat'] assert isinstance(logit, torch.FloatTensor if DEVICE.type == 'cpu' else torch.cuda.FloatTensor) assert isinstance(trans_feat, torch.FloatTensor if DEVICE.type == 'cpu' else torch.cuda.FloatTensor) assert len(logit.shape) == 2 assert len(trans_feat.shape) == 3 assert trans_feat.shape[0] == logit.shape[0] # 64 coming from pointnet implementation assert (trans_feat.shape[1] == trans_feat.shape[2]) and (trans_feat.shape[1] == 64) assert DATASET_NUM_CLASS[dataset_name] == logit.shape[1] else: assert NotImplemented def get_inp(task, model, data_batch, batch_proc, dataset_name): check_inp_fmt(task, data_batch, dataset_name) if not batch_proc is None: data_batch = batch_proc(data_batch, DEVICE) check_inp_fmt(task, data_batch, dataset_name) if isinstance(model, nn.DataParallel): model_type = type(model.module) else: model_type = type(model) if task in ['cls', 'cls_trans']: pc = data_batch['pc'] inp = {'pc': pc} else: assert False return inp def get_loss(task, loss_name, data_batch, out, dataset_name): """ Returns the tensor loss function :param task: :param loss_name: :param data_batch: batched data; note not applied data_batch :param out: output from the model :param dataset_name: :return: tensor """ check_out_fmt(task, out, dataset_name) if task == 'cls': label = data_batch['label'].to(out['logit'].device) if loss_name == 'cross_entropy': if 'label_2' in data_batch.keys(): label_2 = data_batch['label_2'].to(out['logit'].device) if isinstance(data_batch['lam'],torch.Tensor): loss = 0 for i in range(data_batch['pc'].shape[0]): loss_tmp = smooth_loss(out['logit'][i].unsqueeze(0), label[i].unsqueeze(0).long()) * (1 - data_batch['lam'][i]) + smooth_loss(out['logit'][i].unsqueeze(0), label_2[i].unsqueeze(0).long()) * data_batch['lam'][i] loss += loss_tmp loss = loss / data_batch['pc'].shape[0] else: loss = smooth_loss(out['logit'], label) * (1 - data_batch['lam']) + smooth_loss(out['logit'], label_2) * data_batch['lam'] else: loss = F.cross_entropy(out['logit'], label) # source: https://github.com/WangYueFt/dgcnn/blob/master/pytorch/util.py elif loss_name == 'smooth': if 'label_2' in data_batch.keys(): label_2 = data_batch['label_2'].to(out['logit'].device) if isinstance(data_batch['lam'],torch.Tensor): loss = 0 for i in range(data_batch['pc'].shape[0]): loss_tmp = smooth_loss(out['logit'][i].unsqueeze(0), label[i].unsqueeze(0).long()) * (1 - data_batch['lam'][i]) + smooth_loss(out['logit'][i].unsqueeze(0), label_2[i].unsqueeze(0).long()) * data_batch['lam'][i] loss += loss_tmp loss = loss / data_batch['pc'].shape[0] else: loss = smooth_loss(out['logit'], label) * (1 - data_batch['lam']) + smooth_loss(out['logit'], label_2) * data_batch['lam'] else: loss = smooth_loss(out['logit'], label) else: assert False elif task == 'cls_trans': label = data_batch['label'].to(out['logit'].device) trans_feat = out['trans_feat'] logit = out['logit'] if loss_name == 'cross_entropy': if 'label_2' in data_batch.keys(): label_2 = data_batch['label_2'].to(out['logit'].device) if isinstance(data_batch['lam'],torch.Tensor): loss = 0 for i in range(data_batch['pc'].shape[0]): loss_tmp = smooth_loss(out['logit'][i].unsqueeze(0), label[i].unsqueeze(0).long()) * (1 - data_batch['lam'][i]) + smooth_loss(out['logit'][i].unsqueeze(0), label_2[i].unsqueeze(0).long()) * data_batch['lam'][i] loss += loss_tmp loss = loss / data_batch['pc'].shape[0] else: loss = smooth_loss(out['logit'], label) * (1 - data_batch['lam']) + smooth_loss(out['logit'], label_2) * data_batch['lam'] else: loss = F.cross_entropy(out['logit'], label) loss += feature_transform_regularizer(trans_feat) * 0.001 elif loss_name == 'smooth': if 'label_2' in data_batch.keys(): label_2 = data_batch['label_2'].to(out['logit'].device) if isinstance(data_batch['lam'],torch.Tensor): loss = 0 for i in range(data_batch['pc'].shape[0]): loss_tmp = smooth_loss(out['logit'][i].unsqueeze(0), label[i].unsqueeze(0).long()) * (1 - data_batch['lam'][i]) + smooth_loss(out['logit'][i].unsqueeze(0), label_2[i].unsqueeze(0).long()) * data_batch['lam'][i] loss += loss_tmp loss = loss / data_batch['pc'].shape[0] else: loss = smooth_loss(out['logit'], label) * (1 - data_batch['lam']) + smooth_loss(out['logit'], label_2) * data_batch['lam'] else: loss = smooth_loss(out['logit'], label) loss += feature_transform_regularizer(trans_feat) * 0.001 else: assert False else: assert False return loss def validate(task, loader, model, dataset_name, adapt = None, confusion = False): model.eval() def get_extra_param(): return None perf = PerfTrackVal(task, extra_param=get_extra_param()) time_dl = 0 time_gi = 0 time_model = 0 time_upd = 0 with torch.no_grad(): bar = ProgressBar(max_value=len(loader)) time5 = time() if confusion: pred = [] ground = [] for i, data_batch in enumerate(loader): time1 = time() inp = get_inp(task, model, data_batch, loader.dataset.batch_proc, dataset_name) time2 = time() if adapt.METHOD == 'bn': model = adapt_bn(inp,model,adapt) elif adapt.METHOD == 'tent': model = adapt_tent(inp,model,adapt) out = model(**inp) if confusion: pred.append(out['logit'].squeeze().cpu()) ground.append(data_batch['label'].squeeze().cpu()) time3 = time() perf.update(data_batch=data_batch, out=out) time4 = time() time_dl += (time1 - time5) time_gi += (time2 - time1) time_model += (time3 - time2) time_upd += (time4 - time3) time5 = time() bar.update(i) print(f"Time DL: {time_dl}, Time Get Inp: {time_gi}, Time Model: {time_model}, Time Update: {time_upd}") if not confusion: return perf.agg() else: pred = np.argmax(torch.cat(pred).numpy(), axis=1) # print(pred) ground = torch.cat(ground).numpy() # print(ground) return perf.agg(), pred, ground def train(task, loader, model, optimizer, loss_name, dataset_name, cfg): model.train() def get_extra_param(): return None perf = PerfTrackTrain(task, extra_param=get_extra_param()) time_forward = 0 time_backward = 0 time_data_loading = 0 time3 = time() for i, data_batch in enumerate(loader): time1 = time() if cfg.AUG.NAME == 'cutmix_r': data_batch = aug_utils.cutmix_r(data_batch,cfg) elif cfg.AUG.NAME == 'cutmix_k': data_batch = aug_utils.cutmix_k(data_batch,cfg) elif cfg.AUG.NAME == 'mixup': data_batch = aug_utils.mixup(data_batch,cfg) elif cfg.AUG.NAME == 'rsmix': data_batch = aug_utils.rsmix(data_batch,cfg) elif cfg.AUG.NAME == 'pgd': data_batch = aug_utils.pgd(data_batch,model, task, loss_name, dataset_name) model.train() # print(data_batch) inp = get_inp(task, model, data_batch, loader.dataset.batch_proc, dataset_name) out = model(**inp) loss = get_loss(task, loss_name, data_batch, out, dataset_name) perf.update_all(data_batch=data_batch, out=out, loss=loss) time2 = time() if loss.ne(loss).any(): print("WARNING: avoiding step as nan in the loss") else: optimizer.zero_grad() loss.backward() bad_grad = False for x in model.parameters(): if x.grad is not None: if x.grad.ne(x.grad).any(): print("WARNING: nan in a gradient") bad_grad = True break if ((x.grad == float('inf')) | (x.grad == float('-inf'))).any(): print("WARNING: inf in a gradient") bad_grad = True break if bad_grad: print("WARNING: avoiding step as bad gradient") else: optimizer.step() time_data_loading += (time1 - time3) time_forward += (time2 - time1) time3 = time() time_backward += (time3 - time2) if i % 50 == 0: print( f"[{i}/{len(loader)}] avg_loss: {perf.agg_loss()}, FW time = {round(time_forward, 2)}, " f"BW time = {round(time_backward, 2)}, DL time = {round(time_data_loading, 2)}") return perf.agg(), perf.agg_loss() def save_checkpoint(id, epoch, model, optimizer, lr_sched, bnm_sched, test_perf, cfg): model.cpu() path = f"./runs/{cfg.EXP.EXP_ID}/model_{id}.pth" torch.save({ 'cfg': vars(cfg), 'epoch': epoch, 'model_state': model.state_dict(), 'optimizer_state': optimizer.state_dict(), 'lr_sched_state': lr_sched.state_dict(), 'bnm_sched_state': bnm_sched.state_dict() if bnm_sched is not None else None, 'test_perf': test_perf, }, path) print('Checkpoint saved to %s' % path) model.to(DEVICE) def load_best_checkpoint(model, cfg): path = f"./runs/{cfg.EXP.EXP_ID}/model_best.pth" checkpoint = torch.load(path) model.load_state_dict(checkpoint['model_state']) print('Checkpoint loaded from %s' % path) def load_model_opt_sched(model, optimizer, lr_sched, bnm_sched, model_path): print(f'Recovering model and checkpoint from {model_path}') checkpoint = torch.load(model_path) try: model.load_state_dict(checkpoint['model_state']) except: if isinstance(model, nn.DataParallel): model.module.load_state_dict(checkpoint['model_state']) else: model = nn.DataParallel(model) model.load_state_dict(checkpoint['model_state']) model = model.module optimizer.load_state_dict(checkpoint['optimizer_state']) # for backward compatibility with saved models if 'lr_sched_state' in checkpoint: lr_sched.load_state_dict(checkpoint['lr_sched_state']) if checkpoint['bnm_sched_state'] is not None: bnm_sched.load_state_dict(checkpoint['bnm_sched_state']) else: print("WARNING: lr scheduler and bnm scheduler states are not loaded.") return model def get_model(cfg): if cfg.EXP.MODEL_NAME == 'simpleview': model = models.MVModel( task=cfg.EXP.TASK, dataset=cfg.EXP.DATASET, **cfg.MODEL.MV) elif cfg.EXP.MODEL_NAME == 'rscnn': model = models.RSCNN( task=cfg.EXP.TASK, dataset=cfg.EXP.DATASET, **cfg.MODEL.RSCNN) elif cfg.EXP.MODEL_NAME == 'pointnet2': model = models.PointNet2( task=cfg.EXP.TASK, dataset=cfg.EXP.DATASET, **cfg.MODEL.PN2) elif cfg.EXP.MODEL_NAME == 'dgcnn': model = models.DGCNN( task=cfg.EXP.TASK, dataset=cfg.EXP.DATASET) elif cfg.EXP.MODEL_NAME == 'pointnet': model = models.PointNet( task=cfg.EXP.TASK, dataset=cfg.EXP.DATASET) elif cfg.EXP.MODEL_NAME == 'pct': model = models.Pct( task=cfg.EXP.TASK, dataset=cfg.EXP.DATASET) elif cfg.EXP.MODEL_NAME == 'pointMLP': model = models.pointMLP( task=cfg.EXP.TASK, dataset=cfg.EXP.DATASET) elif cfg.EXP.MODEL_NAME == 'pointMLP2': model = models.pointMLP2( task=cfg.EXP.TASK, dataset=cfg.EXP.DATASET) elif cfg.EXP.MODEL_NAME == 'curvenet': model = models.CurveNet( task=cfg.EXP.TASK, dataset=cfg.EXP.DATASET) elif cfg.EXP.MODEL_NAME == 'gdanet': model = models.GDANET( task=cfg.EXP.TASK, dataset=cfg.EXP.DATASET) else: assert False return model def get_metric_from_perf(task, perf, metric_name): if task in ['cls', 'cls_trans']: assert metric_name in ['acc'] metric = perf[metric_name] else: assert False return metric def get_optimizer(optim_name, tr_arg, model): if optim_name == 'vanilla': optimizer = torch.optim.Adam( model.parameters(), lr=tr_arg.learning_rate, weight_decay=tr_arg.l2) lr_sched = lr_scheduler.ReduceLROnPlateau( optimizer, mode='min', factor=tr_arg.lr_decay_factor, patience=tr_arg.lr_reduce_patience, verbose=True, min_lr=tr_arg.lr_clip) bnm_sched = None elif optim_name == 'pct': pass optimizer = torch.optim.Adam( model.parameters(), lr=tr_arg.learning_rate, weight_decay=tr_arg.l2) lr_sched = lr_scheduler.CosineAnnealingLR( optimizer, tr_arg.num_epochs, eta_min=tr_arg.learning_rate) bnm_sched = None else: assert False return optimizer, lr_sched, bnm_sched def entry_train(cfg, resume=False, model_path=""): loader_train = create_dataloader(split='train', cfg=cfg) loader_valid = create_dataloader(split='valid', cfg=cfg) loader_test = create_dataloader(split='test', cfg=cfg) model = get_model(cfg) model.to(DEVICE) print(model) if torch.cuda.device_count() > 1: model = nn.DataParallel(model) optimizer, lr_sched, bnm_sched = get_optimizer(cfg.EXP.OPTIMIZER, cfg.TRAIN, model) if resume: model = load_model_opt_sched(model, optimizer, lr_sched, bnm_sched, model_path) else: assert model_path == "" log_dir = f"./runs/{cfg.EXP.EXP_ID}" if not os.path.exists(log_dir): os.makedirs(log_dir) tb = TensorboardManager(log_dir) track_train = TrackTrain(early_stop_patience=cfg.TRAIN.early_stop) for epoch in range(cfg.TRAIN.num_epochs): print(f'Epoch {epoch}') print('Training..') train_perf, train_loss = train(cfg.EXP.TASK, loader_train, model, optimizer, cfg.EXP.LOSS_NAME, cfg.EXP.DATASET, cfg) pprint.pprint(train_perf, width=80) tb.update('train', epoch, train_perf) if (not cfg.EXP_EXTRA.no_val) and epoch % cfg.EXP_EXTRA.val_eval_freq == 0: print('\nValidating..') val_perf = validate(cfg.EXP.TASK, loader_valid, model, cfg.EXP.DATASET, cfg.ADAPT) pprint.pprint(val_perf, width=80) tb.update('val', epoch, val_perf) else: val_perf = defaultdict(float) if (not cfg.EXP_EXTRA.no_test) and (epoch % cfg.EXP_EXTRA.test_eval_freq == 0): print('\nTesting..') test_perf = validate(cfg.EXP.TASK, loader_test, model, cfg.EXP.DATASET, cfg.ADAPT) pprint.pprint(test_perf, width=80) tb.update('test', epoch, test_perf) else: test_perf = defaultdict(float) track_train.record_epoch( epoch_id=epoch, train_metric=get_metric_from_perf(cfg.EXP.TASK, train_perf, cfg.EXP.METRIC), val_metric=get_metric_from_perf(cfg.EXP.TASK, val_perf, cfg.EXP.METRIC), test_metric=get_metric_from_perf(cfg.EXP.TASK, test_perf, cfg.EXP.METRIC)) if (not cfg.EXP_EXTRA.no_val) and track_train.save_model(epoch_id=epoch, split='val'): print('Saving best model on the validation set') save_checkpoint('best_val', epoch, model, optimizer, lr_sched, bnm_sched, test_perf, cfg) if (not cfg.EXP_EXTRA.no_test) and track_train.save_model(epoch_id=epoch, split='test'): print('Saving best model on the test set') save_checkpoint('best_test', epoch, model, optimizer, lr_sched, bnm_sched, test_perf, cfg) if (not cfg.EXP_EXTRA.no_val) and track_train.early_stop(epoch_id=epoch): print(f"Early stopping at {epoch} as val acc did not improve for {cfg.TRAIN.early_stop} epochs.") break if (not (cfg.EXP_EXTRA.save_ckp == 0)) and (epoch % cfg.EXP_EXTRA.save_ckp == 0): save_checkpoint(f'{epoch}', epoch, model, optimizer, lr_sched, bnm_sched, test_perf, cfg) if cfg.EXP.OPTIMIZER == 'vanilla': assert bnm_sched is None lr_sched.step(train_loss) else: lr_sched.step() print('Saving the final model') save_checkpoint('final', epoch, model, optimizer, lr_sched, bnm_sched, test_perf, cfg) print('\nTesting on the final model..') last_test_perf = validate(cfg.EXP.TASK, loader_test, model, cfg.EXP.DATASET, cfg.ADAPT) pprint.pprint(last_test_perf, width=80) tb.close() def entry_test(cfg, test_or_valid, model_path="", confusion = False): split = "test" if test_or_valid else "valid" loader_test = create_dataloader(split=split, cfg=cfg) model = get_model(cfg) model.to(DEVICE) print(model) if torch.cuda.device_count() > 1: model = nn.DataParallel(model) optimizer, lr_sched, bnm_sched = get_optimizer(cfg.EXP.OPTIMIZER, cfg.TRAIN, model) model = load_model_opt_sched(model, optimizer, lr_sched, bnm_sched, model_path) model.eval() if confusion: test_perf, pred, ground = validate(cfg.EXP.TASK, loader_test, model, cfg.EXP.DATASET, cfg.ADAPT, confusion) print(pred.shape, ground.shape) #### some hardcoding ####### np.save('./output/' + cfg.EXP.MODEL_NAME + '_' + cfg.DATALOADER.MODELNET40_C.corruption + '_' + str(cfg.DATALOADER.MODELNET40_C.severity) + '_pred.npy',pred ) np.save('./output/' + cfg.EXP.MODEL_NAME + '_' + cfg.DATALOADER.MODELNET40_C.corruption + '_' + str(cfg.DATALOADER.MODELNET40_C.severity) + '_ground.npy',ground) #### #### #### #### #### #### else: test_perf = validate(cfg.EXP.TASK, loader_test, model, cfg.EXP.DATASET, cfg.ADAPT, confusion) print("Model: {} Corruption: {} Severity: {} Acc: {} Class Acc: {}".format(cfg.EXP.MODEL_NAME, cfg.DATALOADER.MODELNET40_C.corruption, cfg.DATALOADER.MODELNET40_C.severity,test_perf['acc'],test_perf['class_acc']),file=file_object,flush=True) pprint.pprint(test_perf, width=80) return test_perf def rscnn_vote_evaluation(cfg, model_path, log_file): model = get_model(cfg) checkpoint = torch.load(model_path) try: model.load_state_dict(checkpoint['model_state']) except: print("WARNING: using dataparallel to load data") model = nn.DataParallel(model) model.load_state_dict(checkpoint['model_state']) print(f"Checkpoint loaded from {model_path}") model.to(DEVICE) model.eval() assert cfg.EXP.DATASET in ["modelnet40_rscnn"] loader_test = create_dataloader(split='test', cfg=cfg) rscnn_voting_evaluate_cls( loader=loader_test, model=model, data_batch_to_points_target=lambda x: (x['pc'], x['label']), points_to_inp=lambda x: {'pc': x}, out_to_prob=lambda x: F.softmax(x['logit'], dim=1), log_file=log_file ) def pn2_vote_evaluation(cfg, model_path, log_file): assert cfg.EXP.DATASET in ["modelnet40_pn2"] loader_test = create_dataloader(split='test', cfg=cfg) model = get_model(cfg) checkpoint = torch.load(model_path) try: model.load_state_dict(checkpoint['model_state']) except: print("WARNING: using dataparallel to load data") model = nn.DataParallel(model) model.load_state_dict(checkpoint['model_state']) print(f"Checkpoint loaded from {model_path}") model.to(DEVICE) model.eval() pn2_vote_evaluate_cls(loader_test, model, log_file) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.set_defaults(entry=lambda cmd_args: parser.print_help()) parser.add_argument('--entry', type=str, default="train") parser.add_argument('--exp-config', type=str, default="") parser.add_argument('--model-path', type=str, default="") parser.add_argument('--resume', action="store_true", default=False) # parser.add_argument('--gpu',type=str,default='0', # help="Which gpu to use") parser.add_argument('--corruption',type=str,default='uniform', help="Which corruption to use") parser.add_argument('--output',type=str,default='./test.txt', help="path to output file") parser.add_argument('--severity',type=int,default=1, help="Which severity to use") parser.add_argument('--confusion', action="store_true", default=False, help="whether to output confusion matrix data") cmd_args = parser.parse_args() # os.environ['CUDA_VISIBLE_DEVICES'] = cmd_args.gpu if cmd_args.entry == "train": assert not cmd_args.exp_config == "" if not cmd_args.resume: assert cmd_args.model_path == "" cfg = get_cfg_defaults() cfg.merge_from_file(cmd_args.exp_config) if cfg.EXP.EXP_ID == "": cfg.EXP.EXP_ID = str(datetime.now())[:-7].replace(' ', '-') cfg.freeze() print(cfg) random.seed(cfg.EXP.SEED) np.random.seed(cfg.EXP.SEED) torch.manual_seed(cfg.EXP.SEED) entry_train(cfg, cmd_args.resume, cmd_args.model_path) elif cmd_args.entry in ["test", "valid"]: file_object = open(cmd_args.output, 'a') assert not cmd_args.exp_config == "" assert not cmd_args.model_path == "" cfg = get_cfg_defaults() cfg.merge_from_file(cmd_args.exp_config) if cfg.EXP.DATASET == "modelnet40_c": cfg.DATALOADER.MODELNET40_C.corruption = cmd_args.corruption cfg.DATALOADER.MODELNET40_C.severity = cmd_args.severity cfg.freeze() print(cfg) random.seed(cfg.EXP.SEED) np.random.seed(cfg.EXP.SEED) torch.manual_seed(cfg.EXP.SEED) test_or_valid = cmd_args.entry == "test" entry_test(cfg, test_or_valid, cmd_args.model_path,cmd_args.confusion) elif cmd_args.entry in ["rscnn_vote", "pn2_vote"]: assert not cmd_args.exp_config == "" assert not cmd_args.model_path == "" log_file = f"vote_log/{cmd_args.model_path.replace('/', '_')}_{cmd_args.entry.replace('/', '_')}.log" cfg = get_cfg_defaults() cfg.merge_from_file(cmd_args.exp_config) cfg.freeze() print(cfg) seed = cfg.EXP.SEED random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = True torch.backends.cudnn.deterministic = True if cmd_args.entry == "rscnn_vote": rscnn_vote_evaluation(cfg, cmd_args.model_path, log_file) elif cmd_args.entry == "pn2_vote": pn2_vote_evaluation(cfg, cmd_args.model_path, log_file) else: assert False ================================================ FILE: models/__init__.py ================================================ ''' Description: Autor: Jiachen Sun Date: 2022-02-17 13:35:52 LastEditors: Jiachen Sun LastEditTime: 2022-02-22 23:36:25 ''' from .mv import MVModel from .rscnn import RSCNN from .pointnet2 import PointNet2 from .dgcnn import DGCNN from .pointnet import PointNet from .pct import Pct from .pointmlp import pointMLP from .pointmlp2 import pointMLP2 from .curvenet import CurveNet from .gdanet import GDANET ================================================ FILE: models/curvenet.py ================================================ ''' Description: Autor: Jiachen Sun Date: 2022-02-17 20:37:07 LastEditors: Jiachen Sun LastEditTime: 2022-02-17 20:42:20 ''' import torch.nn as nn import torch.nn.functional as F from CurveNet.core.models.curvenet_cls import CurveNet as CurveNet_og from all_utils import DATASET_NUM_CLASS class CurveNet(nn.Module): def __init__(self, task, dataset): super().__init__() self.task = task self.dataset = dataset if task == "cls": num_classes = DATASET_NUM_CLASS[dataset] self.model = CurveNet_og(num_classes=num_classes) else: assert False def forward(self, pc, cls=None): pc = pc.to(next(self.parameters()).device) pc = pc.permute(0, 2, 1).contiguous() if self.task == 'cls': assert cls is None logit = self.model(pc) out = {'logit': logit} else: assert False return out ================================================ FILE: models/dgcnn.py ================================================ import torch.nn as nn import torch.nn.functional as F from dgcnn.pytorch.model import DGCNN as DGCNN_original from all_utils import DATASET_NUM_CLASS class DGCNN(nn.Module): def __init__(self, task, dataset): super().__init__() self.task = task self.dataset = dataset if task == "cls": num_classes = DATASET_NUM_CLASS[dataset] # default arguments class Args: def __init__(self): self.k = 20 self.emb_dims = 1024 self.dropout = 0.5 self.leaky_relu = 1 args = Args() self.model = DGCNN_original(args, output_channels=num_classes) else: assert False def forward(self, pc, cls=None): pc = pc.to(next(self.parameters()).device) pc = pc.permute(0, 2, 1).contiguous() if self.task == 'cls': assert cls is None logit = self.model(pc) out = {'logit': logit} else: assert False return out ================================================ FILE: models/gdanet.py ================================================ ''' Description: Autor: Jiachen Sun Date: 2022-02-22 23:22:17 LastEditors: Jiachen Sun LastEditTime: 2022-02-23 00:16:25 ''' import torch import torch.nn as nn from GDANet.model.GDANet_cls import GDANET as GDANET_og from all_utils import DATASET_NUM_CLASS class GDANET(nn.Module): def __init__(self, task, dataset): super().__init__() self.task = task num_class = DATASET_NUM_CLASS[dataset] if task == 'cls': self.model = GDANET_og(number_class=num_class) else: assert False def forward(self, pc, normal=None, cls=None): # batch_size = pc.shape[0] pc=pc.permute(0,2,1).contiguous() pc = pc.to(next(self.parameters()).device) if self.task == 'cls': assert cls is None assert normal is None logit = self.model(pc) out = {'logit': logit} else: assert False return out ================================================ FILE: models/model_utils.py ================================================ import torch.nn as nn # from syncbn_pyt.modules.nn import BatchNorm2d as BatchNorm2dSync class Squeeze(nn.Module): def __init__(self): super().__init__() def forward(self, inp): return inp.squeeze() class BatchNormPoint(nn.Module): def __init__(self, feat_size, sync_bn=False): super().__init__() self.feat_size = feat_size self.sync_bn=sync_bn if self.sync_bn: self.bn = BatchNorm2dSync(feat_size) else: self.bn = nn.BatchNorm1d(feat_size) def forward(self, x): assert len(x.shape) == 3 s1, s2, s3 = x.shape[0], x.shape[1], x.shape[2] assert s3 == self.feat_size if self.sync_bn: # 4d input for BatchNorm2dSync x = x.view(s1 * s2, self.feat_size, 1, 1) x = self.bn(x) else: x = x.view(s1 * s2, self.feat_size) x = self.bn(x) return x.view(s1, s2, s3) ================================================ FILE: models/mv.py ================================================ import torch import torch.nn as nn from all_utils import DATASET_NUM_CLASS from models.model_utils import Squeeze, BatchNormPoint from models.mv_utils import PCViews class MVModel(nn.Module): def __init__(self, task, dataset, backbone, feat_size): super().__init__() assert task == 'cls' self.task = task self.num_class = DATASET_NUM_CLASS[dataset] self.dropout_p = 0.5 self.feat_size = feat_size pc_views = PCViews() self.num_views = pc_views.num_views self._get_img = pc_views.get_img img_layers, in_features = self.get_img_layers( backbone, feat_size=feat_size) self.img_model = nn.Sequential(*img_layers) self.final_fc = MVFC( num_views=self.num_views, in_features=in_features, out_features=self.num_class, dropout_p=self.dropout_p) def forward(self, pc): """ :param pc: :return: """ pc = pc.cuda() img = self.get_img(pc) feat = self.img_model(img) logit = self.final_fc(feat) out = {'logit': logit} return out def get_img(self, pc): img = self._get_img(pc) img = torch.tensor(img).float() img = img.to(next(self.parameters()).device) assert len(img.shape) == 3 img = img.unsqueeze(3) # [num_pc * num_views, 1, RESOLUTION, RESOLUTION] img = img.permute(0, 3, 1, 2) return img @staticmethod def get_img_layers(backbone, feat_size): """ Return layers for the image model """ from models.resnet import _resnet, BasicBlock assert backbone == 'resnet18' layers = [2, 2, 2, 2] block = BasicBlock backbone_mod = _resnet( arch=None, block=block, layers=layers, pretrained=False, progress=False, feature_size=feat_size, zero_init_residual=True) all_layers = [x for x in backbone_mod.children()] in_features = all_layers[-1].in_features # all layers except the final fc layer and the initial conv layers # WARNING: this is checked only for resnet models main_layers = all_layers[4:-1] img_layers = [ nn.Conv2d(1, feat_size, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False), nn.BatchNorm2d(feat_size, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True), nn.ReLU(inplace=True), *main_layers, Squeeze() ] return img_layers, in_features class MVFC(nn.Module): """ Final FC layers for the MV model """ def __init__(self, num_views, in_features, out_features, dropout_p): super().__init__() self.num_views = num_views self.in_features = in_features self.model = nn.Sequential( BatchNormPoint(in_features), # dropout before concatenation so that each view drops features independently nn.Dropout(dropout_p), nn.Flatten(), nn.Linear(in_features=in_features * self.num_views, out_features=in_features), nn.BatchNorm1d(in_features), nn.ReLU(), nn.Dropout(dropout_p), nn.Linear(in_features=in_features, out_features=out_features, bias=True)) def forward(self, feat): feat = feat.view((-1, self.num_views, self.in_features)) out = self.model(feat) return out ================================================ FILE: models/mv_utils.py ================================================ import numpy as np import torch RESOLUTION = 128 TRANS = -1.4 def euler2mat(angle): """Convert euler angles to rotation matrix. :param angle: [3] or [b, 3] :return rotmat: [3] or [b, 3, 3] source https://github.com/ClementPinard/SfmLearner-Pytorch/blob/master/inverse_warp.py """ if len(angle.size()) == 1: x, y, z = angle[0], angle[1], angle[2] _dim = 0 _view = [3, 3] elif len(angle.size()) == 2: b, _ = angle.size() x, y, z = angle[:, 0], angle[:, 1], angle[:, 2] _dim = 1 _view = [b, 3, 3] else: assert False cosz = torch.cos(z) sinz = torch.sin(z) # zero = torch.zeros([b], requires_grad=False, device=angle.device)[0] # one = torch.ones([b], requires_grad=False, device=angle.device)[0] zero = z.detach()*0 one = zero.detach()+1 zmat = torch.stack([cosz, -sinz, zero, sinz, cosz, zero, zero, zero, one], dim=_dim).reshape(_view) cosy = torch.cos(y) siny = torch.sin(y) ymat = torch.stack([cosy, zero, siny, zero, one, zero, -siny, zero, cosy], dim=_dim).reshape(_view) cosx = torch.cos(x) sinx = torch.sin(x) xmat = torch.stack([one, zero, zero, zero, cosx, -sinx, zero, sinx, cosx], dim=_dim).reshape(_view) rot_mat = xmat @ ymat @ zmat # print(rot_mat) return rot_mat def distribute(depth, _x, _y, size_x, size_y, image_height, image_width): """ Distributes the depth associated with each point to the discrete coordinates (image_height, image_width) in a region of size (size_x, size_y). :param depth: :param _x: :param _y: :param size_x: :param size_y: :param image_height: :param image_width: :return: """ assert size_x % 2 == 0 or size_x == 1 assert size_y % 2 == 0 or size_y == 1 batch, _ = depth.size() epsilon = torch.tensor([1e-12], requires_grad=False, device=depth.device) _i = torch.linspace(-size_x / 2, (size_x / 2) - 1, size_x, requires_grad=False, device=depth.device) _j = torch.linspace(-size_y / 2, (size_y / 2) - 1, size_y, requires_grad=False, device=depth.device) extended_x = _x.unsqueeze(2).repeat([1, 1, size_x]) + _i # [batch, num_points, size_x] extended_y = _y.unsqueeze(2).repeat([1, 1, size_y]) + _j # [batch, num_points, size_y] extended_x = extended_x.unsqueeze(3).repeat([1, 1, 1, size_y]) # [batch, num_points, size_x, size_y] extended_y = extended_y.unsqueeze(2).repeat([1, 1, size_x, 1]) # [batch, num_points, size_x, size_y] extended_x.ceil_() extended_y.ceil_() value = depth.unsqueeze(2).unsqueeze(3).repeat([1, 1, size_x, size_y]) # [batch, num_points, size_x, size_y] # all points that will be finally used masked_points = ((extended_x >= 0) * (extended_x <= image_height - 1) * (extended_y >= 0) * (extended_y <= image_width - 1) * (value >= 0)) true_extended_x = extended_x true_extended_y = extended_y # to prevent error extended_x = (extended_x % image_height) extended_y = (extended_y % image_width) # [batch, num_points, size_x, size_y] distance = torch.abs((extended_x - _x.unsqueeze(2).unsqueeze(3)) * (extended_y - _y.unsqueeze(2).unsqueeze(3))) weight = (masked_points.float() * (1 / (value + epsilon))) # [batch, num_points, size_x, size_y] weighted_value = value * weight weight = weight.view([batch, -1]) weighted_value = weighted_value.view([batch, -1]) coordinates = (extended_x.view([batch, -1]) * image_width) + extended_y.view( [batch, -1]) coord_max = image_height * image_width true_coordinates = (true_extended_x.view([batch, -1]) * image_width) + true_extended_y.view( [batch, -1]) true_coordinates[~masked_points.view([batch, -1])] = coord_max weight_scattered = torch.zeros( [batch, image_width * image_height], device=depth.device).scatter_add(1, coordinates.long(), weight) masked_zero_weight_scattered = (weight_scattered == 0.0) weight_scattered += masked_zero_weight_scattered.float() weighed_value_scattered = torch.zeros( [batch, image_width * image_height], device=depth.device).scatter_add(1, coordinates.long(), weighted_value) return weighed_value_scattered, weight_scattered def points2depth(points, image_height, image_width, size_x=4, size_y=4): """ :param points: [B, num_points, 3] :param image_width: :param image_height: :param size_x: :param size_y: :return: depth_recovered: [B, image_width, image_height] """ epsilon = torch.tensor([1e-12], requires_grad=False, device=points.device) # epsilon not needed, kept here to ensure exact replication of old version coord_x = (points[:, :, 0] / (points[:, :, 2] + epsilon)) * (image_width / image_height) # [batch, num_points] coord_y = (points[:, :, 1] / (points[:, :, 2] + epsilon)) # [batch, num_points] batch, total_points, _ = points.size() depth = points[:, :, 2] # [batch, num_points] # pdb.set_trace() _x = ((coord_x + 1) * image_height) / 2 _y = ((coord_y + 1) * image_width) / 2 weighed_value_scattered, weight_scattered = distribute( depth=depth, _x=_x, _y=_y, size_x=size_x, size_y=size_y, image_height=image_height, image_width=image_width) depth_recovered = (weighed_value_scattered / weight_scattered).view([ batch, image_height, image_width ]) return depth_recovered # source: https://discuss.pytorch.org/t/batched-index-select/9115/6 def batched_index_select(inp, dim, index): """ input: B x * x ... x * dim: 0 < scalar index: B x M """ views = [inp.shape[0]] + \ [1 if i != dim else -1 for i in range(1, len(inp.shape))] expanse = list(inp.shape) expanse[0] = -1 expanse[dim] = -1 index = index.view(views).expand(expanse) return torch.gather(inp, dim, index) def point_fea_img_fea(point_fea, point_coo, h, w): """ each point_coo is of the form (x*w + h). points not in the canvas are removed :param point_fea: [batch_size, num_points, feat_size] :param point_coo: [batch_size, num_points] :return: """ assert len(point_fea.shape) == 3 assert len(point_coo.shape) == 2 assert point_fea.shape[0:2] == point_coo.shape coo_max = ((h - 1) * w) + (w - 1) mask_point_coo = (point_coo >= 0) * (point_coo <= coo_max) point_coo *= mask_point_coo.float() point_fea *= mask_point_coo.float().unsqueeze(-1) bs, _, fs = point_fea.shape point_coo = point_coo.unsqueeze(2).repeat([1, 1, fs]) img_fea = torch.zeros([bs, h * w, fs], device=point_fea.device).scatter_add(1, point_coo.long(), point_fea) return img_fea def distribute_img_fea_points(img_fea, point_coord): """ :param img_fea: [B, C, H, W] :param point_coord: [B, num_points], each coordinate is a scalar value given by (x * W) + y :return point_fea: [B, num_points, C], for points with coordinates outside the image, we return 0 """ B, C, H, W = list(img_fea.size()) img_fea = img_fea.permute(0, 2, 3, 1).view([B, H*W, C]) coord_max = ((H - 1) * W) + (W - 1) mask_point_coord = (point_coord >= 0) * (point_coord <= coord_max) mask_point_coord = mask_point_coord.float() point_coord = mask_point_coord * point_coord point_fea = batched_index_select( inp=img_fea, dim=1, index=point_coord.long()) point_fea = mask_point_coord.unsqueeze(-1) * point_fea return point_fea class PCViews: """For creating images from PC based on the view information. Faster as the repeated operations are done only once whie initialization. """ def __init__(self): _views = np.asarray([ [[0 * np.pi / 2, 0, np.pi / 2], [0, 0, TRANS]], [[1 * np.pi / 2, 0, np.pi / 2], [0, 0, TRANS]], [[2 * np.pi / 2, 0, np.pi / 2], [0, 0, TRANS]], [[3 * np.pi / 2, 0, np.pi / 2], [0, 0, TRANS]], [[0, -np.pi / 2, np.pi / 2], [0, 0, TRANS]], [[0, np.pi / 2, np.pi / 2], [0, 0, TRANS]]]) self.num_views = 6 angle = torch.tensor(_views[:, 0, :]).float().cuda() self.rot_mat = euler2mat(angle).transpose(1, 2) self.translation = torch.tensor(_views[:, 1, :]).float().cuda() self.translation = self.translation.unsqueeze(1) def get_img(self, points): """Get image based on the prespecified specifications. Args: points (torch.tensor): of size [B, _, 3] Returns: img (torch.tensor): of size [B * self.num_views, RESOLUTION, RESOLUTION] """ b, _, _ = points.shape v = self.translation.shape[0] _points = self.point_transform( points=torch.repeat_interleave(points, v, dim=0), rot_mat=self.rot_mat.repeat(b, 1, 1), translation=self.translation.repeat(b, 1, 1)) img = points2depth( points=_points, image_height=RESOLUTION, image_width=RESOLUTION, size_x=1, size_y=1, ) return img @staticmethod def point_transform(points, rot_mat, translation): """ :param points: [batch, num_points, 3] :param rot_mat: [batch, 3] :param translation: [batch, 1, 3] :return: """ points = torch.matmul(points.to('cuda:0'), rot_mat.to('cuda:0')) points = points - translation return points ================================================ FILE: models/pct.py ================================================ import torch.nn as nn from PCT_Pytorch.model import Pct as Pct_original from all_utils import DATASET_NUM_CLASS class Pct(nn.Module): def __init__(self, task, dataset): super().__init__() self.task = task self.dataset = dataset if task == "cls": num_classes = DATASET_NUM_CLASS[dataset] # default arguments class Args: def __init__(self): self.dropout = 0.5 args = Args() self.model = Pct_original(args, output_channels=num_classes) else: assert False def forward(self, pc, cls=None): pc = pc.to(next(self.parameters()).device) pc = pc.permute(0, 2, 1).contiguous() if self.task == 'cls': assert cls is None logit = self.model(pc) out = {'logit': logit} else: assert False return out ================================================ FILE: models/pointmlp.py ================================================ ''' Description: Autor: Jiachen Sun Date: 2022-02-17 20:50:58 LastEditors: Jiachen Sun LastEditTime: 2022-02-21 21:18:02 ''' import torch.nn as nn from pointMLP.classification_ModelNet40.models.pointmlp import pointMLP as pointMLP_original from all_utils import DATASET_NUM_CLASS class pointMLP(nn.Module): def __init__(self, task, dataset): super().__init__() self.task = task self.dataset = dataset if task == "cls": num_classes = DATASET_NUM_CLASS[dataset] self.model = pointMLP_original(num_classes=num_classes) else: assert False def forward(self, pc, cls=None): pc = pc.to(next(self.parameters()).device) pc = pc.permute(0, 2, 1).contiguous() if self.task == 'cls': assert cls is None logit = self.model(pc) out = {'logit': logit} else: assert False return out ================================================ FILE: models/pointmlp2.py ================================================ ''' Description: Autor: Jiachen Sun Date: 2022-02-21 21:16:25 LastEditors: Jiachen Sun LastEditTime: 2022-02-21 21:17:57 ''' import torch.nn as nn from pointMLP.classification_ModelNet40.models.pointmlp import pointMLPElite as pointMLP_original from all_utils import DATASET_NUM_CLASS class pointMLP2(nn.Module): def __init__(self, task, dataset): super().__init__() self.task = task self.dataset = dataset if task == "cls": num_classes = DATASET_NUM_CLASS[dataset] self.model = pointMLP_original(num_classes=num_classes) else: assert False def forward(self, pc, cls=None): pc = pc.to(next(self.parameters()).device) pc = pc.permute(0, 2, 1).contiguous() if self.task == 'cls': assert cls is None logit = self.model(pc) out = {'logit': logit} else: assert False return out ================================================ FILE: models/pointnet.py ================================================ # based on: https://github.com/fxia22/pointnet.pytorch/blob/master/utils/train_classification.py import torch.nn as nn from pointnet_pyt.pointnet.model import PointNetCls from all_utils import DATASET_NUM_CLASS class PointNet(nn.Module): def __init__(self, dataset, task): super().__init__() self.task = task num_class = DATASET_NUM_CLASS[dataset] if self.task == 'cls_trans': self.model = PointNetCls(k=num_class, feature_transform=True) else: assert False def forward(self, pc, cls=None): pc = pc.to(next(self.parameters()).device) pc = pc.transpose(2, 1).float() if self.task == 'cls_trans': logit, _, trans_feat = self.model(pc) else: assert False out = {'logit': logit, 'trans_feat': trans_feat} return out ================================================ FILE: models/pointnet2.py ================================================ ''' Description: Autor: Jiachen Sun Date: 2022-02-16 22:23:16 LastEditors: Jiachen Sun LastEditTime: 2022-02-24 22:36:59 ''' import torch import torch.nn as nn from pointnet2_pyt.pointnet2.models.pointnet2_msg_cls import Pointnet2MSG from all_utils import DATASET_NUM_CLASS class PointNet2(nn.Module): def __init__(self, task, dataset, version_cls): super().__init__() self.task = task num_class = DATASET_NUM_CLASS[dataset] if task == 'cls': self.model = Pointnet2MSG(num_classes=num_class, input_channels=0, use_xyz=True, version=version_cls) else: assert False def forward(self, pc, normal=None, cls=None): pc = pc.to(next(self.parameters()).device) if self.task == 'cls': assert cls is None assert normal is None logit = self.model(pc) out = {'logit': logit} else: assert False return out ================================================ FILE: models/resnet.py ================================================ import torch import torch.nn as nn from torchvision.models.utils import load_state_dict_from_url __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d', 'wide_resnet50_2', 'wide_resnet101_2'] model_urls = { 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth', 'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth', 'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth', } def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation) def conv1x1(in_planes, out_planes, stride=1): """1x1 convolution""" return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) class BasicBlock(nn.Module): expansion = 1 def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None): super(BasicBlock, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d if groups != 1 or base_width != 64: raise ValueError('BasicBlock only supports groups=1 and base_width=64') if dilation > 1: raise NotImplementedError("Dilation > 1 not supported in BasicBlock") # Both self.conv1 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = norm_layer(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = norm_layer(planes) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) return out class Bottleneck(nn.Module): # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2) # while original implementation places the stride at the first 1x1 convolution(self.conv1) # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385. # This variant is also known as ResNet V1.5 and improves accuracy according to # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch. expansion = 4 def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64, dilation=1, norm_layer=None): super(Bottleneck, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d width = int(planes * (base_width / 64.)) * groups # Both self.conv2 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv1x1(inplanes, width) self.bn1 = norm_layer(width) self.conv2 = conv3x3(width, width, stride, groups, dilation) self.bn2 = norm_layer(width) self.conv3 = conv1x1(width, planes * self.expansion) self.bn3 = norm_layer(planes * self.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) return out class ResNet(nn.Module): def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None, feature_size=64): super(ResNet, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d self._norm_layer = norm_layer self.inplanes = feature_size self.dilation = 1 if replace_stride_with_dilation is None: # each element in the tuple indicates if we should replace # the 2x2 stride with a dilated convolution instead replace_stride_with_dilation = [False, False, False] if len(replace_stride_with_dilation) != 3: raise ValueError("replace_stride_with_dilation should be None " "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) self.groups = groups self.base_width = width_per_group self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = norm_layer(self.inplanes) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, feature_size, layers[0]) self.layer2 = self._make_layer(block, feature_size * 2, layers[1], stride=2, dilate=replace_stride_with_dilation[0]) self.layer3 = self._make_layer(block, feature_size * 4, layers[2], stride=2, dilate=replace_stride_with_dilation[1]) self.layer4 = self._make_layer(block, feature_size * 8, layers[3], stride=2, dilate=replace_stride_with_dilation[2]) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(feature_size * 8 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) # Zero-initialize the last BN in each residual branch, # so that the residual branch starts with zeros, and each residual block behaves like an identity. # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 if zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): nn.init.constant_(m.bn3.weight, 0) elif isinstance(m, BasicBlock): nn.init.constant_(m.bn2.weight, 0) def _make_layer(self, block, planes, blocks, stride=1, dilate=False): norm_layer = self._norm_layer downsample = None previous_dilation = self.dilation if dilate: self.dilation *= stride stride = 1 if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), norm_layer(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes, groups=self.groups, base_width=self.base_width, dilation=self.dilation, norm_layer=norm_layer)) return nn.Sequential(*layers) def _forward_impl(self, x): # See note [TorchScript super()] x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x def forward(self, x): return self._forward_impl(x) def _resnet(arch, block, layers, pretrained, progress, **kwargs): model = ResNet(block, layers, **kwargs) if pretrained: state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) model.load_state_dict(state_dict) return model def resnet18(pretrained=False, progress=True, **kwargs): r"""ResNet-18 model from `"Deep Residual Learning for Image Recognition" `_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs) def resnet34(pretrained=False, progress=True, **kwargs): r"""ResNet-34 model from `"Deep Residual Learning for Image Recognition" `_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, **kwargs) def resnet50(pretrained=False, progress=True, **kwargs): r"""ResNet-50 model from `"Deep Residual Learning for Image Recognition" `_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) def resnet101(pretrained=False, progress=True, **kwargs): r"""ResNet-101 model from `"Deep Residual Learning for Image Recognition" `_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs) def resnet152(pretrained=False, progress=True, **kwargs): r"""ResNet-152 model from `"Deep Residual Learning for Image Recognition" `_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress, **kwargs) def resnext50_32x4d(pretrained=False, progress=True, **kwargs): r"""ResNeXt-50 32x4d model from `"Aggregated Residual Transformation for Deep Neural Networks" `_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ kwargs['groups'] = 32 kwargs['width_per_group'] = 4 return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) def resnext101_32x8d(pretrained=False, progress=True, **kwargs): r"""ResNeXt-101 32x8d model from `"Aggregated Residual Transformation for Deep Neural Networks" `_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ kwargs['groups'] = 32 kwargs['width_per_group'] = 8 return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs) def wide_resnet50_2(pretrained=False, progress=True, **kwargs): r"""Wide ResNet-50-2 model from `"Wide Residual Networks" `_ The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ kwargs['width_per_group'] = 64 * 2 return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) def wide_resnet101_2(pretrained=False, progress=True, **kwargs): r"""Wide ResNet-101-2 model from `"Wide Residual Networks" `_ The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ kwargs['width_per_group'] = 64 * 2 return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs) ================================================ FILE: models/rscnn.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from rs_cnn.models import RSCNN_MSN_Seg, RSCNN_SSN_Cls from all_utils import DATASET_NUM_CLASS # distilled from: # https://github.com/Yochengliu/Relation-Shape-CNN/blob/master/models/rscnn_ssn_cls.py # https://github.com/Yochengliu/Relation-Shape-CNN/blob/master/models/rscnn_msn_seg.py class RSCNN(nn.Module): def __init__(self, task, dataset, ssn_or_msn): """ Returns a model :param cls_or_seg: (bool) if true cls else seg :param ssn_or_msn: (bool) if true ssn else msn """ super().__init__() self.task = task self.dataset = dataset num_classes = DATASET_NUM_CLASS[self.dataset] if self.task == 'cls': assert ssn_or_msn # source: https://github.com/Yochengliu/Relation-Shape-CNN/blob/master/cfgs/config_ssn_cls.yaml # source: https://github.com/Yochengliu/Relation-Shape-CNN/blob/master/train_cls.py#L73 rscnn_params = { 'num_classes':num_classes, 'input_channels': 0, 'relation_prior': 1, 'use_xyz': True } self.model = RSCNN_SSN_Cls(**rscnn_params) else: assert False def forward(self, pc, cls=None): pc = pc.to(next(self.parameters()).device) if self.task == 'cls': assert cls is None out = {'logit': self.model(pc)} else: assert False return out ================================================ FILE: pc_utils.py ================================================ import numpy as np import torch # source: https://github.com/charlesq34/pointnet2/blob/74bb67f3702e8aec55a7b8765dd728b18456030c/utils/provider.py#L187-L198 def jitter_point_cloud(batch_data, sigma=0.01, clip=0.05): """ Randomly jitter points. jittering is per point. Input: BxNx3 array, original batch of point clouds Return: BxNx3 array, jittered batch of point clouds """ B, N, C = batch_data.shape assert(clip > 0) jittered_data = np.clip(sigma * np.random.randn(B, N, C), -1*clip, clip) jittered_data += batch_data return jittered_data # source: https://github.com/charlesq34/pointnet2/blob/74bb67f3702e8aec55a7b8765dd728b18456030c/utils/provider.py#L32-L50 def rotate_point_cloud(batch_data): """ Randomly rotate the point clouds to augument the dataset rotation is per shape based along up direction Input: BxNx3 array, original batch of point clouds Return: BxNx3 array, rotated batch of point clouds """ rotated_data = np.zeros(batch_data.shape, dtype=np.float32) for k in range(batch_data.shape[0]): rotation_angle = np.random.uniform() * 2 * np.pi cosval = np.cos(rotation_angle) sinval = np.sin(rotation_angle) rotation_matrix = np.array([[cosval, 0, sinval], [0, 1, 0], [-sinval, 0, cosval]]) shape_pc = batch_data[k, ...] rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) return rotated_data # source: https://github.com/WangYueFt/dgcnn/blob/master/pytorch/data.py def translate_pointcloud(pointcloud): xyz1 = np.random.uniform(low=2./3., high=3./2., size=[3]) xyz2 = np.random.uniform(low=-0.2, high=0.2, size=[3]) translated_pointcloud = np.add(np.multiply(pointcloud, xyz1), xyz2).astype('float32') return translated_pointcloud # based on https://github.com/Yochengliu/Relation-Shape-CNN/blob/master/data/data_utils.py#L81 class PointcloudScaleAndTranslate(object): def __init__(self, scale_low=2. / 3., scale_high=3. / 2., translate_range=0.2, no_z_aug=False): """ :param scale_low: :param scale_high: :param translate_range: :param no_z: no translation and scaling along the z axis """ self.scale_low = scale_low self.scale_high = scale_high self.translate_range = translate_range self.no_z_aug = no_z_aug def __call__(self, pc): bsize = pc.size()[0] for i in range(bsize): xyz1 = np.random.uniform(low=self.scale_low, high=self.scale_high, size=[3]) xyz2 = np.random.uniform(low=-self.translate_range, high=self.translate_range, size=[3]) if self.no_z_aug: xyz1[2] = 1.0 xyz2[2] = 0.0 pc[i, :, 0:3] = torch.mul(pc[i, :, 0:3], torch.from_numpy(xyz1).float().cuda()) + torch.from_numpy(xyz2).float().cuda() return pc ================================================ FILE: pointMLP/.gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so .idea # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ pip-wheel-metadata/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # PEP 582; used by e.g. github.com/David-OConnor/pyflow __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ .DS_Store ================================================ FILE: pointMLP/LICENSE ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: pointMLP/README.md ================================================ # Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework （ICLR 2022） [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rethinking-network-design-and-local-geometry-1/3d-point-cloud-classification-on-modelnet40)](https://paperswithcode.com/sota/3d-point-cloud-classification-on-modelnet40?p=rethinking-network-design-and-local-geometry-1) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rethinking-network-design-and-local-geometry-1/3d-point-cloud-classification-on-scanobjectnn)](https://paperswithcode.com/sota/3d-point-cloud-classification-on-scanobjectnn?p=rethinking-network-design-and-local-geometry-1) [Project Sites]() | [arXiv](https://arxiv.org/abs/2202.07123) | Primary contact: [Xu Ma](mailto:ma.xu1@northeastern.edu)
Overview of one stage in PointMLP. Given an input point cloud, PointMLP progressively extract local features using residual point MLP blocks. In each stage, we first transform local point using a geometric affine module, then local points are are extracted before and after aggregation respectively. By repeating multiple stages, PointMLP progressively enlarge the receptive field and model entire point cloud geometric information. ## BibTeX @inproceedings{ ma2022rethinking, title={Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual {MLP} Framework}, author={Xu Ma and Can Qin and Haoxuan You and Haoxi Ran and Yun Fu}, booktitle={International Conference on Learning Representations}, year={2022}, url={https://openreview.net/forum?id=3Pbra-_u76D} } ## Model Zoo - The codes/models/logs for submission version (without bug fixed) can be found here [commit:d2b8dbaa](http://github.com/13952522076/pointMLP-pytorch/tree/d2b8dbaa06eb6176b222dcf2ad248f8438582026). - On ModelNet40, fixed pointMLP achieves a result of **91.5% mAcc** and **94.1% OA** without voting, logs and pretrained models can be found [[here]](https://web.northeastern.edu/smilelab/xuma/pointMLP/checkpoints/fixstd/modelnet40/pointMLP-20220209053148-404/). - On ScanObjectNN, fixed pointMLP achieves a result of **84.4% mAcc** and **86.1% OA** without voting, logs and pretrained models can be found [[here]](https://web.northeastern.edu/smilelab/xuma/pointMLP/checkpoints/fixstd/scanobjectnn/pointMLP-20220204021453/). - Stay tuned. More elite versions and voting results will be uploaded. ## News & Updates: - [ ] updated more pretrained models - [ ] double check the part seg utils - [ ] project page - [x] update std bug (unstable testing in previous version) - [x] paper/codes release :point_right::point_right::point_right:**NOTE:** The codes/models/logs for submission version (without bug fixed) can be found here [commit:d2b8dbaa](http://github.com/13952522076/pointMLP-pytorch/tree/d2b8dbaa06eb6176b222dcf2ad248f8438582026). ## Install ```bash # 1. clone this repo git clone https://github.com/ma-xu/pointMLP-pytorch.git cd pointMLP-pytorch # 2. create a conda virtual environment and activate it conda create -n pointmlp python=3.7 -y conda activate pointmlp # 3. install required libs, pytorch 1.8.1, torchvision 0.9.1, etc. pip install -r requirements.txt # 4. install CUDA kernels pip install pointnet2_ops_lib/. ``` ## Useage ### Classification ModelNet40 **Train**: The dataset will be automatically downloaded, run following command to train. By default, it will create a fold named "checkpoints/{modelName}-{msg}-{randomseed}", which includes args.txt, best_checkpoint.pth, last_checkpoint.pth, log.txt, out.txt. ```bash cd pointMLP-pytorch/classification_ModelNet40 # train pointMLP python main.py --model pointMLP # train pointMLP-elite python main.py --model pointMLPElite # please add other paramemters as you wish. ``` To conduct voting testing, run ```bash # please modify the msg accrodingly python voting.py --model pointMLP --msg demo ``` ### Classification ScanObjectNN The dataset will be automatically downloaded - Train pointMLP/pointMLPElite ```bash # train pointMLP python main.py --model pointMLP # train pointMLP-elite python main.py --model pointMLPElite # please add other paramemters as you wish. ``` By default, it will create a fold named "checkpoints/{modelName}-{msg}-{randomseed}", which includes args.txt, best_checkpoint.pth, last_checkpoint.pth, log.txt, out.txt. ### Part segmentation - Make data folder and download the dataset ```bash cd pointMLP-pytorch/part_segmentation mkdir data cd data wget https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip --no-check-certificate unzip shapenetcore_partanno_segmentation_benchmark_v0_normal.zip ``` - Train pointMLP ```bash # train pointMLP python main.py --model pointMLP # please add other paramemters as you wish. ``` ## Acknowledgment Our implementation is mainly based on the following codebases. We gratefully thank the authors for their wonderful works. [CurveNet](https://github.com/tiangexiang/CurveNet), [PAConv](https://github.com/CVMI-Lab/PAConv), [GDANet](https://github.com/mutianxu/GDANet), [Pointnet2_PyTorch](https://github.com/erikwijmans/Pointnet2_PyTorch) ## LICENSE PointMLP is under the Apache-2.0 license. Please contact the authors for commercial use. ================================================ FILE: pointMLP/classification_ModelNet40/data.py ================================================ import os import glob import h5py import numpy as np from torch.utils.data import Dataset os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE" def download(): BASE_DIR = os.path.dirname(os.path.abspath(__file__)) DATA_DIR = os.path.join(BASE_DIR, 'data') if not os.path.exists(DATA_DIR): os.mkdir(DATA_DIR) if not os.path.exists(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048')): www = 'https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip' zipfile = os.path.basename(www) os.system('wget %s --no-check-certificate; unzip %s' % (www, zipfile)) os.system('mv %s %s' % (zipfile[:-4], DATA_DIR)) os.system('rm %s' % (zipfile)) def load_data(partition): download() BASE_DIR = os.path.dirname(os.path.abspath(__file__)) DATA_DIR = os.path.join(BASE_DIR, 'data') all_data = [] all_label = [] for h5_name in glob.glob(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048', 'ply_data_%s*.h5'%partition)): # print(f"h5_name: {h5_name}") f = h5py.File(h5_name,'r') data = f['data'][:].astype('float32') label = f['label'][:].astype('int64') f.close() all_data.append(data) all_label.append(label) all_data = np.concatenate(all_data, axis=0) all_label = np.concatenate(all_label, axis=0) return all_data, all_label def random_point_dropout(pc, max_dropout_ratio=0.875): ''' batch_pc: BxNx3 ''' # for b in range(batch_pc.shape[0]): dropout_ratio = np.random.random()*max_dropout_ratio # 0~0.875 drop_idx = np.where(np.random.random((pc.shape[0]))<=dropout_ratio)[0] # print ('use random drop', len(drop_idx)) if len(drop_idx)>0: pc[drop_idx,:] = pc[0,:] # set to the first point return pc def translate_pointcloud(pointcloud): xyz1 = np.random.uniform(low=2./3., high=3./2., size=[3]) xyz2 = np.random.uniform(low=-0.2, high=0.2, size=[3]) translated_pointcloud = np.add(np.multiply(pointcloud, xyz1), xyz2).astype('float32') return translated_pointcloud def jitter_pointcloud(pointcloud, sigma=0.01, clip=0.02): N, C = pointcloud.shape pointcloud += np.clip(sigma * np.random.randn(N, C), -1*clip, clip) return pointcloud class ModelNet40(Dataset): def __init__(self, num_points, partition='train'): self.data, self.label = load_data(partition) self.num_points = num_points self.partition = partition def __getitem__(self, item): pointcloud = self.data[item][:self.num_points] label = self.label[item] if self.partition == 'train': # pointcloud = random_point_dropout(pointcloud) # open for dgcnn not for our idea for all pointcloud = translate_pointcloud(pointcloud) np.random.shuffle(pointcloud) return pointcloud, label def __len__(self): return self.data.shape[0] if __name__ == '__main__': train = ModelNet40(1024) test = ModelNet40(1024, 'test') # for data, label in train: # print(data.shape) # print(label.shape) from torch.utils.data import DataLoader train_loader = DataLoader(ModelNet40(partition='train', num_points=1024), num_workers=4, batch_size=32, shuffle=True, drop_last=True) for batch_idx, (data, label) in enumerate(train_loader): print(f"batch_idx: {batch_idx} | data shape: {data.shape} | ;lable shape: {label.shape}") train_set = ModelNet40(partition='train', num_points=1024) test_set = ModelNet40(partition='test', num_points=1024) print(f"train_set size {train_set.__len__()}") print(f"test_set size {test_set.__len__()}") ================================================ FILE: pointMLP/classification_ModelNet40/helper.py ================================================ import torch import torch.nn.functional as F def cal_loss(pred, gold, smoothing=True): ''' Calculate cross entropy loss, apply label smoothing if needed. ''' gold = gold.contiguous().view(-1) if smoothing: eps = 0.2 n_class = pred.size(1) one_hot = torch.zeros_like(pred).scatter(1, gold.view(-1, 1), 1) one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1) log_prb = F.log_softmax(pred, dim=1) loss = -(one_hot * log_prb).sum(dim=1).mean() else: loss = F.cross_entropy(pred, gold, reduction='mean') return loss ================================================ FILE: pointMLP/classification_ModelNet40/main.py ================================================ """ Usage: python main.py --model PointMLP --msg demo """ import argparse import os import logging import datetime import torch import torch.nn.parallel import torch.backends.cudnn as cudnn import torch.optim import torch.utils.data import torch.utils.data.distributed from torch.utils.data import DataLoader import models as models from utils import Logger, mkdir_p, progress_bar, save_model, save_args, cal_loss from data import ModelNet40 from torch.optim.lr_scheduler import CosineAnnealingLR import sklearn.metrics as metrics import numpy as np def parse_args(): """Parameters""" parser = argparse.ArgumentParser('training') parser.add_argument('-c', '--checkpoint', type=str, metavar='PATH', help='path to save checkpoint (default: checkpoint)') parser.add_argument('--msg', type=str, help='message after checkpoint') parser.add_argument('--batch_size', type=int, default=32, help='batch size in training') parser.add_argument('--model', default='PointNet', help='model name [default: pointnet_cls]') parser.add_argument('--epoch', default=300, type=int, help='number of epoch in training') parser.add_argument('--num_points', type=int, default=1024, help='Point Number') parser.add_argument('--learning_rate', default=0.1, type=float, help='learning rate in training') parser.add_argument('--min_lr', default=0.005, type=float, help='min lr') parser.add_argument('--weight_decay', type=float, default=2e-4, help='decay rate') parser.add_argument('--seed', type=int, help='random seed') parser.add_argument('--workers', default=8, type=int, help='workers') return parser.parse_args() def main(): args = parse_args() if args.seed is None: args.seed = np.random.randint(1, 10000) os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE" assert torch.cuda.is_available(), "Please ensure codes are executed in cuda." device = 'cuda' if args.seed is not None: torch.manual_seed(args.seed) np.random.seed(args.seed) torch.cuda.manual_seed_all(args.seed) torch.cuda.manual_seed(args.seed) torch.set_printoptions(10) torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True os.environ['PYTHONHASHSEED'] = str(args.seed) time_str = str(datetime.datetime.now().strftime('-%Y%m%d%H%M%S')) if args.msg is None: message = time_str else: message = "-" + args.msg args.checkpoint = 'checkpoints/' + args.model + message + '-' + str(args.seed) if not os.path.isdir(args.checkpoint): mkdir_p(args.checkpoint) screen_logger = logging.getLogger("Model") screen_logger.setLevel(logging.INFO) formatter = logging.Formatter('%(message)s') file_handler = logging.FileHandler(os.path.join(args.checkpoint, "out.txt")) file_handler.setLevel(logging.INFO) file_handler.setFormatter(formatter) screen_logger.addHandler(file_handler) def printf(str): screen_logger.info(str) print(str) # Model printf(f"args: {args}") printf('==> Building model..') net = models.__dict__[args.model]() criterion = cal_loss net = net.to(device) # criterion = criterion.to(device) if device == 'cuda': net = torch.nn.DataParallel(net) cudnn.benchmark = True best_test_acc = 0. # best test accuracy best_train_acc = 0. best_test_acc_avg = 0. best_train_acc_avg = 0. best_test_loss = float("inf") best_train_loss = float("inf") start_epoch = 0 # start from epoch 0 or last checkpoint epoch optimizer_dict = None if not os.path.isfile(os.path.join(args.checkpoint, "last_checkpoint.pth")): save_args(args) logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title="ModelNet" + args.model) logger.set_names(["Epoch-Num", 'Learning-Rate', 'Train-Loss', 'Train-acc-B', 'Train-acc', 'Valid-Loss', 'Valid-acc-B', 'Valid-acc']) else: printf(f"Resuming last checkpoint from {args.checkpoint}") checkpoint_path = os.path.join(args.checkpoint, "last_checkpoint.pth") checkpoint = torch.load(checkpoint_path) net.load_state_dict(checkpoint['net']) start_epoch = checkpoint['epoch'] best_test_acc = checkpoint['best_test_acc'] best_train_acc = checkpoint['best_train_acc'] best_test_acc_avg = checkpoint['best_test_acc_avg'] best_train_acc_avg = checkpoint['best_train_acc_avg'] best_test_loss = checkpoint['best_test_loss'] best_train_loss = checkpoint['best_train_loss'] logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title="ModelNet" + args.model, resume=True) optimizer_dict = checkpoint['optimizer'] printf('==> Preparing data..') train_loader = DataLoader(ModelNet40(partition='train', num_points=args.num_points), num_workers=args.workers, batch_size=args.batch_size, shuffle=True, drop_last=True) test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), num_workers=args.workers, batch_size=args.batch_size // 2, shuffle=False, drop_last=False) optimizer = torch.optim.SGD(net.parameters(), lr=args.learning_rate, momentum=0.9, weight_decay=args.weight_decay) if optimizer_dict is not None: optimizer.load_state_dict(optimizer_dict) scheduler = CosineAnnealingLR(optimizer, args.epoch, eta_min=args.min_lr, last_epoch=start_epoch - 1) for epoch in range(start_epoch, args.epoch): printf('Epoch(%d/%s) Learning Rate %s:' % (epoch + 1, args.epoch, optimizer.param_groups[0]['lr'])) train_out = train(net, train_loader, optimizer, criterion, device) # {"loss", "acc", "acc_avg", "time"} test_out = validate(net, test_loader, criterion, device) scheduler.step() if test_out["acc"] > best_test_acc: best_test_acc = test_out["acc"] is_best = True else: is_best = False best_test_acc = test_out["acc"] if (test_out["acc"] > best_test_acc) else best_test_acc best_train_acc = train_out["acc"] if (train_out["acc"] > best_train_acc) else best_train_acc best_test_acc_avg = test_out["acc_avg"] if (test_out["acc_avg"] > best_test_acc_avg) else best_test_acc_avg best_train_acc_avg = train_out["acc_avg"] if (train_out["acc_avg"] > best_train_acc_avg) else best_train_acc_avg best_test_loss = test_out["loss"] if (test_out["loss"] < best_test_loss) else best_test_loss best_train_loss = train_out["loss"] if (train_out["loss"] < best_train_loss) else best_train_loss save_model( net, epoch, path=args.checkpoint, acc=test_out["acc"], is_best=is_best, best_test_acc=best_test_acc, # best test accuracy best_train_acc=best_train_acc, best_test_acc_avg=best_test_acc_avg, best_train_acc_avg=best_train_acc_avg, best_test_loss=best_test_loss, best_train_loss=best_train_loss, optimizer=optimizer.state_dict() ) logger.append([epoch, optimizer.param_groups[0]['lr'], train_out["loss"], train_out["acc_avg"], train_out["acc"], test_out["loss"], test_out["acc_avg"], test_out["acc"]]) printf( f"Training loss:{train_out['loss']} acc_avg:{train_out['acc_avg']}% acc:{train_out['acc']}% time:{train_out['time']}s") printf( f"Testing loss:{test_out['loss']} acc_avg:{test_out['acc_avg']}% " f"acc:{test_out['acc']}% time:{test_out['time']}s [best test acc: {best_test_acc}%] \n\n") logger.close() printf(f"++++++++" * 2 + "Final results" + "++++++++" * 2) printf(f"++ Last Train time: {train_out['time']} | Last Test time: {test_out['time']} ++") printf(f"++ Best Train loss: {best_train_loss} | Best Test loss: {best_test_loss} ++") printf(f"++ Best Train acc_B: {best_train_acc_avg} | Best Test acc_B: {best_test_acc_avg} ++") printf(f"++ Best Train acc: {best_train_acc} | Best Test acc: {best_test_acc} ++") printf(f"++++++++" * 5) def train(net, trainloader, optimizer, criterion, device): net.train() train_loss = 0 correct = 0 total = 0 train_pred = [] train_true = [] time_cost = datetime.datetime.now() for batch_idx, (data, label) in enumerate(trainloader): data, label = data.to(device), label.to(device).squeeze() data = data.permute(0, 2, 1) # so, the input data shape is [batch, 3, 1024] optimizer.zero_grad() logits = net(data) loss = criterion(logits, label) loss.backward() torch.nn.utils.clip_grad_norm_(net.parameters(), 1) optimizer.step() train_loss += loss.item() preds = logits.max(dim=1)[1] train_true.append(label.cpu().numpy()) train_pred.append(preds.detach().cpu().numpy()) total += label.size(0) correct += preds.eq(label).sum().item() progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)' % (train_loss / (batch_idx + 1), 100. * correct / total, correct, total)) time_cost = int((datetime.datetime.now() - time_cost).total_seconds()) train_true = np.concatenate(train_true) train_pred = np.concatenate(train_pred) return { "loss": float("%.3f" % (train_loss / (batch_idx + 1))), "acc": float("%.3f" % (100. * metrics.accuracy_score(train_true, train_pred))), "acc_avg": float("%.3f" % (100. * metrics.balanced_accuracy_score(train_true, train_pred))), "time": time_cost } def validate(net, testloader, criterion, device): net.eval() test_loss = 0 correct = 0 total = 0 test_true = [] test_pred = [] time_cost = datetime.datetime.now() with torch.no_grad(): for batch_idx, (data, label) in enumerate(testloader): data, label = data.to(device), label.to(device).squeeze() data = data.permute(0, 2, 1) logits = net(data) loss = criterion(logits, label) test_loss += loss.item() preds = logits.max(dim=1)[1] test_true.append(label.cpu().numpy()) test_pred.append(preds.detach().cpu().numpy()) total += label.size(0) correct += preds.eq(label).sum().item() progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)' % (test_loss / (batch_idx + 1), 100. * correct / total, correct, total)) time_cost = int((datetime.datetime.now() - time_cost).total_seconds()) test_true = np.concatenate(test_true) test_pred = np.concatenate(test_pred) return { "loss": float("%.3f" % (test_loss / (batch_idx + 1))), "acc": float("%.3f" % (100. * metrics.accuracy_score(test_true, test_pred))), "acc_avg": float("%.3f" % (100. * metrics.balanced_accuracy_score(test_true, test_pred))), "time": time_cost } if __name__ == '__main__': main() ================================================ FILE: pointMLP/classification_ModelNet40/models/__init__.py ================================================ from __future__ import absolute_import from .pointmlp import pointMLP, pointMLPElite ================================================ FILE: pointMLP/classification_ModelNet40/models/pointmlp.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F # from torch import einsum # from einops import rearrange, repeat from PCT_Pytorch.pointnet2_ops_lib.pointnet2_ops import pointnet2_utils # from pointnet2_ops import pointnet2_utils def get_activation(activation): if activation.lower() == 'gelu': return nn.GELU() elif activation.lower() == 'rrelu': return nn.RReLU(inplace=True) elif activation.lower() == 'selu': return nn.SELU(inplace=True) elif activation.lower() == 'silu': return nn.SiLU(inplace=True) elif activation.lower() == 'hardswish': return nn.Hardswish(inplace=True) elif activation.lower() == 'leakyrelu': return nn.LeakyReLU(inplace=True) else: return nn.ReLU(inplace=True) def square_distance(src, dst): """ Calculate Euclid distance between each two points. src^T * dst = xn * xm + yn * ym + zn * zm； sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn; sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm; dist = (xn - xm)^2 + (yn - ym)^2 + (zn - zm)^2 = sum(src**2,dim=-1)+sum(dst**2,dim=-1)-2*src^T*dst Input: src: source points, [B, N, C] dst: target points, [B, M, C] Output: dist: per-point square distance, [B, N, M] """ B, N, _ = src.shape _, M, _ = dst.shape dist = -2 * torch.matmul(src, dst.permute(0, 2, 1)) dist += torch.sum(src ** 2, -1).view(B, N, 1) dist += torch.sum(dst ** 2, -1).view(B, 1, M) return dist def index_points(points, idx): """ Input: points: input points data, [B, N, C] idx: sample index data, [B, S] Return: new_points:, indexed points data, [B, S, C] """ device = points.device B = points.shape[0] view_shape = list(idx.shape) view_shape[1:] = [1] * (len(view_shape) - 1) repeat_shape = list(idx.shape) repeat_shape[0] = 1 batch_indices = torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape) new_points = points[batch_indices, idx, :] return new_points def farthest_point_sample(xyz, npoint): """ Input: xyz: pointcloud data, [B, N, 3] npoint: number of samples Return: centroids: sampled pointcloud index, [B, npoint] """ device = xyz.device B, N, C = xyz.shape centroids = torch.zeros(B, npoint, dtype=torch.long).to(device) distance = torch.ones(B, N).to(device) * 1e10 farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device) batch_indices = torch.arange(B, dtype=torch.long).to(device) for i in range(npoint): centroids[:, i] = farthest centroid = xyz[batch_indices, farthest, :].view(B, 1, 3) dist = torch.sum((xyz - centroid) ** 2, -1) distance = torch.min(distance, dist) farthest = torch.max(distance, -1)[1] return centroids def query_ball_point(radius, nsample, xyz, new_xyz): """ Input: radius: local region radius nsample: max sample number in local region xyz: all points, [B, N, 3] new_xyz: query points, [B, S, 3] Return: group_idx: grouped points index, [B, S, nsample] """ device = xyz.device B, N, C = xyz.shape _, S, _ = new_xyz.shape group_idx = torch.arange(N, dtype=torch.long).to(device).view(1, 1, N).repeat([B, S, 1]) sqrdists = square_distance(new_xyz, xyz) group_idx[sqrdists > radius ** 2] = N group_idx = group_idx.sort(dim=-1)[0][:, :, :nsample] group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, nsample]) mask = group_idx == N group_idx[mask] = group_first[mask] return group_idx def knn_point(nsample, xyz, new_xyz): """ Input: nsample: max sample number in local region xyz: all points, [B, N, C] new_xyz: query points, [B, S, C] Return: group_idx: grouped points index, [B, S, nsample] """ sqrdists = square_distance(new_xyz, xyz) _, group_idx = torch.topk(sqrdists, nsample, dim=-1, largest=False, sorted=False) return group_idx class LocalGrouper(nn.Module): def __init__(self, channel, groups, kneighbors, use_xyz=True, normalize="center", **kwargs): """ Give xyz[b,p,3] and fea[b,p,d], return new_xyz[b,g,3] and new_fea[b,g,k,d] :param groups: groups number :param kneighbors: k-nerighbors :param kwargs: others """ super(LocalGrouper, self).__init__() self.groups = groups self.kneighbors = kneighbors self.use_xyz = use_xyz if normalize is not None: self.normalize = normalize.lower() else: self.normalize = None if self.normalize not in ["center", "anchor"]: print(f"Unrecognized normalize parameter (self.normalize), set to None. Should be one of [center, anchor].") self.normalize = None if self.normalize is not None: add_channel=3 if self.use_xyz else 0 self.affine_alpha = nn.Parameter(torch.ones([1,1,1,channel + add_channel])) self.affine_beta = nn.Parameter(torch.zeros([1, 1, 1, channel + add_channel])) def forward(self, xyz, points): B, N, C = xyz.shape S = self.groups xyz = xyz.contiguous() # xyz [btach, points, xyz] # fps_idx = torch.multinomial(torch.linspace(0, N - 1, steps=N).repeat(B, 1).to(xyz.device), num_samples=self.groups, replacement=False).long() # fps_idx = farthest_point_sample(xyz, self.groups).long() fps_idx = pointnet2_utils.furthest_point_sample(xyz, self.groups).long() # [B, npoint] new_xyz = index_points(xyz, fps_idx) # [B, npoint, 3] new_points = index_points(points, fps_idx) # [B, npoint, d] idx = knn_point(self.kneighbors, xyz, new_xyz) # idx = query_ball_point(radius, nsample, xyz, new_xyz) grouped_xyz = index_points(xyz, idx) # [B, npoint, k, 3] grouped_points = index_points(points, idx) # [B, npoint, k, d] if self.use_xyz: grouped_points = torch.cat([grouped_points, grouped_xyz],dim=-1) # [B, npoint, k, d+3] if self.normalize is not None: if self.normalize =="center": mean = torch.mean(grouped_points, dim=2, keepdim=True) if self.normalize =="anchor": mean = torch.cat([new_points, new_xyz],dim=-1) if self.use_xyz else new_points mean = mean.unsqueeze(dim=-2) # [B, npoint, 1, d+3] std = torch.std((grouped_points-mean).reshape(B,-1),dim=-1,keepdim=True).unsqueeze(dim=-1).unsqueeze(dim=-1) grouped_points = (grouped_points-mean)/(std + 1e-5) grouped_points = self.affine_alpha*grouped_points + self.affine_beta new_points = torch.cat([grouped_points, new_points.view(B, S, 1, -1).repeat(1, 1, self.kneighbors, 1)], dim=-1) return new_xyz, new_points class ConvBNReLU1D(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=1, bias=True, activation='relu'): super(ConvBNReLU1D, self).__init__() self.act = get_activation(activation) self.net = nn.Sequential( nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, bias=bias), nn.BatchNorm1d(out_channels), self.act ) def forward(self, x): return self.net(x) class ConvBNReLURes1D(nn.Module): def __init__(self, channel, kernel_size=1, groups=1, res_expansion=1.0, bias=True, activation='relu'): super(ConvBNReLURes1D, self).__init__() self.act = get_activation(activation) self.net1 = nn.Sequential( nn.Conv1d(in_channels=channel, out_channels=int(channel * res_expansion), kernel_size=kernel_size, groups=groups, bias=bias), nn.BatchNorm1d(int(channel * res_expansion)), self.act ) if groups > 1: self.net2 = nn.Sequential( nn.Conv1d(in_channels=int(channel * res_expansion), out_channels=channel, kernel_size=kernel_size, groups=groups, bias=bias), nn.BatchNorm1d(channel), self.act, nn.Conv1d(in_channels=channel, out_channels=channel, kernel_size=kernel_size, bias=bias), nn.BatchNorm1d(channel), ) else: self.net2 = nn.Sequential( nn.Conv1d(in_channels=int(channel * res_expansion), out_channels=channel, kernel_size=kernel_size, bias=bias), nn.BatchNorm1d(channel) ) def forward(self, x): return self.act(self.net2(self.net1(x)) + x) class PreExtraction(nn.Module): def __init__(self, channels, out_channels, blocks=1, groups=1, res_expansion=1, bias=True, activation='relu', use_xyz=True): """ input: [b,g,k,d]: output:[b,d,g] :param channels: :param blocks: """ super(PreExtraction, self).__init__() in_channels = 3+2*channels if use_xyz else 2*channels self.transfer = ConvBNReLU1D(in_channels, out_channels, bias=bias, activation=activation) operation = [] for _ in range(blocks): operation.append( ConvBNReLURes1D(out_channels, groups=groups, res_expansion=res_expansion, bias=bias, activation=activation) ) self.operation = nn.Sequential(*operation) def forward(self, x): b, n, s, d = x.size() # torch.Size([32, 512, 32, 6]) x = x.permute(0, 1, 3, 2) x = x.reshape(-1, d, s) x = self.transfer(x) batch_size, _, _ = x.size() x = self.operation(x) # [b, d, k] x = F.adaptive_max_pool1d(x, 1).view(batch_size, -1) x = x.reshape(b, n, -1).permute(0, 2, 1) return x class PosExtraction(nn.Module): def __init__(self, channels, blocks=1, groups=1, res_expansion=1, bias=True, activation='relu'): """ input[b,d,g]; output[b,d,g] :param channels: :param blocks: """ super(PosExtraction, self).__init__() operation = [] for _ in range(blocks): operation.append( ConvBNReLURes1D(channels, groups=groups, res_expansion=res_expansion, bias=bias, activation=activation) ) self.operation = nn.Sequential(*operation) def forward(self, x): # [b, d, g] return self.operation(x) class Model(nn.Module): def __init__(self, points=1024, class_num=40, embed_dim=64, groups=1, res_expansion=1.0, activation="relu", bias=True, use_xyz=True, normalize="center", dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2], k_neighbors=[32, 32, 32, 32], reducers=[2, 2, 2, 2], **kwargs): super(Model, self).__init__() self.stages = len(pre_blocks) self.class_num = class_num self.points = points self.embedding = ConvBNReLU1D(3, embed_dim, bias=bias, activation=activation) assert len(pre_blocks) == len(k_neighbors) == len(reducers) == len(pos_blocks) == len(dim_expansion), \ "Please check stage number consistent for pre_blocks, pos_blocks k_neighbors, reducers." self.local_grouper_list = nn.ModuleList() self.pre_blocks_list = nn.ModuleList() self.pos_blocks_list = nn.ModuleList() last_channel = embed_dim anchor_points = self.points for i in range(len(pre_blocks)): out_channel = last_channel * dim_expansion[i] pre_block_num = pre_blocks[i] pos_block_num = pos_blocks[i] kneighbor = k_neighbors[i] reduce = reducers[i] anchor_points = anchor_points // reduce # append local_grouper_list local_grouper = LocalGrouper(last_channel, anchor_points, kneighbor, use_xyz, normalize) # [b,g,k,d] self.local_grouper_list.append(local_grouper) # append pre_block_list pre_block_module = PreExtraction(last_channel, out_channel, pre_block_num, groups=groups, res_expansion=res_expansion, bias=bias, activation=activation, use_xyz=use_xyz) self.pre_blocks_list.append(pre_block_module) # append pos_block_list pos_block_module = PosExtraction(out_channel, pos_block_num, groups=groups, res_expansion=res_expansion, bias=bias, activation=activation) self.pos_blocks_list.append(pos_block_module) last_channel = out_channel self.act = get_activation(activation) self.classifier = nn.Sequential( nn.Linear(last_channel, 512), nn.BatchNorm1d(512), self.act, nn.Dropout(0.5), nn.Linear(512, 256), nn.BatchNorm1d(256), self.act, nn.Dropout(0.5), nn.Linear(256, self.class_num) ) def forward(self, x): xyz = x.permute(0, 2, 1) batch_size, _, _ = x.size() x = self.embedding(x) # B,D,N for i in range(self.stages): # Give xyz[b, p, 3] and fea[b, p, d], return new_xyz[b, g, 3] and new_fea[b, g, k, d] xyz, x = self.local_grouper_list[i](xyz, x.permute(0, 2, 1)) # [b,g,3] [b,g,k,d] x = self.pre_blocks_list[i](x) # [b,d,g] x = self.pos_blocks_list[i](x) # [b,d,g] x = F.adaptive_max_pool1d(x, 1).squeeze(dim=-1) x = self.classifier(x) return x def pointMLP(num_classes=40, **kwargs) -> Model: return Model(points=1024, class_num=num_classes, embed_dim=64, groups=1, res_expansion=1.0, activation="relu", bias=False, use_xyz=False, normalize="anchor", dim_expansion=[2, 2, 2, 2], pre_blocks=[2, 2, 2, 2], pos_blocks=[2, 2, 2, 2], k_neighbors=[24, 24, 24, 24], reducers=[2, 2, 2, 2], **kwargs) def pointMLPElite(num_classes=40, **kwargs) -> Model: return Model(points=1024, class_num=num_classes, embed_dim=32, groups=1, res_expansion=0.25, activation="relu", bias=False, use_xyz=False, normalize="anchor", dim_expansion=[2, 2, 2, 1], pre_blocks=[1, 1, 2, 1], pos_blocks=[1, 1, 2, 1], k_neighbors=[24,24,24,24], reducers=[2, 2, 2, 2], **kwargs) if __name__ == '__main__': data = torch.rand(2, 3, 1024) print("===> testing pointMLP ...") model = pointMLP() out = model(data) print(out.shape) ================================================ FILE: pointMLP/classification_ModelNet40/utils/__init__.py ================================================ """Useful utils """ from .misc import * from .logger import * from .progress.progress.bar import Bar as Bar ================================================ FILE: pointMLP/classification_ModelNet40/utils/logger.py ================================================ # A simple torch style logger # (C) Wei YANG 2017 from __future__ import absolute_import import matplotlib.pyplot as plt import os import sys import numpy as np __all__ = ['Logger', 'LoggerMonitor', 'savefig'] def savefig(fname, dpi=None): dpi = 150 if dpi == None else dpi plt.savefig(fname, dpi=dpi) def plot_overlap(logger, names=None): names = logger.names if names == None else names numbers = logger.numbers for _, name in enumerate(names): x = np.arange(len(numbers[name])) plt.plot(x, np.asarray(numbers[name])) return [logger.title + '(' + name + ')' for name in names] class Logger(object): '''Save training process to log file with simple plot function.''' def __init__(self, fpath, title=None, resume=False): self.file = None self.resume = resume self.title = '' if title == None else title if fpath is not None: if resume: self.file = open(fpath, 'r') name = self.file.readline() self.names = name.rstrip().split('\t') self.numbers = {} for _, name in enumerate(self.names): self.numbers[name] = [] for numbers in self.file: numbers = numbers.rstrip().split('\t') for i in range(0, len(numbers)): self.numbers[self.names[i]].append(numbers[i]) self.file.close() self.file = open(fpath, 'a') else: self.file = open(fpath, 'w') def set_names(self, names): if self.resume: pass # initialize numbers as empty list self.numbers = {} self.names = names for _, name in enumerate(self.names): self.file.write(name) self.file.write('\t') self.numbers[name] = [] self.file.write('\n') self.file.flush() def append(self, numbers): assert len(self.names) == len(numbers), 'Numbers do not match names' for index, num in enumerate(numbers): self.file.write("{0:.6f}".format(num)) self.file.write('\t') self.numbers[self.names[index]].append(num) self.file.write('\n') self.file.flush() def plot(self, names=None): names = self.names if names == None else names numbers = self.numbers for _, name in enumerate(names): x = np.arange(len(numbers[name])) plt.plot(x, np.asarray(numbers[name])) plt.legend([self.title + '(' + name + ')' for name in names]) plt.grid(True) def close(self): if self.file is not None: self.file.close() class LoggerMonitor(object): '''Load and visualize multiple logs.''' def __init__ (self, paths): '''paths is a distionary with {name:filepath} pair''' self.loggers = [] for title, path in paths.items(): logger = Logger(path, title=title, resume=True) self.loggers.append(logger) def plot(self, names=None): plt.figure() plt.subplot(121) legend_text = [] for logger in self.loggers: legend_text += plot_overlap(logger, names) plt.legend(legend_text, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) plt.grid(True) if __name__ == '__main__': # # Example # logger = Logger('test.txt') # logger.set_names(['Train loss', 'Valid loss','Test loss']) # length = 100 # t = np.arange(length) # train_loss = np.exp(-t / 10.0) + np.random.rand(length) * 0.1 # valid_loss = np.exp(-t / 10.0) + np.random.rand(length) * 0.1 # test_loss = np.exp(-t / 10.0) + np.random.rand(length) * 0.1 # for i in range(0, length): # logger.append([train_loss[i], valid_loss[i], test_loss[i]]) # logger.plot() # Example: logger monitor paths = { 'resadvnet20':'/home/wyang/code/pytorch-classification/checkpoint/cifar10/resadvnet20/log.txt', 'resadvnet32':'/home/wyang/code/pytorch-classification/checkpoint/cifar10/resadvnet32/log.txt', 'resadvnet44':'/home/wyang/code/pytorch-classification/checkpoint/cifar10/resadvnet44/log.txt', } field = ['Valid Acc.'] monitor = LoggerMonitor(paths) monitor.plot(names=field) savefig('test.eps') ================================================ FILE: pointMLP/classification_ModelNet40/utils/misc.py ================================================ '''Some helper functions for PyTorch, including: - get_mean_and_std: calculate the mean and std value of dataset. - msr_init: net parameter initialization. - progress_bar: progress bar mimic xlua.progress. ''' import errno import os import sys import time import math import torch import shutil import numpy as np import random import torch.nn.functional as F import torch.nn as nn import torch.nn.init as init from torch.autograd import Variable __all__ = ['get_mean_and_std', 'init_params', 'mkdir_p', 'AverageMeter', 'progress_bar','save_model',"save_args","set_seed", "IOStream", "cal_loss"] def get_mean_and_std(dataset): '''Compute the mean and std value of dataset.''' dataloader = trainloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True, num_workers=2) mean = torch.zeros(3) std = torch.zeros(3) print('==> Computing mean and std..') for inputs, targets in dataloader: for i in range(3): mean[i] += inputs[:,i,:,:].mean() std[i] += inputs[:,i,:,:].std() mean.div_(len(dataset)) std.div_(len(dataset)) return mean, std def init_params(net): '''Init layer parameters.''' for m in net.modules(): if isinstance(m, nn.Conv2d): init.kaiming_normal(m.weight, mode='fan_out') if m.bias: init.constant(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): init.constant(m.weight, 1) init.constant(m.bias, 0) elif isinstance(m, nn.Linear): init.normal(m.weight, std=1e-3) if m.bias: init.constant(m.bias, 0) def mkdir_p(path): '''make dir if not exist''' try: os.makedirs(path) except OSError as exc: # Python >2.5 if exc.errno == errno.EEXIST and os.path.isdir(path): pass else: raise class AverageMeter(object): """Computes and stores the average and current value Imported from https://github.com/pytorch/examples/blob/master/imagenet/main.py#L247-L262 """ def __init__(self): self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count TOTAL_BAR_LENGTH = 65. last_time = time.time() begin_time = last_time def progress_bar(current, total, msg=None): global last_time, begin_time if current == 0: begin_time = time.time() # Reset for new bar. cur_len = int(TOTAL_BAR_LENGTH*current/total) rest_len = int(TOTAL_BAR_LENGTH - cur_len) - 1 sys.stdout.write(' [') for i in range(cur_len): sys.stdout.write('=') sys.stdout.write('>') for i in range(rest_len): sys.stdout.write('.') sys.stdout.write(']') cur_time = time.time() step_time = cur_time - last_time last_time = cur_time tot_time = cur_time - begin_time L = [] L.append(' Step: %s' % format_time(step_time)) L.append(' | Tot: %s' % format_time(tot_time)) if msg: L.append(' | ' + msg) msg = ''.join(L) sys.stdout.write(msg) # for i in range(term_width-int(TOTAL_BAR_LENGTH)-len(msg)-3): # sys.stdout.write(' ') # Go back to the center of the bar. # for i in range(term_width-int(TOTAL_BAR_LENGTH/2)+2): # sys.stdout.write('\b') sys.stdout.write(' %d/%d ' % (current+1, total)) if current < total-1: sys.stdout.write('\r') else: sys.stdout.write('\n') sys.stdout.flush() def format_time(seconds): days = int(seconds / 3600/24) seconds = seconds - days*3600*24 hours = int(seconds / 3600) seconds = seconds - hours*3600 minutes = int(seconds / 60) seconds = seconds - minutes*60 secondsf = int(seconds) seconds = seconds - secondsf millis = int(seconds*1000) f = '' i = 1 if days > 0: f += str(days) + 'D' i += 1 if hours > 0 and i <= 2: f += str(hours) + 'h' i += 1 if minutes > 0 and i <= 2: f += str(minutes) + 'm' i += 1 if secondsf > 0 and i <= 2: f += str(secondsf) + 's' i += 1 if millis > 0 and i <= 2: f += str(millis) + 'ms' i += 1 if f == '': f = '0ms' return f def save_model(net, epoch, path, acc, is_best, **kwargs): state = { 'net': net.state_dict(), 'epoch': epoch, 'acc': acc } for key, value in kwargs.items(): state[key] = value filepath = os.path.join(path, "last_checkpoint.pth") torch.save(state, filepath) if is_best: shutil.copyfile(filepath, os.path.join(path, 'best_checkpoint.pth')) def save_args(args): file = open(os.path.join(args.checkpoint, 'args.txt'), "w") for k, v in vars(args).items(): file.write(f"{k}:\t {v}\n") file.close() def set_seed(seed=None): if seed is None: return random.seed(seed) os.environ['PYTHONHASHSEED'] = ("%s" % seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True # create a file and write the text into it class IOStream(): def __init__(self, path): self.f = open(path, 'a') def cprint(self, text): print(text) self.f.write(text+'\n') self.f.flush() def close(self): self.f.close() def cal_loss(pred, gold, smoothing=True): ''' Calculate cross entropy loss, apply label smoothing if needed. ''' gold = gold.contiguous().view(-1) if smoothing: eps = 0.2 n_class = pred.size(1) one_hot = torch.zeros_like(pred).scatter(1, gold.view(-1, 1), 1) one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1) log_prb = F.log_softmax(pred, dim=1) loss = -(one_hot * log_prb).sum(dim=1).mean() else: loss = F.cross_entropy(pred, gold, reduction='mean') return loss ================================================ FILE: pointMLP/classification_ModelNet40/utils/progress/.gitignore ================================================ *.pyc *.egg-info build/ dist/ ================================================ FILE: pointMLP/classification_ModelNet40/utils/progress/LICENSE ================================================ # Copyright (c) 2012 Giorgos Verigakis # # Permission to use, copy, modify, and distribute this software for any # purpose with or without fee is hereby granted, provided that the above # copyright notice and this permission notice appear in all copies. # # THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES # WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF # MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR # ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES # WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN # ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF # OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. ================================================ FILE: pointMLP/classification_ModelNet40/utils/progress/MANIFEST.in ================================================ include README.rst LICENSE ================================================ FILE: pointMLP/classification_ModelNet40/utils/progress/README.rst ================================================ Easy progress reporting for Python ================================== |pypi| |demo| .. |pypi| image:: https://img.shields.io/pypi/v/progress.svg .. |demo| image:: https://raw.github.com/verigak/progress/master/demo.gif :alt: Demo Bars ---- There are 7 progress bars to choose from: - ``Bar`` - ``ChargingBar`` - ``FillingSquaresBar`` - ``FillingCirclesBar`` - ``IncrementalBar`` - ``PixelBar`` - ``ShadyBar`` To use them, just call ``next`` to advance and ``finish`` to finish: .. code-block:: python from progress.bar import Bar bar = Bar('Processing', max=20) for i in range(20): # Do some work bar.next() bar.finish() The result will be a bar like the following: :: Processing |############# | 42/100 To simplify the common case where the work is done in an iterator, you can use the ``iter`` method: .. code-block:: python for i in Bar('Processing').iter(it): # Do some work Progress bars are very customizable, you can change their width, their fill character, their suffix and more: .. code-block:: python bar = Bar('Loading', fill='@', suffix='%(percent)d%%') This will produce a bar like the following: :: Loading |@@@@@@@@@@@@@ | 42% You can use a number of template arguments in ``message`` and ``suffix``: ========== ================================ Name Value ========== ================================ index current value max maximum value remaining max - index progress index / max percent progress * 100 avg simple moving average time per item (in seconds) elapsed elapsed time in seconds elapsed_td elapsed as a timedelta (useful for printing as a string) eta avg * remaining eta_td eta as a timedelta (useful for printing as a string) ========== ================================ Instead of passing all configuration options on instatiation, you can create your custom subclass: .. code-block:: python class FancyBar(Bar): message = 'Loading' fill = '*' suffix = '%(percent).1f%% - %(eta)ds' You can also override any of the arguments or create your own: .. code-block:: python class SlowBar(Bar): suffix = '%(remaining_hours)d hours remaining' @property def remaining_hours(self): return self.eta // 3600 Spinners ======== For actions with an unknown number of steps you can use a spinner: .. code-block:: python from progress.spinner import Spinner spinner = Spinner('Loading ') while state != 'FINISHED': # Do some work spinner.next() There are 5 predefined spinners: - ``Spinner`` - ``PieSpinner`` - ``MoonSpinner`` - ``LineSpinner`` - ``PixelSpinner`` Other ===== There are a number of other classes available too, please check the source or subclass one of them to create your own. License ======= progress is licensed under ISC ================================================ FILE: pointMLP/classification_ModelNet40/utils/progress/progress/__init__.py ================================================ # Copyright (c) 2012 Giorgos Verigakis # # Permission to use, copy, modify, and distribute this software for any # purpose with or without fee is hereby granted, provided that the above # copyright notice and this permission notice appear in all copies. # # THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES # WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF # MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR # ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES # WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN # ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF # OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. from __future__ import division from collections import deque from datetime import timedelta from math import ceil from sys import stderr from time import time __version__ = '1.3' class Infinite(object): file = stderr sma_window = 10 # Simple Moving Average window def __init__(self, *args, **kwargs): self.index = 0 self.start_ts = time() self.avg = 0 self._ts = self.start_ts self._xput = deque(maxlen=self.sma_window) for key, val in kwargs.items(): setattr(self, key, val) def __getitem__(self, key): if key.startswith('_'): return None return getattr(self, key, None) @property def elapsed(self): return int(time() - self.start_ts) @property def elapsed_td(self): return timedelta(seconds=self.elapsed) def update_avg(self, n, dt): if n > 0: self._xput.append(dt / n) self.avg = sum(self._xput) / len(self._xput) def update(self): pass def start(self): pass def finish(self): pass def next(self, n=1): now = time() dt = now - self._ts self.update_avg(n, dt) self._ts = now self.index = self.index + n self.update() def iter(self, it): try: for x in it: yield x self.next() finally: self.finish() class Progress(Infinite): def __init__(self, *args, **kwargs): super(Progress, self).__init__(*args, **kwargs) self.max = kwargs.get('max', 100) @property def eta(self): return int(ceil(self.avg * self.remaining)) @property def eta_td(self): return timedelta(seconds=self.eta) @property def percent(self): return self.progress * 100 @property def progress(self): return min(1, self.index / self.max) @property def remaining(self): return max(self.max - self.index, 0) def start(self): self.update() def goto(self, index): incr = index - self.index self.next(incr) def iter(self, it): try: self.max = len(it) except TypeError: pass try: for x in it: yield x self.next() finally: self.finish() ================================================ FILE: pointMLP/classification_ModelNet40/utils/progress/progress/bar.py ================================================ # -*- coding: utf-8 -*- # Copyright (c) 2012 Giorgos Verigakis # # Permission to use, copy, modify, and distribute this software for any # purpose with or without fee is hereby granted, provided that the above # copyright notice and this permission notice appear in all copies. # # THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES # WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF # MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR # ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES # WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN # ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF # OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. from __future__ import unicode_literals from . import Progress from .helpers import WritelnMixin class Bar(WritelnMixin, Progress): width = 32 message = '' suffix = '%(index)d/%(max)d' bar_prefix = ' |' bar_suffix = '| ' empty_fill = ' ' fill = '#' hide_cursor = True def update(self): filled_length = int(self.width * self.progress) empty_length = self.width - filled_length message = self.message % self bar = self.fill * filled_length empty = self.empty_fill * empty_length suffix = self.suffix % self line = ''.join([message, self.bar_prefix, bar, empty, self.bar_suffix, suffix]) self.writeln(line) class ChargingBar(Bar): suffix = '%(percent)d%%' bar_prefix = ' ' bar_suffix = ' ' empty_fill = '∙' fill = '█' class FillingSquaresBar(ChargingBar): empty_fill = '▢' fill = '▣' class FillingCirclesBar(ChargingBar): empty_fill = '◯' fill = '◉' class IncrementalBar(Bar): phases = (' ', '▏', '▎', '▍', '▌', '▋', '▊', '▉', '█') def update(self): nphases = len(self.phases) filled_len = self.width * self.progress nfull = int(filled_len) # Number of full chars phase = int((filled_len - nfull) * nphases) # Phase of last char nempty = self.width - nfull # Number of empty chars message = self.message % self bar = self.phases[-1] * nfull current = self.phases[phase] if phase > 0 else '' empty = self.empty_fill * max(0, nempty - len(current)) suffix = self.suffix % self line = ''.join([message, self.bar_prefix, bar, current, empty, self.bar_suffix, suffix]) self.writeln(line) class PixelBar(IncrementalBar): phases = ('⡀', '⡄', '⡆', '⡇', '⣇', '⣧', '⣷', '⣿') class ShadyBar(IncrementalBar): phases = (' ', '░', '▒', '▓', '█') ================================================ FILE: pointMLP/classification_ModelNet40/utils/progress/progress/counter.py ================================================ # -*- coding: utf-8 -*- # Copyright (c) 2012 Giorgos Verigakis # # Permission to use, copy, modify, and distribute this software for any # purpose with or without fee is hereby granted, provided that the above # copyright notice and this permission notice appear in all copies. # # THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES # WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF # MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR # ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES # WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN # ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF # OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. from __future__ import unicode_literals from . import Infinite, Progress from .helpers import WriteMixin class Counter(WriteMixin, Infinite): message = '' hide_cursor = True def update(self): self.write(str(self.index)) class Countdown(WriteMixin, Progress): hide_cursor = True def update(self): self.write(str(self.remaining)) class Stack(WriteMixin, Progress): phases = (' ', '▁', '▂', '▃', '▄', '▅', '▆', '▇', '█') hide_cursor = True def update(self): nphases = len(self.phases) i = min(nphases - 1, int(self.progress * nphases)) self.write(self.phases[i]) class Pie(Stack): phases = ('○', '◔', '◑', '◕', '●') ================================================ FILE: pointMLP/classification_ModelNet40/utils/progress/progress/helpers.py ================================================ # Copyright (c) 2012 Giorgos Verigakis # # Permission to use, copy, modify, and distribute this software for any # purpose with or without fee is hereby granted, provided that the above # copyright notice and this permission notice appear in all copies. # # THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES # WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF # MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR # ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES # WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN # ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF # OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. from __future__ import print_function HIDE_CURSOR = '\x1b[?25l' SHOW_CURSOR = '\x1b[?25h' class WriteMixin(object): hide_cursor = False def __init__(self, message=None, **kwargs): super(WriteMixin, self).__init__(**kwargs) self._width = 0 if message: self.message = message if self.file.isatty(): if self.hide_cursor: print(HIDE_CURSOR, end='', file=self.file) print(self.message, end='', file=self.file) self.file.flush() def write(self, s): if self.file.isatty(): b = '\b' * self._width c = s.ljust(self._width) print(b + c, end='', file=self.file) self._width = max(self._width, len(s)) self.file.flush() def finish(self): if self.file.isatty() and self.hide_cursor: print(SHOW_CURSOR, end='', file=self.file) class WritelnMixin(object): hide_cursor = False def __init__(self, message=None, **kwargs): super(WritelnMixin, self).__init__(**kwargs) if message: self.message = message if self.file.isatty() and self.hide_cursor: print(HIDE_CURSOR, end='', file=self.file) def clearln(self): if self.file.isatty(): print('\r\x1b[K', end='', file=self.file) def writeln(self, line): if self.file.isatty(): self.clearln() print(line, end='', file=self.file) self.file.flush() def finish(self): if self.file.isatty(): print(file=self.file) if self.hide_cursor: print(SHOW_CURSOR, end='', file=self.file) from signal import signal, SIGINT from sys import exit class SigIntMixin(object): """Registers a signal handler that calls finish on SIGINT""" def __init__(self, *args, **kwargs): super(SigIntMixin, self).__init__(*args, **kwargs) signal(SIGINT, self._sigint_handler) def _sigint_handler(self, signum, frame): self.finish() exit(0) ================================================ FILE: pointMLP/classification_ModelNet40/utils/progress/progress/spinner.py ================================================ # -*- coding: utf-8 -*- # Copyright (c) 2012 Giorgos Verigakis # # Permission to use, copy, modify, and distribute this software for any # purpose with or without fee is hereby granted, provided that the above # copyright notice and this permission notice appear in all copies. # # THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES # WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF # MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR # ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES # WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN # ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF # OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. from __future__ import unicode_literals from . import Infinite from .helpers import WriteMixin class Spinner(WriteMixin, Infinite): message = '' phases = ('-', '\\', '|', '/') hide_cursor = True def update(self): i = self.index % len(self.phases) self.write(self.phases[i]) class PieSpinner(Spinner): phases = ['◷', '◶', '◵', '◴'] class MoonSpinner(Spinner): phases = ['◑', '◒', '◐', '◓'] class LineSpinner(Spinner): phases = ['⎺', '⎻', '⎼', '⎽', '⎼', '⎻'] class PixelSpinner(Spinner): phases = ['⣾','⣷', '⣯', '⣟', '⡿', '⢿', '⣻', '⣽'] ================================================ FILE: pointMLP/classification_ModelNet40/utils/progress/setup.py ================================================ #!/usr/bin/env python from setuptools import setup import progress setup( name='progress', version=progress.__version__, description='Easy to use progress bars', long_description=open('README.rst').read(), author='Giorgos Verigakis', author_email='verigak@gmail.com', url='http://github.com/verigak/progress/', license='ISC', packages=['progress'], classifiers=[ 'Environment :: Console', 'Intended Audience :: Developers', 'License :: OSI Approved :: ISC License (ISCL)', 'Programming Language :: Python :: 2.6', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', ] ) ================================================ FILE: pointMLP/classification_ModelNet40/utils/progress/test_progress.py ================================================ #!/usr/bin/env python from __future__ import print_function import random import time from progress.bar import (Bar, ChargingBar, FillingSquaresBar, FillingCirclesBar, IncrementalBar, PixelBar, ShadyBar) from progress.spinner import (Spinner, PieSpinner, MoonSpinner, LineSpinner, PixelSpinner) from progress.counter import Counter, Countdown, Stack, Pie def sleep(): t = 0.01 t += t * random.uniform(-0.1, 0.1) # Add some variance time.sleep(t) for bar_cls in (Bar, ChargingBar, FillingSquaresBar, FillingCirclesBar): suffix = '%(index)d/%(max)d [%(elapsed)d / %(eta)d / %(eta_td)s]' bar = bar_cls(bar_cls.__name__, suffix=suffix) for i in bar.iter(range(200)): sleep() for bar_cls in (IncrementalBar, PixelBar, ShadyBar): suffix = '%(percent)d%% [%(elapsed_td)s / %(eta)d / %(eta_td)s]' bar = bar_cls(bar_cls.__name__, suffix=suffix) for i in bar.iter(range(200)): sleep() for spin in (Spinner, PieSpinner, MoonSpinner, LineSpinner, PixelSpinner): for i in spin(spin.__name__ + ' ').iter(range(100)): sleep() print() for singleton in (Counter, Countdown, Stack, Pie): for i in singleton(singleton.__name__ + ' ').iter(range(100)): sleep() print() bar = IncrementalBar('Random', suffix='%(index)d') for i in range(100): bar.goto(random.randint(0, 100)) sleep() bar.finish() ================================================ FILE: pointMLP/classification_ModelNet40/voting.py ================================================ import argparse import os import datetime import torch import torch.nn.parallel import torch.backends.cudnn as cudnn import torch.optim import torch.utils.data import torch.utils.data.distributed from torch.utils.data import DataLoader import models as models from utils import progress_bar, IOStream from data import ModelNet40 import sklearn.metrics as metrics from helper import cal_loss import numpy as np import torch.nn.functional as F model_names = sorted(name for name in models.__dict__ if callable(models.__dict__[name])) def parse_args(): """Parameters""" parser = argparse.ArgumentParser('training') parser.add_argument('-c', '--checkpoint', type=str, metavar='PATH', help='path to save checkpoint (default: checkpoint)') parser.add_argument('--msg', type=str, help='message after checkpoint') parser.add_argument('--batch_size', type=int, default=32, help='batch size in training') parser.add_argument('--model', default='model31A', help='model name [default: pointnet_cls]') parser.add_argument('--num_classes', default=40, type=int, choices=[10, 40], help='training on ModelNet10/40') parser.add_argument('--num_points', type=int, default=1024, help='Point Number') parser.add_argument('--seed', type=int, help='random seed (default: 1)') # Voting evaluation, referring: https://github.com/CVMI-Lab/PAConv/blob/main/obj_cls/eval_voting.py parser.add_argument('--NUM_PEPEAT', type=int, default=300) parser.add_argument('--NUM_VOTE', type=int, default=10) parser.add_argument('--validate', action='store_true', help='Validate the original testing result.') return parser.parse_args() class PointcloudScale(object): # input random scaling def __init__(self, scale_low=2. / 3., scale_high=3. / 2.): self.scale_low = scale_low self.scale_high = scale_high def __call__(self, pc): bsize = pc.size()[0] for i in range(bsize): xyz1 = np.random.uniform(low=self.scale_low, high=self.scale_high, size=[3]) pc[i, :, 0:3] = torch.mul(pc[i, :, 0:3], torch.from_numpy(xyz1).float().cuda()) return pc def main(): args = parse_args() print(f"args: {args}") os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE" if args.seed is None: args.seed = np.random.randint(1, 10000) print(f"random seed is set to {args.seed}, the speed will slow down.") torch.manual_seed(args.seed) np.random.seed(args.seed) torch.cuda.manual_seed_all(args.seed) torch.cuda.manual_seed(args.seed) torch.set_printoptions(10) torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True os.environ['PYTHONHASHSEED'] = str(args.seed) if torch.cuda.is_available(): device = 'cuda' else: device = 'cpu' print(f"==> Using device: {device}") if args.msg is None: message = str(datetime.datetime.now().strftime('-%Y%m%d%H%M%S')) else: message = "-" + args.msg args.checkpoint = 'checkpoints/' + args.model + message print('==> Preparing data..') test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), num_workers=4, batch_size=args.batch_size // 2, shuffle=False, drop_last=False) # Model print('==> Building model..') net = models.__dict__[args.model]() criterion = cal_loss net = net.to(device) checkpoint_path = os.path.join(args.checkpoint, 'best_checkpoint.pth') checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) # criterion = criterion.to(device) if device == 'cuda': net = torch.nn.DataParallel(net) cudnn.benchmark = True net.load_state_dict(checkpoint['net']) if args.validate: test_out = validate(net, test_loader, criterion, device) print(f"Vanilla out: {test_out}") print(f"Note 1: Please also load the random seed parameter (if forgot, see out.txt).\n" f"Note 2: This result may vary little on different GPUs (and number of GPUs), we tested 2080Ti, P100, and V100.\n" f"[note : Original result is achieved with V100 GPUs.]\n\n\n") # Interestingly, we get original best_test_acc on 4 V100 gpus, but this model is trained on one V100 gpu. # On different GPUs, and different number of GPUs, both OA and mean_acc vary a little. # Also, the batch size also affect the testing results, could not understand. print(f"===> start voting evaluation...") voting(net, test_loader, device, args) def validate(net, testloader, criterion, device): net.eval() test_loss = 0 correct = 0 total = 0 test_true = [] test_pred = [] time_cost = datetime.datetime.now() with torch.no_grad(): for batch_idx, (data, label) in enumerate(testloader): data, label = data.to(device), label.to(device).squeeze() data = data.permute(0, 2, 1) logits = net(data) loss = criterion(logits, label) test_loss += loss.item() preds = logits.max(dim=1)[1] test_true.append(label.cpu().numpy()) test_pred.append(preds.detach().cpu().numpy()) total += label.size(0) correct += preds.eq(label).sum().item() progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)' % (test_loss / (batch_idx + 1), 100. * correct / total, correct, total)) time_cost = int((datetime.datetime.now() - time_cost).total_seconds()) test_true = np.concatenate(test_true) test_pred = np.concatenate(test_pred) return { "loss": float("%.3f" % (test_loss / (batch_idx + 1))), "acc": float("%.3f" % (100. * metrics.accuracy_score(test_true, test_pred))), "acc_avg": float("%.3f" % (100. * metrics.balanced_accuracy_score(test_true, test_pred))), "time": time_cost } def voting(net, testloader, device, args): name = '/evaluate_voting' + str(datetime.datetime.now().strftime('-%Y%m%d%H%M%S')) + 'seed_' + str( args.seed) + '.log' io = IOStream(args.checkpoint + name) io.cprint(str(args)) net.eval() best_acc = 0 best_mean_acc = 0 # pointscale = PointcloudScale(scale_low=0.8, scale_high=1.18) # set the range of scaling # pointscale = PointcloudScale() pointscale = PointcloudScale(scale_low=0.85, scale_high=1.15) for i in range(args.NUM_PEPEAT): test_true = [] test_pred = [] for batch_idx, (data, label) in enumerate(testloader): data, label = data.to(device), label.to(device).squeeze() pred = 0 for v in range(args.NUM_VOTE): new_data = data # batch_size = data.size()[0] if v > 0: new_data.data = pointscale(new_data.data) with torch.no_grad(): pred += F.softmax(net(new_data.permute(0, 2, 1)), dim=1) # sum 10 preds pred /= args.NUM_VOTE # avg the preds! label = label.view(-1) pred_choice = pred.max(dim=1)[1] test_true.append(label.cpu().numpy()) test_pred.append(pred_choice.detach().cpu().numpy()) test_true = np.concatenate(test_true) test_pred = np.concatenate(test_pred) test_acc = 100. * metrics.accuracy_score(test_true, test_pred) test_mean_acc = 100. * metrics.balanced_accuracy_score(test_true, test_pred) if test_acc > best_acc: best_acc = test_acc if test_mean_acc > best_mean_acc: best_mean_acc = test_mean_acc outstr = 'Voting %d, test acc: %.3f, test mean acc: %.3f, [current best(all_acc: %.3f mean_acc: %.3f)]' % \ (i, test_acc, test_mean_acc, best_acc, best_mean_acc) io.cprint(outstr) final_outstr = 'Final voting test acc: %.6f,' % (best_acc * 100) io.cprint(final_outstr) if __name__ == '__main__': main() ================================================ FILE: pointnet2_pyt/.gitignore ================================================ __pycache__ *.pth* .autoenv* runs build checkpoints *.prof .lvimrc .vimtags .ccls .ccls-cache/ dist/ pointnet2.egg-info/ *.zip *.so .tox/ .mypy_cache **/*.pyc ================================================ FILE: pointnet2_pyt/.pre-commit-config.yaml ================================================ exclude: 'build|egg-info|dist' repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v1.2.3 hooks: - id: trailing-whitespace - id: check-added-large-files - id: end-of-file-fixer - repo: https://github.com/ambv/black rev: stable hooks: - id: black language_version: python3.6 - repo: local hooks: - id: clang-format name: Run clang-format entry: clang-format --style google -i types: [text] files: '.*\.cpp$|.*\.h$|.*\.cu$|.*\.hpp$' language: system ================================================ FILE: pointnet2_pyt/.travis.yml ================================================ dist: trusty language: python python: - "3.6" install: - pip install black script: - black --check . - find . -not -path '*/\.*' | grep -E ".*\.cpp$|.*\.h$|.*\.cu$|.*\.hpp$" | xargs -I {} bash -c "diff -u <(cat {}) <(clang-format --style google {})" ================================================ FILE: pointnet2_pyt/MANIFEST.in ================================================ graft pointnet2/_ext-src/include/ ================================================ FILE: pointnet2_pyt/README.rst ================================================ Pointnet2/Pointnet++ PyTorch ============================ * Implemention of Pointnet2/Pointnet++ written in `PyTorch `_. * Supports Multi-GPU via `nn.DataParallel `_. * Supports PyTorch version >= 1.0.0. Use `v1.0 `_ for support of older versions of PyTorch. See the official code release for the paper (in tensorflow), `charlesq34/pointnet2 `_, for official model definitions and hyper-parameters. The custom ops used by Pointnet++ are currently **ONLY** supported on the GPU using CUDA. Setup ----- * Install ``python`` -- This repo is tested with ``2.7``, ``3.5``, and ``3.6`` * Install dependencies :: pip install -r requirements.txt * Building `_ext` module :: python setup.py build_ext --inplace * Optionally, you can also install this repo as a package :: pip install -e . Example training ------------------ Two training examples are provided by ``pointnet2/train/train_sem_seg.py`` and ``pointnet2/train/train_cls.py``. The datasets for both will be downloaded automatically by default. They can be run via :: python -m pointnet2.train.train_cls python -m pointnet2.train.train_sem_seg Both scripts will print training progress after every epoch to the command line. Use the ``--visdom`` flag to enable logging to visdom and more detailed logging of training progress. Contributing ------------ This repository uses `black `_ for linting and style enforcement on python code. For c++/cuda code, `clang-format `_ is used for style. The simplest way to comply with style is via `pre-commit `_ :: pip install pre-commit pre-commit install Citation -------- :: @article{pytorchpointnet++, Author = {Erik Wijmans}, Title = {Pointnet++ Pytorch}, Journal = {https://github.com/erikwijmans/Pointnet2_PyTorch}, Year = {2018} } @inproceedings{qi2017pointnet++, title={Pointnet++: Deep hierarchical feature learning on point sets in a metric space}, author={Qi, Charles Ruizhongtai and Yi, Li and Su, Hao and Guibas, Leonidas J}, booktitle={Advances in Neural Information Processing Systems}, pages={5099--5108}, year={2017} } ================================================ FILE: pointnet2_pyt/UNLICENSE ================================================ This is free and unencumbered software released into the public domain. Anyone is free to copy, modify, publish, use, compile, sell, or distribute this software, either in source code form or as a compiled binary, for any purpose, commercial or non-commercial, and by any means. In jurisdictions that recognize copyright laws, the author or authors of this software dedicate any and all copyright interest in the software to the public domain. We make this dedication for the benefit of the public at large and to the detriment of our heirs and successors. We intend this dedication to be an overt act of relinquishment in perpetuity of all present and future rights to this software under copyright law. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. For more information, please refer to ================================================ FILE: pointnet2_pyt/__init__.py ================================================ ================================================ FILE: pointnet2_pyt/pointnet2/__init__.py ================================================ ''' Description: Autor: Jiachen Sun Date: 2022-02-16 22:23:16 LastEditors: Jiachen Sun LastEditTime: 2022-02-24 23:12:32 ''' from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) __version__ = "2.1.1" try: __POINTNET2_SETUP__ except NameError: __POINTNET2_SETUP__ = False if not __POINTNET2_SETUP__: from pointnet2_pyt.pointnet2 import utils from pointnet2_pyt.pointnet2 import data from pointnet2_pyt.pointnet2 import models ================================================ FILE: pointnet2_pyt/pointnet2/_ext-src/include/ball_query.h ================================================ #pragma once #include at::Tensor ball_query(at::Tensor new_xyz, at::Tensor xyz, const float radius, const int nsample); ================================================ FILE: pointnet2_pyt/pointnet2/_ext-src/include/cuda_utils.h ================================================ #ifndef _CUDA_UTILS_H #define _CUDA_UTILS_H #include #include #include #include #include #include #define TOTAL_THREADS 512 inline int opt_n_threads(int work_size) { const int pow_2 = std::log(static_cast(work_size)) / std::log(2.0); return max(min(1 << pow_2, TOTAL_THREADS), 1); } inline dim3 opt_block_config(int x, int y) { const int x_threads = opt_n_threads(x); const int y_threads = max(min(opt_n_threads(y), TOTAL_THREADS / x_threads), 1); dim3 block_config(x_threads, y_threads, 1); return block_config; } #define CUDA_CHECK_ERRORS() \ do { \ cudaError_t err = cudaGetLastError(); \ if (cudaSuccess != err) { \ fprintf(stderr, "CUDA kernel failed : %s\n%s at L:%d in %s\n", \ cudaGetErrorString(err), __PRETTY_FUNCTION__, __LINE__, \ __FILE__); \ exit(-1); \ } \ } while (0) #endif ================================================ FILE: pointnet2_pyt/pointnet2/_ext-src/include/group_points.h ================================================ #pragma once #include at::Tensor group_points(at::Tensor points, at::Tensor idx); at::Tensor group_points_grad(at::Tensor grad_out, at::Tensor idx, const int n); ================================================ FILE: pointnet2_pyt/pointnet2/_ext-src/include/interpolate.h ================================================ #pragma once #include #include std::vector three_nn(at::Tensor unknowns, at::Tensor knows); at::Tensor three_interpolate(at::Tensor points, at::Tensor idx, at::Tensor weight); at::Tensor three_interpolate_grad(at::Tensor grad_out, at::Tensor idx, at::Tensor weight, const int m); ================================================ FILE: pointnet2_pyt/pointnet2/_ext-src/include/sampling.h ================================================ #pragma once #include at::Tensor gather_points(at::Tensor points, at::Tensor idx); at::Tensor gather_points_grad(at::Tensor grad_out, at::Tensor idx, const int n); at::Tensor furthest_point_sampling(at::Tensor points, const int nsamples); ================================================ FILE: pointnet2_pyt/pointnet2/_ext-src/include/utils.h ================================================ #pragma once #include #include #define CHECK_CUDA(x) \ do { \ AT_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor"); \ } while (0) #define CHECK_CONTIGUOUS(x) \ do { \ AT_CHECK(x.is_contiguous(), #x " must be a contiguous tensor"); \ } while (0) #define CHECK_IS_INT(x) \ do { \ AT_CHECK(x.scalar_type() == at::ScalarType::Int, \ #x " must be an int tensor"); \ } while (0) #define CHECK_IS_FLOAT(x) \ do { \ AT_CHECK(x.scalar_type() == at::ScalarType::Float, \ #x " must be a float tensor"); \ } while (0) ================================================ FILE: pointnet2_pyt/pointnet2/_ext-src/src/ball_query.cpp ================================================ #include "ball_query.h" #include "utils.h" void query_ball_point_kernel_wrapper(int b, int n, int m, float radius, int nsample, const float *new_xyz, const float *xyz, int *idx); at::Tensor ball_query(at::Tensor new_xyz, at::Tensor xyz, const float radius, const int nsample) { CHECK_CONTIGUOUS(new_xyz); CHECK_CONTIGUOUS(xyz); CHECK_IS_FLOAT(new_xyz); CHECK_IS_FLOAT(xyz); if (new_xyz.type().is_cuda()) { CHECK_CUDA(xyz); } at::Tensor idx = torch::zeros({new_xyz.size(0), new_xyz.size(1), nsample}, at::device(new_xyz.device()).dtype(at::ScalarType::Int)); if (new_xyz.type().is_cuda()) { query_ball_point_kernel_wrapper(xyz.size(0), xyz.size(1), new_xyz.size(1), radius, nsample, new_xyz.data(), xyz.data(), idx.data()); } else { AT_CHECK(false, "CPU not supported"); } return idx; } ================================================ FILE: pointnet2_pyt/pointnet2/_ext-src/src/ball_query_gpu.cu ================================================ #include #include #include #include "cuda_utils.h" // input: new_xyz(b, m, 3) xyz(b, n, 3) // output: idx(b, m, nsample) __global__ void query_ball_point_kernel(int b, int n, int m, float radius, int nsample, const float *__restrict__ new_xyz, const float *__restrict__ xyz, int *__restrict__ idx) { int batch_index = blockIdx.x; xyz += batch_index * n * 3; new_xyz += batch_index * m * 3; idx += m * nsample * batch_index; int index = threadIdx.x; int stride = blockDim.x; float radius2 = radius * radius; for (int j = index; j < m; j += stride) { float new_x = new_xyz[j * 3 + 0]; float new_y = new_xyz[j * 3 + 1]; float new_z = new_xyz[j * 3 + 2]; for (int k = 0, cnt = 0; k < n && cnt < nsample; ++k) { float x = xyz[k * 3 + 0]; float y = xyz[k * 3 + 1]; float z = xyz[k * 3 + 2]; float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z); if (d2 < radius2) { if (cnt == 0) { for (int l = 0; l < nsample; ++l) { idx[j * nsample + l] = k; } } idx[j * nsample + cnt] = k; ++cnt; } } } } void query_ball_point_kernel_wrapper(int b, int n, int m, float radius, int nsample, const float *new_xyz, const float *xyz, int *idx) { cudaStream_t stream = at::cuda::getCurrentCUDAStream(); query_ball_point_kernel<<>>( b, n, m, radius, nsample, new_xyz, xyz, idx); CUDA_CHECK_ERRORS(); } ================================================ FILE: pointnet2_pyt/pointnet2/_ext-src/src/bindings.cpp ================================================ #include "ball_query.h" #include "group_points.h" #include "interpolate.h" #include "sampling.h" PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("gather_points", &gather_points); m.def("gather_points_grad", &gather_points_grad); m.def("furthest_point_sampling", &furthest_point_sampling); m.def("three_nn", &three_nn); m.def("three_interpolate", &three_interpolate); m.def("three_interpolate_grad", &three_interpolate_grad); m.def("ball_query", &ball_query); m.def("group_points", &group_points); m.def("group_points_grad", &group_points_grad); } ================================================ FILE: pointnet2_pyt/pointnet2/_ext-src/src/group_points.cpp ================================================ #include "group_points.h" #include "utils.h" void group_points_kernel_wrapper(int b, int c, int n, int npoints, int nsample, const float *points, const int *idx, float *out); void group_points_grad_kernel_wrapper(int b, int c, int n, int npoints, int nsample, const float *grad_out, const int *idx, float *grad_points); at::Tensor group_points(at::Tensor points, at::Tensor idx) { CHECK_CONTIGUOUS(points); CHECK_CONTIGUOUS(idx); CHECK_IS_FLOAT(points); CHECK_IS_INT(idx); if (points.type().is_cuda()) { CHECK_CUDA(idx); } at::Tensor output = torch::zeros({points.size(0), points.size(1), idx.size(1), idx.size(2)}, at::device(points.device()).dtype(at::ScalarType::Float)); if (points.type().is_cuda()) { group_points_kernel_wrapper(points.size(0), points.size(1), points.size(2), idx.size(1), idx.size(2), points.data(), idx.data(), output.data()); } else { AT_CHECK(false, "CPU not supported"); } return output; } at::Tensor group_points_grad(at::Tensor grad_out, at::Tensor idx, const int n) { CHECK_CONTIGUOUS(grad_out); CHECK_CONTIGUOUS(idx); CHECK_IS_FLOAT(grad_out); CHECK_IS_INT(idx); if (grad_out.type().is_cuda()) { CHECK_CUDA(idx); } at::Tensor output = torch::zeros({grad_out.size(0), grad_out.size(1), n}, at::device(grad_out.device()).dtype(at::ScalarType::Float)); if (grad_out.type().is_cuda()) { group_points_grad_kernel_wrapper( grad_out.size(0), grad_out.size(1), n, idx.size(1), idx.size(2), grad_out.data(), idx.data(), output.data()); } else { AT_CHECK(false, "CPU not supported"); } return output; } ================================================ FILE: pointnet2_pyt/pointnet2/_ext-src/src/group_points_gpu.cu ================================================ #include #include #include "cuda_utils.h" // input: points(b, c, n) idx(b, npoints, nsample) // output: out(b, c, npoints, nsample) __global__ void group_points_kernel(int b, int c, int n, int npoints, int nsample, const float *__restrict__ points, const int *__restrict__ idx, float *__restrict__ out) { int batch_index = blockIdx.x; points += batch_index * n * c; idx += batch_index * npoints * nsample; out += batch_index * npoints * nsample * c; const int index = threadIdx.y * blockDim.x + threadIdx.x; const int stride = blockDim.y * blockDim.x; for (int i = index; i < c * npoints; i += stride) { const int l = i / npoints; const int j = i % npoints; for (int k = 0; k < nsample; ++k) { int ii = idx[j * nsample + k]; out[(l * npoints + j) * nsample + k] = points[l * n + ii]; } } } void group_points_kernel_wrapper(int b, int c, int n, int npoints, int nsample, const float *points, const int *idx, float *out) { cudaStream_t stream = at::cuda::getCurrentCUDAStream(); group_points_kernel<<>>( b, c, n, npoints, nsample, points, idx, out); CUDA_CHECK_ERRORS(); } // input: grad_out(b, c, npoints, nsample), idx(b, npoints, nsample) // output: grad_points(b, c, n) __global__ void group_points_grad_kernel(int b, int c, int n, int npoints, int nsample, const float *__restrict__ grad_out, const int *__restrict__ idx, float *__restrict__ grad_points) { int batch_index = blockIdx.x; grad_out += batch_index * npoints * nsample * c; idx += batch_index * npoints * nsample; grad_points += batch_index * n * c; const int index = threadIdx.y * blockDim.x + threadIdx.x; const int stride = blockDim.y * blockDim.x; for (int i = index; i < c * npoints; i += stride) { const int l = i / npoints; const int j = i % npoints; for (int k = 0; k < nsample; ++k) { int ii = idx[j * nsample + k]; atomicAdd(grad_points + l * n + ii, grad_out[(l * npoints + j) * nsample + k]); } } } void group_points_grad_kernel_wrapper(int b, int c, int n, int npoints, int nsample, const float *grad_out, const int *idx, float *grad_points) { cudaStream_t stream = at::cuda::getCurrentCUDAStream(); group_points_grad_kernel<<>>( b, c, n, npoints, nsample, grad_out, idx, grad_points); CUDA_CHECK_ERRORS(); } ================================================ FILE: pointnet2_pyt/pointnet2/_ext-src/src/interpolate.cpp ================================================ #include "interpolate.h" #include "utils.h" void three_nn_kernel_wrapper(int b, int n, int m, const float *unknown, const float *known, float *dist2, int *idx); void three_interpolate_kernel_wrapper(int b, int c, int m, int n, const float *points, const int *idx, const float *weight, float *out); void three_interpolate_grad_kernel_wrapper(int b, int c, int n, int m, const float *grad_out, const int *idx, const float *weight, float *grad_points); std::vector three_nn(at::Tensor unknowns, at::Tensor knows) { CHECK_CONTIGUOUS(unknowns); CHECK_CONTIGUOUS(knows); CHECK_IS_FLOAT(unknowns); CHECK_IS_FLOAT(knows); if (unknowns.type().is_cuda()) { CHECK_CUDA(knows); } at::Tensor idx = torch::zeros({unknowns.size(0), unknowns.size(1), 3}, at::device(unknowns.device()).dtype(at::ScalarType::Int)); at::Tensor dist2 = torch::zeros({unknowns.size(0), unknowns.size(1), 3}, at::device(unknowns.device()).dtype(at::ScalarType::Float)); if (unknowns.type().is_cuda()) { three_nn_kernel_wrapper(unknowns.size(0), unknowns.size(1), knows.size(1), unknowns.data(), knows.data(), dist2.data(), idx.data()); } else { AT_CHECK(false, "CPU not supported"); } return {dist2, idx}; } at::Tensor three_interpolate(at::Tensor points, at::Tensor idx, at::Tensor weight) { CHECK_CONTIGUOUS(points); CHECK_CONTIGUOUS(idx); CHECK_CONTIGUOUS(weight); CHECK_IS_FLOAT(points); CHECK_IS_INT(idx); CHECK_IS_FLOAT(weight); if (points.type().is_cuda()) { CHECK_CUDA(idx); CHECK_CUDA(weight); } at::Tensor output = torch::zeros({points.size(0), points.size(1), idx.size(1)}, at::device(points.device()).dtype(at::ScalarType::Float)); if (points.type().is_cuda()) { three_interpolate_kernel_wrapper( points.size(0), points.size(1), points.size(2), idx.size(1), points.data(), idx.data(), weight.data(), output.data()); } else { AT_CHECK(false, "CPU not supported"); } return output; } at::Tensor three_interpolate_grad(at::Tensor grad_out, at::Tensor idx, at::Tensor weight, const int m) { CHECK_CONTIGUOUS(grad_out); CHECK_CONTIGUOUS(idx); CHECK_CONTIGUOUS(weight); CHECK_IS_FLOAT(grad_out); CHECK_IS_INT(idx); CHECK_IS_FLOAT(weight); if (grad_out.type().is_cuda()) { CHECK_CUDA(idx); CHECK_CUDA(weight); } at::Tensor output = torch::zeros({grad_out.size(0), grad_out.size(1), m}, at::device(grad_out.device()).dtype(at::ScalarType::Float)); if (grad_out.type().is_cuda()) { three_interpolate_grad_kernel_wrapper( grad_out.size(0), grad_out.size(1), grad_out.size(2), m, grad_out.data(), idx.data(), weight.data(), output.data()); } else { AT_CHECK(false, "CPU not supported"); } return output; } ================================================ FILE: pointnet2_pyt/pointnet2/_ext-src/src/interpolate_gpu.cu ================================================ #include #include #include #include "cuda_utils.h" // input: unknown(b, n, 3) known(b, m, 3) // output: dist2(b, n, 3), idx(b, n, 3) __global__ void three_nn_kernel(int b, int n, int m, const float *__restrict__ unknown, const float *__restrict__ known, float *__restrict__ dist2, int *__restrict__ idx) { int batch_index = blockIdx.x; unknown += batch_index * n * 3; known += batch_index * m * 3; dist2 += batch_index * n * 3; idx += batch_index * n * 3; int index = threadIdx.x; int stride = blockDim.x; for (int j = index; j < n; j += stride) { float ux = unknown[j * 3 + 0]; float uy = unknown[j * 3 + 1]; float uz = unknown[j * 3 + 2]; double best1 = 1e40, best2 = 1e40, best3 = 1e40; int besti1 = 0, besti2 = 0, besti3 = 0; for (int k = 0; k < m; ++k) { float x = known[k * 3 + 0]; float y = known[k * 3 + 1]; float z = known[k * 3 + 2]; float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z); if (d < best1) { best3 = best2; besti3 = besti2; best2 = best1; besti2 = besti1; best1 = d; besti1 = k; } else if (d < best2) { best3 = best2; besti3 = besti2; best2 = d; besti2 = k; } else if (d < best3) { best3 = d; besti3 = k; } } dist2[j * 3 + 0] = best1; dist2[j * 3 + 1] = best2; dist2[j * 3 + 2] = best3; idx[j * 3 + 0] = besti1; idx[j * 3 + 1] = besti2; idx[j * 3 + 2] = besti3; } } void three_nn_kernel_wrapper(int b, int n, int m, const float *unknown, const float *known, float *dist2, int *idx) { cudaStream_t stream = at::cuda::getCurrentCUDAStream(); three_nn_kernel<<>>(b, n, m, unknown, known, dist2, idx); CUDA_CHECK_ERRORS(); } // input: points(b, c, m), idx(b, n, 3), weight(b, n, 3) // output: out(b, c, n) __global__ void three_interpolate_kernel(int b, int c, int m, int n, const float *__restrict__ points, const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ out) { int batch_index = blockIdx.x; points += batch_index * m * c; idx += batch_index * n * 3; weight += batch_index * n * 3; out += batch_index * n * c; const int index = threadIdx.y * blockDim.x + threadIdx.x; const int stride = blockDim.y * blockDim.x; for (int i = index; i < c * n; i += stride) { const int l = i / n; const int j = i % n; float w1 = weight[j * 3 + 0]; float w2 = weight[j * 3 + 1]; float w3 = weight[j * 3 + 2]; int i1 = idx[j * 3 + 0]; int i2 = idx[j * 3 + 1]; int i3 = idx[j * 3 + 2]; out[i] = points[l * m + i1] * w1 + points[l * m + i2] * w2 + points[l * m + i3] * w3; } } void three_interpolate_kernel_wrapper(int b, int c, int m, int n, const float *points, const int *idx, const float *weight, float *out) { cudaStream_t stream = at::cuda::getCurrentCUDAStream(); three_interpolate_kernel<<>>( b, c, m, n, points, idx, weight, out); CUDA_CHECK_ERRORS(); } // input: grad_out(b, c, n), idx(b, n, 3), weight(b, n, 3) // output: grad_points(b, c, m) __global__ void three_interpolate_grad_kernel( int b, int c, int n, int m, const float *__restrict__ grad_out, const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ grad_points) { int batch_index = blockIdx.x; grad_out += batch_index * n * c; idx += batch_index * n * 3; weight += batch_index * n * 3; grad_points += batch_index * m * c; const int index = threadIdx.y * blockDim.x + threadIdx.x; const int stride = blockDim.y * blockDim.x; for (int i = index; i < c * n; i += stride) { const int l = i / n; const int j = i % n; float w1 = weight[j * 3 + 0]; float w2 = weight[j * 3 + 1]; float w3 = weight[j * 3 + 2]; int i1 = idx[j * 3 + 0]; int i2 = idx[j * 3 + 1]; int i3 = idx[j * 3 + 2]; atomicAdd(grad_points + l * m + i1, grad_out[i] * w1); atomicAdd(grad_points + l * m + i2, grad_out[i] * w2); atomicAdd(grad_points + l * m + i3, grad_out[i] * w3); } } void three_interpolate_grad_kernel_wrapper(int b, int c, int n, int m, const float *grad_out, const int *idx, const float *weight, float *grad_points) { cudaStream_t stream = at::cuda::getCurrentCUDAStream(); three_interpolate_grad_kernel<<>>( b, c, n, m, grad_out, idx, weight, grad_points); CUDA_CHECK_ERRORS(); } ================================================ FILE: pointnet2_pyt/pointnet2/_ext-src/src/sampling.cpp ================================================ #include "sampling.h" #include "utils.h" void gather_points_kernel_wrapper(int b, int c, int n, int npoints, const float *points, const int *idx, float *out); void gather_points_grad_kernel_wrapper(int b, int c, int n, int npoints, const float *grad_out, const int *idx, float *grad_points); void furthest_point_sampling_kernel_wrapper(int b, int n, int m, const float *dataset, float *temp, int *idxs); at::Tensor gather_points(at::Tensor points, at::Tensor idx) { CHECK_CONTIGUOUS(points); CHECK_CONTIGUOUS(idx); CHECK_IS_FLOAT(points); CHECK_IS_INT(idx); if (points.type().is_cuda()) { CHECK_CUDA(idx); } at::Tensor output = torch::zeros({points.size(0), points.size(1), idx.size(1)}, at::device(points.device()).dtype(at::ScalarType::Float)); if (points.type().is_cuda()) { gather_points_kernel_wrapper(points.size(0), points.size(1), points.size(2), idx.size(1), points.data(), idx.data(), output.data()); } else { AT_CHECK(false, "CPU not supported"); } return output; } at::Tensor gather_points_grad(at::Tensor grad_out, at::Tensor idx, const int n) { CHECK_CONTIGUOUS(grad_out); CHECK_CONTIGUOUS(idx); CHECK_IS_FLOAT(grad_out); CHECK_IS_INT(idx); if (grad_out.type().is_cuda()) { CHECK_CUDA(idx); } at::Tensor output = torch::zeros({grad_out.size(0), grad_out.size(1), n}, at::device(grad_out.device()).dtype(at::ScalarType::Float)); if (grad_out.type().is_cuda()) { gather_points_grad_kernel_wrapper(grad_out.size(0), grad_out.size(1), n, idx.size(1), grad_out.data(), idx.data(), output.data()); } else { AT_CHECK(false, "CPU not supported"); } return output; } at::Tensor furthest_point_sampling(at::Tensor points, const int nsamples) { CHECK_CONTIGUOUS(points); CHECK_IS_FLOAT(points); at::Tensor output = torch::zeros({points.size(0), nsamples}, at::device(points.device()).dtype(at::ScalarType::Int)); at::Tensor tmp = torch::full({points.size(0), points.size(1)}, 1e10, at::device(points.device()).dtype(at::ScalarType::Float)); if (points.type().is_cuda()) { furthest_point_sampling_kernel_wrapper( points.size(0), points.size(1), nsamples, points.data(), tmp.data(), output.data()); } else { AT_CHECK(false, "CPU not supported"); } return output; } ================================================ FILE: pointnet2_pyt/pointnet2/_ext-src/src/sampling_gpu.cu ================================================ #include #include #include "cuda_utils.h" // input: points(b, c, n) idx(b, m) // output: out(b, c, m) __global__ void gather_points_kernel(int b, int c, int n, int m, const float *__restrict__ points, const int *__restrict__ idx, float *__restrict__ out) { for (int i = blockIdx.x; i < b; i += gridDim.x) { for (int l = blockIdx.y; l < c; l += gridDim.y) { for (int j = threadIdx.x; j < m; j += blockDim.x) { int a = idx[i * m + j]; out[(i * c + l) * m + j] = points[(i * c + l) * n + a]; } } } } void gather_points_kernel_wrapper(int b, int c, int n, int npoints, const float *points, const int *idx, float *out) { gather_points_kernel<<>>(b, c, n, npoints, points, idx, out); CUDA_CHECK_ERRORS(); } // input: grad_out(b, c, m) idx(b, m) // output: grad_points(b, c, n) __global__ void gather_points_grad_kernel(int b, int c, int n, int m, const float *__restrict__ grad_out, const int *__restrict__ idx, float *__restrict__ grad_points) { for (int i = blockIdx.x; i < b; i += gridDim.x) { for (int l = blockIdx.y; l < c; l += gridDim.y) { for (int j = threadIdx.x; j < m; j += blockDim.x) { int a = idx[i * m + j]; atomicAdd(grad_points + (i * c + l) * n + a, grad_out[(i * c + l) * m + j]); } } } } void gather_points_grad_kernel_wrapper(int b, int c, int n, int npoints, const float *grad_out, const int *idx, float *grad_points) { gather_points_grad_kernel<<>>( b, c, n, npoints, grad_out, idx, grad_points); CUDA_CHECK_ERRORS(); } __device__ void __update(float *__restrict__ dists, int *__restrict__ dists_i, int idx1, int idx2) { const float v1 = dists[idx1], v2 = dists[idx2]; const int i1 = dists_i[idx1], i2 = dists_i[idx2]; dists[idx1] = max(v1, v2); dists_i[idx1] = v2 > v1 ? i2 : i1; } // Input dataset: (b, n, 3), tmp: (b, n) // Ouput idxs (b, m) template __global__ void furthest_point_sampling_kernel( int b, int n, int m, const float *__restrict__ dataset, float *__restrict__ temp, int *__restrict__ idxs) { if (m <= 0) return; __shared__ float dists[block_size]; __shared__ int dists_i[block_size]; int batch_index = blockIdx.x; dataset += batch_index * n * 3; temp += batch_index * n; idxs += batch_index * m; int tid = threadIdx.x; const int stride = block_size; int old = 0; if (threadIdx.x == 0) idxs[0] = old; __syncthreads(); for (int j = 1; j < m; j++) { int besti = 0; float best = -1; float x1 = dataset[old * 3 + 0]; float y1 = dataset[old * 3 + 1]; float z1 = dataset[old * 3 + 2]; for (int k = tid; k < n; k += stride) { float x2, y2, z2; x2 = dataset[k * 3 + 0]; y2 = dataset[k * 3 + 1]; z2 = dataset[k * 3 + 2]; float mag = (x2 * x2) + (y2 * y2) + (z2 * z2); if (mag <= 1e-3) continue; float d = (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1); float d2 = min(d, temp[k]); temp[k] = d2; besti = d2 > best ? k : besti; best = d2 > best ? d2 : best; } dists[tid] = best; dists_i[tid] = besti; __syncthreads(); if (block_size >= 512) { if (tid < 256) { __update(dists, dists_i, tid, tid + 256); } __syncthreads(); } if (block_size >= 256) { if (tid < 128) { __update(dists, dists_i, tid, tid + 128); } __syncthreads(); } if (block_size >= 128) { if (tid < 64) { __update(dists, dists_i, tid, tid + 64); } __syncthreads(); } if (block_size >= 64) { if (tid < 32) { __update(dists, dists_i, tid, tid + 32); } __syncthreads(); } if (block_size >= 32) { if (tid < 16) { __update(dists, dists_i, tid, tid + 16); } __syncthreads(); } if (block_size >= 16) { if (tid < 8) { __update(dists, dists_i, tid, tid + 8); } __syncthreads(); } if (block_size >= 8) { if (tid < 4) { __update(dists, dists_i, tid, tid + 4); } __syncthreads(); } if (block_size >= 4) { if (tid < 2) { __update(dists, dists_i, tid, tid + 2); } __syncthreads(); } if (block_size >= 2) { if (tid < 1) { __update(dists, dists_i, tid, tid + 1); } __syncthreads(); } old = dists_i[0]; if (tid == 0) idxs[j] = old; } } void furthest_point_sampling_kernel_wrapper(int b, int n, int m, const float *dataset, float *temp, int *idxs) { unsigned int n_threads = opt_n_threads(n); cudaStream_t stream = at::cuda::getCurrentCUDAStream(); switch (n_threads) { case 512: furthest_point_sampling_kernel<512> <<>>(b, n, m, dataset, temp, idxs); break; case 256: furthest_point_sampling_kernel<256> <<>>(b, n, m, dataset, temp, idxs); break; case 128: furthest_point_sampling_kernel<128> <<>>(b, n, m, dataset, temp, idxs); break; case 64: furthest_point_sampling_kernel<64> <<>>(b, n, m, dataset, temp, idxs); break; case 32: furthest_point_sampling_kernel<32> <<>>(b, n, m, dataset, temp, idxs); break; case 16: furthest_point_sampling_kernel<16> <<>>(b, n, m, dataset, temp, idxs); break; case 8: furthest_point_sampling_kernel<8> <<>>(b, n, m, dataset, temp, idxs); break; case 4: furthest_point_sampling_kernel<4> <<>>(b, n, m, dataset, temp, idxs); break; case 2: furthest_point_sampling_kernel<2> <<>>(b, n, m, dataset, temp, idxs); break; case 1: furthest_point_sampling_kernel<1> <<>>(b, n, m, dataset, temp, idxs); break; default: furthest_point_sampling_kernel<512> <<>>(b, n, m, dataset, temp, idxs); } CUDA_CHECK_ERRORS(); } ================================================ FILE: pointnet2_pyt/pointnet2/data/.gitignore ================================================ indoor3d_sem_seg_hdf5_data modelnet40_ply_hdf5_2048 ================================================ FILE: pointnet2_pyt/pointnet2/data/Indoor3DSemSegLoader.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) import torch import torch.utils.data as data import numpy as np import os import h5py import subprocess import shlex BASE_DIR = os.path.dirname(os.path.abspath(__file__)) def _get_data_files(list_filename): with open(list_filename) as f: return [line.rstrip() for line in f] def _load_data_file(name): f = h5py.File(name) data = f["data"][:] label = f["label"][:] return data, label class Indoor3DSemSeg(data.Dataset): def __init__(self, num_points, train=True, download=True, data_precent=1.0): super().__init__() self.data_precent = data_precent self.folder = "indoor3d_sem_seg_hdf5_data" self.data_dir = os.path.join(BASE_DIR, self.folder) self.url = ( "https://shapenet.cs.stanford.edu/media/indoor3d_sem_seg_hdf5_data.zip" ) if download and not os.path.exists(self.data_dir): zipfile = os.path.join(BASE_DIR, os.path.basename(self.url)) subprocess.check_call( shlex.split("curl {} -o {}".format(self.url, zipfile)) ) subprocess.check_call( shlex.split("unzip {} -d {}".format(zipfile, BASE_DIR)) ) subprocess.check_call(shlex.split("rm {}".format(zipfile))) self.train, self.num_points = train, num_points all_files = _get_data_files(os.path.join(self.data_dir, "all_files.txt")) room_filelist = _get_data_files( os.path.join(self.data_dir, "room_filelist.txt") ) data_batchlist, label_batchlist = [], [] for f in all_files: data, label = _load_data_file(os.path.join(BASE_DIR, f)) data_batchlist.append(data) label_batchlist.append(label) data_batches = np.concatenate(data_batchlist, 0) labels_batches = np.concatenate(label_batchlist, 0) test_area = "Area_5" train_idxs, test_idxs = [], [] for i, room_name in enumerate(room_filelist): if test_area in room_name: test_idxs.append(i) else: train_idxs.append(i) if self.train: self.points = data_batches[train_idxs, ...] self.labels = labels_batches[train_idxs, ...] else: self.points = data_batches[test_idxs, ...] self.labels = labels_batches[test_idxs, ...] def __getitem__(self, idx): pt_idxs = np.arange(0, self.num_points) np.random.shuffle(pt_idxs) current_points = torch.from_numpy(self.points[idx, pt_idxs].copy()).type( torch.FloatTensor ) current_labels = torch.from_numpy(self.labels[idx, pt_idxs].copy()).type( torch.LongTensor ) return current_points, current_labels def __len__(self): return int(self.points.shape[0] * self.data_precent) def set_num_points(self, pts): self.num_points = pts def randomize(self): pass if __name__ == "__main__": dset = Indoor3DSemSeg(16, "./", train=True) print(dset[0]) print(len(dset)) dloader = torch.utils.data.DataLoader(dset, batch_size=32, shuffle=True) for i, data in enumerate(dloader, 0): inputs, labels = data if i == len(dloader) - 1: print(inputs.size()) ================================================ FILE: pointnet2_pyt/pointnet2/data/ModelNet40Loader.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) import torch import torch.utils.data as data import numpy as np import os import h5py import subprocess import shlex BASE_DIR = os.path.dirname(os.path.abspath(__file__)) def _get_data_files(list_filename): with open(list_filename) as f: return [line.rstrip()[5:] for line in f] def _load_data_file(name): f = h5py.File(name) data = f["data"][:] label = f["label"][:] return data, label class ModelNet40Cls(data.Dataset): def __init__(self, num_points, transforms=None, train=True, download=True): super().__init__() self.transforms = transforms self.folder = "modelnet40_ply_hdf5_2048" self.data_dir = os.path.join(BASE_DIR, self.folder) self.url = "https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip" if download and not os.path.exists(self.data_dir): zipfile = os.path.join(BASE_DIR, os.path.basename(self.url)) subprocess.check_call( shlex.split("curl {} -o {}".format(self.url, zipfile)) ) subprocess.check_call( shlex.split("unzip {} -d {}".format(zipfile, BASE_DIR)) ) subprocess.check_call(shlex.split("rm {}".format(zipfile))) self.train = train if self.train: self.files = _get_data_files(os.path.join(self.data_dir, "train_files.txt")) else: self.files = _get_data_files(os.path.join(self.data_dir, "test_files.txt")) point_list, label_list = [], [] for f in self.files: points, labels = _load_data_file(os.path.join(BASE_DIR, f)) point_list.append(points) label_list.append(labels) self.points = np.concatenate(point_list, 0) self.labels = np.concatenate(label_list, 0) self.set_num_points(num_points) def __getitem__(self, idx): pt_idxs = np.arange(0, self.num_points) np.random.shuffle(pt_idxs) current_points = self.points[idx, pt_idxs].copy() label = torch.from_numpy(self.labels[idx]).type(torch.LongTensor) if self.transforms is not None: current_points = self.transforms(current_points) return current_points, label def __len__(self): return self.points.shape[0] def set_num_points(self, pts): self.num_points = min(self.points.shape[1], pts) def randomize(self): pass if __name__ == "__main__": from torchvision import transforms import data_utils as d_utils transforms = transforms.Compose( [ d_utils.PointcloudToTensor(), d_utils.PointcloudRotate(axis=np.array([1, 0, 0])), d_utils.PointcloudScale(), d_utils.PointcloudTranslate(), d_utils.PointcloudJitter(), ] ) dset = ModelNet40Cls(16, train=True, transforms=transforms) print(dset[0][0]) print(dset[0][1]) print(len(dset)) dloader = torch.utils.data.DataLoader(dset, batch_size=32, shuffle=True) ================================================ FILE: pointnet2_pyt/pointnet2/data/__init__.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) from .ModelNet40Loader import ModelNet40Cls from .Indoor3DSemSegLoader import Indoor3DSemSeg ================================================ FILE: pointnet2_pyt/pointnet2/data/data_utils.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) import torch import numpy as np def angle_axis(angle, axis): # type: (float, np.ndarray) -> float r"""Returns a 4x4 rotation matrix that performs a rotation around axis by angle Parameters ---------- angle : float Angle to rotate by axis: np.ndarray Axis to rotate about Returns ------- torch.Tensor 3x3 rotation matrix """ u = axis / np.linalg.norm(axis) cosval, sinval = np.cos(angle), np.sin(angle) # yapf: disable cross_prod_mat = np.array([[0.0, -u[2], u[1]], [u[2], 0.0, -u[0]], [-u[1], u[0], 0.0]]) R = torch.from_numpy( cosval * np.eye(3) + sinval * cross_prod_mat + (1.0 - cosval) * np.outer(u, u) ) # yapf: enable return R.float() class PointcloudScale(object): def __init__(self, lo=0.8, hi=1.25): self.lo, self.hi = lo, hi def __call__(self, points): scaler = np.random.uniform(self.lo, self.hi) points[:, 0:3] *= scaler return points class PointcloudRotate(object): def __init__(self, axis=np.array([0.0, 1.0, 0.0])): self.axis = axis def __call__(self, points): rotation_angle = np.random.uniform() * 2 * np.pi rotation_matrix = angle_axis(rotation_angle, self.axis) normals = points.size(1) > 3 if not normals: return torch.matmul(points, rotation_matrix.t()) else: pc_xyz = points[:, 0:3] pc_normals = points[:, 3:] points[:, 0:3] = torch.matmul(pc_xyz, rotation_matrix.t()) points[:, 3:] = torch.matmul(pc_normals, rotation_matrix.t()) return points class PointcloudRotatePerturbation(object): def __init__(self, angle_sigma=0.06, angle_clip=0.18): self.angle_sigma, self.angle_clip = angle_sigma, angle_clip def _get_angles(self): angles = np.clip( self.angle_sigma * np.random.randn(3), -self.angle_clip, self.angle_clip ) return angles def __call__(self, points): angles = self._get_angles() Rx = angle_axis(angles[0], np.array([1.0, 0.0, 0.0])) Ry = angle_axis(angles[1], np.array([0.0, 1.0, 0.0])) Rz = angle_axis(angles[2], np.array([0.0, 0.0, 1.0])) rotation_matrix = torch.matmul(torch.matmul(Rz, Ry), Rx) normals = points.size(1) > 3 if not normals: return torch.matmul(points, rotation_matrix.t()) else: pc_xyz = points[:, 0:3] pc_normals = points[:, 3:] points[:, 0:3] = torch.matmul(pc_xyz, rotation_matrix.t()) points[:, 3:] = torch.matmul(pc_normals, rotation_matrix.t()) return points class PointcloudJitter(object): def __init__(self, std=0.01, clip=0.05): self.std, self.clip = std, clip def __call__(self, points): jittered_data = ( points.new(points.size(0), 3) .normal_(mean=0.0, std=self.std) .clamp_(-self.clip, self.clip) ) points[:, 0:3] += jittered_data return points class PointcloudTranslate(object): def __init__(self, translate_range=0.1): self.translate_range = translate_range def __call__(self, points): translation = np.random.uniform(-self.translate_range, self.translate_range) points[:, 0:3] += translation return points class PointcloudToTensor(object): def __call__(self, points): return torch.from_numpy(points).float() class PointcloudRandomInputDropout(object): def __init__(self, max_dropout_ratio=0.875): assert max_dropout_ratio >= 0 and max_dropout_ratio < 1 self.max_dropout_ratio = max_dropout_ratio def __call__(self, points): pc = points.numpy() dropout_ratio = np.random.random() * self.max_dropout_ratio # 0~0.875 drop_idx = np.where(np.random.random((pc.shape[0])) <= dropout_ratio)[0] if len(drop_idx) > 0: pc[drop_idx] = pc[0] # set to the first point return torch.from_numpy(pc).float() ================================================ FILE: pointnet2_pyt/pointnet2/models/__init__.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) from .pointnet2_msg_sem import Pointnet2MSG as Pointnet2SemMSG from .pointnet2_ssg_sem import Pointnet2SSG as Pointnet2SemSSG from .pointnet2_msg_cls import Pointnet2MSG as Pointnet2ClsMSG from .pointnet2_ssg_cls import Pointnet2SSG as Pointnet2ClsSSG ================================================ FILE: pointnet2_pyt/pointnet2/models/pointnet2_msg_cls.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) import torch import torch.nn as nn import etw_pytorch_utils as pt_utils from collections import namedtuple from pointnet2.utils.pointnet2_modules import PointnetSAModuleMSG, PointnetSAModule def model_fn_decorator(criterion): ModelReturn = namedtuple("ModelReturn", ["preds", "loss", "acc"]) def model_fn(model, data, epoch=0, eval=False): with torch.set_grad_enabled(not eval): inputs, labels = data inputs = inputs.to("cuda", non_blocking=True) labels = labels.to("cuda", non_blocking=True) preds = model(inputs) labels = labels.view(-1) loss = criterion(preds, labels) _, classes = torch.max(preds, -1) acc = (classes == labels).float().sum() / labels.numel() return ModelReturn(preds, loss, {"acc": acc.item(), "loss": loss.item()}) return model_fn class Pointnet2MSG(nn.Module): r""" PointNet2 with multi-scale grouping Classification network Parameters ---------- num_classes: int Number of semantics classes to predict over -- size of softmax classifier input_channels: int = 3 Number of input channels in the feature descriptor for each point. If the point cloud is Nx9, this value should be 6 as in an Nx9 point cloud, 3 of the channels are xyz, and 6 are feature descriptors use_xyz: bool = True Whether or not to use the xyz position of a point as a feature """ def __init__(self, num_classes, input_channels=3, use_xyz=True, version=1.0): super(Pointnet2MSG, self).__init__() self.SA_modules = nn.ModuleList() self.SA_modules.append( PointnetSAModuleMSG( npoint=512, radii=[0.1, 0.2, 0.4], nsamples=[16, 32, 128], mlps=[ [input_channels, 32, 32, 64], [input_channels, 64, 64, 128], [input_channels, 64, 96, 128], ], use_xyz=use_xyz, ) ) input_channels = 64 + 128 + 128 self.SA_modules.append( PointnetSAModuleMSG( npoint=128, radii=[0.2, 0.4, 0.8], nsamples=[32, 64, 128], mlps=[ [input_channels, 64, 64, 128], [input_channels, 128, 128, 256], [input_channels, 128, 128, 256], ], use_xyz=use_xyz, ) ) self.SA_modules.append( PointnetSAModule(mlp=[128 + 256 + 256, 256, 512, 1024], use_xyz=use_xyz) ) if version == 1.0: self.FC_layer = ( pt_utils.Seq(1024) .fc(512, bn=True) # potentially different for original one # https://github.com/charlesq34/pointnet2/blob/master/models/pointnet2_cls_msg.py#L34 .dropout(0.5) .fc(256, bn=True) # potentially different for original one # https://github.com/charlesq34/pointnet2/blob/master/models/pointnet2_cls_msg.py#L34 .dropout(0.5) .fc(num_classes, activation=None) ) elif version == 2.0: self.FC_layer = ( pt_utils.Seq(1024) .fc(512, bn=True) # potentially different for original one # https://github.com/charlesq34/pointnet2/blob/master/models/pointnet2_cls_msg.py#L34 .dropout(0.6) .fc(256, bn=True) # potentially different for original one # https://github.com/charlesq34/pointnet2/blob/master/models/pointnet2_cls_msg.py#L34 .dropout(0.6) .fc(num_classes, activation=None) ) else: assert False def _break_up_pc(self, pc): xyz = pc[..., 0:3].contiguous() features = pc[..., 3:].transpose(1, 2).contiguous() if pc.size(-1) > 3 else None return xyz, features def forward(self, pointcloud): # type: (Pointnet2MSG, torch.cuda.FloatTensor) -> pt_utils.Seq r""" Forward pass of the network Parameters ---------- pointcloud: Variable(torch.cuda.FloatTensor) (B, N, 3 + input_channels) tensor Point cloud to run predicts on Each point in the point-cloud MUST be formated as (x, y, z, features...) """ xyz, features = self._break_up_pc(pointcloud) for module in self.SA_modules: xyz, features = module(xyz, features) return self.FC_layer(features.squeeze(-1)) # arguments found out based on https://github.com/charlesq34/pointnet2/commit/74c52aa30458d1695e093a179cd335b7885b3244 # commit class Pointnet2MSG5K(nn.Module): r""" PointNet2 with multi-scale grouping Classification network Parameters ---------- num_classes: int Number of semantics classes to predict over -- size of softmax classifier input_channels: int = 3 Number of input channels in the feature descriptor for each point. If the point cloud is Nx9, this value should be 6 as in an Nx9 point cloud, 3 of the channels are xyz, and 6 are feature descriptors use_xyz: bool = True Whether or not to use the xyz position of a point as a feature """ def __init__(self, num_classes, input_channels=3, use_xyz=True): super(Pointnet2MSG5K, self).__init__() self.SA_modules = nn.ModuleList() self.SA_modules.append( PointnetSAModuleMSG( npoint=512, radii=[0.1, 0.2, 0.4], nsamples=[32,64,128], mlps=[ [input_channels, 32, 32, 64], [input_channels, 64, 64, 128], [input_channels, 64, 96, 128], ], use_xyz=use_xyz, ) ) input_channels = 64 + 128 + 128 self.SA_modules.append( PointnetSAModuleMSG( npoint=128, radii=[0.2, 0.4, 0.8], nsamples=[64,64,128], mlps=[ [input_channels, 64, 64, 128], [input_channels, 128, 128, 256], [input_channels, 128, 128, 256], ], use_xyz=use_xyz, ) ) self.SA_modules.append( PointnetSAModule(mlp=[128 + 256 + 256, 256, 512, 1024], use_xyz=use_xyz) ) self.FC_layer = ( pt_utils.Seq(1024) .fc(512, bn=True) .dropout(0.5) .fc(256, bn=True) .dropout(0.5) .fc(num_classes, activation=None) ) def _break_up_pc(self, pc): xyz = pc[..., 0:3].contiguous() features = pc[..., 3:].transpose(1, 2).contiguous() if pc.size(-1) > 3 else None return xyz, features def forward(self, pointcloud): # type: (Pointnet2MSG, torch.cuda.FloatTensor) -> pt_utils.Seq r""" Forward pass of the network Parameters ---------- pointcloud: Variable(torch.cuda.FloatTensor) (B, N, 3 + input_channels) tensor Point cloud to run predicts on Each point in the point-cloud MUST be formated as (x, y, z, features...) """ xyz, features = self._break_up_pc(pointcloud) for module in self.SA_modules: xyz, features = module(xyz, features) return self.FC_layer(features.squeeze(-1)) ================================================ FILE: pointnet2_pyt/pointnet2/models/pointnet2_msg_sem.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) import torch import torch.nn as nn import etw_pytorch_utils as pt_utils from collections import namedtuple from pointnet2.utils.pointnet2_modules import PointnetFPModule, PointnetSAModuleMSG def model_fn_decorator(criterion): ModelReturn = namedtuple("ModelReturn", ["preds", "loss", "acc"]) def model_fn(model, data, epoch=0, eval=False): with torch.set_grad_enabled(not eval): inputs, labels = data inputs = inputs.to("cuda", non_blocking=True) labels = labels.to("cuda", non_blocking=True) preds = model(inputs) loss = criterion(preds.view(labels.numel(), -1), labels.view(-1)) _, classes = torch.max(preds, -1) acc = (classes == labels).float().sum() / labels.numel() return ModelReturn(preds, loss, {"acc": acc.item(), "loss": loss.item()}) return model_fn class Pointnet2MSG(nn.Module): r""" PointNet2 with multi-scale grouping Semantic segmentation network that uses feature propogation layers Parameters ---------- num_classes: int Number of semantics classes to predict over -- size of softmax classifier that run for each point input_channels: int = 6 Number of input channels in the feature descriptor for each point. If the point cloud is Nx9, this value should be 6 as in an Nx9 point cloud, 3 of the channels are xyz, and 6 are feature descriptors use_xyz: bool = True Whether or not to use the xyz position of a point as a feature """ def __init__(self, num_classes, input_channels=6, use_xyz=True): super(Pointnet2MSG, self).__init__() self.SA_modules = nn.ModuleList() c_in = input_channels self.SA_modules.append( PointnetSAModuleMSG( npoint=1024, radii=[0.05, 0.1], nsamples=[16, 32], mlps=[[c_in, 16, 16, 32], [c_in, 32, 32, 64]], use_xyz=use_xyz, ) ) c_out_0 = 32 + 64 c_in = c_out_0 self.SA_modules.append( PointnetSAModuleMSG( npoint=256, radii=[0.1, 0.2], nsamples=[16, 32], mlps=[[c_in, 64, 64, 128], [c_in, 64, 96, 128]], use_xyz=use_xyz, ) ) c_out_1 = 128 + 128 c_in = c_out_1 self.SA_modules.append( PointnetSAModuleMSG( npoint=64, radii=[0.2, 0.4], nsamples=[16, 32], mlps=[[c_in, 128, 196, 256], [c_in, 128, 196, 256]], use_xyz=use_xyz, ) ) c_out_2 = 256 + 256 c_in = c_out_2 self.SA_modules.append( PointnetSAModuleMSG( npoint=16, radii=[0.4, 0.8], nsamples=[16, 32], mlps=[[c_in, 256, 256, 512], [c_in, 256, 384, 512]], use_xyz=use_xyz, ) ) c_out_3 = 512 + 512 self.FP_modules = nn.ModuleList() self.FP_modules.append(PointnetFPModule(mlp=[256 + input_channels, 128, 128])) self.FP_modules.append(PointnetFPModule(mlp=[512 + c_out_0, 256, 256])) self.FP_modules.append(PointnetFPModule(mlp=[512 + c_out_1, 512, 512])) self.FP_modules.append(PointnetFPModule(mlp=[c_out_3 + c_out_2, 512, 512])) self.FC_layer = ( pt_utils.Seq(128) .conv1d(128, bn=True) .dropout() .conv1d(num_classes, activation=None) ) def _break_up_pc(self, pc): xyz = pc[..., 0:3].contiguous() features = pc[..., 3:].transpose(1, 2).contiguous() if pc.size(-1) > 3 else None return xyz, features def forward(self, pointcloud): # type: (Pointnet2MSG, torch.cuda.FloatTensor) -> pt_utils.Seq r""" Forward pass of the network Parameters ---------- pointcloud: Variable(torch.cuda.FloatTensor) (B, N, 3 + input_channels) tensor Point cloud to run predicts on Each point in the point-cloud MUST be formated as (x, y, z, features...) """ xyz, features = self._break_up_pc(pointcloud) l_xyz, l_features = [xyz], [features] for i in range(len(self.SA_modules)): li_xyz, li_features = self.SA_modules[i](l_xyz[i], l_features[i]) l_xyz.append(li_xyz) l_features.append(li_features) for i in range(-1, -(len(self.FP_modules) + 1), -1): l_features[i - 1] = self.FP_modules[i]( l_xyz[i - 1], l_xyz[i], l_features[i - 1], l_features[i] ) return self.FC_layer(l_features[0]).transpose(1, 2).contiguous() if __name__ == "__main__": from torch.autograd import Variable import numpy as np import torch.optim as optim B = 2 N = 32 inputs = torch.randn(B, N, 6).cuda() labels = torch.from_numpy(np.random.randint(0, 3, size=B * N)).view(B, N).cuda() model = Pointnet2MSG(3, input_channels=3) model.cuda() optimizer = optim.Adam(model.parameters(), lr=1e-2) print("Testing with xyz") model_fn = model_fn_decorator(nn.CrossEntropyLoss()) for _ in range(5): optimizer.zero_grad() _, loss, _ = model_fn(model, (inputs, labels)) loss.backward() print(loss.data[0]) optimizer.step() # with use_xyz=False inputs = torch.randn(B, N, 6).cuda() labels = torch.from_numpy(np.random.randint(0, 3, size=B * N)).view(B, N).cuda() model = Pointnet2MSG(3, input_channels=3, use_xyz=False) model.cuda() optimizer = optim.Adam(model.parameters(), lr=1e-2) print("Testing without xyz") model_fn = model_fn_decorator(nn.CrossEntropyLoss()) for _ in range(5): optimizer.zero_grad() _, loss, _ = model_fn(model, (inputs, labels)) loss.backward() print(loss.data[0]) optimizer.step() ================================================ FILE: pointnet2_pyt/pointnet2/models/pointnet2_ssg_cls.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) import torch import torch.nn as nn import etw_pytorch_utils as pt_utils from collections import namedtuple from pointnet2.utils.pointnet2_modules import PointnetSAModule def model_fn_decorator(criterion): ModelReturn = namedtuple("ModelReturn", ["preds", "loss", "acc"]) def model_fn(model, data, epoch=0, eval=False): with torch.set_grad_enabled(not eval): inputs, labels = data inputs = inputs.to("cuda", non_blocking=True) labels = labels.to("cuda", non_blocking=True) preds = model(inputs) labels = labels.view(-1) loss = criterion(preds, labels) _, classes = torch.max(preds, -1) acc = (classes == labels).float().sum() / labels.numel() return ModelReturn(preds, loss, {"acc": acc.item(), "loss": loss.item()}) return model_fn class Pointnet2SSG(nn.Module): r""" PointNet2 with single-scale grouping Classification network Parameters ---------- num_classes: int Number of semantics classes to predict over -- size of softmax classifier input_channels: int = 3 Number of input channels in the feature descriptor for each point. If the point cloud is Nx9, this value should be 6 as in an Nx9 point cloud, 3 of the channels are xyz, and 6 are feature descriptors use_xyz: bool = True Whether or not to use the xyz position of a point as a feature """ def __init__(self, num_classes, input_channels=3, use_xyz=True): super(Pointnet2SSG, self).__init__() self.SA_modules = nn.ModuleList() self.SA_modules.append( PointnetSAModule( npoint=512, radius=0.2, nsample=64, mlp=[input_channels, 64, 64, 128], use_xyz=use_xyz, ) ) self.SA_modules.append( PointnetSAModule( npoint=128, radius=0.4, nsample=64, mlp=[128, 128, 128, 256], use_xyz=use_xyz, ) ) self.SA_modules.append( PointnetSAModule(mlp=[256, 256, 512, 1024], use_xyz=use_xyz) ) self.FC_layer = ( pt_utils.Seq(1024) .fc(512, bn=True) .dropout(0.5) .fc(256, bn=True) .dropout(0.5) .fc(num_classes, activation=None) ) def _break_up_pc(self, pc): xyz = pc[..., 0:3].contiguous() features = pc[..., 3:].transpose(1, 2).contiguous() if pc.size(-1) > 3 else None return xyz, features def forward(self, pointcloud): # type: (Pointnet2SSG, torch.cuda.FloatTensor) -> pt_utils.Seq r""" Forward pass of the network Parameters ---------- pointcloud: Variable(torch.cuda.FloatTensor) (B, N, 3 + input_channels) tensor Point cloud to run predicts on Each point in the point-cloud MUST be formated as (x, y, z, features...) """ xyz, features = self._break_up_pc(pointcloud) for module in self.SA_modules: xyz, features = module(xyz, features) return self.FC_layer(features.squeeze(-1)) ================================================ FILE: pointnet2_pyt/pointnet2/models/pointnet2_ssg_sem.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) import torch import torch.nn as nn import etw_pytorch_utils as pt_utils from collections import namedtuple from pointnet2.utils.pointnet2_modules import PointnetSAModule, PointnetFPModule def model_fn_decorator(criterion): ModelReturn = namedtuple("ModelReturn", ["preds", "loss", "acc"]) def model_fn(model, data, epoch=0, eval=False): with torch.set_grad_enabled(not eval): inputs, labels = data inputs = inputs.to("cuda", non_blocking=True) labels = labels.to("cuda", non_blocking=True) preds = model(inputs) loss = criterion(preds.view(labels.numel(), -1), labels.view(-1)) _, classes = torch.max(preds, -1) acc = (classes == labels).float().sum() / labels.numel() return ModelReturn(preds, loss, {"acc": acc.item(), "loss": loss.item()}) return model_fn class Pointnet2SSG(nn.Module): r""" PointNet2 with single-scale grouping Semantic segmentation network that uses feature propogation layers Parameters ---------- num_classes: int Number of semantics classes to predict over -- size of softmax classifier that run for each point input_channels: int = 6 Number of input channels in the feature descriptor for each point. If the point cloud is Nx9, this value should be 6 as in an Nx9 point cloud, 3 of the channels are xyz, and 6 are feature descriptors use_xyz: bool = True Whether or not to use the xyz position of a point as a feature """ def __init__(self, num_classes, input_channels=3, use_xyz=True): super(Pointnet2SSG, self).__init__() self.SA_modules = nn.ModuleList() self.SA_modules.append( PointnetSAModule( npoint=1024, radius=0.1, nsample=32, mlp=[input_channels, 32, 32, 64], use_xyz=use_xyz, ) ) self.SA_modules.append( PointnetSAModule( npoint=256, radius=0.2, nsample=32, mlp=[64, 64, 64, 128], use_xyz=use_xyz, ) ) self.SA_modules.append( PointnetSAModule( npoint=64, radius=0.4, nsample=32, mlp=[128, 128, 128, 256], use_xyz=use_xyz, ) ) self.SA_modules.append( PointnetSAModule( npoint=16, radius=0.8, nsample=32, mlp=[256, 256, 256, 512], use_xyz=use_xyz, ) ) self.FP_modules = nn.ModuleList() self.FP_modules.append( PointnetFPModule(mlp=[128 + input_channels, 128, 128, 128]) ) self.FP_modules.append(PointnetFPModule(mlp=[256 + 64, 256, 128])) self.FP_modules.append(PointnetFPModule(mlp=[256 + 128, 256, 256])) self.FP_modules.append(PointnetFPModule(mlp=[512 + 256, 256, 256])) self.FC_layer = ( pt_utils.Seq(128) .conv1d(128, bn=True) .dropout() .conv1d(num_classes, activation=None) ) def _break_up_pc(self, pc): xyz = pc[..., 0:3].contiguous() features = pc[..., 3:].transpose(1, 2).contiguous() if pc.size(-1) > 3 else None return xyz, features def forward(self, pointcloud): # type: (Pointnet2SSG, torch.cuda.FloatTensor) -> pt_utils.Seq r""" Forward pass of the network Parameters ---------- pointcloud: Variable(torch.cuda.FloatTensor) (B, N, 3 + input_channels) tensor Point cloud to run predicts on Each point in the point-cloud MUST be formated as (x, y, z, features...) """ xyz, features = self._break_up_pc(pointcloud) l_xyz, l_features = [xyz], [features] for i in range(len(self.SA_modules)): li_xyz, li_features = self.SA_modules[i](l_xyz[i], l_features[i]) l_xyz.append(li_xyz) l_features.append(li_features) for i in range(-1, -(len(self.FP_modules) + 1), -1): l_features[i - 1] = self.FP_modules[i]( l_xyz[i - 1], l_xyz[i], l_features[i - 1], l_features[i] ) return self.FC_layer(l_features[0]).transpose(1, 2).contiguous() ================================================ FILE: pointnet2_pyt/pointnet2/train/__init__.py ================================================ ================================================ FILE: pointnet2_pyt/pointnet2/train/train_cls.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) import torch import torch.optim as optim import torch.optim.lr_scheduler as lr_sched import torch.nn as nn from torch.utils.data import DataLoader from torchvision import transforms import etw_pytorch_utils as pt_utils import pprint import os.path as osp import os import argparse from pointnet2.models import Pointnet2ClsMSG as Pointnet from pointnet2.models.pointnet2_msg_cls import model_fn_decorator from pointnet2.data import ModelNet40Cls import pointnet2.data.data_utils as d_utils torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = True def parse_args(): parser = argparse.ArgumentParser( description="Arguments for cls training", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) parser.add_argument("-batch_size", type=int, default=16, help="Batch size") parser.add_argument( "-num_points", type=int, default=4096, help="Number of points to train with" ) parser.add_argument( "-weight_decay", type=float, default=1e-5, help="L2 regularization coeff" ) parser.add_argument("-lr", type=float, default=1e-2, help="Initial learning rate") parser.add_argument( "-lr_decay", type=float, default=0.7, help="Learning rate decay gamma" ) parser.add_argument( "-decay_step", type=float, default=2e5, help="Learning rate decay step" ) parser.add_argument( "-bn_momentum", type=float, default=0.5, help="Initial batch norm momentum" ) parser.add_argument( "-bnm_decay", type=float, default=0.5, help="Batch norm momentum decay gamma" ) parser.add_argument( "-checkpoint", type=str, default=None, help="Checkpoint to start from" ) parser.add_argument( "-epochs", type=int, default=200, help="Number of epochs to train for" ) parser.add_argument( "-run_name", type=str, default="cls_run_1", help="Name for run in tensorboard_logger", ) parser.add_argument("--visdom-port", type=int, default=8097) parser.add_argument("--visdom", action="store_true") return parser.parse_args() lr_clip = 1e-5 bnm_clip = 1e-2 if __name__ == "__main__": args = parse_args() transforms = transforms.Compose( [ d_utils.PointcloudToTensor(), d_utils.PointcloudScale(), d_utils.PointcloudRotate(), d_utils.PointcloudRotatePerturbation(), d_utils.PointcloudTranslate(), d_utils.PointcloudJitter(), d_utils.PointcloudRandomInputDropout(), ] ) test_set = ModelNet40Cls(args.num_points, transforms=transforms, train=False) test_loader = DataLoader( test_set, batch_size=args.batch_size, shuffle=True, num_workers=2, pin_memory=True, ) train_set = ModelNet40Cls(args.num_points, transforms=transforms) train_loader = DataLoader( train_set, batch_size=args.batch_size, shuffle=True, num_workers=2, pin_memory=True, ) model = Pointnet(input_channels=0, num_classes=40, use_xyz=True) model.cuda() optimizer = optim.Adam( model.parameters(), lr=args.lr, weight_decay=args.weight_decay ) lr_lbmd = lambda it: max( args.lr_decay ** (int(it * args.batch_size / args.decay_step)), lr_clip / args.lr, ) bn_lbmd = lambda it: max( args.bn_momentum * args.bnm_decay ** (int(it * args.batch_size / args.decay_step)), bnm_clip, ) # default value it = -1 # for the initialize value of `LambdaLR` and `BNMomentumScheduler` best_loss = 1e10 start_epoch = 1 # load status from checkpoint if args.checkpoint is not None: checkpoint_status = pt_utils.load_checkpoint( model, optimizer, filename=args.checkpoint.split(".")[0] ) if checkpoint_status is not None: it, start_epoch, best_loss = checkpoint_status lr_scheduler = lr_sched.LambdaLR(optimizer, lr_lambda=lr_lbmd, last_epoch=it) bnm_scheduler = pt_utils.BNMomentumScheduler( model, bn_lambda=bn_lbmd, last_epoch=it ) it = max(it, 0) # for the initialize value of `trainer.train` model_fn = model_fn_decorator(nn.CrossEntropyLoss()) if args.visdom: viz = pt_utils.VisdomViz(port=args.visdom_port) else: viz = pt_utils.CmdLineViz() viz.text(pprint.pformat(vars(args))) if not osp.isdir("checkpoints"): os.makedirs("checkpoints") trainer = pt_utils.Trainer( model, model_fn, optimizer, checkpoint_name="checkpoints/pointnet2_cls", best_name="checkpoints/pointnet2_cls_best", lr_scheduler=lr_scheduler, bnm_scheduler=bnm_scheduler, viz=viz, ) trainer.train( it, start_epoch, args.epochs, train_loader, test_loader, best_loss=best_loss ) if start_epoch == args.epochs: _ = trainer.eval_epoch(test_loader) ================================================ FILE: pointnet2_pyt/pointnet2/train/train_sem_seg.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) import torch.optim as optim import torch.optim.lr_scheduler as lr_sched import torch.nn as nn from torch.utils.data import DataLoader import etw_pytorch_utils as pt_utils import pprint import os.path as osp import os import argparse from pointnet2.models import Pointnet2SemMSG as Pointnet from pointnet2.models.pointnet2_msg_sem import model_fn_decorator from pointnet2.data import Indoor3DSemSeg parser = argparse.ArgumentParser(description="Arg parser") parser.add_argument( "-batch_size", type=int, default=32, help="Batch size [default: 32]" ) parser.add_argument( "-num_points", type=int, default=4096, help="Number of points to train with [default: 4096]", ) parser.add_argument( "-weight_decay", type=float, default=0, help="L2 regularization coeff [default: 0.0]", ) parser.add_argument( "-lr", type=float, default=1e-2, help="Initial learning rate [default: 1e-2]" ) parser.add_argument( "-lr_decay", type=float, default=0.5, help="Learning rate decay gamma [default: 0.5]", ) parser.add_argument( "-decay_step", type=float, default=2e5, help="Learning rate decay step [default: 20]", ) parser.add_argument( "-bn_momentum", type=float, default=0.9, help="Initial batch norm momentum [default: 0.9]", ) parser.add_argument( "-bn_decay", type=float, default=0.5, help="Batch norm momentum decay gamma [default: 0.5]", ) parser.add_argument( "-checkpoint", type=str, default=None, help="Checkpoint to start from" ) parser.add_argument( "-epochs", type=int, default=200, help="Number of epochs to train for" ) parser.add_argument( "-run_name", type=str, default="sem_seg_run_1", help="Name for run in tensorboard_logger", ) parser.add_argument("--visdom-port", type=int, default=8097) parser.add_argument("--visdom", action="store_true") lr_clip = 1e-5 bnm_clip = 1e-2 if __name__ == "__main__": args = parser.parse_args() test_set = Indoor3DSemSeg(args.num_points, train=False) test_loader = DataLoader( test_set, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=2, ) train_set = Indoor3DSemSeg(args.num_points) train_loader = DataLoader( train_set, batch_size=args.batch_size, pin_memory=True, num_workers=2, shuffle=True, ) model = Pointnet(num_classes=13, input_channels=6, use_xyz=True) model.cuda() optimizer = optim.Adam( model.parameters(), lr=args.lr, weight_decay=args.weight_decay ) lr_lbmd = lambda it: max( args.lr_decay ** (int(it * args.batch_size / args.decay_step)), lr_clip / args.lr, ) bnm_lmbd = lambda it: max( args.bn_momentum * args.bn_decay ** (int(it * args.batch_size / args.decay_step)), bnm_clip, ) # default value it = -1 # for the initialize value of `LambdaLR` and `BNMomentumScheduler` best_loss = 1e10 start_epoch = 1 # load status from checkpoint if args.checkpoint is not None: checkpoint_status = pt_utils.load_checkpoint( model, optimizer, filename=args.checkpoint.split(".")[0] ) if checkpoint_status is not None: it, start_epoch, best_loss = checkpoint_status lr_scheduler = lr_sched.LambdaLR(optimizer, lr_lambda=lr_lbmd, last_epoch=it) bnm_scheduler = pt_utils.BNMomentumScheduler( model, bn_lambda=bnm_lmbd, last_epoch=it ) it = max(it, 0) # for the initialize value of `trainer.train` model_fn = model_fn_decorator(nn.CrossEntropyLoss()) if args.visdom: viz = pt_utils.VisdomViz(port=args.visdom_port) else: viz = pt_utils.CmdLineViz() viz.text(pprint.pformat(vars(args))) if not osp.isdir("checkpoints"): os.makedirs("checkpoints") trainer = pt_utils.Trainer( model, model_fn, optimizer, checkpoint_name="checkpoints/pointnet2_semseg", best_name="checkpoints/pointnet2_semseg_best", lr_scheduler=lr_scheduler, bnm_scheduler=bnm_scheduler, viz=viz, ) trainer.train( it, start_epoch, args.epochs, train_loader, test_loader, best_loss=best_loss ) if start_epoch == args.epochs: _ = trainer.eval_epoch(test_loader) ================================================ FILE: pointnet2_pyt/pointnet2/utils/.gitignore ================================================ build _ext ================================================ FILE: pointnet2_pyt/pointnet2/utils/__init__.py ================================================ ''' Description: Autor: Jiachen Sun Date: 2022-02-16 22:23:16 LastEditors: Jiachen Sun LastEditTime: 2022-02-23 16:50:08 ''' from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) from PCT_Pytorch.pointnet2_ops_lib.pointnet2_ops import pointnet2_utils from PCT_Pytorch.pointnet2_ops_lib.pointnet2_ops import pointnet2_modules ================================================ FILE: pointnet2_pyt/pointnet2/utils/linalg_utils.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) import torch from enum import Enum import numpy as np PDist2Order = Enum("PDist2Order", "d_first d_second") def pdist2(X, Z=None, order=PDist2Order.d_second): # type: (torch.Tensor, torch.Tensor, PDist2Order) -> torch.Tensor r""" Calculates the pairwise distance between X and Z D[b, i, j] = l2 distance X[b, i] and Z[b, j] Parameters --------- X : torch.Tensor X is a (B, N, d) tensor. There are B batches, and N vectors of dimension d Z: torch.Tensor Z is a (B, M, d) tensor. If Z is None, then Z = X Returns ------- torch.Tensor Distance matrix is size (B, N, M) """ if order == PDist2Order.d_second: if X.dim() == 2: X = X.unsqueeze(0) if Z is None: Z = X G = np.matmul(X, Z.transpose(-2, -1)) S = (X * X).sum(-1, keepdim=True) R = S.transpose(-2, -1) else: if Z.dim() == 2: Z = Z.unsqueeze(0) G = np.matmul(X, Z.transpose(-2, -1)) S = (X * X).sum(-1, keepdim=True) R = (Z * Z).sum(-1, keepdim=True).transpose(-2, -1) else: if X.dim() == 2: X = X.unsqueeze(0) if Z is None: Z = X G = np.matmul(X.transpose(-2, -1), Z) R = (X * X).sum(-2, keepdim=True) S = R.transpose(-2, -1) else: if Z.dim() == 2: Z = Z.unsqueeze(0) G = np.matmul(X.transpose(-2, -1), Z) S = (X * X).sum(-2, keepdim=True).transpose(-2, -1) R = (Z * Z).sum(-2, keepdim=True) return torch.abs(R + S - 2 * G).squeeze(0) def pdist2_slow(X, Z=None): if Z is None: Z = X D = torch.zeros(X.size(0), X.size(2), Z.size(2)) for b in range(D.size(0)): for i in range(D.size(1)): for j in range(D.size(2)): D[b, i, j] = torch.dist(X[b, :, i], Z[b, :, j]) return D if __name__ == "__main__": X = torch.randn(2, 3, 5) Z = torch.randn(2, 3, 3) print(pdist2(X, order=PDist2Order.d_first)) print(pdist2_slow(X)) print(torch.dist(pdist2(X, order=PDist2Order.d_first), pdist2_slow(X))) ================================================ FILE: pointnet2_pyt/pointnet2/utils/pointnet2_modules.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) import torch import torch.nn as nn import torch.nn.functional as F import etw_pytorch_utils as pt_utils from pointnet2.utils import pointnet2_utils if False: # Workaround for type hints without depending on the `typing` module from typing import * class _PointnetSAModuleBase(nn.Module): def __init__(self): super(_PointnetSAModuleBase, self).__init__() self.npoint = None self.groupers = None self.mlps = None def forward(self, xyz, features=None): # type: (_PointnetSAModuleBase, torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor] r""" Parameters ---------- xyz : torch.Tensor (B, N, 3) tensor of the xyz coordinates of the features features : torch.Tensor (B, C, N) tensor of the descriptors of the the features Returns ------- new_xyz : torch.Tensor (B, npoint, 3) tensor of the new features' xyz new_features : torch.Tensor (B, \sum_k(mlps[k][-1]), npoint) tensor of the new_features descriptors """ new_features_list = [] B = xyz.shape[0] xyz_flipped = xyz.transpose(1, 2).contiguous() new_xyz = ( pointnet2_utils.gather_operation( xyz_flipped, pointnet2_utils.furthest_point_sample(xyz, self.npoint) ) .transpose(1, 2) .contiguous() if self.npoint is not None else torch.zeros((B, 1, 3)).to(xyz.device) ) for i in range(len(self.groupers)): new_features = self.groupers[i]( xyz, new_xyz, features ) # (B, C, npoint, nsample) new_features = self.mlps[i](new_features) # (B, mlp[-1], npoint, nsample) new_features = F.max_pool2d( new_features, kernel_size=[1, new_features.size(3)] ) # (B, mlp[-1], npoint, 1) new_features = new_features.squeeze(-1) # (B, mlp[-1], npoint) new_features_list.append(new_features) return new_xyz, torch.cat(new_features_list, dim=1) class PointnetSAModuleMSG(_PointnetSAModuleBase): r"""Pointnet set abstrction layer with multiscale grouping Parameters ---------- npoint : int Number of features radii : list of float32 list of radii to group with nsamples : list of int32 Number of samples in each ball query mlps : list of list of int32 Spec of the pointnet before the global max_pool for each scale bn : bool Use batchnorm """ def __init__(self, npoint, radii, nsamples, mlps, bn=True, use_xyz=True): # type: (PointnetSAModuleMSG, int, List[float], List[int], List[List[int]], bool, bool) -> None super(PointnetSAModuleMSG, self).__init__() assert len(radii) == len(nsamples) == len(mlps) self.npoint = npoint self.groupers = nn.ModuleList() self.mlps = nn.ModuleList() for i in range(len(radii)): radius = radii[i] nsample = nsamples[i] self.groupers.append( pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz) if npoint is not None else pointnet2_utils.GroupAll(use_xyz) ) mlp_spec = mlps[i] if use_xyz: mlp_spec[0] += 3 self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn)) class PointnetSAModule(PointnetSAModuleMSG): r"""Pointnet set abstrction layer Parameters ---------- npoint : int Number of features radius : float Radius of ball nsample : int Number of samples in the ball query mlp : list Spec of the pointnet before the global max_pool bn : bool Use batchnorm """ def __init__( self, mlp, npoint=None, radius=None, nsample=None, bn=True, use_xyz=True ): # type: (PointnetSAModule, List[int], int, float, int, bool, bool) -> None super(PointnetSAModule, self).__init__( mlps=[mlp], npoint=npoint, radii=[radius], nsamples=[nsample], bn=bn, use_xyz=use_xyz, ) class PointnetFPModule(nn.Module): r"""Propigates the features of one set to another Parameters ---------- mlp : list Pointnet module parameters bn : bool Use batchnorm """ def __init__(self, mlp, bn=True): # type: (PointnetFPModule, List[int], bool) -> None super(PointnetFPModule, self).__init__() self.mlp = pt_utils.SharedMLP(mlp, bn=bn) def forward(self, unknown, known, unknow_feats, known_feats): # type: (PointnetFPModule, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor) -> torch.Tensor r""" Parameters ---------- unknown : torch.Tensor (B, n, 3) tensor of the xyz positions of the unknown features known : torch.Tensor (B, m, 3) tensor of the xyz positions of the known features unknow_feats : torch.Tensor (B, C1, n) tensor of the features to be propigated to known_feats : torch.Tensor (B, C2, m) tensor of features to be propigated Returns ------- new_features : torch.Tensor (B, mlp[-1], n) tensor of the features of the unknown features """ if known is not None: dist, idx = pointnet2_utils.three_nn(unknown, known) dist_recip = 1.0 / (dist + 1e-8) norm = torch.sum(dist_recip, dim=2, keepdim=True) weight = dist_recip / norm interpolated_feats = pointnet2_utils.three_interpolate( known_feats, idx, weight ) else: interpolated_feats = known_feats.expand( *(known_feats.size()[0:2] + [unknown.size(1)]) ) if unknow_feats is not None: new_features = torch.cat( [interpolated_feats, unknow_feats], dim=1 ) # (B, C2 + C1, n) else: new_features = interpolated_feats new_features = new_features.unsqueeze(-1) new_features = self.mlp(new_features) return new_features.squeeze(-1) if __name__ == "__main__": from torch.autograd import Variable torch.manual_seed(1) torch.cuda.manual_seed_all(1) xyz = Variable(torch.randn(2, 9, 3).cuda(), requires_grad=True) xyz_feats = Variable(torch.randn(2, 9, 6).cuda(), requires_grad=True) test_module = PointnetSAModuleMSG( npoint=2, radii=[5.0, 10.0], nsamples=[6, 3], mlps=[[9, 3], [9, 6]] ) test_module.cuda() print(test_module(xyz, xyz_feats)) # test_module = PointnetFPModule(mlp=[6, 6]) # test_module.cuda() # from torch.autograd import gradcheck # inputs = (xyz, xyz, None, xyz_feats) # test = gradcheck(test_module, inputs, eps=1e-6, atol=1e-4) # print(test) for _ in range(1): _, new_features = test_module(xyz, xyz_feats) new_features.backward(torch.cuda.FloatTensor(*new_features.size()).fill_(1)) print(new_features) print(xyz.grad) ================================================ FILE: pointnet2_pyt/pointnet2/utils/pointnet2_utils.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) import torch from torch.autograd import Function import torch.nn as nn import etw_pytorch_utils as pt_utils import sys try: import builtins except: import __builtin__ as builtins try: import pointnet2._ext as _ext except ImportError: if not getattr(builtins, "__POINTNET2_SETUP__", False): raise ImportError( "Could not import _ext module.\n" "Please see the setup instructions in the README: " "https://github.com/erikwijmans/Pointnet2_PyTorch/blob/master/README.rst" ) if False: # Workaround for type hints without depending on the `typing` module from typing import * class RandomDropout(nn.Module): def __init__(self, p=0.5, inplace=False): super(RandomDropout, self).__init__() self.p = p self.inplace = inplace def forward(self, X): theta = torch.Tensor(1).uniform_(0, self.p)[0] return pt_utils.feature_dropout_no_scaling(X, theta, self.train, self.inplace) class FurthestPointSampling(Function): @staticmethod def forward(ctx, xyz, npoint): # type: (Any, torch.Tensor, int) -> torch.Tensor r""" Uses iterative furthest point sampling to select a set of npoint features that have the largest minimum distance Parameters ---------- xyz : torch.Tensor (B, N, 3) tensor where N > npoint npoint : int32 number of features in the sampled set Returns ------- torch.Tensor (B, npoint) tensor containing the set """ return _ext.furthest_point_sampling(xyz, npoint) @staticmethod def backward(xyz, a=None): return None, None furthest_point_sample = FurthestPointSampling.apply class GatherOperation(Function): @staticmethod def forward(ctx, features, idx): # type: (Any, torch.Tensor, torch.Tensor) -> torch.Tensor r""" Parameters ---------- features : torch.Tensor (B, C, N) tensor idx : torch.Tensor (B, npoint) tensor of the features to gather Returns ------- torch.Tensor (B, C, npoint) tensor """ _, C, N = features.size() ctx.for_backwards = (idx, C, N) return _ext.gather_points(features, idx) @staticmethod def backward(ctx, grad_out): idx, C, N = ctx.for_backwards grad_features = _ext.gather_points_grad(grad_out.contiguous(), idx, N) return grad_features, None gather_operation = GatherOperation.apply class ThreeNN(Function): @staticmethod def forward(ctx, unknown, known): # type: (Any, torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor] r""" Find the three nearest neighbors of unknown in known Parameters ---------- unknown : torch.Tensor (B, n, 3) tensor of known features known : torch.Tensor (B, m, 3) tensor of unknown features Returns ------- dist : torch.Tensor (B, n, 3) l2 distance to the three nearest neighbors idx : torch.Tensor (B, n, 3) index of 3 nearest neighbors """ dist2, idx = _ext.three_nn(unknown, known) return torch.sqrt(dist2), idx @staticmethod def backward(ctx, a=None, b=None): return None, None three_nn = ThreeNN.apply class ThreeInterpolate(Function): @staticmethod def forward(ctx, features, idx, weight): # type(Any, torch.Tensor, torch.Tensor, torch.Tensor) -> Torch.Tensor r""" Performs weight linear interpolation on 3 features Parameters ---------- features : torch.Tensor (B, c, m) Features descriptors to be interpolated from idx : torch.Tensor (B, n, 3) three nearest neighbors of the target features in features weight : torch.Tensor (B, n, 3) weights Returns ------- torch.Tensor (B, c, n) tensor of the interpolated features """ B, c, m = features.size() n = idx.size(1) ctx.three_interpolate_for_backward = (idx, weight, m) return _ext.three_interpolate(features, idx, weight) @staticmethod def backward(ctx, grad_out): # type: (Any, torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor] r""" Parameters ---------- grad_out : torch.Tensor (B, c, n) tensor with gradients of ouputs Returns ------- grad_features : torch.Tensor (B, c, m) tensor with gradients of features None None """ idx, weight, m = ctx.three_interpolate_for_backward grad_features = _ext.three_interpolate_grad( grad_out.contiguous(), idx, weight, m ) return grad_features, None, None three_interpolate = ThreeInterpolate.apply class GroupingOperation(Function): @staticmethod def forward(ctx, features, idx): # type: (Any, torch.Tensor, torch.Tensor) -> torch.Tensor r""" Parameters ---------- features : torch.Tensor (B, C, N) tensor of features to group idx : torch.Tensor (B, npoint, nsample) tensor containing the indicies of features to group with Returns ------- torch.Tensor (B, C, npoint, nsample) tensor """ B, nfeatures, nsample = idx.size() _, C, N = features.size() ctx.for_backwards = (idx, N) return _ext.group_points(features, idx) @staticmethod def backward(ctx, grad_out): # type: (Any, torch.tensor) -> Tuple[torch.Tensor, torch.Tensor] r""" Parameters ---------- grad_out : torch.Tensor (B, C, npoint, nsample) tensor of the gradients of the output from forward Returns ------- torch.Tensor (B, C, N) gradient of the features None """ idx, N = ctx.for_backwards grad_features = _ext.group_points_grad(grad_out.contiguous(), idx, N) return grad_features, None grouping_operation = GroupingOperation.apply class BallQuery(Function): @staticmethod def forward(ctx, radius, nsample, xyz, new_xyz): # type: (Any, float, int, torch.Tensor, torch.Tensor) -> torch.Tensor r""" Parameters ---------- radius : float radius of the balls nsample : int maximum number of features in the balls xyz : torch.Tensor (B, N, 3) xyz coordinates of the features new_xyz : torch.Tensor (B, npoint, 3) centers of the ball query Returns ------- torch.Tensor (B, npoint, nsample) tensor with the indicies of the features that form the query balls """ return _ext.ball_query(new_xyz, xyz, radius, nsample) @staticmethod def backward(ctx, a=None): return None, None, None, None ball_query = BallQuery.apply class QueryAndGroup(nn.Module): r""" Groups with a ball query of radius Parameters --------- radius : float32 Radius of ball nsample : int32 Maximum number of features to gather in the ball """ def __init__(self, radius, nsample, use_xyz=True): # type: (QueryAndGroup, float, int, bool) -> None super(QueryAndGroup, self).__init__() self.radius, self.nsample, self.use_xyz = radius, nsample, use_xyz def forward(self, xyz, new_xyz, features=None): # type: (QueryAndGroup, torch.Tensor. torch.Tensor, torch.Tensor) -> Tuple[Torch.Tensor] r""" Parameters ---------- xyz : torch.Tensor xyz coordinates of the features (B, N, 3) new_xyz : torch.Tensor centriods (B, npoint, 3) features : torch.Tensor Descriptors of the features (B, C, N) Returns ------- new_features : torch.Tensor (B, 3 + C, npoint, nsample) tensor """ idx = ball_query(self.radius, self.nsample, xyz, new_xyz) xyz_trans = xyz.transpose(1, 2).contiguous() grouped_xyz = grouping_operation(xyz_trans, idx) # (B, 3, npoint, nsample) grouped_xyz -= new_xyz.transpose(1, 2).unsqueeze(-1) if features is not None: grouped_features = grouping_operation(features, idx) if self.use_xyz: new_features = torch.cat( [grouped_xyz, grouped_features], dim=1 ) # (B, C + 3, npoint, nsample) else: new_features = grouped_features else: assert ( self.use_xyz ), "Cannot have not features and not use xyz as a feature!" new_features = grouped_xyz return new_features class GroupAll(nn.Module): r""" Groups all features Parameters --------- """ def __init__(self, use_xyz=True): # type: (GroupAll, bool) -> None super(GroupAll, self).__init__() self.use_xyz = use_xyz def forward(self, xyz, new_xyz, features=None): # type: (GroupAll, torch.Tensor, torch.Tensor, torch.Tensor) -> Tuple[torch.Tensor] r""" Parameters ---------- xyz : torch.Tensor xyz coordinates of the features (B, N, 3) new_xyz : torch.Tensor Ignored features : torch.Tensor Descriptors of the features (B, C, N) Returns ------- new_features : torch.Tensor (B, C + 3, 1, N) tensor """ grouped_xyz = xyz.transpose(1, 2).unsqueeze(2) if features is not None: grouped_features = features.unsqueeze(2) if self.use_xyz: new_features = torch.cat( [grouped_xyz, grouped_features], dim=1 ) # (B, 3 + C, 1, N) else: new_features = grouped_features else: new_features = grouped_xyz return new_features ================================================ FILE: pointnet2_pyt/setup.py ================================================ ''' Description: Autor: Jiachen Sun Date: 2022-02-16 22:23:16 LastEditors: Jiachen Sun LastEditTime: 2022-02-24 23:16:38 ''' from __future__ import division, absolute_import, with_statement, print_function from setuptools import setup, find_packages from torch.utils.cpp_extension import BuildExtension, CUDAExtension import glob try: import builtins except: import __builtin__ as builtins builtins.__POINTNET2_SETUP__ = True import pointnet2 # _ext_src_root = "pointnet2/_ext-src" # _ext_sources = glob.glob("{}/src/*.cpp".format(_ext_src_root)) + glob.glob( # "{}/src/*.cu".format(_ext_src_root) # ) # _ext_headers = glob.glob("{}/include/*".format(_ext_src_root)) requirements = ["etw_pytorch_utils==1.1.1", "h5py", "enum34", "future"] setup( name="pointnet2", version=pointnet2.__version__, author="Erik Wijmans", packages=find_packages(), install_requires=requirements, # ext_modules=[ # CUDAExtension( # name="pointnet2._ext", # sources=_ext_sources, # extra_compile_args={ # "cxx": ["-O2", "-I{}".format("{}/include".format(_ext_src_root))], # "nvcc": ["-O2", "-I{}".format("{}/include".format(_ext_src_root))], # }, # ) # ], # cmdclass={"build_ext": BuildExtension}, ) ================================================ FILE: pointnet2_pyt/tests/conftest.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) import pytest import torch import numpy as np pytest_plugins = ["helpers_namespace"] def _test_loop(model, model_fn, inputs, labels): optimizer = torch.optim.Adam(model.parameters(), lr=1e-5) prev_loss = 1e10 for _ in range(5): optimizer.zero_grad() _, loss, _ = model_fn(model, (inputs, labels)) loss.backward() optimizer.step() assert loss.item() < prev_loss + 1.0, "Loss spiked upwards" prev_loss = loss.item() @pytest.helpers.register def cls_test_xyz(model, model_fn): B, N = 4, 2048 inputs = torch.randn(B, N, 6).cuda() labels = torch.from_numpy(np.random.randint(0, 3, size=B)).cuda() model.cuda() _test_loop(model, model_fn, inputs, labels) @pytest.helpers.register def cls_test_no_xyz(model, model_fn): B, N = 4, 2048 inputs = torch.randn(B, N, 3).cuda() labels = torch.from_numpy(np.random.randint(0, 3, size=B)).cuda() model.cuda() _test_loop(model, model_fn, inputs, labels) @pytest.helpers.register def semseg_test_xyz(model, model_fn): B, N = 4, 2048 inputs = torch.randn(B, N, 6).cuda() labels = torch.from_numpy(np.random.randint(0, 3, size=B * N)).view(B, N).cuda() model.cuda() _test_loop(model, model_fn, inputs, labels) @pytest.helpers.register def semseg_test_no_xyz(model, model_fn): B, N = 4, 2048 inputs = torch.randn(B, N, 3).cuda() labels = torch.from_numpy(np.random.randint(0, 3, size=B * N)).view(B, N).cuda() model.cuda() _test_loop(model, model_fn, inputs, labels) ================================================ FILE: pointnet2_pyt/tests/test_cls_msg.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) from pointnet2.models.pointnet2_msg_cls import model_fn_decorator, Pointnet2MSG import torch.nn as nn import pytest def test_xyz(): model = Pointnet2MSG(3, input_channels=3) pytest.helpers.cls_test_xyz(model, model_fn_decorator(nn.CrossEntropyLoss())) def test_no_xyz(): model = Pointnet2MSG(3, input_channels=0) pytest.helpers.cls_test_no_xyz(model, model_fn_decorator(nn.CrossEntropyLoss())) ================================================ FILE: pointnet2_pyt/tests/test_cls_ssg.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) from pointnet2.models.pointnet2_ssg_cls import model_fn_decorator, Pointnet2SSG import torch.nn as nn import pytest def test_xyz(): model = Pointnet2SSG(3, input_channels=3) pytest.helpers.cls_test_xyz(model, model_fn_decorator(nn.CrossEntropyLoss())) def test_no_xyz(): model = Pointnet2SSG(3, input_channels=0) pytest.helpers.cls_test_no_xyz(model, model_fn_decorator(nn.CrossEntropyLoss())) ================================================ FILE: pointnet2_pyt/tests/test_semseg_msg.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) from pointnet2.models.pointnet2_msg_sem import model_fn_decorator, Pointnet2MSG import torch.nn as nn import pytest def test_xyz(): model = Pointnet2MSG(3, input_channels=3) pytest.helpers.semseg_test_xyz(model, model_fn_decorator(nn.CrossEntropyLoss())) def test_no_xyz(): model = Pointnet2MSG(3, input_channels=0) pytest.helpers.semseg_test_no_xyz(model, model_fn_decorator(nn.CrossEntropyLoss())) ================================================ FILE: pointnet2_pyt/tests/test_semseg_ssg.py ================================================ from __future__ import ( division, absolute_import, with_statement, print_function, unicode_literals, ) from pointnet2.models.pointnet2_ssg_sem import model_fn_decorator, Pointnet2SSG import torch.nn as nn import pytest def test_xyz(): model = Pointnet2SSG(3, input_channels=3) pytest.helpers.semseg_test_xyz(model, model_fn_decorator(nn.CrossEntropyLoss())) def test_no_xyz(): model = Pointnet2SSG(3, input_channels=0) pytest.helpers.semseg_test_no_xyz(model, model_fn_decorator(nn.CrossEntropyLoss())) ================================================ FILE: pointnet2_pyt/tox.ini ================================================ [tox] envlist = py27 py35 py36 [testenv] # install pytest in the virtualenv where commands will be executed deps = numpy torch>=1.0 git+git://github.com/erikwijmans/etw_pytorch_utils.git@v1.1.1#egg=etw_pytorch_utils pytest pytest-helpers-namespace commands = pytest ================================================ FILE: pointnet2_tf/LICENSE ================================================ PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. Copyright (c) 2017, Geometric Computation Group of Stanford University The MIT License (MIT) Copyright (c) 2017 Charles R. Qi Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: pointnet2_tf/README.md ================================================ ### PointNet++: *Deep Hierarchical Feature Learning on Point Sets in a Metric Space* Created by Charles R. Qi, Li (Eric) Yi, Hao Su, Leonidas J. Guibas from Stanford University. ![prediction example](https://github.com/charlesq34/pointnet2/blob/master/doc/teaser.jpg) ### Citation If you find our work useful in your research, please consider citing: @article{qi2017pointnetplusplus, title={PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space}, author={Qi, Charles R and Yi, Li and Su, Hao and Guibas, Leonidas J}, journal={arXiv preprint arXiv:1706.02413}, year={2017} } ### Introduction This work is based on our NIPS'17 paper. You can find arXiv version of the paper here or check project webpage for a quick overview. PointNet++ is a follow-up project that builds on and extends PointNet. It is version 2.0 of the PointNet architecture. PointNet (the v1 model) either transforms features of *individual points* independently or process global features of the *entire point set*. However, in many cases there are well defined distance metrics such as Euclidean distance for 3D point clouds collected by 3D sensors or geodesic distance for manifolds like isometric shape surfaces. In PointNet++ we want to respect *spatial localities* of those point sets. PointNet++ learns hierarchical features with increasing scales of contexts, just like that in convolutional neural networks. Besides, we also observe one challenge that is not present in convnets (with images) -- non-uniform densities in natural point clouds. To deal with those non-uniform densities, we further propose special layers that are able to intelligently aggregate information from different scales. In this repository we release code and data for our PointNet++ classification and segmentation networks as well as a few utility scripts for training, testing and data processing and visualization. ### Installation Install TensorFlow. The code is tested under TF1.2 GPU version and Python 2.7 (version 3 should also work) on Ubuntu 14.04. There are also some dependencies for a few Python libraries for data processing and visualizations like `cv2`, `h5py` etc. It's highly recommended that you have access to GPUs. #### Compile Customized TF Operators The TF operators are included under `tf_ops`, you need to compile them (check `tf_xxx_compile.sh` under each ops subfolder) first. Update `nvcc` and `python` path if necessary. The code is tested under TF1.2.0. If you are using earlier version it's possible that you need to remove the `-D_GLIBCXX_USE_CXX11_ABI=0` flag in g++ command in order to compile correctly. To compile the operators in TF version >=1.4, you need to modify the compile scripts slightly. First, find Tensorflow include and library paths. TF_INC=$(python -c 'import tensorflow as tf; print(tf.sysconfig.get_include())') TF_LIB=$(python -c 'import tensorflow as tf; print(tf.sysconfig.get_lib())') Then, add flags of `-I$TF_INC/external/nsync/public -L$TF_LIB -ltensorflow_framework` to the `g++` commands. ### Usage #### Shape Classification To train a PointNet++ model to classify ModelNet40 shapes (using point clouds with XYZ coordinates): python train.py To see all optional arguments for training: python train.py -h If you have multiple GPUs on your machine, you can also run the multi-GPU version training (our implementation is similar to the tensorflow cifar10 tutorial): CUDA_VISIBLE_DEVICES=0,1 python train_multi_gpu.py --num_gpus 2 After training, to evaluate the classification accuracies (with optional multi-angle voting): python evaluate.py --num_votes 12 Side Note: For the XYZ+normal experiment reported in our paper: (1) 5000 points are used and (2) a further random data dropout augmentation is used during training (see commented line after `augment_batch_data` in `train.py` and (3) the model architecture is updated such that the `nsample=128` in the first two set abstraction levels, which is suited for the larger point density in 5000-point samplings. To use normal features for classification: You can get our sampled point clouds of ModelNet40 (XYZ and normal from mesh, 10k points per shape) here (1.6GB). Move the uncompressed data folder to `data/modelnet40_normal_resampled` #### Object Part Segmentation To train a model to segment object parts for ShapeNet models: cd part_seg python train.py Preprocessed ShapeNetPart dataset (XYZ, normal and part labels) can be found here (674MB). Move the uncompressed data folder to `data/shapenetcore_partanno_segmentation_benchmark_v0_normal` #### Semantic Scene Parsing See `scannet/README` and `scannet/train.py` for details. #### Visualization Tools We have provided a handy point cloud visualization tool under `utils`. Run `sh compile_render_balls_so.sh` to compile it and then you can try the demo with `python show3d_balls.py` The original code is from here. #### Prepare Your Own Data You can refer to here on how to prepare your own HDF5 files for either classification or segmentation. Or you can refer to `modelnet_dataset.py` on how to read raw data files and prepare mini-batches from them. A more advanced way is to use TensorFlow's dataset APIs, for which you can find more documentations here. ### License Our code is released under MIT License (see LICENSE file for details). ### Updates * 02/23/2018: Added support for multi-gpu training for the classification task. * 02/23/2018: Adopted a new way for data loading. No longer require manual data downloading to train a classification network. * 02/06/2018: Added sample training code for ScanNet semantic segmentation. ### Related Projects * PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation by Qi et al. (CVPR 2017 Oral Presentation). Code and data released in GitHub. * Frustum PointNets for 3D Object Detection from RGB-D Data by Qi et al. (CVPR 2018) A novel framework for 3D object detection with RGB-D data. Based on 2D boxes from a 2D object detector on RGB images, we extrude the depth maps in 2D boxes to point clouds in 3D space and then realize instance segmentation and 3D bounding box estimation using PointNet/PointNet++. The method proposed has achieved first place on KITTI 3D object detection benchmark on all categories (last checked on 11/30/2017). Code and data release TBD. ================================================ FILE: pointnet2_tf/data/README.md ================================================ #### Point Cloud Data You can get our sampled point clouds of ModelNet40 (XYZ and normal from mesh, 10k points per shape) at this link. The ShapeNetPart dataset (XYZ, normal and part labels) can be found here. Uncompress the downloaded data in this directory. ================================================ FILE: pointnet2_tf/evaluate.py ================================================ ''' Evaluate classification performance with optional voting. Will use H5 dataset in default. If using normal, will shift to the normal dataset. ''' import tensorflow as tf import numpy as np import argparse import socket import importlib import time import os import scipy.misc import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = BASE_DIR sys.path.append(BASE_DIR) sys.path.append(os.path.join(ROOT_DIR, 'models')) sys.path.append(os.path.join(ROOT_DIR, 'utils')) import provider import modelnet_dataset import modelnet_h5_dataset parser = argparse.ArgumentParser() parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]') parser.add_argument('--model', default='pointnet2_cls_ssg', help='Model name. [default: pointnet2_cls_ssg]') parser.add_argument('--batch_size', type=int, default=16, help='Batch Size during training [default: 16]') parser.add_argument('--num_point', type=int, default=1024, help='Point Number [256/512/1024/2048] [default: 1024]') parser.add_argument('--model_path', default='log/model.ckpt', help='model checkpoint file path [default: log/model.ckpt]') parser.add_argument('--dump_dir', default='dump', help='dump folder path [dump]') parser.add_argument('--normal', action='store_true', help='Whether to use normal information') parser.add_argument('--num_votes', type=int, default=1, help='Aggregate classification scores from multiple rotations [default: 1]') FLAGS = parser.parse_args() BATCH_SIZE = FLAGS.batch_size NUM_POINT = FLAGS.num_point MODEL_PATH = FLAGS.model_path GPU_INDEX = FLAGS.gpu MODEL = importlib.import_module(FLAGS.model) # import network module DUMP_DIR = FLAGS.dump_dir if not os.path.exists(DUMP_DIR): os.mkdir(DUMP_DIR) LOG_FOUT = open(os.path.join(DUMP_DIR, 'log_evaluate.txt'), 'w') LOG_FOUT.write(str(FLAGS)+'\n') NUM_CLASSES = 40 SHAPE_NAMES = [line.rstrip() for line in \ open(os.path.join(ROOT_DIR, 'data/modelnet40_ply_hdf5_2048/shape_names.txt'))] HOSTNAME = socket.gethostname() # Shapenet official train/test split if FLAGS.normal: assert(NUM_POINT<=10000) DATA_PATH = os.path.join(ROOT_DIR, 'data/modelnet40_normal_resampled') TRAIN_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='train', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE) TEST_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='test', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE) else: assert(NUM_POINT<=2048) TRAIN_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/train_files.txt'), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=True) TEST_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/test_files.txt'), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=False) def log_string(out_str): LOG_FOUT.write(out_str+'\n') LOG_FOUT.flush() print(out_str) def evaluate(num_votes): is_training = False with tf.device('/gpu:'+str(GPU_INDEX)): pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT) is_training_pl = tf.placeholder(tf.bool, shape=()) # simple model pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl) MODEL.get_loss(pred, labels_pl, end_points) losses = tf.get_collection('losses') total_loss = tf.add_n(losses, name='total_loss') # Add ops to save and restore all the variables. saver = tf.train.Saver() # Create a session config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True config.log_device_placement = False sess = tf.Session(config=config) # Restore variables from disk. saver.restore(sess, MODEL_PATH) log_string("Model restored.") ops = {'pointclouds_pl': pointclouds_pl, 'labels_pl': labels_pl, 'is_training_pl': is_training_pl, 'pred': pred, 'loss': total_loss} eval_one_epoch(sess, ops, num_votes) def eval_one_epoch(sess, ops, num_votes=1, topk=1): is_training = False # Make sure batch data is of same size cur_batch_data = np.zeros((BATCH_SIZE,NUM_POINT,TEST_DATASET.num_channel())) cur_batch_label = np.zeros((BATCH_SIZE), dtype=np.int32) total_correct = 0 total_seen = 0 loss_sum = 0 batch_idx = 0 shape_ious = [] total_seen_class = [0 for _ in range(NUM_CLASSES)] total_correct_class = [0 for _ in range(NUM_CLASSES)] while TEST_DATASET.has_next_batch(): batch_data, batch_label = TEST_DATASET.next_batch(augment=False) bsize = batch_data.shape[0] print('Batch: %03d, batch size: %d'%(batch_idx, bsize)) # for the last batch in the epoch, the bsize:end are from last batch cur_batch_data[0:bsize,...] = batch_data cur_batch_label[0:bsize] = batch_label batch_pred_sum = np.zeros((BATCH_SIZE, NUM_CLASSES)) # score for classes for vote_idx in range(num_votes): # Shuffle point order to achieve different farthest samplings shuffled_indices = np.arange(NUM_POINT) np.random.shuffle(shuffled_indices) if FLAGS.normal: rotated_data = provider.rotate_point_cloud_by_angle_with_normal(cur_batch_data[:, shuffled_indices, :], vote_idx/float(num_votes) * np.pi * 2) else: rotated_data = provider.rotate_point_cloud_by_angle(cur_batch_data[:, shuffled_indices, :], vote_idx/float(num_votes) * np.pi * 2) feed_dict = {ops['pointclouds_pl']: rotated_data, ops['labels_pl']: cur_batch_label, ops['is_training_pl']: is_training} loss_val, pred_val = sess.run([ops['loss'], ops['pred']], feed_dict=feed_dict) batch_pred_sum += pred_val pred_val = np.argmax(batch_pred_sum, 1) correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize]) total_correct += correct total_seen += bsize loss_sum += loss_val batch_idx += 1 for i in range(bsize): l = batch_label[i] total_seen_class[l] += 1 total_correct_class[l] += (pred_val[i] == l) log_string('eval mean loss: %f' % (loss_sum / float(batch_idx))) log_string('eval accuracy: %f'% (total_correct / float(total_seen))) log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float)))) class_accuracies = np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float) for i, name in enumerate(SHAPE_NAMES): log_string('%10s:\t%0.3f' % (name, class_accuracies[i])) if __name__=='__main__': with tf.Graph().as_default(): evaluate(num_votes=FLAGS.num_votes) LOG_FOUT.close() ================================================ FILE: pointnet2_tf/modelnet_dataset.py ================================================ ''' ModelNet dataset. Support ModelNet40, ModelNet10, XYZ and normal channels. Up to 10000 points. ''' import os import os.path import json import numpy as np import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = BASE_DIR sys.path.append(os.path.join(ROOT_DIR, 'utils')) import provider def pc_normalize(pc): l = pc.shape[0] centroid = np.mean(pc, axis=0) pc = pc - centroid m = np.max(np.sqrt(np.sum(pc**2, axis=1))) pc = pc / m return pc class ModelNetDataset(): def __init__(self, root, batch_size = 32, npoints = 1024, split='train', normalize=True, normal_channel=False, modelnet10=False, cache_size=15000, shuffle=None): self.root = root self.batch_size = batch_size self.npoints = npoints self.normalize = normalize if modelnet10: self.catfile = os.path.join(self.root, 'modelnet10_shape_names.txt') else: self.catfile = os.path.join(self.root, 'shape_names.txt') self.cat = [line.rstrip() for line in open(self.catfile)] self.classes = dict(zip(self.cat, range(len(self.cat)))) self.normal_channel = normal_channel shape_ids = {} if modelnet10: shape_ids['train'] = [line.rstrip() for line in open(os.path.join(self.root, 'modelnet10_train.txt'))] shape_ids['test']= [line.rstrip() for line in open(os.path.join(self.root, 'modelnet10_test.txt'))] else: shape_ids['train'] = [line.rstrip() for line in open(os.path.join(self.root, 'modelnet40_train.txt'))] shape_ids['test']= [line.rstrip() for line in open(os.path.join(self.root, 'modelnet40_test.txt'))] assert(split=='train' or split=='test') shape_names = ['_'.join(x.split('_')[0:-1]) for x in shape_ids[split]] # list of (shape_name, shape_txt_file_path) tuple self.datapath = [(shape_names[i], os.path.join(self.root, shape_names[i], shape_ids[split][i])+'.txt') for i in range(len(shape_ids[split]))] self.cache_size = cache_size # how many data points to cache in memory self.cache = {} # from index to (point_set, cls) tuple if shuffle is None: if split == 'train': self.shuffle = True else: self.shuffle = False else: self.shuffle = shuffle self.reset() def _augment_batch_data(self, batch_data): if self.normal_channel: rotated_data = provider.rotate_point_cloud_with_normal(batch_data) rotated_data = provider.rotate_perturbation_point_cloud_with_normal(rotated_data) else: rotated_data = provider.rotate_point_cloud(batch_data) rotated_data = provider.rotate_perturbation_point_cloud(rotated_data) jittered_data = provider.random_scale_point_cloud(rotated_data[:,:,0:3]) jittered_data = provider.shift_point_cloud(jittered_data) jittered_data = provider.jitter_point_cloud(jittered_data) rotated_data[:,:,0:3] = jittered_data return provider.shuffle_points(rotated_data) def _get_item(self, index): if index in self.cache: point_set, cls = self.cache[index] else: fn = self.datapath[index] cls = self.classes[self.datapath[index][0]] cls = np.array([cls]).astype(np.int32) point_set = np.loadtxt(fn[1],delimiter=',').astype(np.float32) # Take the first npoints point_set = point_set[0:self.npoints,:] if self.normalize: point_set[:,0:3] = pc_normalize(point_set[:,0:3]) if not self.normal_channel: point_set = point_set[:,0:3] if len(self.cache) < self.cache_size: self.cache[index] = (point_set, cls) return point_set, cls def __getitem__(self, index): return self._get_item(index) def __len__(self): return len(self.datapath) def num_channel(self): if self.normal_channel: return 6 else: return 3 def reset(self): self.idxs = np.arange(0, len(self.datapath)) if self.shuffle: np.random.shuffle(self.idxs) self.num_batches = (len(self.datapath)+self.batch_size-1) // self.batch_size self.batch_idx = 0 def has_next_batch(self): return self.batch_idx < self.num_batches def next_batch(self, augment=False): ''' returned dimension may be smaller than self.batch_size ''' start_idx = self.batch_idx * self.batch_size end_idx = min((self.batch_idx+1) * self.batch_size, len(self.datapath)) bsize = end_idx - start_idx batch_data = np.zeros((bsize, self.npoints, self.num_channel())) batch_label = np.zeros((bsize), dtype=np.int32) for i in range(bsize): ps,cls = self._get_item(self.idxs[i+start_idx]) batch_data[i] = ps batch_label[i] = cls self.batch_idx += 1 if augment: batch_data = self._augment_batch_data(batch_data) return batch_data, batch_label if __name__ == '__main__': d = ModelNetDataset(root = '../data/modelnet40_normal_resampled', split='test') print(d.shuffle) print(len(d)) import time tic = time.time() for i in range(10): ps, cls = d[i] print(time.time() - tic) print(ps.shape, type(ps), cls) print(d.has_next_batch()) ps_batch, cls_batch = d.next_batch(True) print(ps_batch.shape) print(cls_batch.shape) ================================================ FILE: pointnet2_tf/modelnet_h5_dataset.py ================================================ ''' ModelNet dataset. Support ModelNet40, XYZ channels. Up to 2048 points. Faster IO than ModelNetDataset in the first epoch. ''' import os import sys import numpy as np import h5py from .utils import provider # updated datapath DATA_DIR = 'data' if not os.path.exists(DATA_DIR): os.mkdir(DATA_DIR) if not os.path.exists(os.path.join(DATA_DIR, 'modelnet40_ply_hdf5_2048')): www = 'https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip' zipfile = os.path.basename(www) os.system('wget %s; unzip %s' % (www, zipfile)) os.system('mv %s %s' % (zipfile[:-4], DATA_DIR)) os.system('rm %s' % (zipfile)) def shuffle_data(data, labels): """ Shuffle data and labels. Input: data: B,N,... numpy array label: B,... numpy array Return: shuffled data, label and shuffle indices """ idx = np.arange(len(labels)) np.random.shuffle(idx) return data[idx, ...], labels[idx], idx def getDataFiles(list_filename): return [line.rstrip() for line in open(list_filename)] def load_h5(h5_filename): f = h5py.File(h5_filename, 'r') data = f['data'][:] label = f['label'][:] return (data, label) def loadDataFile(filename): return load_h5(filename) class ModelNetH5Dataset(object): def __init__(self, list_filename, batch_size = 32, npoints = 1024, shuffle=True): self.list_filename = list_filename self.batch_size = batch_size self.npoints = npoints self.shuffle = shuffle self.h5_files = getDataFiles(self.list_filename) self.reset() def reset(self): ''' reset order of h5 files ''' self.file_idxs = np.arange(0, len(self.h5_files)) if self.shuffle: np.random.shuffle(self.file_idxs) self.current_data = None self.current_label = None self.current_file_idx = 0 self.batch_idx = 0 def _augment_batch_data(self, batch_data): rotated_data = provider.rotate_point_cloud(batch_data) rotated_data = provider.rotate_perturbation_point_cloud(rotated_data) jittered_data = provider.random_scale_point_cloud(rotated_data[:,:,0:3]) jittered_data = provider.shift_point_cloud(jittered_data) jittered_data = provider.jitter_point_cloud(jittered_data) rotated_data[:,:,0:3] = jittered_data return provider.shuffle_points(rotated_data) def _get_data_filename(self): return self.h5_files[self.file_idxs[self.current_file_idx]] def _load_data_file(self, filename): self.current_data,self.current_label = load_h5(filename) self.current_label = np.squeeze(self.current_label) self.batch_idx = 0 if self.shuffle: self.current_data, self.current_label, _ = shuffle_data(self.current_data,self.current_label) def _has_next_batch_in_file(self): return self.batch_idx*self.batch_size < self.current_data.shape[0] def num_channel(self): return 3 def has_next_batch(self): # TODO: add backend thread to load data if (self.current_data is None) or (not self._has_next_batch_in_file()): if self.current_file_idx >= len(self.h5_files): return False self._load_data_file(self._get_data_filename()) self.batch_idx = 0 self.current_file_idx += 1 return self._has_next_batch_in_file() def next_batch(self, augment=False): ''' returned dimension may be smaller than self.batch_size ''' start_idx = self.batch_idx * self.batch_size end_idx = min((self.batch_idx+1) * self.batch_size, self.current_data.shape[0]) bsize = end_idx - start_idx batch_label = np.zeros((bsize), dtype=np.int32) data_batch = self.current_data[start_idx:end_idx, 0:self.npoints, :].copy() label_batch = self.current_label[start_idx:end_idx].copy() self.batch_idx += 1 if augment: data_batch = self._augment_batch_data(data_batch) return data_batch, label_batch if __name__=='__main__': d = ModelNetH5Dataset('data/modelnet40_ply_hdf5_2048/train_files.txt') print(d.shuffle) print(d.has_next_batch()) ps_batch, cls_batch = d.next_batch(True) print(ps_batch.shape) print(cls_batch.shape) ================================================ FILE: pointnet2_tf/models/pointnet2_cls_msg.py ================================================ import os import sys BASE_DIR = os.path.dirname(__file__) sys.path.append(BASE_DIR) sys.path.append(os.path.join(BASE_DIR, '../utils')) import tensorflow as tf import numpy as np import tf_util from pointnet_util import pointnet_sa_module, pointnet_sa_module_msg def placeholder_inputs(batch_size, num_point): pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3)) labels_pl = tf.placeholder(tf.int32, shape=(batch_size)) return pointclouds_pl, labels_pl def get_model(point_cloud, is_training, bn_decay=None): """ Classification PointNet, input is BxNx3, output Bx40 """ batch_size = point_cloud.get_shape()[0].value num_point = point_cloud.get_shape()[1].value end_points = {} l0_xyz = point_cloud l0_points = None # Set abstraction layers l1_xyz, l1_points = pointnet_sa_module_msg(l0_xyz, l0_points, 512, [0.1,0.2,0.4], [16,32,128], [[32,32,64], [64,64,128], [64,96,128]], is_training, bn_decay, scope='layer1', use_nchw=True) l2_xyz, l2_points = pointnet_sa_module_msg(l1_xyz, l1_points, 128, [0.2,0.4,0.8], [32,64,128], [[64,64,128], [128,128,256], [128,128,256]], is_training, bn_decay, scope='layer2') l3_xyz, l3_points, _ = pointnet_sa_module(l2_xyz, l2_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=None, group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer3') # Fully connected layers net = tf.reshape(l3_points, [batch_size, -1]) net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay) net = tf_util.dropout(net, keep_prob=0.4, is_training=is_training, scope='dp1') net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training, scope='fc2', bn_decay=bn_decay) net = tf_util.dropout(net, keep_prob=0.4, is_training=is_training, scope='dp2') net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3') return net, end_points def get_loss(pred, label, end_points): """ pred: B*NUM_CLASSES, label: B, """ loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label) classify_loss = tf.reduce_mean(loss) tf.summary.scalar('classify loss', classify_loss) tf.add_to_collection('losses', classify_loss) return classify_loss if __name__=='__main__': with tf.Graph().as_default(): inputs = tf.zeros((32,1024,3)) net, _ = get_model(inputs, tf.constant(True)) print(net) ================================================ FILE: pointnet2_tf/models/pointnet2_cls_ssg.py ================================================ """ PointNet++ Model for point clouds classification """ import os import sys BASE_DIR = os.path.dirname(__file__) sys.path.append(BASE_DIR) sys.path.append(os.path.join(BASE_DIR, '../utils')) import tensorflow as tf import numpy as np import tf_util from pointnet_util import pointnet_sa_module def placeholder_inputs(batch_size, num_point): pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3)) labels_pl = tf.placeholder(tf.int32, shape=(batch_size)) return pointclouds_pl, labels_pl def get_model(point_cloud, is_training, bn_decay=None): """ Classification PointNet, input is BxNx3, output Bx40 """ batch_size = point_cloud.get_shape()[0].value num_point = point_cloud.get_shape()[1].value end_points = {} l0_xyz = point_cloud l0_points = None end_points['l0_xyz'] = l0_xyz # Set abstraction layers # Note: When using NCHW for layer 2, we see increased GPU memory usage (in TF1.4). # So we only use NCHW for layer 1 until this issue can be resolved. l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points, npoint=512, radius=0.2, nsample=32, mlp=[64,64,128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1', use_nchw=True) l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points, npoint=128, radius=0.4, nsample=64, mlp=[128,128,256], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2') l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=None, group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer3') # Fully connected layers net = tf.reshape(l3_points, [batch_size, -1]) net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay) net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1') net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training, scope='fc2', bn_decay=bn_decay) net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp2') net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3') return net, end_points def get_loss(pred, label, end_points): """ pred: B*NUM_CLASSES, label: B, """ loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label) classify_loss = tf.reduce_mean(loss) tf.summary.scalar('classify loss', classify_loss) tf.add_to_collection('losses', classify_loss) return classify_loss if __name__=='__main__': with tf.Graph().as_default(): inputs = tf.zeros((32,1024,3)) output, _ = get_model(inputs, tf.constant(True)) print(output) ================================================ FILE: pointnet2_tf/models/pointnet2_part_seg.py ================================================ import os import sys BASE_DIR = os.path.dirname(__file__) sys.path.append(BASE_DIR) sys.path.append(os.path.join(BASE_DIR, '../utils')) import tensorflow as tf import numpy as np import tf_util from pointnet_util import pointnet_sa_module, pointnet_fp_module def placeholder_inputs(batch_size, num_point): pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 6)) labels_pl = tf.placeholder(tf.int32, shape=(batch_size, num_point)) return pointclouds_pl, labels_pl def get_model(point_cloud, is_training, bn_decay=None): """ Part segmentation PointNet, input is BxNx6 (XYZ NormalX NormalY NormalZ), output Bx50 """ batch_size = point_cloud.get_shape()[0].value num_point = point_cloud.get_shape()[1].value end_points = {} l0_xyz = tf.slice(point_cloud, [0,0,0], [-1,-1,3]) l0_points = tf.slice(point_cloud, [0,0,3], [-1,-1,3]) # Set Abstraction layers l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points, npoint=512, radius=0.2, nsample=64, mlp=[64,64,128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1') l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points, npoint=128, radius=0.4, nsample=64, mlp=[128,128,256], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2') l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=None, group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer3') # Feature Propagation layers l2_points = pointnet_fp_module(l2_xyz, l3_xyz, l2_points, l3_points, [256,256], is_training, bn_decay, scope='fa_layer1') l1_points = pointnet_fp_module(l1_xyz, l2_xyz, l1_points, l2_points, [256,128], is_training, bn_decay, scope='fa_layer2') l0_points = pointnet_fp_module(l0_xyz, l1_xyz, tf.concat([l0_xyz,l0_points],axis=-1), l1_points, [128,128,128], is_training, bn_decay, scope='fa_layer3') # FC layers net = tf_util.conv1d(l0_points, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay) end_points['feats'] = net net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1') net = tf_util.conv1d(net, 50, 1, padding='VALID', activation_fn=None, scope='fc2') return net, end_points def get_loss(pred, label): """ pred: BxNxC, label: BxN, """ loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label) classify_loss = tf.reduce_mean(loss) tf.summary.scalar('classify loss', classify_loss) tf.add_to_collection('losses', classify_loss) return classify_loss if __name__=='__main__': with tf.Graph().as_default(): inputs = tf.zeros((32,2048,6)) net, _ = get_model(inputs, tf.constant(True)) print(net) ================================================ FILE: pointnet2_tf/models/pointnet2_part_seg_msg_one_hot.py ================================================ import os import sys BASE_DIR = os.path.dirname(__file__) sys.path.append(BASE_DIR) sys.path.append(os.path.join(BASE_DIR, '../utils')) import tensorflow as tf import numpy as np import tf_util from pointnet_util import pointnet_sa_module, pointnet_sa_module_msg, pointnet_fp_module def placeholder_inputs(batch_size, num_point): pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 6)) labels_pl = tf.placeholder(tf.int32, shape=(batch_size, num_point)) cls_labels_pl = tf.placeholder(tf.int32, shape=(batch_size)) return pointclouds_pl, labels_pl, cls_labels_pl NUM_CATEGORIES = 16 def get_model(point_cloud, cls_label, is_training, bn_decay=None): """ Classification PointNet, input is BxNx3, output Bx40 """ batch_size = point_cloud.get_shape()[0].value num_point = point_cloud.get_shape()[1].value end_points = {} l0_xyz = tf.slice(point_cloud, [0,0,0], [-1,-1,3]) l0_points = tf.slice(point_cloud, [0,0,3], [-1,-1,3]) # Set abstraction layers l1_xyz, l1_points = pointnet_sa_module_msg(l0_xyz, l0_points, 512, [0.1,0.2,0.4], [32,64,128], [[32,32,64], [64,64,128], [64,96,128]], is_training, bn_decay, scope='layer1') l2_xyz, l2_points = pointnet_sa_module_msg(l1_xyz, l1_points, 128, [0.4,0.8], [64,128], [[128,128,256],[128,196,256]], is_training, bn_decay, scope='layer2') l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=None, radius=None, nsample=None, mlp=[256,512,1024], mlp2=None, group_all=True, is_training=is_training, bn_decay=bn_decay, scope='layer3') # Feature propagation layers l2_points = pointnet_fp_module(l2_xyz, l3_xyz, l2_points, l3_points, [256,256], is_training, bn_decay, scope='fa_layer1') l1_points = pointnet_fp_module(l1_xyz, l2_xyz, l1_points, l2_points, [256,128], is_training, bn_decay, scope='fa_layer2') cls_label_one_hot = tf.one_hot(cls_label, depth=NUM_CATEGORIES, on_value=1.0, off_value=0.0) cls_label_one_hot = tf.reshape(cls_label_one_hot, [batch_size, 1, NUM_CATEGORIES]) cls_label_one_hot = tf.tile(cls_label_one_hot, [1,num_point,1]) l0_points = pointnet_fp_module(l0_xyz, l1_xyz, tf.concat([cls_label_one_hot, l0_xyz, l0_points],axis=-1), l1_points, [128,128], is_training, bn_decay, scope='fp_layer3') # FC layers net = tf_util.conv1d(l0_points, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay) end_points['feats'] = net net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1') net = tf_util.conv1d(net, 50, 1, padding='VALID', activation_fn=None, scope='fc2') return net, end_points def get_loss(pred, label): """ pred: BxNxC, label: BxN, """ loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label) classify_loss = tf.reduce_mean(loss) tf.summary.scalar('classify loss', classify_loss) tf.add_to_collection('losses', classify_loss) return classify_loss if __name__=='__main__': with tf.Graph().as_default(): inputs = tf.zeros((32,2048,6)) cls_labels = tf.zeros((32),dtype=tf.int32) output, ep = get_model(inputs, cls_labels, tf.constant(True)) print(output) ================================================ FILE: pointnet2_tf/models/pointnet2_sem_seg.py ================================================ import os import sys BASE_DIR = os.path.dirname(__file__) sys.path.append(BASE_DIR) sys.path.append(os.path.join(BASE_DIR, '../utils')) import tensorflow as tf import numpy as np import tf_util from pointnet_util import pointnet_sa_module, pointnet_fp_module def placeholder_inputs(batch_size, num_point): pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3)) labels_pl = tf.placeholder(tf.int32, shape=(batch_size, num_point)) smpws_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point)) return pointclouds_pl, labels_pl, smpws_pl def get_model(point_cloud, is_training, num_class, bn_decay=None): """ Semantic segmentation PointNet, input is BxNx3, output Bxnum_class """ batch_size = point_cloud.get_shape()[0].value num_point = point_cloud.get_shape()[1].value end_points = {} l0_xyz = point_cloud l0_points = None end_points['l0_xyz'] = l0_xyz # Layer 1 l1_xyz, l1_points, l1_indices = pointnet_sa_module(l0_xyz, l0_points, npoint=1024, radius=0.1, nsample=32, mlp=[32,32,64], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer1') l2_xyz, l2_points, l2_indices = pointnet_sa_module(l1_xyz, l1_points, npoint=256, radius=0.2, nsample=32, mlp=[64,64,128], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer2') l3_xyz, l3_points, l3_indices = pointnet_sa_module(l2_xyz, l2_points, npoint=64, radius=0.4, nsample=32, mlp=[128,128,256], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer3') l4_xyz, l4_points, l4_indices = pointnet_sa_module(l3_xyz, l3_points, npoint=16, radius=0.8, nsample=32, mlp=[256,256,512], mlp2=None, group_all=False, is_training=is_training, bn_decay=bn_decay, scope='layer4') # Feature Propagation layers l3_points = pointnet_fp_module(l3_xyz, l4_xyz, l3_points, l4_points, [256,256], is_training, bn_decay, scope='fa_layer1') l2_points = pointnet_fp_module(l2_xyz, l3_xyz, l2_points, l3_points, [256,256], is_training, bn_decay, scope='fa_layer2') l1_points = pointnet_fp_module(l1_xyz, l2_xyz, l1_points, l2_points, [256,128], is_training, bn_decay, scope='fa_layer3') l0_points = pointnet_fp_module(l0_xyz, l1_xyz, l0_points, l1_points, [128,128,128], is_training, bn_decay, scope='fa_layer4') # FC layers net = tf_util.conv1d(l0_points, 128, 1, padding='VALID', bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay) end_points['feats'] = net net = tf_util.dropout(net, keep_prob=0.5, is_training=is_training, scope='dp1') net = tf_util.conv1d(net, num_class, 1, padding='VALID', activation_fn=None, scope='fc2') return net, end_points def get_loss(pred, label, smpw): """ pred: BxNxC, label: BxN, smpw: BxN """ classify_loss = tf.losses.sparse_softmax_cross_entropy(labels=label, logits=pred, weights=smpw) tf.summary.scalar('classify loss', classify_loss) tf.add_to_collection('losses', classify_loss) return classify_loss if __name__=='__main__': with tf.Graph().as_default(): inputs = tf.zeros((32,2048,3)) net, _ = get_model(inputs, tf.constant(True), 10) print(net) ================================================ FILE: pointnet2_tf/models/pointnet_cls_basic.py ================================================ ''' PointNet version 1 Model Reference: https://github.com/charlesq34/pointnet ''' import tensorflow as tf import numpy as np import math import sys import os BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) sys.path.append(os.path.join(BASE_DIR, '../utils')) import tf_util def placeholder_inputs(batch_size, num_point): pointclouds_pl = tf.placeholder(tf.float32, shape=(batch_size, num_point, 3)) labels_pl = tf.placeholder(tf.int32, shape=(batch_size)) return pointclouds_pl, labels_pl def get_model(point_cloud, is_training, bn_decay=None): """ Classification PointNet, input is BxNx3, output Bx40 """ batch_size = point_cloud.get_shape()[0].value num_point = point_cloud.get_shape()[1].value end_points = {} input_image = tf.expand_dims(point_cloud, -1) # Point functions (MLP implemented as conv2d) net = tf_util.conv2d(input_image, 64, [1,3], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='conv1', bn_decay=bn_decay) net = tf_util.conv2d(net, 64, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='conv2', bn_decay=bn_decay) net = tf_util.conv2d(net, 64, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='conv3', bn_decay=bn_decay) net = tf_util.conv2d(net, 128, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='conv4', bn_decay=bn_decay) net = tf_util.conv2d(net, 1024, [1,1], padding='VALID', stride=[1,1], bn=True, is_training=is_training, scope='conv5', bn_decay=bn_decay) # Symmetric function: max pooling net = tf_util.max_pool2d(net, [num_point,1], padding='VALID', scope='maxpool') # MLP on global point cloud vector net = tf.reshape(net, [batch_size, -1]) net = tf_util.fully_connected(net, 512, bn=True, is_training=is_training, scope='fc1', bn_decay=bn_decay) net = tf_util.fully_connected(net, 256, bn=True, is_training=is_training, scope='fc2', bn_decay=bn_decay) net = tf_util.dropout(net, keep_prob=0.7, is_training=is_training, scope='dp1') net = tf_util.fully_connected(net, 40, activation_fn=None, scope='fc3') return net, end_points def get_loss(pred, label, end_points): """ pred: B*NUM_CLASSES, label: B, """ loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=pred, labels=label) classify_loss = tf.reduce_mean(loss) tf.summary.scalar('classify loss', classify_loss) tf.add_to_collection('losses', classify_loss) return classify_loss if __name__=='__main__': with tf.Graph().as_default(): inputs = tf.zeros((32,1024,3)) outputs = get_model(inputs, tf.constant(True)) print(outputs) ================================================ FILE: pointnet2_tf/part_seg/command.sh ================================================ python train.py --model pointnet2_part_seg --log_dir log --gpu 1 --max_epoch 201 > log.txt 2>&1 & ================================================ FILE: pointnet2_tf/part_seg/command_one_hot.sh ================================================ python train_one_hot.py --batch_size 8 --model pointnet2_part_seg_msg_one_hot --log_dir log_msg_one_hot --gpu 0 --max_epoch 201 > log_msg_one_hot.txt 2>&1 & ================================================ FILE: pointnet2_tf/part_seg/evaluate.py ================================================ import argparse import math from datetime import datetime import h5py import numpy as np import tensorflow as tf import socket import importlib import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = os.path.dirname(BASE_DIR) sys.path.append(BASE_DIR) sys.path.append(os.path.join(ROOT_DIR, 'models')) sys.path.append(os.path.join(ROOT_DIR, 'utils')) import tf_util import part_dataset_all_normal parser = argparse.ArgumentParser() parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]') parser.add_argument('--model', default='pointnet2_part_seg', help='Model name [default: pointnet2_part_seg]') parser.add_argument('--model_path', default='log/model.ckpt', help='model checkpoint file path [default: log/model.ckpt]') parser.add_argument('--log_dir', default='log_eval', help='Log dir [default: log_eval]') parser.add_argument('--num_point', type=int, default=2048, help='Point Number [default: 2048]') parser.add_argument('--batch_size', type=int, default=32, help='Batch Size during training [default: 32]') FLAGS = parser.parse_args() VOTE_NUM = 12 EPOCH_CNT = 0 BATCH_SIZE = FLAGS.batch_size NUM_POINT = FLAGS.num_point GPU_INDEX = FLAGS.gpu MODEL_PATH = FLAGS.model_path MODEL = importlib.import_module(FLAGS.model) # import network module MODEL_FILE = os.path.join(ROOT_DIR, 'models', FLAGS.model+'.py') LOG_DIR = FLAGS.log_dir if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR) os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w') LOG_FOUT.write(str(FLAGS)+'\n') NUM_CLASSES = 50 # Shapenet official train/test split DATA_PATH = os.path.join(ROOT_DIR, 'data', 'shapenetcore_partanno_segmentation_benchmark_v0_normal') TEST_DATASET = part_dataset_all_normal.PartNormalDataset(root=DATA_PATH, npoints=NUM_POINT, classification=False, split='test') def log_string(out_str): LOG_FOUT.write(out_str+'\n') LOG_FOUT.flush() print(out_str) def evaluate(): with tf.Graph().as_default(): with tf.device('/gpu:'+str(GPU_INDEX)): pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT) is_training_pl = tf.placeholder(tf.bool, shape=()) print is_training_pl print "--- Get model and loss" pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl) loss = MODEL.get_loss(pred, labels_pl) saver = tf.train.Saver() # Create a session config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True sess = tf.Session(config=config) # Restore variables from disk. saver.restore(sess, MODEL_PATH) ops = {'pointclouds_pl': pointclouds_pl, 'labels_pl': labels_pl, 'is_training_pl': is_training_pl, 'pred': pred, 'loss': loss} eval_one_epoch(sess, ops) def get_batch(dataset, idxs, start_idx, end_idx): bsize = end_idx-start_idx batch_data = np.zeros((bsize, NUM_POINT, 6)) batch_label = np.zeros((bsize, NUM_POINT), dtype=np.int32) for i in range(bsize): ps,normal,seg = dataset[idxs[i+start_idx]] batch_data[i,:,0:3] = ps batch_data[i,:,3:6] = normal batch_label[i,:] = seg return batch_data, batch_label def eval_one_epoch(sess, ops): """ ops: dict mapping from string to tf ops """ is_training = False test_idxs = np.arange(0, len(TEST_DATASET)) # Test on all data: last batch might be smaller than BATCH_SIZE num_batches = (len(TEST_DATASET)+BATCH_SIZE-1)/BATCH_SIZE total_correct = 0 total_seen = 0 loss_sum = 0 total_seen_class = [0 for _ in range(NUM_CLASSES)] total_correct_class = [0 for _ in range(NUM_CLASSES)] seg_classes = TEST_DATASET.seg_classes shape_ious = {cat:[] for cat in seg_classes.keys()} seg_label_to_cat = {} # {0:Airplane, 1:Airplane, ...49:Table} for cat in seg_classes.keys(): for label in seg_classes[cat]: seg_label_to_cat[label] = cat log_string(str(datetime.now())) log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT)) batch_data = np.zeros((BATCH_SIZE, NUM_POINT, 6)) batch_label = np.zeros((BATCH_SIZE, NUM_POINT)).astype(np.int32) for batch_idx in range(num_batches): if batch_idx %20==0: log_string('%03d/%03d'%(batch_idx, num_batches)) start_idx = batch_idx * BATCH_SIZE end_idx = min(len(TEST_DATASET), (batch_idx+1) * BATCH_SIZE) cur_batch_size = end_idx-start_idx cur_batch_data, cur_batch_label = get_batch(TEST_DATASET, test_idxs, start_idx, end_idx) if cur_batch_size == BATCH_SIZE: batch_data = cur_batch_data batch_label = cur_batch_label else: batch_data[0:cur_batch_size] = cur_batch_data batch_label[0:cur_batch_size] = cur_batch_label # --------------------------------------------------------------------- loss_val = 0 pred_val = np.zeros((BATCH_SIZE, NUM_POINT, NUM_CLASSES)) for _ in range(VOTE_NUM): feed_dict = {ops['pointclouds_pl']: batch_data, ops['labels_pl']: batch_label, ops['is_training_pl']: is_training} temp_loss_val, temp_pred_val = sess.run([ops['loss'], ops['pred']], feed_dict=feed_dict) loss_val += temp_loss_val pred_val += temp_pred_val loss_val /= float(VOTE_NUM) # --------------------------------------------------------------------- # Select valid data cur_pred_val = pred_val[0:cur_batch_size] # Constrain pred to the groundtruth classes (selected by seg_classes[cat]) cur_pred_val_logits = cur_pred_val cur_pred_val = np.zeros((cur_batch_size, NUM_POINT)).astype(np.int32) for i in range(cur_batch_size): cat = seg_label_to_cat[cur_batch_label[i,0]] logits = cur_pred_val_logits[i,:,:] cur_pred_val[i,:] = np.argmax(logits[:,seg_classes[cat]], 1) + seg_classes[cat][0] correct = np.sum(cur_pred_val == cur_batch_label) total_correct += correct total_seen += (cur_batch_size*NUM_POINT) if cur_batch_size==BATCH_SIZE: loss_sum += loss_val for l in range(NUM_CLASSES): total_seen_class[l] += np.sum(cur_batch_label==l) total_correct_class[l] += (np.sum((cur_pred_val==l) & (cur_batch_label==l))) for i in range(cur_batch_size): segp = cur_pred_val[i,:] segl = cur_batch_label[i,:] cat = seg_label_to_cat[segl[0]] part_ious = [0.0 for _ in range(len(seg_classes[cat]))] for l in seg_classes[cat]: if (np.sum(segl==l) == 0) and (np.sum(segp==l) == 0): # part is not present, no prediction as well part_ious[l-seg_classes[cat][0]] = 1.0 else: part_ious[l-seg_classes[cat][0]] = np.sum((segl==l) & (segp==l)) / float(np.sum((segl==l) | (segp==l))) shape_ious[cat].append(np.mean(part_ious)) all_shape_ious = [] for cat in shape_ious.keys(): for iou in shape_ious[cat]: all_shape_ious.append(iou) shape_ious[cat] = np.mean(shape_ious[cat]) print len(all_shape_ious) mean_shape_ious = np.mean(shape_ious.values()) log_string('eval mean loss: %f' % (loss_sum / float(len(TEST_DATASET)/BATCH_SIZE))) log_string('eval accuracy: %f'% (total_correct / float(total_seen))) log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float)))) for cat in sorted(shape_ious.keys()): log_string('eval mIoU of %s:\t %f'%(cat, shape_ious[cat])) log_string('eval mean mIoU: %f' % (mean_shape_ious)) log_string('eval mean mIoU (all shapes): %f' % (np.mean(all_shape_ious))) if __name__ == "__main__": log_string('pid: %s'%(str(os.getpid()))) evaluate() LOG_FOUT.close() ================================================ FILE: pointnet2_tf/part_seg/part_dataset.py ================================================ ''' Dataset for shapenet part segmentaion. ''' import os import os.path import json import numpy as np import sys def pc_normalize(pc): l = pc.shape[0] centroid = np.mean(pc, axis=0) pc = pc - centroid m = np.max(np.sqrt(np.sum(pc**2, axis=1))) pc = pc / m return pc class PartDataset(): def __init__(self, root, npoints = 2500, classification = False, class_choice = None, split='train', normalize=True): self.npoints = npoints self.root = root self.catfile = os.path.join(self.root, 'synsetoffset2category.txt') self.cat = {} self.classification = classification self.normalize = normalize with open(self.catfile, 'r') as f: for line in f: ls = line.strip().split() self.cat[ls[0]] = ls[1] #print(self.cat) if not class_choice is None: self.cat = {k:v for k,v in self.cat.items() if k in class_choice} self.meta = {} with open(os.path.join(self.root, 'train_test_split', 'shuffled_train_file_list.json'), 'r') as f: train_ids = set([str(d.split('/')[2]) for d in json.load(f)]) with open(os.path.join(self.root, 'train_test_split', 'shuffled_val_file_list.json'), 'r') as f: val_ids = set([str(d.split('/')[2]) for d in json.load(f)]) with open(os.path.join(self.root, 'train_test_split', 'shuffled_test_file_list.json'), 'r') as f: test_ids = set([str(d.split('/')[2]) for d in json.load(f)]) for item in self.cat: #print('category', item) self.meta[item] = [] dir_point = os.path.join(self.root, self.cat[item], 'points') dir_seg = os.path.join(self.root, self.cat[item], 'points_label') #print(dir_point, dir_seg) fns = sorted(os.listdir(dir_point)) #print(fns[0][0:-4]) if split=='trainval': fns = [fn for fn in fns if ((fn[0:-4] in train_ids) or (fn[0:-4] in val_ids))] elif split=='train': fns = [fn for fn in fns if fn[0:-4] in train_ids] elif split=='val': fns = [fn for fn in fns if fn[0:-4] in val_ids] elif split=='test': fns = [fn for fn in fns if fn[0:-4] in test_ids] else: print('Unknown split: %s. Exiting..'%(split)) exit(-1) #print(os.path.basename(fns)) for fn in fns: token = (os.path.splitext(os.path.basename(fn))[0]) self.meta[item].append((os.path.join(dir_point, token + '.pts'), os.path.join(dir_seg, token + '.seg'))) self.datapath = [] for item in self.cat: for fn in self.meta[item]: self.datapath.append((item, fn[0], fn[1])) self.classes = dict(zip(self.cat, range(len(self.cat)))) self.num_seg_classes = 0 if not self.classification: for i in range(len(self.datapath)/50): l = len(np.unique(np.loadtxt(self.datapath[i][-1]).astype(np.uint8))) if l > self.num_seg_classes: self.num_seg_classes = l #print(self.num_seg_classes) self.cache = {} # from index to (point_set, cls, seg) tuple self.cache_size = 10000 def __getitem__(self, index): if index in self.cache: point_set, seg, cls = self.cache[index] else: fn = self.datapath[index] cls = self.classes[self.datapath[index][0]] cls = np.array([cls]).astype(np.int32) point_set = np.loadtxt(fn[1]).astype(np.float32) if self.normalize: point_set = pc_normalize(point_set) seg = np.loadtxt(fn[2]).astype(np.int64) - 1 #print(point_set.shape, seg.shape) if len(self.cache) < self.cache_size: self.cache[index] = (point_set, seg, cls) choice = np.random.choice(len(seg), self.npoints, replace=True) #resample point_set = point_set[choice, :] seg = seg[choice] if self.classification: return point_set, cls else: return point_set, seg def __len__(self): return len(self.datapath) if __name__ == '__main__': d = PartDataset(root = '../data/shapenetcore_partanno_segmentation_benchmark_v0', class_choice = ['Airplane'], split='test') print(len(d)) import time tic = time.time() for i in range(100): ps, seg = d[i] print np.max(seg), np.min(seg) print(time.time() - tic) print(ps.shape, type(ps), seg.shape,type(seg)) d = PartDataset(root = '../data/shapenetcore_partanno_segmentation_benchmark_v0', classification = True) print(len(d)) ps, cls = d[0] print(ps.shape, type(ps), cls.shape,type(cls)) ================================================ FILE: pointnet2_tf/part_seg/part_dataset_all_normal.py ================================================ ''' Dataset for ShapeNetPart segmentation ''' import os import os.path import json import numpy as np import sys def pc_normalize(pc): l = pc.shape[0] centroid = np.mean(pc, axis=0) pc = pc - centroid m = np.max(np.sqrt(np.sum(pc**2, axis=1))) pc = pc / m return pc class PartNormalDataset(): def __init__(self, root, npoints = 2500, classification = False, split='train', normalize=True, return_cls_label = False): self.npoints = npoints self.root = root self.catfile = os.path.join(self.root, 'synsetoffset2category.txt') self.cat = {} self.classification = classification self.normalize = normalize self.return_cls_label = return_cls_label with open(self.catfile, 'r') as f: for line in f: ls = line.strip().split() self.cat[ls[0]] = ls[1] self.cat = {k:v for k,v in self.cat.items()} #print(self.cat) self.meta = {} with open(os.path.join(self.root, 'train_test_split', 'shuffled_train_file_list.json'), 'r') as f: train_ids = set([str(d.split('/')[2]) for d in json.load(f)]) with open(os.path.join(self.root, 'train_test_split', 'shuffled_val_file_list.json'), 'r') as f: val_ids = set([str(d.split('/')[2]) for d in json.load(f)]) with open(os.path.join(self.root, 'train_test_split', 'shuffled_test_file_list.json'), 'r') as f: test_ids = set([str(d.split('/')[2]) for d in json.load(f)]) for item in self.cat: #print('category', item) self.meta[item] = [] dir_point = os.path.join(self.root, self.cat[item]) fns = sorted(os.listdir(dir_point)) #print(fns[0][0:-4]) if split=='trainval': fns = [fn for fn in fns if ((fn[0:-4] in train_ids) or (fn[0:-4] in val_ids))] elif split=='train': fns = [fn for fn in fns if fn[0:-4] in train_ids] elif split=='val': fns = [fn for fn in fns if fn[0:-4] in val_ids] elif split=='test': fns = [fn for fn in fns if fn[0:-4] in test_ids] else: print('Unknown split: %s. Exiting..'%(split)) exit(-1) #print(os.path.basename(fns)) for fn in fns: token = (os.path.splitext(os.path.basename(fn))[0]) self.meta[item].append(os.path.join(dir_point, token + '.txt')) self.datapath = [] for item in self.cat: for fn in self.meta[item]: self.datapath.append((item, fn)) self.classes = dict(zip(self.cat, range(len(self.cat)))) # Mapping from category ('Chair') to a list of int [10,11,12,13] as segmentation labels self.seg_classes = {'Earphone': [16, 17, 18], 'Motorbike': [30, 31, 32, 33, 34, 35], 'Rocket': [41, 42, 43], 'Car': [8, 9, 10, 11], 'Laptop': [28, 29], 'Cap': [6, 7], 'Skateboard': [44, 45, 46], 'Mug': [36, 37], 'Guitar': [19, 20, 21], 'Bag': [4, 5], 'Lamp': [24, 25, 26, 27], 'Table': [47, 48, 49], 'Airplane': [0, 1, 2, 3], 'Pistol': [38, 39, 40], 'Chair': [12, 13, 14, 15], 'Knife': [22, 23]} for cat in sorted(self.seg_classes.keys()): print(cat, self.seg_classes[cat]) self.cache = {} # from index to (point_set, cls, seg) tuple self.cache_size = 20000 def __getitem__(self, index): if index in self.cache: point_set, normal, seg, cls = self.cache[index] else: fn = self.datapath[index] cat = self.datapath[index][0] cls = self.classes[cat] cls = np.array([cls]).astype(np.int32) data = np.loadtxt(fn[1]).astype(np.float32) point_set = data[:,0:3] if self.normalize: point_set = pc_normalize(point_set) normal = data[:,3:6] seg = data[:,-1].astype(np.int32) if len(self.cache) < self.cache_size: self.cache[index] = (point_set, normal, seg, cls) choice = np.random.choice(len(seg), self.npoints, replace=True) #resample point_set = point_set[choice, :] seg = seg[choice] normal = normal[choice,:] if self.classification: return point_set, normal, cls else: if self.return_cls_label: return point_set, normal, seg, cls else: return point_set, normal, seg def __len__(self): return len(self.datapath) if __name__ == '__main__': d = PartNormalDataset(root = '../data/shapenetcore_partanno_segmentation_benchmark_v0_normal', split='trainval', npoints=3000) print(len(d)) i = 500 ps, normal, seg = d[i] print d.datapath[i] print np.max(seg), np.min(seg) print(ps.shape, seg.shape, normal.shape) print ps print normal sys.path.append('../utils') import show3d_balls show3d_balls.showpoints(ps, normal+1, ballradius=8) d = PartNormalDataset(root = '../data/shapenetcore_partanno_segmentation_benchmark_v0_normal', classification = True) print(len(d)) ps, normal, cls = d[0] print(ps.shape, type(ps), cls.shape,type(cls)) ================================================ FILE: pointnet2_tf/part_seg/test.py ================================================ import tensorflow as tf import numpy as np import argparse import socket import importlib import time import os import scipy.misc import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) sys.path.append(os.path.join(BASE_DIR, 'models')) sys.path.append(os.path.join(BASE_DIR, 'utils')) import provider import show3d_balls sys.path.append(os.path.join(ROOT_DIR, 'data_prep')) import part_dataset parser = argparse.ArgumentParser() parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]') parser.add_argument('--num_point', type=int, default=2048, help='Point Number [default: 2048]') parser.add_argument('--category', default='Airplane', help='Which single class to train on [default: Airplane]') parser.add_argument('--model', default='pointnet2_part_seg', help='Model name [default: pointnet2_part_seg]') parser.add_argument('--model_path', default='log/model.ckpt', help='model checkpoint file path [default: log/model.ckpt]') FLAGS = parser.parse_args() MODEL_PATH = FLAGS.model_path GPU_INDEX = FLAGS.gpu NUM_POINT = FLAGS.num_point MODEL = importlib.import_module(FLAGS.model) # import network module NUM_CLASSES = 4 DATA_PATH = os.path.join(ROOT_DIR, 'data', 'shapenetcore_partanno_segmentation_benchmark_v0_normal') TEST_DATASET = part_dataset.PartDataset(root=DATA_PATH, npoints=NUM_POINT, classification=False, class_choice=FLAGS.category, split='test') def get_model(batch_size, num_point): with tf.Graph().as_default(): with tf.device('/gpu:'+str(GPU_INDEX)): pointclouds_pl, labels_pl = MODEL.placeholder_inputs(batch_size, num_point) is_training_pl = tf.placeholder(tf.bool, shape=()) pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl) loss = MODEL.get_loss(pred, labels_pl, end_points) saver = tf.train.Saver() # Create a session config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True sess = tf.Session(config=config) # Restore variables from disk. saver.restore(sess, MODEL_PATH) ops = {'pointclouds_pl': pointclouds_pl, 'labels_pl': labels_pl, 'is_training_pl': is_training_pl, 'pred': pred, 'loss': loss} return sess, ops def inference(sess, ops, pc, batch_size): ''' pc: BxNx3 array, return BxN pred ''' assert pc.shape[0]%batch_size == 0 num_batches = pc.shape[0]/batch_size logits = np.zeros((pc.shape[0], pc.shape[1], NUM_CLASSES)) for i in range(num_batches): feed_dict = {ops['pointclouds_pl']: pc[i*batch_size:(i+1)*batch_size,...], ops['is_training_pl']: False} batch_logits = sess.run(ops['pred'], feed_dict=feed_dict) logits[i*batch_size:(i+1)*batch_size,...] = batch_logits return np.argmax(logits, 2) if __name__=='__main__': import matplotlib.pyplot as plt cmap = plt.cm.get_cmap("hsv", 4) cmap = np.array([cmap(i) for i in range(10)])[:,:3] for i in range(len(TEST_DATASET)): ps, seg = TEST_DATASET[i] sess, ops = get_model(batch_size=1, num_point=ps.shape[0]) segp = inference(sess, ops, np.expand_dims(ps,0), batch_size=1) segp = segp.squeeze() gt = cmap[seg, :] pred = cmap[segp, :] show3d_balls.showpoints(ps, gt, pred, ballradius=8) ================================================ FILE: pointnet2_tf/part_seg/train.py ================================================ import argparse import math from datetime import datetime import h5py import numpy as np import tensorflow as tf import socket import importlib import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = os.path.dirname(BASE_DIR) sys.path.append(BASE_DIR) sys.path.append(os.path.join(ROOT_DIR, 'models')) sys.path.append(os.path.join(ROOT_DIR, 'utils')) import provider import tf_util import part_dataset_all_normal parser = argparse.ArgumentParser() parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]') parser.add_argument('--model', default='model', help='Model name [default: model]') parser.add_argument('--log_dir', default='log', help='Log dir [default: log]') parser.add_argument('--num_point', type=int, default=2048, help='Point Number [default: 2048]') parser.add_argument('--max_epoch', type=int, default=201, help='Epoch to run [default: 201]') parser.add_argument('--batch_size', type=int, default=32, help='Batch Size during training [default: 32]') parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]') parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]') parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]') parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]') parser.add_argument('--decay_rate', type=float, default=0.7, help='Decay rate for lr decay [default: 0.7]') FLAGS = parser.parse_args() EPOCH_CNT = 0 BATCH_SIZE = FLAGS.batch_size NUM_POINT = FLAGS.num_point MAX_EPOCH = FLAGS.max_epoch BASE_LEARNING_RATE = FLAGS.learning_rate GPU_INDEX = FLAGS.gpu MOMENTUM = FLAGS.momentum OPTIMIZER = FLAGS.optimizer DECAY_STEP = FLAGS.decay_step DECAY_RATE = FLAGS.decay_rate MODEL = importlib.import_module(FLAGS.model) # import network module MODEL_FILE = os.path.join(ROOT_DIR, 'models', FLAGS.model+'.py') LOG_DIR = FLAGS.log_dir if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR) os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w') LOG_FOUT.write(str(FLAGS)+'\n') BN_INIT_DECAY = 0.5 BN_DECAY_DECAY_RATE = 0.5 BN_DECAY_DECAY_STEP = float(DECAY_STEP) BN_DECAY_CLIP = 0.99 HOSTNAME = socket.gethostname() NUM_CLASSES = 50 # Shapenet official train/test split DATA_PATH = os.path.join(ROOT_DIR, 'data', 'shapenetcore_partanno_segmentation_benchmark_v0_normal') TRAIN_DATASET = part_dataset_all_normal.PartNormalDataset(root=DATA_PATH, npoints=NUM_POINT, classification=False, split='trainval') TEST_DATASET = part_dataset_all_normal.PartNormalDataset(root=DATA_PATH, npoints=NUM_POINT, classification=False, split='test') def log_string(out_str): LOG_FOUT.write(out_str+'\n') LOG_FOUT.flush() print(out_str) def get_learning_rate(batch): learning_rate = tf.train.exponential_decay( BASE_LEARNING_RATE, # Base learning rate. batch * BATCH_SIZE, # Current index into the dataset. DECAY_STEP, # Decay step. DECAY_RATE, # Decay rate. staircase=True) learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE! return learning_rate def get_bn_decay(batch): bn_momentum = tf.train.exponential_decay( BN_INIT_DECAY, batch*BATCH_SIZE, BN_DECAY_DECAY_STEP, BN_DECAY_DECAY_RATE, staircase=True) bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum) return bn_decay def train(): with tf.Graph().as_default(): with tf.device('/gpu:'+str(GPU_INDEX)): pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT) is_training_pl = tf.placeholder(tf.bool, shape=()) # Note the global_step=batch parameter to minimize. # That tells the optimizer to helpfully increment the 'batch' parameter for you every time it trains. batch = tf.Variable(0) bn_decay = get_bn_decay(batch) tf.summary.scalar('bn_decay', bn_decay) print "--- Get model and loss" # Get model and loss pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl, bn_decay=bn_decay) loss = MODEL.get_loss(pred, labels_pl) tf.summary.scalar('loss', loss) correct = tf.equal(tf.argmax(pred, 2), tf.to_int64(labels_pl)) accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE*NUM_POINT) tf.summary.scalar('accuracy', accuracy) print "--- Get training operator" # Get training operator learning_rate = get_learning_rate(batch) tf.summary.scalar('learning_rate', learning_rate) if OPTIMIZER == 'momentum': optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM) elif OPTIMIZER == 'adam': optimizer = tf.train.AdamOptimizer(learning_rate) train_op = optimizer.minimize(loss, global_step=batch) # Add ops to save and restore all the variables. saver = tf.train.Saver() # Create a session config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True config.log_device_placement = False sess = tf.Session(config=config) # Add summary writers merged = tf.summary.merge_all() train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'), sess.graph) test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'), sess.graph) # Init variables init = tf.global_variables_initializer() sess.run(init) ops = {'pointclouds_pl': pointclouds_pl, 'labels_pl': labels_pl, 'is_training_pl': is_training_pl, 'pred': pred, 'loss': loss, 'train_op': train_op, 'merged': merged, 'step': batch, 'end_points': end_points} best_acc = -1 for epoch in range(MAX_EPOCH): log_string('**** EPOCH %03d ****' % (epoch)) sys.stdout.flush() train_one_epoch(sess, ops, train_writer) eval_one_epoch(sess, ops, test_writer) # Save the variables to disk. if epoch % 10 == 0: save_path = saver.save(sess, os.path.join(LOG_DIR, "model.ckpt")) log_string("Model saved in file: %s" % save_path) def get_batch(dataset, idxs, start_idx, end_idx): bsize = end_idx-start_idx batch_data = np.zeros((bsize, NUM_POINT, 6)) batch_label = np.zeros((bsize, NUM_POINT), dtype=np.int32) for i in range(bsize): ps,normal,seg = dataset[idxs[i+start_idx]] batch_data[i,:,0:3] = ps batch_data[i,:,3:6] = normal batch_label[i,:] = seg return batch_data, batch_label def train_one_epoch(sess, ops, train_writer): """ ops: dict mapping from string to tf ops """ is_training = True # Shuffle train samples train_idxs = np.arange(0, len(TRAIN_DATASET)) np.random.shuffle(train_idxs) num_batches = len(TRAIN_DATASET)/BATCH_SIZE log_string(str(datetime.now())) total_correct = 0 total_seen = 0 loss_sum = 0 for batch_idx in range(num_batches): start_idx = batch_idx * BATCH_SIZE end_idx = (batch_idx+1) * BATCH_SIZE batch_data, batch_label = get_batch(TRAIN_DATASET, train_idxs, start_idx, end_idx) # Augment batched point clouds by rotation and jittering #aug_data = batch_data #aug_data = provider.random_scale_point_cloud(batch_data) batch_data[:,:,0:3] = provider.jitter_point_cloud(batch_data[:,:,0:3]) feed_dict = {ops['pointclouds_pl']: batch_data, ops['labels_pl']: batch_label, ops['is_training_pl']: is_training,} summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'], ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict) train_writer.add_summary(summary, step) pred_val = np.argmax(pred_val, 2) correct = np.sum(pred_val == batch_label) total_correct += correct total_seen += (BATCH_SIZE*NUM_POINT) loss_sum += loss_val if (batch_idx+1)%10 == 0: log_string(' -- %03d / %03d --' % (batch_idx+1, num_batches)) log_string('mean loss: %f' % (loss_sum / 10)) log_string('accuracy: %f' % (total_correct / float(total_seen))) total_correct = 0 total_seen = 0 loss_sum = 0 def eval_one_epoch(sess, ops, test_writer): """ ops: dict mapping from string to tf ops """ global EPOCH_CNT is_training = False test_idxs = np.arange(0, len(TEST_DATASET)) # Test on all data: last batch might be smaller than BATCH_SIZE num_batches = (len(TEST_DATASET)+BATCH_SIZE-1)/BATCH_SIZE total_correct = 0 total_seen = 0 loss_sum = 0 total_seen_class = [0 for _ in range(NUM_CLASSES)] total_correct_class = [0 for _ in range(NUM_CLASSES)] seg_classes = TEST_DATASET.seg_classes shape_ious = {cat:[] for cat in seg_classes.keys()} seg_label_to_cat = {} # {0:Airplane, 1:Airplane, ...49:Table} for cat in seg_classes.keys(): for label in seg_classes[cat]: seg_label_to_cat[label] = cat log_string(str(datetime.now())) log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT)) batch_data = np.zeros((BATCH_SIZE, NUM_POINT, 3)) batch_label = np.zeros((BATCH_SIZE, NUM_POINT)).astype(np.int32) for batch_idx in range(num_batches): if batch_idx %20==0: log_string('%03d/%03d'%(batch_idx, num_batches)) start_idx = batch_idx * BATCH_SIZE end_idx = min(len(TEST_DATASET), (batch_idx+1) * BATCH_SIZE) cur_batch_size = end_idx-start_idx cur_batch_data, cur_batch_label = get_batch(TEST_DATASET, test_idxs, start_idx, end_idx) if cur_batch_size == BATCH_SIZE: batch_data = cur_batch_data batch_label = cur_batch_label else: batch_data[0:cur_batch_size] = cur_batch_data batch_label[0:cur_batch_size] = cur_batch_label # --------------------------------------------------------------------- feed_dict = {ops['pointclouds_pl']: batch_data, ops['labels_pl']: batch_label, ops['is_training_pl']: is_training} summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'], ops['loss'], ops['pred']], feed_dict=feed_dict) test_writer.add_summary(summary, step) # --------------------------------------------------------------------- # Select valid data cur_pred_val = pred_val[0:cur_batch_size] # Constrain pred to the groundtruth classes (selected by seg_classes[cat]) cur_pred_val_logits = cur_pred_val cur_pred_val = np.zeros((cur_batch_size, NUM_POINT)).astype(np.int32) for i in range(cur_batch_size): cat = seg_label_to_cat[cur_batch_label[i,0]] logits = cur_pred_val_logits[i,:,:] cur_pred_val[i,:] = np.argmax(logits[:,seg_classes[cat]], 1) + seg_classes[cat][0] correct = np.sum(cur_pred_val == cur_batch_label) total_correct += correct total_seen += (cur_batch_size*NUM_POINT) if cur_batch_size==BATCH_SIZE: loss_sum += loss_val for l in range(NUM_CLASSES): total_seen_class[l] += np.sum(cur_batch_label==l) total_correct_class[l] += (np.sum((cur_pred_val==l) & (cur_batch_label==l))) for i in range(cur_batch_size): segp = cur_pred_val[i,:] segl = cur_batch_label[i,:] cat = seg_label_to_cat[segl[0]] part_ious = [0.0 for _ in range(len(seg_classes[cat]))] for l in seg_classes[cat]: if (np.sum(segl==l) == 0) and (np.sum(segp==l) == 0): # part is not present, no prediction as well part_ious[l-seg_classes[cat][0]] = 1.0 else: part_ious[l-seg_classes[cat][0]] = np.sum((segl==l) & (segp==l)) / float(np.sum((segl==l) | (segp==l))) shape_ious[cat].append(np.mean(part_ious)) all_shape_ious = [] for cat in shape_ious.keys(): for iou in shape_ious[cat]: all_shape_ious.append(iou) shape_ious[cat] = np.mean(shape_ious[cat]) mean_shape_ious = np.mean(shape_ious.values()) log_string('eval mean loss: %f' % (loss_sum / float(len(TEST_DATASET)/BATCH_SIZE))) log_string('eval accuracy: %f'% (total_correct / float(total_seen))) log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float)))) for cat in sorted(shape_ious.keys()): log_string('eval mIoU of %s:\t %f'%(cat, shape_ious[cat])) log_string('eval mean mIoU: %f' % (mean_shape_ious)) log_string('eval mean mIoU (all shapes): %f' % (np.mean(all_shape_ious))) EPOCH_CNT += 1 return total_correct/float(total_seen) if __name__ == "__main__": log_string('pid: %s'%(str(os.getpid()))) train() LOG_FOUT.close() ================================================ FILE: pointnet2_tf/part_seg/train_one_hot.py ================================================ import argparse import math from datetime import datetime import h5py import numpy as np import tensorflow as tf import socket import importlib import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = os.path.dirname(BASE_DIR) sys.path.append(BASE_DIR) sys.path.append(os.path.join(ROOT_DIR, 'models')) sys.path.append(os.path.join(ROOT_DIR, 'utils')) import provider import tf_util import part_dataset_all_normal parser = argparse.ArgumentParser() parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]') parser.add_argument('--model', default='model', help='Model name [default: model]') parser.add_argument('--log_dir', default='log', help='Log dir [default: log]') parser.add_argument('--num_point', type=int, default=2048, help='Point Number [default: 2048]') parser.add_argument('--max_epoch', type=int, default=201, help='Epoch to run [default: 201]') parser.add_argument('--batch_size', type=int, default=32, help='Batch Size during training [default: 32]') parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]') parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]') parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]') parser.add_argument('--decay_step', type=int, default=16881*20, help='Decay step for lr decay [default: 200000]') parser.add_argument('--decay_rate', type=float, default=0.5, help='Decay rate for lr decay [default: 0.7]') FLAGS = parser.parse_args() EPOCH_CNT = 0 BATCH_SIZE = FLAGS.batch_size NUM_POINT = FLAGS.num_point MAX_EPOCH = FLAGS.max_epoch BASE_LEARNING_RATE = FLAGS.learning_rate GPU_INDEX = FLAGS.gpu MOMENTUM = FLAGS.momentum OPTIMIZER = FLAGS.optimizer DECAY_STEP = FLAGS.decay_step DECAY_RATE = FLAGS.decay_rate MODEL = importlib.import_module(FLAGS.model) # import network module MODEL_FILE = os.path.join(ROOT_DIR, 'models', FLAGS.model+'.py') LOG_DIR = FLAGS.log_dir if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR) os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w') LOG_FOUT.write(str(FLAGS)+'\n') BN_INIT_DECAY = 0.5 BN_DECAY_DECAY_RATE = 0.5 BN_DECAY_DECAY_STEP = float(DECAY_STEP) BN_DECAY_CLIP = 0.99 HOSTNAME = socket.gethostname() NUM_CLASSES = 50 # Shapenet official train/test split DATA_PATH = os.path.join(ROOT_DIR, 'data', 'shapenetcore_partanno_segmentation_benchmark_v0_normal') TRAIN_DATASET = part_dataset_all_normal.PartNormalDataset(root=DATA_PATH, npoints=NUM_POINT, classification=False, split='trainval', return_cls_label=True) TEST_DATASET = part_dataset_all_normal.PartNormalDataset(root=DATA_PATH, npoints=NUM_POINT, classification=False, split='test', return_cls_label=True) def log_string(out_str): LOG_FOUT.write(out_str+'\n') LOG_FOUT.flush() print(out_str) def get_learning_rate(batch): learning_rate = tf.train.exponential_decay( BASE_LEARNING_RATE, # Base learning rate. batch * BATCH_SIZE, # Current index into the dataset. DECAY_STEP, # Decay step. DECAY_RATE, # Decay rate. staircase=True) learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE! return learning_rate def get_bn_decay(batch): bn_momentum = tf.train.exponential_decay( BN_INIT_DECAY, batch*BATCH_SIZE, BN_DECAY_DECAY_STEP, BN_DECAY_DECAY_RATE, staircase=True) bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum) return bn_decay def train(): with tf.Graph().as_default(): with tf.device('/gpu:'+str(GPU_INDEX)): pointclouds_pl, labels_pl, cls_labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT) is_training_pl = tf.placeholder(tf.bool, shape=()) print is_training_pl # Note the global_step=batch parameter to minimize. # That tells the optimizer to helpfully increment the 'batch' parameter for you every time it trains. batch = tf.Variable(0) bn_decay = get_bn_decay(batch) tf.summary.scalar('bn_decay', bn_decay) print "--- Get model and loss" # Get model and loss pred, end_points = MODEL.get_model(pointclouds_pl, cls_labels_pl, is_training_pl, bn_decay=bn_decay) loss = MODEL.get_loss(pred, labels_pl) tf.summary.scalar('loss', loss) correct = tf.equal(tf.argmax(pred, 2), tf.to_int64(labels_pl)) accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE*NUM_POINT) tf.summary.scalar('accuracy', accuracy) print "--- Get training operator" # Get training operator learning_rate = get_learning_rate(batch) tf.summary.scalar('learning_rate', learning_rate) if OPTIMIZER == 'momentum': optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM) elif OPTIMIZER == 'adam': optimizer = tf.train.AdamOptimizer(learning_rate) train_op = optimizer.minimize(loss, global_step=batch) # Add ops to save and restore all the variables. saver = tf.train.Saver() # Create a session config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True config.log_device_placement = False sess = tf.Session(config=config) # Add summary writers merged = tf.summary.merge_all() train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'), sess.graph) test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'), sess.graph) # Init variables init = tf.global_variables_initializer() sess.run(init) #sess.run(init, {is_training_pl: True}) ops = {'pointclouds_pl': pointclouds_pl, 'labels_pl': labels_pl, 'cls_labels_pl': cls_labels_pl, 'is_training_pl': is_training_pl, 'pred': pred, 'loss': loss, 'train_op': train_op, 'merged': merged, 'step': batch, 'end_points': end_points} best_acc = -1 for epoch in range(MAX_EPOCH): log_string('**** EPOCH %03d ****' % (epoch)) sys.stdout.flush() train_one_epoch(sess, ops, train_writer) eval_one_epoch(sess, ops, test_writer) # Save the variables to disk. if epoch % 10 == 0: save_path = saver.save(sess, os.path.join(LOG_DIR, "model.ckpt")) log_string("Model saved in file: %s" % save_path) def get_batch(dataset, idxs, start_idx, end_idx): bsize = end_idx-start_idx batch_data = np.zeros((bsize, NUM_POINT, 6)) batch_label = np.zeros((bsize, NUM_POINT), dtype=np.int32) batch_cls_label = np.zeros((bsize,), dtype=np.int32) for i in range(bsize): ps,normal,seg,cls = dataset[idxs[i+start_idx]] batch_data[i,:,0:3] = ps batch_data[i,:,3:6] = normal batch_label[i,:] = seg batch_cls_label[i] = cls return batch_data, batch_label, batch_cls_label def train_one_epoch(sess, ops, train_writer): """ ops: dict mapping from string to tf ops """ is_training = True # Shuffle train samples train_idxs = np.arange(0, len(TRAIN_DATASET)) np.random.shuffle(train_idxs) num_batches = len(TRAIN_DATASET)/BATCH_SIZE log_string(str(datetime.now())) total_correct = 0 total_seen = 0 loss_sum = 0 for batch_idx in range(num_batches): start_idx = batch_idx * BATCH_SIZE end_idx = (batch_idx+1) * BATCH_SIZE batch_data, batch_label, batch_cls_label = get_batch(TRAIN_DATASET, train_idxs, start_idx, end_idx) # Augment batched point clouds by rotation and jittering #aug_data = batch_data #aug_data = provider.random_scale_point_cloud(batch_data) batch_data[:,:,0:3] = provider.jitter_point_cloud(batch_data[:,:,0:3]) feed_dict = {ops['pointclouds_pl']: batch_data, ops['labels_pl']: batch_label, ops['cls_labels_pl']: batch_cls_label, ops['is_training_pl']: is_training,} summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'], ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict) train_writer.add_summary(summary, step) pred_val = np.argmax(pred_val, 2) correct = np.sum(pred_val == batch_label) total_correct += correct total_seen += (BATCH_SIZE*NUM_POINT) loss_sum += loss_val if (batch_idx+1)%10 == 0: log_string(' -- %03d / %03d --' % (batch_idx+1, num_batches)) log_string('mean loss: %f' % (loss_sum / 10)) log_string('accuracy: %f' % (total_correct / float(total_seen))) total_correct = 0 total_seen = 0 loss_sum = 0 def eval_one_epoch(sess, ops, test_writer): """ ops: dict mapping from string to tf ops """ global EPOCH_CNT is_training = False test_idxs = np.arange(0, len(TEST_DATASET)) # Test on all data: last batch might be smaller than BATCH_SIZE num_batches = (len(TEST_DATASET)+BATCH_SIZE-1)/BATCH_SIZE total_correct = 0 total_seen = 0 loss_sum = 0 total_seen_class = [0 for _ in range(NUM_CLASSES)] total_correct_class = [0 for _ in range(NUM_CLASSES)] seg_classes = TEST_DATASET.seg_classes shape_ious = {cat:[] for cat in seg_classes.keys()} seg_label_to_cat = {} # {0:Airplane, 1:Airplane, ...49:Table} for cat in seg_classes.keys(): for label in seg_classes[cat]: seg_label_to_cat[label] = cat log_string(str(datetime.now())) log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT)) batch_data = np.zeros((BATCH_SIZE, NUM_POINT, 3)) batch_label = np.zeros((BATCH_SIZE, NUM_POINT)).astype(np.int32) batch_cls_label = np.zeros((BATCH_SIZE,)).astype(np.int32) for batch_idx in range(num_batches): if batch_idx %20==0: log_string('%03d/%03d'%(batch_idx, num_batches)) start_idx = batch_idx * BATCH_SIZE end_idx = min(len(TEST_DATASET), (batch_idx+1) * BATCH_SIZE) cur_batch_size = end_idx-start_idx cur_batch_data, cur_batch_label, cur_batch_cls_label = get_batch(TEST_DATASET, test_idxs, start_idx, end_idx) if cur_batch_size == BATCH_SIZE: batch_data = cur_batch_data batch_label = cur_batch_label batch_cls_label = cur_batch_cls_label else: batch_data[0:cur_batch_size] = cur_batch_data batch_label[0:cur_batch_size] = cur_batch_label batch_cls_label[0:cur_batch_size] = cur_batch_cls_label # --------------------------------------------------------------------- feed_dict = {ops['pointclouds_pl']: batch_data, ops['labels_pl']: batch_label, ops['cls_labels_pl']: batch_cls_label, ops['is_training_pl']: is_training} summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'], ops['loss'], ops['pred']], feed_dict=feed_dict) test_writer.add_summary(summary, step) # --------------------------------------------------------------------- # Select valid data cur_pred_val = pred_val[0:cur_batch_size] # Constrain pred to the groundtruth classes (selected by seg_classes[cat]) cur_pred_val_logits = cur_pred_val cur_pred_val = np.zeros((cur_batch_size, NUM_POINT)).astype(np.int32) for i in range(cur_batch_size): cat = seg_label_to_cat[cur_batch_label[i,0]] logits = cur_pred_val_logits[i,:,:] cur_pred_val[i,:] = np.argmax(logits[:,seg_classes[cat]], 1) + seg_classes[cat][0] correct = np.sum(cur_pred_val == cur_batch_label) total_correct += correct total_seen += (cur_batch_size*NUM_POINT) if cur_batch_size==BATCH_SIZE: loss_sum += loss_val for l in range(NUM_CLASSES): total_seen_class[l] += np.sum(cur_batch_label==l) total_correct_class[l] += (np.sum((cur_pred_val==l) & (cur_batch_label==l))) for i in range(cur_batch_size): segp = cur_pred_val[i,:] segl = cur_batch_label[i,:] cat = seg_label_to_cat[segl[0]] part_ious = [0.0 for _ in range(len(seg_classes[cat]))] for l in seg_classes[cat]: if (np.sum(segl==l) == 0) and (np.sum(segp==l) == 0): # part is not present, no prediction as well part_ious[l-seg_classes[cat][0]] = 1.0 else: part_ious[l-seg_classes[cat][0]] = np.sum((segl==l) & (segp==l)) / float(np.sum((segl==l) | (segp==l))) shape_ious[cat].append(np.mean(part_ious)) all_shape_ious = [] for cat in shape_ious.keys(): for iou in shape_ious[cat]: all_shape_ious.append(iou) shape_ious[cat] = np.mean(shape_ious[cat]) mean_shape_ious = np.mean(shape_ious.values()) log_string('eval mean loss: %f' % (loss_sum / float(len(TEST_DATASET)/BATCH_SIZE))) log_string('eval accuracy: %f'% (total_correct / float(total_seen))) log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float)))) for cat in sorted(shape_ious.keys()): log_string('eval mIoU of %s:\t %f'%(cat, shape_ious[cat])) log_string('eval mean mIoU: %f' % (mean_shape_ious)) log_string('eval mean mIoU (all shapes): %f' % (np.mean(all_shape_ious))) EPOCH_CNT += 1 return total_correct/float(total_seen) if __name__ == "__main__": log_string('pid: %s'%(str(os.getpid()))) train() LOG_FOUT.close() ================================================ FILE: pointnet2_tf/scannet/README.md ================================================ ### ScanNet Data Original dataset website: http://www.scan-net.org/ You can get our preprocessed data at here (1.72GB) and refer to the code in `scannet_util.py` for data loading. Note that the virtual scan data is generated on the fly from our preprocessed data. Some code we used for scannet preprocessing is also included in `preprocessing` folder. You have to download the original ScanNet data and make small modifications in paths in order to run them. Note: To use ScanNetV2 data, change the tsv file to `scannetv2-labels.combined.tsv` and also update `scannet_util.py` to read the raw class and NYU40 names in the right columns (shifted by 1 compared to the V1 tsv). ================================================ FILE: pointnet2_tf/scannet/pc_util.py ================================================ """ Utility functions for processing point clouds. Author: Charles R. Qi, Hao Su Date: November 2016 """ import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) # Draw point cloud from eulerangles import euler2mat # Point cloud IO import numpy as np from plyfile import PlyData, PlyElement # ---------------------------------------- # Point Cloud/Volume Conversions # ---------------------------------------- def point_cloud_label_to_surface_voxel_label(point_cloud, label, res=0.0484): coordmax = np.max(point_cloud,axis=0) coordmin = np.min(point_cloud,axis=0) nvox = np.ceil((coordmax-coordmin)/res) vidx = np.ceil((point_cloud-coordmin)/res) vidx = vidx[:,0]+vidx[:,1]*nvox[0]+vidx[:,2]*nvox[0]*nvox[1] uvidx = np.unique(vidx) if label.ndim==1: uvlabel = [np.argmax(np.bincount(label[vidx==uv].astype(np.uint32))) for uv in uvidx] else: assert(label.ndim==2) uvlabel = np.zeros(len(uvidx),label.shape[1]) for i in range(label.shape[1]): uvlabel[:,i] = np.array([np.argmax(np.bincount(label[vidx==uv,i].astype(np.uint32))) for uv in uvidx]) return uvidx, uvlabel, nvox def point_cloud_label_to_surface_voxel_label_fast(point_cloud, label, res=0.0484): coordmax = np.max(point_cloud,axis=0) coordmin = np.min(point_cloud,axis=0) nvox = np.ceil((coordmax-coordmin)/res) vidx = np.ceil((point_cloud-coordmin)/res) vidx = vidx[:,0]+vidx[:,1]*nvox[0]+vidx[:,2]*nvox[0]*nvox[1] uvidx, vpidx = np.unique(vidx,return_index=True) if label.ndim==1: uvlabel = label[vpidx] else: assert(label.ndim==2) uvlabel = label[vpidx,:] return uvidx, uvlabel, nvox def point_cloud_to_volume_batch(point_clouds, vsize=12, radius=1.0, flatten=True): """ Input is BxNx3 batch of point cloud Output is Bx(vsize^3) """ vol_list = [] for b in range(point_clouds.shape[0]): vol = point_cloud_to_volume(np.squeeze(point_clouds[b,:,:]), vsize, radius) if flatten: vol_list.append(vol.flatten()) else: vol_list.append(np.expand_dims(np.expand_dims(vol, -1), 0)) if flatten: return np.vstack(vol_list) else: return np.concatenate(vol_list, 0) def point_cloud_to_volume(points, vsize, radius=1.0): """ input is Nx3 points. output is vsize*vsize*vsize assumes points are in range [-radius, radius] """ vol = np.zeros((vsize,vsize,vsize)) voxel = 2*radius/float(vsize) locations = (points + radius)/voxel locations = locations.astype(int) vol[locations[:,0],locations[:,1],locations[:,2]] = 1.0 return vol #a = np.zeros((16,1024,3)) #print point_cloud_to_volume_batch(a, 12, 1.0, False).shape def volume_to_point_cloud(vol): """ vol is occupancy grid (value = 0 or 1) of size vsize*vsize*vsize return Nx3 numpy array. """ vsize = vol.shape[0] assert(vol.shape[1] == vsize and vol.shape[1] == vsize) points = [] for a in range(vsize): for b in range(vsize): for c in range(vsize): if vol[a,b,c] == 1: points.append(np.array([a,b,c])) if len(points) == 0: return np.zeros((0,3)) points = np.vstack(points) return points def point_cloud_to_volume_v2_batch(point_clouds, vsize=12, radius=1.0, num_sample=128): """ Input is BxNx3 a batch of point cloud Output is BxVxVxVxnum_samplex3 Added on Feb 19 """ vol_list = [] for b in range(point_clouds.shape[0]): vol = point_cloud_to_volume_v2(point_clouds[b,:,:], vsize, radius, num_sample) vol_list.append(np.expand_dims(vol, 0)) return np.concatenate(vol_list, 0) def point_cloud_to_volume_v2(points, vsize, radius=1.0, num_sample=128): """ input is Nx3 points output is vsize*vsize*vsize*num_sample*3 assumes points are in range [-radius, radius] samples num_sample points in each voxel, if there are less than num_sample points, replicate the points Added on Feb 19 """ vol = np.zeros((vsize,vsize,vsize,num_sample,3)) voxel = 2*radius/float(vsize) locations = (points + radius)/voxel locations = locations.astype(int) loc2pc = {} for n in range(points.shape[0]): loc = tuple(locations[n,:]) if loc not in loc2pc: loc2pc[loc] = [] loc2pc[loc].append(points[n,:]) #print loc2pc for i in range(vsize): for j in range(vsize): for k in range(vsize): if (i,j,k) not in loc2pc: vol[i,j,k,:,:] = np.zeros((num_sample,3)) else: pc = loc2pc[(i,j,k)] # a list of (3,) arrays pc = np.vstack(pc) # kx3 # Sample/pad to num_sample points if pc.shape[0]>num_sample: choices = np.random.choice(pc.shape[0], num_sample, replace=False) pc = pc[choices,:] elif pc.shape[0]num_sample: choices = np.random.choice(pc.shape[0], num_sample, replace=False) pc = pc[choices,:] elif pc.shape[0] 0) dx = mask[:, 0] dy = mask[:, 1] dv = disk[disk > 0] # Order points by z-buffer zorder = np.argsort(points[:, 2]) points = points[zorder, :] points[:, 2] = (points[:, 2] - np.min(points[:, 2])) / (np.max(points[:, 2] - np.min(points[:, 2]))) max_depth = np.max(points[:, 2]) for i in range(points.shape[0]): j = points.shape[0] - i - 1 x = points[j, 0] y = points[j, 1] xc = canvasSize/2 + (x*space) yc = canvasSize/2 + (y*space) xc = int(np.round(xc)) yc = int(np.round(yc)) px = dx + xc py = dy + yc image[px, py] = image[px, py] * 0.7 + dv * (max_depth - points[j, 2]) * 0.3 image = image / np.max(image) return image def point_cloud_three_views(points): """ input points Nx3 numpy array (+y is up direction). return an numpy array gray image of size 500x1500. """ # +y is up direction # xrot is azimuth # yrot is in-plane # zrot is elevation img1 = draw_point_cloud(points, zrot=110/180.0*np.pi, xrot=45/180.0*np.pi, yrot=0/180.0*np.pi) img2 = draw_point_cloud(points, zrot=70/180.0*np.pi, xrot=135/180.0*np.pi, yrot=0/180.0*np.pi) img3 = draw_point_cloud(points, zrot=180.0/180.0*np.pi, xrot=90/180.0*np.pi, yrot=0/180.0*np.pi) image_large = np.concatenate([img1, img2, img3], 1) return image_large def point_cloud_three_views_demo(): """ Demo for draw_point_cloud function """ from PIL import Image points = read_ply('../third_party/mesh_sampling/piano.ply') im_array = point_cloud_three_views(points) img = Image.fromarray(np.uint8(im_array*255.0)) img.save('piano.jpg') if __name__=="__main__": point_cloud_three_views_demo() def pyplot_draw_point_cloud(points, output_filename): """ points is a Nx3 numpy array """ import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.scatter(points[:,0], points[:,1], points[:,2]) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') #savefig(output_filename) def pyplot_draw_volume(vol, output_filename): """ vol is of size vsize*vsize*vsize output an image to output_filename """ points = volume_to_point_cloud(vol) pyplot_draw_point_cloud(points, output_filename) def write_ply_color(points, labels, out_filename, num_classes=None): """ Color (N,3) points with labels (N) within range 0 ~ num_classes-1 as OBJ file """ import matplotlib.pyplot as pyplot labels = labels.astype(int) N = points.shape[0] if num_classes is None: num_classes = np.max(labels)+1 else: assert(num_classes>np.max(labels)) fout = open(out_filename, 'w') colors = [pyplot.cm.hsv(i/float(num_classes)) for i in range(num_classes)] for i in range(N): c = colors[labels[i]] c = [int(x*255) for x in c] fout.write('v %f %f %f %d %d %d\n' % (points[i,0],points[i,1],points[i,2],c[0],c[1],c[2])) fout.close() def write_ply_rgb(points, colors, out_filename, num_classes=None): """ Color (N,3) points with RGB colors (N,3) within range [0,255] as OBJ file """ colors = colors.astype(int) N = points.shape[0] fout = open(out_filename, 'w') for i in range(N): c = colors[i,:] fout.write('v %f %f %f %d %d %d\n' % (points[i,0],points[i,1],points[i,2],c[0],c[1],c[2])) fout.close() ================================================ FILE: pointnet2_tf/scannet/preprocessing/collect_scannet_scenes.py ================================================ import scannet_util CLASS_NAMES = scannet_util.g_label_names RAW2SCANNET = scannet_util.g_raw2scannet import os import json import sys import numpy as np BASE_DIR = os.path.dirname(__file__) sys.path.append(BASE_DIR) sys.path.append('../') import pc_util SCANNET_DIR = 'scannet_clean_2' SCENE_NAMES = [line.rstrip() for line in open('scannet_all.txt')] def collect_one_scene_data_label(scene_name, out_filename): # Over-segmented segments: maps from segment to vertex/point IDs data_folder = os.path.join(SCANNET_DIR, scene_name) mesh_seg_filename = os.path.join(data_folder, '%s_vh_clean_2.0.010000.segs.json'%(scene_name)) #print mesh_seg_filename with open(mesh_seg_filename) as jsondata: d = json.load(jsondata) seg = d['segIndices'] #print len(seg) segid_to_pointid = {} for i in range(len(seg)): if seg[i] not in segid_to_pointid: segid_to_pointid[seg[i]] = [] segid_to_pointid[seg[i]].append(i) # Raw points in XYZRGBA ply_filename = os.path.join(data_folder, '%s_vh_clean_2.ply' % (scene_name)) points = pc_util.read_ply_xyzrgb(ply_filename) log_string(str(points.shape)) # Instances over-segmented segment IDs: annotation on segments instance_segids = [] labels = [] annotation_filename = os.path.join(data_folder, '%s.aggregation.json'%(scene_name)) #print annotation_filename with open(annotation_filename) as jsondata: d = json.load(jsondata) for x in d['segGroups']: instance_segids.append(x['segments']) labels.append(x['label']) #print len(instance_segids) #print labels # Each instance's points instance_points_list = [] instance_labels_list = [] semantic_labels_list = [] for i in range(len(instance_segids)): segids = instance_segids[i] pointids = [] for segid in segids: pointids += segid_to_pointid[segid] instance_points = points[np.array(pointids),:] instance_points_list.append(instance_points) instance_labels_list.append(np.ones((instance_points.shape[0], 1))*i) if labels[i] not in RAW2SCANNET: label = 'unannotated' else: label = RAW2SCANNET[labels[i]] label = CLASS_NAMES.index(label) semantic_labels_list.append(np.ones((instance_points.shape[0], 1))*label) # Refactor data format scene_points = np.concatenate(instance_points_list, 0) scene_points = scene_points[:,0:6] # XYZRGB, disregarding the A instance_labels = np.concatenate(instance_labels_list, 0) semantic_labels = np.concatenate(semantic_labels_list, 0) data = np.concatenate((scene_points, instance_labels, semantic_labels), 1) np.save(out_filename, data) LOG_FOUT = open('log.txt','w') def log_string(out_str): LOG_FOUT.write(out_str+'\n') LOG_FOUT.flush() print(out_str) if __name__=='__main__': output_folder = 'scannet_scenes' if not os.path.exists(output_folder): os.mkdir(output_folder) for scene_name in SCENE_NAMES: log_string(scene_name) try: out_filename = scene_name+'.npy' # scene0000_00.npy collect_one_scene_data_label(scene_name, os.path.join(output_folder, out_filename)) except Exception, e: log_string(scene_name+'ERROR!!') log_string(str(e)) LOG_FOUT.close() ================================================ FILE: pointnet2_tf/scannet/preprocessing/demo.py ================================================ import sys import os BASE_DIR = os.path.dirname(__file__) sys.path.append(BASE_DIR) sys.path.append(os.path.join(BASE_DIR, '../')) import numpy as np import pc_util data = np.load('scannet_scenes/scene0001_01.npy') scene_points = data[:,0:3] colors = data[:,3:6] instance_labels = data[:,6] semantic_labels = data[:,7] output_folder = 'demo_output' if not os.path.exists(output_folder): os.mkdir(output_folder) # Write scene as OBJ file for visualization pc_util.write_ply_rgb(scene_points, colors, os.path.join(output_folder, 'scene.obj')) pc_util.write_ply_color(scene_points, instance_labels, os.path.join(output_folder, 'scene_instance.obj')) pc_util.write_ply_color(scene_points, semantic_labels, os.path.join(output_folder, 'scene_semantic.obj')) ================================================ FILE: pointnet2_tf/scannet/preprocessing/fetch_label_names.py ================================================ ''' scanning through annotation files for all the scenes to get a complete list of categories ''' import os import json scannet_dir = './scannet/' scene_names = [line.rstrip() for line in open('scannet_all.txt')] labels = set() for scene_name in scene_names: path = os.path.join(scannet_dir, scene_name) agg_filename = os.path.join(path, scene_name+'.aggregation.json') with open(agg_filename) as jsondata: d = json.load(jsondata) for x in d['segGroups']: labels.add(x['label']) fout = open('class_names.txt', 'w') for label in list(labels): print label try: fout.write(label+'\n') except: pass fout.close() ================================================ FILE: pointnet2_tf/scannet/preprocessing/scannet-labels.combined.tsv ================================================ category count nyuId nyu40id eigen13id nyuClass nyu40class eigen13class ModelNet40 ModelNet10 ShapeNetCore55 synsetoffset wnsynsetid wnsynsetkey wall 7274 21 1 12 wall wall Wall n04546855 wall.n.01 chair 5419 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 floor 3910 11 2 5 floor floor Floor n03365592 floor.n.01 table 2664 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 door 1400 28 8 12 door door Wall door n03221720 door.n.01 couch 1222 83 6 9 sofa sofa Sofa sofa sofa sofa 04256520 n04256520 sofa.n.01 cabinet 1106 3 3 6 cabinet cabinet Furniture cabinet 02933112 n02933112 cabinet.n.01 shelf 889 42 15 6 shelves shelves Furniture bookshelf bookshelf 02871439 n02871439 bookshelf.n.01 desk 862 36 14 10 desk desk Table desk desk table 04379243 n03179701 desk.n.01 office chair 837 5 5 4 chair chair Chair chair chair chair 03001627 n04373704 swivel_chair.n.01 bed 814 157 4 1 bed bed Bed bed bed bed 02818832 n02818832 bed.n.01 trashcan 688 12 39 6 garbage bin otherfurniture Furniture trash_bin 02747177 n02747177 ashcan.n.01 pillow 608 119 18 7 pillow pillow Objects pillow 03938244 n03938244 pillow.n.01 sink 504 24 34 7 sink sink Objects sink n04223580 sink.n.01 picture 467 64 11 8 picture picture Picture n03931044 picture.n.01 window 432 59 9 13 window window Window n04587648 window.n.01 toilet 402 124 33 7 toilet toilet Objects toilet toilet n04446276 toilet.n.01 bookshelf 400 88 10 6 bookshelf bookshelf Furniture bookshelf bookshelf 02871439 n02871439 bookshelf.n.01 monitor 395 49 40 7 monitor otherprop Objects monitor monitor tv or monitor 03211117 n03782190 monitor.n.04 computer 369 46 40 7 computer otherprop Objects n03082979 computer.n.01 curtain 356 89 16 13 curtain curtain Window curtain n03151077 curtain.n.01 book 335 1 23 2 book books Books n02870526 book.n.11 armchair 318 5 5 4 chair chair Chair chair chair chair 03001627 n02738535 armchair.n.01 coffee table 303 356 39 6 coffee table otherfurniture Furniture table table table 04379243 n03063968 coffee_table.n.01 drawer 290 174 39 6 drawer otherfurniture Furniture n03233905 drawer.n.01 box 283 26 29 7 box box Objects n02883344 box.n.01 refrigerator 269 17 24 6 refridgerator refridgerator Furniture n04070727 refrigerator.n.01 lamp 255 144 35 7 lamp lamp Objects lamp lamp 03636649 n03636649 lamp.n.02 kitchen cabinet 252 3 3 6 cabinet cabinet Furniture n02933112 cabinet.n.01 dining chair 242 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 towel 222 135 27 7 towel towel Objects n04459362 towel.n.01 clothes 214 141 21 7 clothes clothes Objects n02728440 apparel.n.01 tv 210 172 25 11 television television TV tv or monitor 03211117 n03211117 display.n.06 nightstand 206 158 32 6 night stand night stand Furniture night_stand night_stand n03015254 chest_of_drawers.n.01 counter 196 7 12 6 counter counter Furniture table table table 04379243 n03116530 counter.n.01 dresser 180 169 17 6 dresser dresser Furniture dresser dresser n03015254 chest_of_drawers.n.01 countertop 176 7 12 6 counter counter Furniture n03118245 countertop.n.01 stool 165 150 40 7 stool otherprop Objects stool n04326896 stool.n.01 cushion 141 119 18 7 pillow pillow Objects n03151500 cushion.n.03 plant 139 82 40 7 plant otherprop Objects plant n00017222 plant.n.02 ceiling 134 4 22 3 ceiling ceiling Ceiling n02990373 ceiling.n.01 bathtub 134 136 36 7 bathtub bathtub Objects bathtub bathtub tub 02808440 n02808440 bathtub.n.01 bedframe 132 157 4 1 bed bed Bed n02822579 bedstead.n.01 end table 125 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 dining table 123 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 keyboard 118 47 40 7 keyboard otherprop Objects keyboard computer keyboard 03085013 n03085013 computer_keyboard.n.01 bag 116 55 37 7 bag bag Objects suitcase 02773838 n02773838 bag.n.06 backpack 114 206 40 7 backpack otherprop Objects n02769748 backpack.n.01 toilet paper 113 139 40 7 toilet paper otherprop Objects n15075141 toilet_tissue.n.01 printer 111 66 40 7 printer otherprop Objects printer 04004475 n04004475 printer.n.03 tv stand 103 291 39 6 tv stand otherfurniture Furniture tv_stand n03290653 entertainment_center.n.01 whiteboard 102 45 30 7 whiteboard whiteboard Objects n03211616 display_panel.n.01 carpet 99 130 40 7 rug otherprop Objects n04118021 rug.n.01 blanket 99 312 40 7 blanket otherprop Objects n02849154 blanket.n.01 shower curtain 99 123 28 7 shower curtain shower curtain Objects curtain n04209239 shower_curtain.n.01 trash can 94 12 39 6 garbage bin otherfurniture Furniture trash_bin 02747177 n02747177 ashcan.n.01 closet 94 772 39 6 wardrobe otherfurniture Furniture wardrobe stair 89 215 38 7 stairs otherstructure Objects stairs n04314914 step.n.04 microwave 88 13 40 7 microwave otherprop Objects microwave 03761084 n03761084 microwave.n.02 washbasin 86 24 34 7 sink sink Objects sink n04553920 washbasin.n.01 rug 85 130 40 7 rug otherprop Objects n04118021 rug.n.01 stove 78 242 38 7 stove otherstructure Objects stove 04330267 n04330267 stove.n.02 shoe 68 149 40 7 shoe otherprop Objects n04199027 shoe.n.01 computer tower 68 46 40 7 computer otherprop Objects n03082979 computer.n.01 bottle 66 2 40 7 bottle otherprop Objects bottle bottle 02876657 n02876657 bottle.n.01 bin 64 307 40 7 bin otherprop Objects n02839910 bin.n.01 ottoman 63 359 39 6 ottoman otherfurniture Furniture stool n03380724 footstool.n.01 bench 63 204 39 6 bench otherfurniture Furniture bench bench 02828884 n02828884 bench.n.01 board 63 408 38 7 board otherstructure Objects washing machine 62 278 39 6 washing machine otherfurniture Furniture washing_machine 04554684 n04554684 washer.n.03 mirror 62 122 19 7 mirror mirror Objects n03773035 mirror.n.01 copier 61 40 7 otherprop Objects n03257586 duplicator.n.01 basket 60 39 40 7 basket otherprop Objects basket 02801938 n02801938 basket.n.01 sofa chair 59 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 file cabinet 54 3 3 6 cabinet cabinet Furniture cabinet 02933112 n02933112 cabinet.n.01 fan 52 74 40 7 fan otherprop Objects n03320046 fan.n.01 laptop 52 37 40 7 laptop otherprop Objects laptop laptop 03642806 n03642806 laptop.n.01 shower 49 38 7 otherstructure Objects n04208936 shower.n.01 paper 48 15 26 7 paper paper Objects n14974264 paper.n.01 person 48 331 31 7 person person Objects person n05217688 person.n.02 headboard 47 161 39 6 headboard otherfurniture Furniture n03502200 headboard.n.01 paper towel dispenser 47 14 40 7 paper towel dispenser otherprop Objects faucet 45 9 40 7 faucet otherprop Objects faucet 03325088 n03325088 faucet.n.01 oven 43 238 38 7 oven otherstructure Objects n03862676 oven.n.01 footstool 42 359 39 6 ottoman otherfurniture Furniture stool n03380724 footstool.n.01 blinds 42 80 13 13 blinds blinds Window n02851099 blind.n.03 rack 41 50 39 6 stand otherfurniture Furniture n04038440 rack.n.05 plate 39 233 40 7 plate otherprop Objects n03959485 plate.n.04 blackboard 38 225 38 7 blackboard otherstructure Objects n02846511 blackboard.n.01 piano 38 298 39 6 piano otherfurniture Furniture piano piano 03928116 n03928116 piano.n.01 heater 38 111 39 6 heater otherfurniture Furniture n03508101 heater.n.01 soap 37 133 40 7 soap otherprop Objects luggage 36 783 40 7 luggage otherprop Objects n02774630 baggage.n.01 computer desk 36 36 14 10 desk desk Table desk desk table 04379243 n03179701 desk.n.01 rail 36 497 38 7 railing otherstructure Objects radiator 36 236 39 6 radiator otherfurniture Furniture n04041069 radiator.n.02 recycle bin 35 307 40 7 bin otherprop Objects container 34 140 40 7 container otherprop Objects n03094503 container.n.01 wardrobe 34 772 39 6 wardrobe otherfurniture Furniture wardrobe n04550184 wardrobe.n.01 soap dispenser 32 40 7 otherprop Objects n04254120 soap_dispenser.n.01 telephone 32 32 40 7 telephone otherprop Objects telephone 04401088 n04401088 telephone.n.01 bucket 32 427 40 7 bucket otherprop Objects n02909870 bucket.n.01 clock 31 56 40 7 clock otherprop Objects clock 03046257 n03046257 clock.n.01 stand 29 50 39 6 stand otherfurniture Furniture table table table 04379243 n04301000 stand.n.04 light 27 62 38 7 light otherstructure Objects n03665366 light.n.02 laundry basket 27 164 40 7 laundry basket otherprop Objects basket 02801938 n03050864 clothes_hamper.n.01 pipe 27 41 40 7 pipe otherprop Objects round table 26 19 7 10 table table Table table table table 04379243 n04114554 round_table.n.02 roof 25 4 22 3 ceiling ceiling Ceiling n04105068 roof.n.01 clothes dryer 25 39 6 otherfurniture Furniture n03251766 dryer.n.01 coat 23 324 40 7 jacket otherprop Objects n03057021 coat.n.01 guitar 23 300 40 7 guitar otherprop Objects guitar guitar 03467517 n03467517 guitar.n.01 desk chair 23 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 sheet 22 559 40 7 sheet otherprop Objects toilet paper holder 22 647 40 7 toilet paper holder otherprop Objects seat 22 524 39 6 furniture otherfurniture Furniture n04161981 seat.n.03 step 21 38 7 otherstructure Objects n04314914 step.n.04 speaker 20 54 40 7 speaker otherprop Objects speaker 03691459 n03691459 loudspeaker.n.01 vending machine 19 220 40 7 machine otherprop Objects n04525305 vending_machine.n.01 column 19 94 38 7 column otherstructure Objects n03074380 column.n.06 bicycle 18 189 40 7 bicycle otherprop Objects bicycle 02834778 n02834778 bicycle.n.01 ladder 18 48 39 6 ladder otherfurniture Furniture stairs n03632277 ladder.n.01 cover 18 312 40 7 blanket otherprop Objects handle 18 758 40 7 handle otherprop Objects n03485997 handle.n.01 bathroom stall 18 38 7 otherstructure Objects n02873839 booth.n.02 foosball table 17 510 39 6 foosball table otherfurniture Furniture table table table 04379243 n04379243 table.n.02 table lamp 17 144 35 7 lamp lamp Objects lamp lamp 03636649 n04380533 table_lamp.n.01 shower wall 17 21 1 12 wall wall Wall chest 17 344 39 6 chest otherfurniture Furniture dresser dresser cup 17 35 40 7 cup otherprop Objects cup cup or mug 03797390 n03797390 mug.n.04 jacket 16 324 40 7 jacket otherprop Objects n03589791 jacket.n.01 storage bin 16 812 40 7 storage bin otherprop Objects screen 16 40 7 otherprop Objects coffee maker 16 40 7 otherprop Objects n03063338 coffee_maker.n.01 hamper 15 39 40 7 basket otherprop Objects basket 02801938 n03482405 hamper.n.02 dishwasher 15 8 38 7 dishwasher otherstructure Objects dishwasher 03207941 n03207941 dishwasher.n.01 window frame 15 477 38 7 window frame otherstructure Objects n04589593 window_frame.n.01 paper towel 15 113 40 7 paper towel otherprop Objects n03887697 paper_towel.n.01 machine 15 220 40 7 machine otherprop Objects n03699975 machine.n.01 mat 15 143 20 5 floor mat floor mat Floor n03727837 mat.n.01 windowsill 14 38 7 otherstructure Objects n04590263 windowsill.n.01 tap 14 9 40 7 faucet otherprop Objects faucet 03325088 n04559451 water_faucet.n.01 pool table 14 515 39 6 pool table otherfurniture Furniture table table table 04379243 n03982430 pool_table.n.01 hand dryer 14 40 7 otherprop Objects bar 14 51 38 7 bar otherstructure Objects n02788689 bar.n.03 frame 14 38 7 otherstructure Objects rolling chair 14 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 toaster 14 251 40 7 toaster otherprop Objects n04442312 toaster.n.02 wall frame 14 38 7 otherstructure Objects hanger 13 211 40 7 hanger otherprop Objects n03490884 hanger.n.02 conference table 13 19 7 10 table table Table table table table 04379243 n03090000 conference_table.n.01 handrail 13 453 38 7 banister otherstructure Objects n02788148 bannister.n.02 treadmill 13 458 39 6 treadmill otherfurniture Furniture n04477387 treadmill.n.01 bulletin board 13 408 38 7 board otherstructure Objects ironing board 13 313 39 6 ironing board otherfurniture Furniture n03586090 ironing_board.n.01 fireplace 12 372 38 7 fireplace otherstructure Objects n03346455 fireplace.n.01 soap dish 12 638 40 7 soap dish otherprop Objects n04254009 soap_dish.n.01 fabric 12 40 7 otherprop Objects n03309808 fabric.n.01 kitchen counter 12 7 12 6 counter counter Furniture table table table 04379243 n03116530 counter.n.01 glass 12 612 38 7 glass otherstructure Objects n03438257 glass.n.02 doorframe 11 615 38 7 door frame otherstructure Objects n03222722 doorframe.n.01 table cushion 11 40 7 otherprop Objects toilet paper dispenser 11 40 7 otherprop Objects slab 11 38 7 otherstructure Objects n04233405 slab.n.01 mini fridge 11 17 24 6 refridgerator refridgerator Furniture n03273913 electric_refrigerator.n.01 fire extinguisher 11 10 40 7 fire extinguisher otherprop Objects n03345837 fire_extinguisher.n.01 shampoo 11 40 7 otherprop Objects ball 11 60 40 7 ball otherprop Objects hat 11 193 40 7 hat otherprop Objects n03497657 hat.n.01 shower curtain rod 11 40 7 otherprop Objects junk 11 40 7 otherprop Objects n14857897 debris.n.01 soap holder 10 506 40 7 soap holder otherprop Objects staircase 10 215 38 7 stairs otherstructure Objects n04298308 stairway.n.01 toiletry 10 40 7 otherprop Objects n04447443 toiletry.n.01 stall door 10 28 8 12 door door Wall door framed picture 10 64 11 8 picture picture Picture water cooler 10 509 39 6 water cooler otherfurniture Furniture n04559166 water_cooler.n.01 bags 10 40 7 otherprop Objects suitcase 02773838 n02773838 bag.n.06 desk lamp 10 144 35 7 lamp lamp Objects lamp lamp 03636649 n03636649 lamp.n.02 paper cutter 10 108 40 7 paper cutter otherprop Objects n03886940 paper_cutter.n.01 led tv 9 40 7 otherprop Objects switch 9 40 7 otherprop Objects n04372370 switch.n.01 bed sheet 9 559 40 7 sheet otherprop Objects n04188179 sheet.n.03 roof frame 9 38 7 otherstructure Objects tray 9 179 40 7 tray otherprop Objects n04476259 tray.n.01 comforter 9 484 40 7 comforter otherprop Objects n04033995 quilt.n.01 air conditioner 9 79 38 7 air conditioner otherstructure Objects n02686379 air_conditioner.n.01 shower door 9 28 8 12 door door Wall door shirt 9 40 7 otherprop Objects n04197391 shirt.n.01 swivel chair 9 5 5 4 chair chair Chair chair chair chair 03001627 n04373704 swivel_chair.n.01 pillar 9 94 38 7 column otherstructure Objects n03073977 column.n.07 detergent 9 40 7 otherprop Objects ledge 9 38 7 otherstructure Objects n09337253 ledge.n.01 vase 8 78 40 7 vase otherprop Objects vase jar 03593526 n04522168 vase.n.01 toaster oven 8 275 40 7 toaster oven otherprop Objects n04442441 toaster_oven.n.01 bedpost 8 40 7 otherprop Objects n02821415 bedpost.n.01 food 8 40 7 otherprop Objects n00021265 food.n.01 picture frame 8 40 7 otherprop Objects n03931765 picture_frame.n.01 poltrone 8 40 7 otherprop Objects study table 8 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 office table 8 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 mouse 8 103 40 7 mouse otherprop Objects n03793489 mouse.n.04 storage 8 n03744276 memory.n.04 nerf gun 8 40 7 otherprop Objects table chair 8 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 night table 8 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 computer chair 8 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 toilet seat liner dispenser 8 40 7 otherprop Objects backrest 7 5 5 4 chair chair Chair n02767433 back.n.08 chair seat 7 40 7 otherprop Objects sink cabinet 7 3 3 6 cabinet cabinet Furniture cabinet 02933112 n02933112 cabinet.n.01 can 7 329 40 7 can otherprop Objects can 02946921 n02946921 can.n.01 furniture 7 524 39 6 furniture otherfurniture Furniture n03405725 furniture.n.01 cart 7 305 40 7 cart otherprop Objects n03484083 handcart.n.01 stool chair 7 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 step stool 7 276 40 7 step stool otherprop Objects stool n04315713 step_stool.n.01 robe 7 40 7 otherprop Objects n04097866 robe.n.01 table stand 7 50 39 6 stand otherfurniture Furniture stall 7 38 7 otherstructure Objects n02873839 booth.n.02 dispenser 7 40 7 otherprop Objects n03210683 dispenser.n.01 storage container 7 140 40 7 container otherprop Objects side table 7 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 partition 7 21 1 12 wall wall Wall n03894379 partition.n.01 appliance 7 40 7 otherprop Objects lotion 7 40 7 otherprop Objects n03690938 lotion.n.01 pot 7 16 40 7 pot otherprop Objects photo 7 508 40 7 photo otherprop Objects n03925226 photograph.n.01 toilet brush 7 630 40 7 toilet brush otherprop Objects scale 7 639 40 7 scale otherprop Objects n04141975 scale.n.07 tissue box 7 138 40 7 tissue box otherprop Objects remote 7 40 7 otherprop Objects remote_control 04074963 n04074963 remote_control.n.01 light switch 6 301 38 7 light switch otherstructure Objects crate 6 183 39 6 crate otherfurniture Furniture n03127925 crate.n.01 ping pong table 6 625 39 6 ping pong table otherfurniture Furniture table table table 04379243 n04379243 table.n.02 platform 6 38 7 otherstructure Objects pantry 6 38 7 otherstructure Objects n03885535 pantry.n.01 bath cabinet 6 3 3 6 cabinet cabinet Furniture cabinet 02933112 n02933112 cabinet.n.01 slipper 6 40 7 otherprop Objects n04241394 slipper.n.01 sideboard 6 7 12 6 counter counter Furniture holder 6 40 7 otherprop Objects n03525454 holder.n.01 worktop 6 40 7 otherprop Objects outlet 6 40 7 otherprop Objects n04548771 wall_socket.n.01 gas cooker 6 242 38 7 stove otherstructure Objects n03425595 gas_range.n.01 doorhandle 6 652 40 7 knob otherprop Objects n03222959 doorknob.n.01 cutting board 6 247 40 7 cutting board otherprop Objects n03025513 chopping_board.n.01 bathroom sink 6 24 34 7 sink sink Objects sink n04223580 sink.n.01 controller 6 40 7 otherprop Objects n03096960 control.n.09 bedding set 6 40 7 otherprop Objects mount 6 40 7 otherprop Objects decoration 6 40 7 otherprop Objects n03169390 decoration.n.01 tablet 6 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 reading table 6 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 floor covered 6 40 7 otherprop Objects cooker 6 267 40 7 utensil otherprop Objects n03101156 cooker.n.01 file 6 75 40 7 file otherprop Objects filing_cabinet 03337140 n03337140 file.n.03 archive 6 40 7 otherprop Objects n02735086 archive.n.01 trolley 5 504 40 7 trolley otherprop Objects n04335435 streetcar.n.01 wainscoting 5 40 7 otherprop Objects lampshade 5 859 40 7 lamp shade otherprop Objects n03637318 lampshade.n.01 china 5 267 40 7 utensil otherprop Objects n03018209 china.n.02 sign 5 208 40 7 sign otherprop Objects n04217882 signboard.n.01 fax machine 5 68 40 7 fax machine otherprop Objects mirror frame 5 40 7 otherprop Objects projector 5 90 40 7 projector otherprop Objects n04009552 projector.n.02 sweater 5 40 7 otherprop Objects n04370048 sweater.n.01 paint can 5 329 40 7 can otherprop Objects can 02946921 n02946921 can.n.01 heat register 5 40 7 otherprop Objects kitchen table 5 19 7 10 table table Table table table table 04379243 n03620967 kitchen_table.n.01 globe 5 347 40 7 globe otherprop Objects toy 5 389 40 7 toy otherprop Objects n03964744 plaything.n.01 kitchen worktop 5 40 7 otherprop Objects paper roll 5 40 7 otherprop Objects meeting table 5 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 vaze 5 40 7 otherprop Objects wall clock 5 56 40 7 clock otherprop Objects clock 03046257 n04548280 wall_clock.n.01 closet door 5 28 8 12 door door Wall door pack 5 40 7 otherprop Objects doormat 5 143 20 5 floor mat floor mat Floor n03223299 doormat.n.02 tissue 5 648 40 7 tissue otherprop Objects plastic container 5 140 40 7 container otherprop Objects statue 5 294 40 7 sculpture otherprop Objects n04306847 statue.n.01 dollhouse 5 486 39 6 doll house otherfurniture Furniture n03219483 dollhouse.n.01 vacuum 5 40 7 otherprop Objects n04517823 vacuum.n.04 wet floor sign 5 208 40 7 sign otherprop Objects vanity 5 169 17 6 dresser dresser Furniture dresser dresser table 04379243 n03238586 dressing_table.n.01 candle 5 137 40 7 candle otherprop Objects lamp n02948072 candle.n.01 library desk 5 36 14 10 desk desk Table desk desk carton box 5 26 29 7 box box Objects easel 5 50 39 6 stand otherfurniture Furniture n03262809 easel.n.01 wall lamp 5 144 35 7 lamp lamp Objects lamp lamp 03636649 n03636649 lamp.n.02 wall hanging 4 40 7 otherprop Objects n03491178 hanging.n.01 face wash 4 40 7 otherprop Objects corner 4 40 7 otherprop Objects lounge chair 4 5 5 4 chair chair Chair chair chair chair 03001627 n03262932 easy_chair.n.01 beanbag 4 797 39 6 bean bag otherfurniture Furniture n02816656 beanbag.n.01 marker holder 4 40 7 otherprop Objects dumbell 4 40 7 otherprop Objects ping pong paddle 4 40 7 otherprop Objects locker 4 3 3 6 cabinet cabinet Furniture plunger 4 40 7 otherprop Objects n03970156 plunger.n.03 soap bar 4 51 38 7 bar otherstructure Objects student chair 4 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 office object 4 40 7 otherprop Objects stuffed animal 4 177 40 7 stuffed animal otherprop Objects water fountain 4 339 38 7 water fountain otherstructure Objects n03241335 drinking_fountain.n.01 doorknob 4 27 40 7 door knob otherprop Objects n03222959 doorknob.n.01 footrest 4 163 39 6 foot rest otherfurniture Furniture stool n03380724 footstool.n.01 ac unit 4 40 7 otherprop Objects safe 4 26 29 7 box box Objects n04125021 safe.n.01 tile 4 40 7 otherprop Objects n04435180 tile.n.01 easle 4 40 7 otherprop Objects headphone 4 160 40 7 headphones otherprop Objects headphone 03261776 n03261776 earphone.n.01 dress 4 40 7 otherprop Objects n03236735 dress.n.01 rolling cart 4 305 40 7 cart otherprop Objects chest of drawers 4 524 39 6 furniture otherfurniture Furniture dresser dresser n03015254 chest_of_drawers.n.01 plastic bin 4 307 40 7 bin otherprop Objects pail 4 427 40 7 bucket otherprop Objects n02909870 bucket.n.01 dry erase board 4 408 38 7 board otherstructure Objects coatrack 4 40 7 otherprop Objects n03059103 coatrack.n.01 recliner 4 5 5 4 chair chair Chair chair chair chair 03001627 n04062428 recliner.n.01 roomba 4 40 7 otherprop Objects highchair 4 5 5 4 chair chair Chair chair chair chair 03001627 n03518445 highchair.n.01 dish rack 4 581 40 7 dish rack otherprop Objects n03207630 dish_rack.n.01 dartboard 4 408 38 7 board otherstructure Objects n03162940 dartboard.n.01 broom 4 328 40 7 broom otherprop Objects n02906734 broom.n.01 book rack 4 224 39 6 bookrack otherfurniture Furniture eraser 4 100 40 7 eraser otherprop Objects n03294833 eraser.n.01 bath mat 4 40 7 otherprop Objects n02807401 bath_mat.n.01 textile 4 40 7 otherprop Objects n03309808 fabric.n.01 paper box 4 26 29 7 box box Objects guitar case 4 771 40 7 guitar case otherprop Objects mop 4 40 7 otherprop Objects n04367480 swab.n.02 lavatory 4 40 7 otherprop Objects toilet toilet server 4 360 40 7 server otherprop Objects paper towel holder 4 281 40 7 paper towel holder otherprop Objects office supply 4 40 7 otherprop Objects panel 4 408 38 7 board otherstructure Objects toilet brush holder 4 40 7 otherprop Objects magazine 4 71 40 7 magazine otherprop Objects n06595351 magazine.n.01 kitchen rack 4 50 39 6 stand otherfurniture Furniture n04038440 rack.n.05 table object 4 40 7 otherprop Objects range hood 4 380 38 7 range hood otherstructure Objects range_hood n04053677 range_hood.n.01 bath 4 136 36 7 bathtub bathtub Objects bathtub bathtub tub 02808440 n02808440 bathtub.n.01 trim 4 40 7 otherprop Objects n04484160 trimming.n.02 scanner 4 40 7 otherprop Objects bathrobe 4 40 7 otherprop Objects n02807616 bathrobe.n.01 door and post 4 40 7 otherprop Objects pouff 4 40 7 otherprop Objects spring curtain 3 89 16 13 curtain curtain Window curtain recycle 3 40 7 otherprop Objects fax 3 40 7 otherprop Objects n03316105 facsimile.n.02 rolling shelf 3 40 7 otherprop Objects flat-screen television 3 172 25 11 television television TV futon 3 576 39 6 mattress otherfurniture Furniture n03408444 futon.n.01 stack of chairs 3 40 7 otherprop Objects dustpan 3 40 7 otherprop Objects n03259009 dustpan.n.02 hand towel 3 135 27 7 towel towel Objects n03490006 hand_towel.n.01 floor lamp 3 144 35 7 lamp lamp Objects lamp lamp 03636649 n03367059 floor_lamp.n.01 mainboard 3 40 7 otherprop Objects kitchen shelf 3 42 15 6 shelves shelves Furniture bookshelf bookshelf 02871439 n02871439 bookshelf.n.01 organizer 3 40 7 otherprop Objects n03918737 personal_digital_assistant.n.01 freezer 3 17 24 6 refridgerator refridgerator Furniture n03170635 deep-freeze.n.01 furnace 3 551 39 6 furnace otherfurniture Furniture n03404449 furnace.n.01 stock 3 40 7 otherprop Objects map 3 107 40 7 map otherprop Objects n03720163 map.n.01 helmet 3 40 7 otherprop Objects helmet 03513137 n03513137 helmet.n.02 wallpaper 3 38 7 otherstructure Objects wall cabinet 3 3 3 6 cabinet cabinet Furniture cabinet 02933112 n02933112 cabinet.n.01 office equipment 3 40 7 otherprop Objects hair dryer 3 577 40 7 hair dryer otherprop Objects n03483316 hand_blower.n.01 backsplash 3 40 7 otherprop Objects exercise ball 3 60 40 7 ball otherprop Objects jeremiah 3 40 7 otherprop Objects n11082842 jeremiah.n.01 flush 3 40 7 otherprop Objects fridge just 3 40 7 otherprop Objects folded clothes 3 141 21 7 clothes clothes Objects window counter 3 7 12 6 counter counter Furniture iron 3 40 7 otherprop Objects n03584829 iron.n.04 studio light 3 62 38 7 light otherstructure Objects sconce 3 62 38 7 light otherstructure Objects n04148703 sconce.n.03 sofa set 3 40 7 otherprop Objects baseboard 3 40 7 otherprop Objects n02800354 baseboard.n.01 sink counter 3 7 12 6 counter counter Furniture kitchen slab 3 38 7 otherstructure Objects n04233405 slab.n.01 cabinet door 3 28 8 12 door door Wall door exercise machine 3 220 40 7 machine otherprop Objects wood 3 40 7 otherprop Objects teatowels 3 40 7 otherprop Objects workbench 3 204 39 6 bench otherfurniture Furniture bench table 04379243 n04600486 workbench.n.01 backwall 3 40 7 otherprop Objects cubby 3 40 7 otherprop Objects n03144365 cubby.n.01 water bottle 3 2 40 7 bottle otherprop Objects bottle bottle 02876657 n04557648 water_bottle.n.01 kitchen sink 3 24 34 7 sink sink Objects sink n03620889 kitchen_sink.n.01 sink area 3 40 7 otherprop Objects handicap bar 3 51 38 7 bar otherstructure Objects painter 3 594 40 7 cat otherprop Objects n02125311 cougar.n.01 tank 3 40 7 otherprop Objects washstand 3 524 39 6 furniture otherfurniture Furniture n04555400 washstand.n.01 purse 3 181 40 7 purse otherprop Objects n02774152 bag.n.04 surface 3 40 7 otherprop Objects n02688443 airfoil.n.01 towel rack 3 40 7 otherprop Objects n04459773 towel_rack.n.01 decor 3 40 7 otherprop Objects n03579355 interior_decoration.n.01 handwash 3 40 7 otherprop Objects bar stool 3 150 40 7 stool otherprop Objects stool pan 3 589 40 7 pan otherprop Objects n03880531 pan.n.01 air propeller 3 40 7 otherprop Objects paneling 3 21 1 12 wall wall Wall n03882611 paneling.n.01 vent 3 40 7 otherprop Objects kitchen junk 3 40 7 otherprop Objects n14857897 debris.n.01 piano bench 3 460 39 6 piano bench otherfurniture Furniture bench bench 02828884 n02828884 bench.n.01 bunk bed 3 804 39 6 bunk bed otherfurniture Furniture bed bed bed 02818832 n02920259 bunk_bed.n.01 bed lamp 3 144 35 7 lamp lamp Objects lamp lamp 03636649 n03636649 lamp.n.02 shower mat 3 40 7 otherprop Objects bedding 3 40 7 otherprop Objects n02820210 bedclothes.n.01 shoe rack 3 614 40 7 shoe rack otherprop Objects notice board 3 408 38 7 board otherstructure Objects n02916538 bulletin_board.n.02 shower floor 3 11 2 5 floor floor Floor brush 3 40 7 otherprop Objects n02908217 brush.n.02 table football 3 166 40 7 football otherprop Objects padded bench 3 204 39 6 bench otherfurniture Furniture bench bench 02828884 n02828884 bench.n.01 bathroom carpet 3 130 40 7 rug otherprop Objects n04118021 rug.n.01 showerhead 3 650 40 7 shower head otherprop Objects n04209383 showerhead.n.01 loft 3 40 7 otherprop Objects chair w/table 3 40 7 otherprop Objects bedhead 3 40 7 otherprop Objects roll 3 40 7 otherprop Objects n04101375 roll.n.04 comidin 3 40 7 otherprop Objects cardboard box 3 26 29 7 box box Objects cushion stool 2 150 40 7 stool otherprop Objects stool bed cabinet 2 3 3 6 cabinet cabinet Furniture cabinet 02933112 n02933112 cabinet.n.01 pile of clothes 2 141 21 7 clothes clothes Objects case 2 851 40 7 case otherprop Objects slep 2 40 7 otherprop Objects swiffer 2 40 7 otherprop Objects stapler 2 67 40 7 stapler otherprop Objects n04303497 stapler.n.01 cable 2 450 40 7 cables otherprop Objects work desk 2 36 14 10 desk desk Table desk desk floor carpet 2 40 7 otherprop Objects bedside 2 40 7 otherprop Objects n08649711 bedside.n.01 trash bag 2 55 37 7 bag bag Objects heating devce 2 40 7 otherprop Objects sofa table 2 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 ventilator 2 40 7 otherprop Objects n04526964 ventilator.n.01 cat 2 594 40 7 cat otherprop Objects kitchen utensil 2 267 40 7 utensil otherprop Objects n03621049 kitchen_utensil.n.01 counter top for sink 2 24 34 7 sink sink Objects sink bathroom frame 2 38 7 otherstructure Objects banister 2 453 38 7 banister otherstructure Objects n02788148 bannister.n.02 tuvalet kağıdı 2 40 7 otherprop Objects trunk 2 40 7 otherprop Objects blank screen 2 40 7 otherprop Objects tire 2 40 7 otherprop Objects n04440749 tire.n.01 screen curtain 2 89 16 13 curtain curtain Window curtain cooking range 2 40 7 otherprop Objects dressware 2 40 7 otherprop Objects blow up matress 2 40 7 otherprop Objects shred bin 2 307 40 7 bin otherprop Objects air matress 2 40 7 otherprop Objects folder 2 69 40 7 folder otherprop Objects n03376279 folder.n.02 room heater 2 111 39 6 heater otherfurniture Furniture car 2 530 40 7 car otherprop Objects car car 02958343 n02958343 car.n.01 massage armchair 2 40 7 otherprop Objects wardrobe door 2 28 8 12 door door Wall door coffee supply 2 40 7 otherprop Objects tissue holder 2 40 7 otherprop Objects tab 2 40 7 otherprop Objects knickknack 2 40 7 otherprop Objects indoor plant 2 82 40 7 plant otherprop Objects plant notebook 2 210 40 7 notebook otherprop Objects n03832673 notebook.n.02 water dispenser 2 507 40 7 water dispenser otherprop Objects n03210683 dispenser.n.01 cleaning supply 2 40 7 otherprop Objects library table 2 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 bin cover 2 40 7 otherprop Objects teakettle 2 243 40 7 tea kettle otherprop Objects n04397768 teakettle.n.01 reservoir 2 263 40 7 vessel otherprop Objects n04078574 reservoir.n.03 arc sofa 2 83 6 9 sofa sofa Sofa sofa sofa sofa 04256520 n04256520 sofa.n.01 bedside lamp 2 144 35 7 lamp lamp Objects lamp lamp 03636649 n03636649 lamp.n.02 picture window 2 59 9 13 window window Window n03932080 picture_window.n.01 medicine cabinet 2 3 3 6 cabinet cabinet Furniture cabinet 02933112 n03742115 medicine_chest.n.01 cosmetic bag 2 55 37 7 bag bag Objects coffee 2 40 7 otherprop Objects brush holder 2 40 7 otherprop Objects duvet 2 40 7 otherprop Objects n03266749 eiderdown.n.01 flower stand 2 50 39 6 stand otherfurniture Furniture bedcover 2 40 7 otherprop Objects n02822220 bedspread.n.01 side 2 40 7 otherprop Objects n09437454 slope.n.01 weight 2 40 7 otherprop Objects n04571292 weight.n.02 pitcher 2 273 40 7 pitcher otherprop Objects n03950228 pitcher.n.02 schoolbag 2 55 37 7 bag bag Objects n04146343 schoolbag.n.01 wall picture 2 64 11 8 picture picture Picture metal handrail 2 40 7 otherprop Objects icebox 2 17 24 6 refridgerator refridgerator Furniture n04070727 refrigerator.n.01 exercise equipment 2 457 39 6 excercise equipment otherfurniture Furniture loft bed 2 157 4 1 bed bed Bed bed bed bed 02818832 n02818832 bed.n.01 tennis table 2 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 bookbag 2 40 7 otherprop Objects scarf 2 240 40 7 scarf otherprop Objects n04143897 scarf.n.01 cabinet drawer 2 174 39 6 drawer otherfurniture Furniture double door 2 28 8 12 door door Wall door n03226880 double_door.n.01 books in a book shelf 2 40 7 otherprop Objects entertainment stand 2 50 39 6 stand otherfurniture Furniture potty 2 40 7 otherprop Objects n03004275 chamberpot.n.01 john 2 40 7 otherprop Objects toilet toilet n04446276 toilet.n.01 desk accessory 2 40 7 otherprop Objects chair support 2 40 7 otherprop Objects shopping bag 2 55 37 7 bag bag Objects n04204081 shopping_bag.n.01 tree 2 82 40 7 plant otherprop Objects plant n13104059 tree.n.01 dustpan broom 2 328 40 7 broom otherprop Objects stall wall 2 21 1 12 wall wall Wall tường 2 40 7 otherprop Objects bedside drawer 2 174 39 6 drawer otherfurniture Furniture chaise 2 5 5 4 chair chair Chair chair chair chair 03001627 n03002711 chaise_longue.n.01 curtain rod 2 582 38 7 curtain rod otherstructure Objects boardgame 2 40 7 otherprop Objects cccvurtains 2 40 7 otherprop Objects device 2 40 7 otherprop Objects n03183080 device.n.01 armrest 2 40 7 otherprop Objects n02741475 armrest.n.01 alarm 2 525 40 7 alarm otherprop Objects clock 03046257 n02694662 alarm_clock.n.01 towel rail 2 51 38 7 bar otherstructure Objects n04459909 towel_rail.n.01 sliding wood doors 2 40 7 otherprop Objects backbag 2 40 7 otherprop Objects bath curtain 2 89 16 13 curtain curtain Window curtain washcloth 2 40 7 otherprop Objects n04554523 washcloth.n.01 bean bag chair 2 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 toolbox 2 344 39 6 chest otherfurniture Furniture n04452615 toolbox.n.01 rack shelf 2 40 7 otherprop Objects kleenex box 2 26 29 7 box box Objects air freshner 2 40 7 otherprop Objects sewing machine 2 890 40 7 sewing machine otherprop Objects n04179913 sewing_machine.n.01 washing and drying machine 2 220 40 7 machine otherprop Objects hairbrush 2 120 40 7 hair brush otherprop Objects n03475581 hairbrush.n.01 lap desk 2 36 14 10 desk desk Table desk desk hutch 2 40 7 otherprop Objects stack of flat items/possibly cardboard 2 40 7 otherprop Objects under-bed drawer 2 174 39 6 drawer otherfurniture Furniture bleach 2 40 7 otherprop Objects comidine 2 40 7 otherprop Objects bathmat 2 40 7 otherprop Objects cinder block 2 40 7 otherprop Objects n03031957 cinder_block.n.01 mailbox 2 26 29 7 box box Objects mailbox 03710193 n03710193 mailbox.n.01 writing board 2 408 38 7 board otherstructure Objects n04608127 writing_board.n.01 flooring 2 11 2 5 floor floor Floor n03365592 floor.n.01 wall of safety boxes 2 40 7 otherprop Objects vessel 2 263 40 7 vessel otherprop Objects watercraft 04530566 n04530566 vessel.n.02 cube sofa 2 83 6 9 sofa sofa Sofa sofa sofa sofa 04256520 n04256520 sofa.n.01 toothpaste 2 128 40 7 toothpaste otherprop Objects liquid dispencer 2 40 7 otherprop Objects folding chair 2 5 5 4 chair chair Chair chair chair chair 03001627 n03376595 folding_chair.n.01 squash 2 717 40 7 squash otherprop Objects plant n12158798 squash.n.01 grille 2 700 38 7 grill otherstructure Objects centerpiece 2 878 40 7 centerpiece otherprop Objects n02994419 centerpiece.n.02 wall folder 2 69 40 7 folder otherprop Objects towel hanger 2 211 40 7 hanger otherprop Objects n03490884 hanger.n.02 toilet pot 2 16 40 7 pot otherprop Objects aid 2 40 7 otherprop Objects rope 2 560 40 7 rope otherprop Objects n04108268 rope.n.01 envelop rack 2 40 7 otherprop Objects tissue roll 2 764 40 7 tissue roll otherprop Objects rostrum 2 40 7 otherprop Objects n03159640 dais.n.01 owen 2 40 7 otherprop Objects electric panel 2 40 7 otherprop Objects bowl 2 22 40 7 bowl otherprop Objects bowl bowl 02880940 n02880940 bowl.n.03 boiler 2 40 7 otherprop Objects tile wall 2 21 1 12 wall wall Wall reflection 2 64 11 8 picture picture Picture n04068976 reflection.n.05 crib 2 485 39 6 crib otherfurniture Furniture shelves of stuff 2 40 7 otherprop Objects kitchen gadget 2 40 7 otherprop Objects n02729965 appliance.n.01 sliding door 2 28 8 12 door door Wall door n04239074 sliding_door.n.01 paper bag 2 55 37 7 bag bag Objects n04122825 sack.n.01 water heater 2 588 40 7 water heater otherprop Objects n04560113 water_heater.n.01 alarm clock 2 156 40 7 alarm clock otherprop Objects clock 03046257 n02694662 alarm_clock.n.01 chair rail 2 40 7 otherprop Objects corkboard 2 34 40 7 cork board otherprop Objects n14823376 corkboard.n.01 sàn nhà 2 40 7 otherprop Objects conformer 2 40 7 otherprop Objects easy chair 2 5 5 4 chair chair Chair chair chair chair 03001627 n03262932 easy_chair.n.01 sehpa 2 40 7 otherprop Objects library 2 40 7 otherprop Objects bench seat 2 40 7 otherprop Objects music stand 2 820 39 6 music stand otherfurniture Furniture n03801760 music_stand.n.01 office 2 40 7 otherprop Objects n03841666 office.n.01 clutter 2 40 7 otherprop Objects flush for toilet 2 124 33 7 toilet toilet Objects toilet toilet kleenex 2 40 7 otherprop Objects box/storage 2 40 7 otherprop Objects post 2 40 7 otherprop Objects n03988170 post.n.04 plug 2 40 7 otherprop Objects socket 2 40 7 otherprop Objects n04255163 socket.n.02 island 2 456 38 7 kitchen island otherstructure Objects instrument case 2 851 40 7 case otherprop Objects paper tray 2 538 40 7 paper tray otherprop Objects toilet paper package 2 40 7 otherprop Objects antibacterial soap dispenser 2 40 7 otherprop Objects cubicle 2 40 7 otherprop Objects vaccuum 2 40 7 otherprop Objects photography light 2 62 38 7 light otherstructure Objects trump wall 2 21 1 12 wall wall Wall shredder 2 40 7 otherprop Objects n04210120 shredder.n.01 square table 2 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 table support 2 40 7 otherprop Objects sink plumbing 2 40 7 otherprop Objects basin 2 40 7 otherprop Objects pinball machine 2 220 40 7 machine otherprop Objects n03941417 pinball_machine.n.01 meeting 2 40 7 otherprop Objects n08542634 confluence.n.01 mobile 2 40 7 otherprop Objects hatrack 2 40 7 otherprop Objects n03059103 coatrack.n.01 johnny 2 331 31 7 person person Objects person n10628368 rebel.n.01 projector screen 2 53 38 7 projector screen otherstructure Objects valance 2 40 7 otherprop Objects n03111296 cornice.n.01 eliptical 2 40 7 otherprop Objects boot 2 40 7 otherprop Objects window ledge 2 40 7 otherprop Objects clothes hamper 2 39 40 7 basket otherprop Objects basket 02801938 n03050864 clothes_hamper.n.01 wall side 2 40 7 otherprop Objects stairstepper 2 40 7 otherprop Objects bottle recycling bin 2 307 40 7 bin otherprop Objects blend 2 40 7 otherprop Objects tissue dispenser 1 40 7 otherprop Objects entertainment center 1 524 39 6 furniture otherfurniture Furniture n03290653 entertainment_center.n.01 kettle 1 16 40 7 pot otherprop Objects n03612814 kettle.n.01 laundyr supply 1 40 7 otherprop Objects chain 1 40 7 otherprop Objects gown 1 40 7 otherprop Objects w.c. 1 40 7 otherprop Objects toilet toilet n04558478 water_closet.n.01 shelf unit 1 40 7 otherprop Objects walss 1 40 7 otherprop Objects mirror reflection 1 40 7 otherprop Objects bed curtain 1 89 16 13 curtain curtain Window curtain papertray 1 538 40 7 paper tray otherprop Objects tissue paper 1 15 26 7 paper paper Objects milk 1 40 7 otherprop Objects leg rest sofa 1 83 6 9 sofa sofa Sofa sofa sofa sofa 04256520 n04256520 sofa.n.01 double decker 1 40 7 otherprop Objects dekw 1 40 7 otherprop Objects sink counter top 1 40 7 otherprop Objects breadbox 1 40 7 otherprop Objects n02893692 bread-bin.n.01 floor mat 1 143 20 5 floor mat floor mat Floor n03727837 mat.n.01 electrical machine 1 220 40 7 machine otherprop Objects student seat 1 40 7 otherprop Objects privacy partition 1 40 7 otherprop Objects chairlegs 1 40 7 otherprop Objects mail tray 1 618 40 7 mail tray otherprop Objects over the door storage 1 40 7 otherprop Objects door hinge 1 40 7 otherprop Objects cahi 1 40 7 otherprop Objects excercise cycle 1 40 7 otherprop Objects handsoap 1 40 7 otherprop Objects glass door 1 28 8 12 door door Wall door dustbin box 1 26 29 7 box box Objects toy ship 1 40 7 otherprop Objects storage rack 1 448 39 6 storage rack otherfurniture Furniture wall outside room 1 40 7 otherprop Objects ask tray 1 179 40 7 tray otherprop Objects punching bag 1 55 37 7 bag bag Objects n04023962 punching_bag.n.02 storage drawer 1 174 39 6 drawer otherfurniture Furniture cat litter box 1 26 29 7 box box Objects shower rod 1 40 7 otherprop Objects office desk 1 36 14 10 desk desk Table desk desk water filter 1 731 40 7 water filter otherprop Objects n04559620 water_filter.n.01 nicknack 1 40 7 otherprop Objects n02897692 bric-a-brac.n.01 tin of drink 1 40 7 otherprop Objects work 1 40 7 otherprop Objects lustre 1 40 7 otherprop Objects paper shredder 1 40 7 otherprop Objects awllll 1 40 7 otherprop Objects booth 1 19 7 10 table table Table table table table 04379243 n02874214 booth.n.01 folded blanket 1 312 40 7 blanket otherprop Objects bed skirt 1 40 7 otherprop Objects computer speaker 1 54 40 7 speaker otherprop Objects speaker 03691459 n03691459 loudspeaker.n.01 desktop computer 1 46 40 7 computer otherprop Objects n03180011 desktop_computer.n.01 hadnwash 1 40 7 otherprop Objects covered box 1 26 29 7 box box Objects lampbase 1 40 7 otherprop Objects net 1 40 7 otherprop Objects wall pane 1 40 7 otherprop Objects dressing gown 1 40 7 otherprop Objects n03237992 dressing_gown.n.01 footstowindow 2ol 1 40 7 otherprop Objects display/signs 1 40 7 otherprop Objects arifact 1 40 7 otherprop Objects leg 1 40 7 otherprop Objects ceiling fan 1 74 40 7 fan otherprop Objects circular sofa 1 83 6 9 sofa sofa Sofa sofa sofa sofa 04256520 n04256520 sofa.n.01 food/drink 1 40 7 otherprop Objects chair cushion 1 40 7 otherprop Objects water 1 40 7 otherprop Objects n04562658 water_system.n.02 sinl 1 40 7 otherprop Objects sing 1 40 7 otherprop Objects cardboard 1 40 7 otherprop Objects plastic sliding drawers 1 40 7 otherprop Objects celltech 1 40 7 otherprop Objects sink tap 1 40 7 otherprop Objects binder 1 399 40 7 binder otherprop Objects toilet plumbing 1 40 7 otherprop Objects marble 1 60 40 7 ball otherprop Objects n03721047 marble.n.02 table speaker 1 54 40 7 speaker otherprop Objects speaker 03691459 n03691459 loudspeaker.n.01 talbetop 1 40 7 otherprop Objects gym cycle 1 40 7 otherprop Objects table piece 1 40 7 otherprop Objects counter panel 1 40 7 otherprop Objects plug/outlet 1 40 7 otherprop Objects till 1 26 29 7 box box Objects n02976939 cashbox.n.01 sink unit 1 40 7 otherprop Objects urinary 1 40 7 otherprop Objects ball chair 1 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 of shelf 1 40 7 otherprop Objects shower rack 1 614 40 7 shoe rack otherprop Objects full bed 1 157 4 1 bed bed Bed bed bed bed 02818832 n02818832 bed.n.01 sink cupboard 1 40 7 otherprop Objects house shoe 1 149 40 7 shoe otherprop Objects cha 1 40 7 otherprop Objects beachball 1 60 40 7 ball otherprop Objects n02814224 beach_ball.n.01 game table 1 429 40 7 game table otherprop Objects table table table 04379243 n04379243 table.n.02 tiny table 1 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 feminine hygiene waste basket 1 39 40 7 basket otherprop Objects basket 02801938 n02801938 basket.n.01 dicplay case 1 851 40 7 case otherprop Objects end table chair 1 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 cardbox 1 40 7 otherprop Objects envelope 1 476 40 7 envelope otherprop Objects n03291819 envelope.n.01 resevoir 1 40 7 otherprop Objects rabbit 1 40 7 otherprop Objects n02324045 rabbit.n.01 board meeting table 1 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 hold 1 758 40 7 handle otherprop Objects laser printer 1 66 40 7 printer otherprop Objects printer 04004475 n03643737 laser_printer.n.01 glasses case 1 851 40 7 case otherprop Objects n03438863 glasses_case.n.01 thermos 1 693 40 7 flask otherprop Objects bottle bottle 02876657 n04422727 thermos.n.01 shelving unit 1 40 7 otherprop Objects bath towel 1 135 27 7 towel towel Objects n02808304 bath_towel.n.01 monitor stand 1 50 39 6 stand otherfurniture Furniture breakfast bar 1 51 38 7 bar otherstructure Objects flat screen television 1 172 25 11 television television TV dressin table 1 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 dress rack 1 50 39 6 stand otherfurniture Furniture n03238762 dress_rack.n.01 plates of food 1 40 7 otherprop Objects figure 1 40 7 otherprop Objects credenza 1 7 12 6 counter counter Furniture n03129753 credenza.n.01 oejcts 1 40 7 otherprop Objects sinktop 1 40 7 otherprop Objects wall.table 1 40 7 otherprop Objects penholder 1 464 40 7 pen holder otherprop Objects wall panel 1 21 1 12 wall wall Wall n04548503 wall_panel.n.01 stair landing 1 40 7 otherprop Objects wallpapere 1 40 7 otherprop Objects water dispencer 1 507 40 7 water dispenser otherprop Objects therostat 1 110 40 7 thermostat otherprop Objects frying pan 1 318 40 7 frying pan otherprop Objects n03400231 frying_pan.n.01 separator 1 40 7 otherprop Objects n02995998 centrifuge.n.01 divider 1 40 7 otherprop Objects ceiling lamp 1 144 35 7 lamp lamp Objects lamp lamp 03636649 n03636649 lamp.n.02 rod 1 40 7 otherprop Objects pistol 03948459 n03427202 gat.n.01 ;amps 1 40 7 otherprop Objects vaccum 1 40 7 otherprop Objects chair b' 1 40 7 otherprop Objects paint bucket 1 427 40 7 bucket otherprop Objects lighting 1 40 7 otherprop Objects n03667235 lighting.n.02 tablecloth 1 292 40 7 tablecloth otherprop Objects n04380143 tablecloth.n.01 plank 1 408 38 7 board otherstructure Objects n15101854 board.n.02 sink pipe 1 41 40 7 pipe otherprop Objects concrete slab 1 40 7 otherprop Objects mini couch 1 83 6 9 sofa sofa Sofa sofa sofa sofa 04256520 n04256520 sofa.n.01 post it 1 40 7 otherprop Objects stair down 1 40 7 otherprop Objects spout 1 41 40 7 pipe otherprop Objects n04287153 spout.n.01 cutlery 1 40 7 otherprop Objects n03154073 cutter.n.06 magazine rack 1 50 39 6 stand otherfurniture Furniture n03704549 magazine_rack.n.01 mini printer 1 66 40 7 printer otherprop Objects printer 04004475 n04004475 printer.n.03 tray box 1 26 29 7 box box Objects level 2 stair case 1 851 40 7 case otherprop Objects industrial machine 1 220 40 7 machine otherprop Objects giường 1 40 7 otherprop Objects induction cooktop 1 40 7 otherprop Objects artwork 1 40 7 otherprop Objects pillowcase 1 851 40 7 case otherprop Objects n02975412 case.n.19 window wall 1 21 1 12 wall wall Wall minibar 1 7 12 6 counter counter Furniture n03769610 minibar.n.01 laundry detergent 1 40 7 otherprop Objects serving plate 1 233 40 7 plate otherprop Objects dirty basket 1 39 40 7 basket otherprop Objects basket 02801938 n02801938 basket.n.01 reading lamp 1 144 35 7 lamp lamp Objects lamp lamp 03636649 n04057981 reading_lamp.n.01 open cabinet 1 3 3 6 cabinet cabinet Furniture cabinet 02933112 n02933112 cabinet.n.01 hand truck 1 305 40 7 cart otherprop Objects n03490119 hand_truck.n.01 pad 1 40 7 otherprop Objects n03195485 diggings.n.02 laptop table 1 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 ventilator window 1 59 9 13 window window Window esll 1 40 7 otherprop Objects sofa bed 1 157 4 1 bed bed Bed bed bed bed 02818832 n02818832 bed.n.01 safa 1 40 7 otherprop Objects heating system 1 40 7 otherprop Objects n03509025 heating_system.n.01 floor]floo 1 40 7 otherprop Objects facial scrub 1 40 7 otherprop Objects vent hood 1 40 7 otherprop Objects office cupboard 1 40 7 otherprop Objects stand fan 1 74 40 7 fan otherprop Objects storage shelf 1 40 7 otherprop Objects hanging clothes 1 141 21 7 clothes clothes Objects fuse box 1 26 29 7 box box Objects pizza 1 40 7 otherprop Objects personal effect 1 40 7 otherprop Objects drawer organizer 1 40 7 otherprop Objects main board 1 408 38 7 board otherstructure Objects loofa 1 40 7 otherprop Objects shower surround 1 40 7 otherprop Objects bycicle 1 40 7 otherprop Objects all-in-one computer 1 46 40 7 computer otherprop Objects box of tissue 1 648 40 7 tissue otherprop Objects doorlock 1 646 40 7 door lock otherprop Objects n03223162 doorlock.n.01 base unit 1 40 7 otherprop Objects tennis ball 1 60 40 7 ball otherprop Objects n04409515 tennis_ball.n.01 snack machine 1 220 40 7 machine otherprop Objects laptop bag 1 55 37 7 bag bag Objects hallway wall 1 21 1 12 wall wall Wall msic 1 40 7 otherprop Objects file organizer 1 40 7 otherprop Objects fire hose 1 40 7 otherprop Objects n03346004 fire_hose.n.01 media center 1 40 7 otherprop Objects umbrella 1 203 40 7 umbrella otherprop Objects n04507155 umbrella.n.01 barrier 1 40 7 otherprop Objects n02796623 barrier.n.01 dirt 1 40 7 otherprop Objects subwoofer 1 54 40 7 speaker otherprop Objects speaker 03691459 n04349401 subwoofer.n.01 table tennis 1 40 7 otherprop Objects printer/scanner 1 40 7 otherprop Objects drying rack 1 262 39 6 drying rack otherfurniture Furniture wppd [ame;owood paneling 1 40 7 otherprop Objects toilet robe 1 40 7 otherprop Objects printer stand 1 50 39 6 stand otherfurniture Furniture shower screen 1 40 7 otherprop Objects water bed 1 157 4 1 bed bed Bed bed bed bed 02818832 n04557522 water_bed.n.01 display sign 1 208 40 7 sign otherprop Objects diaper bin 1 307 40 7 bin otherprop Objects router 1 303 40 7 router otherprop Objects ashtray 1 377 40 7 ashtray otherprop Objects n02747802 ashtray.n.01 footrest/table 1 40 7 otherprop Objects cleaning brush 1 40 7 otherprop Objects bathroom desk 1 36 14 10 desk desk Table desk desk table 04379243 n03179701 desk.n.01 toilet commode 1 40 7 otherprop Objects staicase handrail 1 40 7 otherprop Objects clothes shelf 1 40 7 otherprop Objects dink 1 40 7 otherprop Objects toilet seat protectors 1 40 7 otherprop Objects stuffware 1 40 7 otherprop Objects low table for storage 1 40 7 otherprop Objects covered stuff 1 40 7 otherprop Objects hood 1 40 7 otherprop Objects floor reflection 1 40 7 otherprop Objects can opener 1 279 40 7 can opener otherprop Objects n02951585 can_opener.n.01 top 1 40 7 otherprop Objects wheely bin 1 307 40 7 bin otherprop Objects book bag 1 55 37 7 bag bag Objects n02870676 book_bag.n.01 body wash 1 40 7 otherprop Objects study chair 1 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 stepladder 1 48 39 6 ladder otherfurniture Furniture stairs n04315599 step_ladder.n.01 paper holder 1 470 40 7 paper holder otherprop Objects white 1 40 7 otherprop Objects yoga ball 1 60 40 7 ball otherprop Objects consol 1 40 7 otherprop Objects cloths container 1 140 40 7 container otherprop Objects shorts 1 192 40 7 shorts otherprop Objects n04204755 short_circuit.n.01 emergency exit windows 1 40 7 otherprop Objects gym 1 40 7 otherprop Objects n03472112 gymnasium.n.02 box of tissues 1 40 7 otherprop Objects trolly 1 221 39 6 trolly otherfurniture Furniture water purifyer 1 93 40 7 water purifier otherprop Objects clutter' 1 40 7 otherprop Objects wallend 1 40 7 otherprop Objects tissue `paper 1 40 7 otherprop Objects mop and bucket 1 427 40 7 bucket otherprop Objects grocery 1 40 7 otherprop Objects n03461385 grocery_store.n.01 worktable 1 19 7 10 table table Table table table table 04379243 n04603729 worktable.n.01 air outlet 1 40 7 otherprop Objects fold 1 97 40 7 pen otherprop Objects n03376159 fold.n.06 toilet commoed 1 40 7 otherprop Objects handfold 1 40 7 otherprop Objects business chair 1 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 llong table chair 1 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 books in a shelf 1 40 7 otherprop Objects chaurs 1 40 7 otherprop Objects tennis table stand 1 50 39 6 stand otherfurniture Furniture kitchen water purifier 1 93 40 7 water purifier otherprop Objects lid 1 533 40 7 lid otherprop Objects electric hob 1 40 7 otherprop Objects baggage 1 783 40 7 luggage otherprop Objects cpnter 1 40 7 otherprop Objects stairs (exit) 1 40 7 otherprop Objects equipment 1 40 7 otherprop Objects n03294048 equipment.n.01 rocking chair 1 5 5 4 chair chair Chair chair chair chair 03001627 n04099969 rocking_chair.n.01 bunkbed 1 804 39 6 bunk bed otherfurniture Furniture divan 1 83 6 9 sofa sofa Sofa sofa sofa sofa 04256520 n03214966 divan.n.01 bottle of hand sanitizer 1 40 7 otherprop Objects sales stall 1 40 7 otherprop Objects dehumidifer 1 40 7 otherprop Objects wall cover 1 40 7 otherprop Objects plumbing 1 40 7 otherprop Objects n03969041 plumbing.n.01 elliptical machine 1 220 40 7 machine otherprop Objects playball 1 40 7 otherprop Objects safety bar 1 51 38 7 bar otherstructure Objects stereo 1 84 40 7 stereo otherprop Objects n04315948 stereo.n.01 havlu 1 40 7 otherprop Objects handbag 1 40 7 otherprop Objects n02774152 bag.n.04 furnance 1 551 39 6 furnace otherfurniture Furniture shleves 1 40 7 otherprop Objects coat hanger 1 400 40 7 coat hanger otherprop Objects n03057920 coat_hanger.n.01 erar wall 1 21 1 12 wall wall Wall bed bench 1 204 39 6 bench otherfurniture Furniture bench bench 02828884 n02828884 bench.n.01 tissu 1 648 40 7 tissue otherprop Objects plastic tub 1 232 40 7 plastic tub otherprop Objects bathtub bathtub tub 02808440 n02808440 bathtub.n.01 potholder 1 40 7 otherprop Objects n03992115 potholder.n.01 coffee mug 1 263 40 7 vessel otherprop Objects cup or mug 03797390 n03063599 coffee_mug.n.01 tennis rcket bag 1 55 37 7 bag bag Objects stand lamp 1 144 35 7 lamp lamp Objects lamp lamp 03636649 n03636649 lamp.n.02 bed/sofa 1 40 7 otherprop Objects laundry supply 1 40 7 otherprop Objects document shredder 1 40 7 otherprop Objects gas-range stove 1 242 38 7 stove otherstructure Objects stove 04330267 n04330267 stove.n.02 table soccer 1 40 7 otherprop Objects cookingrange 1 40 7 otherprop Objects bookbags 1 40 7 otherprop Objects downstairs 1 40 7 otherprop Objects coffeepot 1 893 40 7 coffee pot otherprop Objects n03063689 coffeepot.n.01 jar 1 70 40 7 jar otherprop Objects jar 03593526 n03593526 jar.n.01 rear wall 1 21 1 12 wall wall Wall part of chair 1 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 tape 1 109 40 7 tape otherprop Objects drawer table 1 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 woodenrack 1 40 7 otherprop Objects toilet floor 1 11 2 5 floor floor Floor cushioned seating 1 40 7 otherprop Objects bulls eye 1 40 7 otherprop Objects soft mat 1 40 7 otherprop Objects snack box 1 26 29 7 box box Objects desk organizer 1 40 7 otherprop Objects footboard 1 559 40 7 sheet otherprop Objects n03379461 footboard.n.02 wall hook 1 40 7 otherprop Objects chopping board 1 408 38 7 board otherstructure Objects n03025513 chopping_board.n.01 round picture 1 64 11 8 picture picture Picture chimney 1 702 38 7 chimney otherstructure Objects n03017428 chimney.n.01 studio screen 1 40 7 otherprop Objects personal belonging 1 40 7 otherprop Objects roll of paper 1 15 26 7 paper paper Objects gaming wheel 1 40 7 otherprop Objects landlord 1 331 31 7 person person Objects person n10245236 landlord.n.01 ebd 1 40 7 otherprop Objects heater radiator 1 236 39 6 radiator otherfurniture Furniture cabinet above 1 40 7 otherprop Objects weighted plate 1 233 40 7 plate otherprop Objects travelling bag 1 55 37 7 bag bag Objects suitcase 02773838 n02773838 bag.n.06 desk material 1 40 7 otherprop Objects door wall 1 21 1 12 wall wall Wall traffic cone 1 6 40 7 cone otherprop Objects cone computer mouse 1 103 40 7 mouse otherprop Objects n03793489 mouse.n.04 coathanger 1 400 40 7 coat hanger otherprop Objects bureau 1 524 39 6 furniture otherfurniture Furniture dresser dresser n03015254 chest_of_drawers.n.01 tyre 1 40 7 otherprop Objects n04440749 tire.n.01 armchairchair 1 40 7 otherprop Objects oven range 1 40 7 otherprop Objects pants 1 40 7 otherprop Objects n04489008 trouser.n.01 chiropractic chair 1 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 keg 1 343 39 6 barrel otherfurniture Furniture n03610418 keg.n.02 spray 1 40 7 otherprop Objects n02754103 atomizer.n.01 paper trimmer 1 40 7 otherprop Objects standing whiteboard 1 45 30 7 whiteboard whiteboard Objects desk drawer 1 475 39 6 desk drawer otherfurniture Furniture window/windowed door 1 28 8 12 door door Wall door soapbox 1 671 40 7 soap box otherprop Objects pillow sofa 1 83 6 9 sofa sofa Sofa sofa sofa sofa 04256520 n04256520 sofa.n.01 centre table 1 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 doorway 1 609 38 7 door way otherstructure Objects door n03224032 doorway.n.01 wall and whiteboard 1 45 30 7 whiteboard whiteboard Objects laptop computer 1 46 40 7 computer otherprop Objects laptop laptop 03642806 n03642806 laptop.n.01 scanner/copier 1 40 7 otherprop Objects suitcase w/clothes 1 40 7 otherprop Objects power pusher 1 40 7 otherprop Objects shower faucet handle 1 758 40 7 handle otherprop Objects walk 1 40 7 otherprop Objects n04544979 walk.n.05 matte 1 40 7 otherprop Objects atm machine 1 220 40 7 machine otherprop Objects garage door 1 850 38 7 garage door otherstructure Objects door wals 1 40 7 otherprop Objects cabinet aisle 1 40 7 otherprop Objects table light 1 62 38 7 light otherstructure Objects guillotine paper trimmer 1 40 7 otherprop Objects round 2\ 1 40 7 otherprop Objects teddy 1 40 7 otherprop Objects n03013580 chemise.n.01 white board/divider 1 40 7 otherprop Objects white wall 1 21 1 12 wall wall Wall mark 1 40 7 otherprop Objects n04681387 crisscross.n.01 partition wall 1 21 1 12 wall wall Wall shag rug 1 130 40 7 rug otherprop Objects n04183217 shag_rug.n.01 upstair way 1 40 7 otherprop Objects music stand' 1 820 39 6 music stand otherfurniture Furniture recamier 1 40 7 otherprop Objects venthole 1 40 7 otherprop Objects n04526241 vent.n.01 dining seat 1 40 7 otherprop Objects toilet cover 1 40 7 otherprop Objects personal item 1 40 7 otherprop Objects tallboy 1 524 39 6 furniture otherfurniture Furniture dresser dresser n03518305 highboy.n.01 drawers unit 1 40 7 otherprop Objects teapot 1 678 40 7 tea pot otherprop Objects n04398044 teapot.n.01 cook cabinet 1 3 3 6 cabinet cabinet Furniture cabinet 02933112 n02933112 cabinet.n.01 wok pan 1 589 40 7 pan otherprop Objects tv tray 1 179 40 7 tray otherprop Objects round chair 1 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 sawhorse 1 40 7 otherprop Objects n04140631 sawhorse.n.01 kitchen range 1 242 38 7 stove otherstructure Objects n04330340 stove.n.01 busdrver 1 40 7 otherprop Objects barricade 1 40 7 otherprop Objects wall ornament 1 40 7 otherprop Objects color printer 1 66 40 7 printer otherprop Objects printer 04004475 n04004475 printer.n.03 sticker 1 725 40 7 sticker otherprop Objects n07272545 gummed_label.n.01 exit sign 1 86 40 7 exit sign otherprop Objects gas stove 1 242 38 7 stove otherstructure Objects stove 04330267 n04330267 stove.n.02 venta hood 1 40 7 otherprop Objects copier/printer 1 40 7 otherprop Objects wall-mounted lamp 1 144 35 7 lamp lamp Objects lamp lamp 03636649 n03636649 lamp.n.02 item box 1 26 29 7 box box Objects water puifyer 1 40 7 otherprop Objects wall papper 1 40 7 otherprop Objects salt and peper 1 737 40 7 salt and pepper otherprop Objects printer four 1 40 7 otherprop Objects towel fastener 1 40 7 otherprop Objects basth 1 40 7 otherprop Objects flipflops 1 40 7 otherprop Objects bonus 1 40 7 otherprop Objects kitchen box 1 26 29 7 box box Objects n02883344 box.n.01 central heating unit 1 40 7 otherprop Objects hanging tubelight 1 40 7 otherprop Objects soccer ball 1 837 40 7 soccer ball otherprop Objects n04254680 soccer_ball.n.01 almarah 1 40 7 otherprop Objects canopy 1 40 7 otherprop Objects med box 1 26 29 7 box box Objects drain 1 567 38 7 drain otherstructure Objects panelling 1 21 1 12 wall wall Wall n03882611 paneling.n.01 bed stand 1 50 39 6 stand otherfurniture Furniture deal 1 408 38 7 board otherstructure Objects n15102622 deal.n.04 massage 1 40 7 otherprop Objects safety rail 1 497 38 7 railing otherstructure Objects n04127395 safety_rail.n.01 vacuumer 1 40 7 otherprop Objects binfl 1 40 7 otherprop Objects lightbulb 1 566 40 7 light bulb otherprop Objects lamp n03665924 light_bulb.n.01 door hydraulic 1 40 7 otherprop Objects induction cook top 1 40 7 otherprop Objects bedstand 1 40 7 otherprop Objects calander 1 40 7 otherprop Objects set of seats 1 40 7 otherprop Objects chocolate bar dispenser 1 40 7 otherprop Objects wall unit tv 1 40 7 otherprop Objects broomstick 1 328 40 7 broom otherprop Objects n02907082 broomstick.n.01 bath faucet 1 9 40 7 faucet otherprop Objects faucet 03325088 n03325088 faucet.n.01 folded cloth 1 40 7 otherprop Objects supply 1 40 7 otherprop Objects under oven drawer 1 174 39 6 drawer otherfurniture Furniture kinect 1 823 40 7 kinect otherprop Objects cash 1 40 7 otherprop Objects n10886222 cash.n.03 dining side wall 1 21 1 12 wall wall Wall log 1 40 7 otherprop Objects n03686658 log.n.05 garden gnome 1 40 7 otherprop Objects coucnb 1 40 7 otherprop Objects dart 1 40 7 otherprop Objects n03162818 dart.n.01 dust pan and brush 1 40 7 otherprop Objects smoke alarm 1 525 40 7 alarm otherprop Objects n03343737 fire_alarm.n.02 kitchen top 1 40 7 otherprop Objects toilet flush 1 40 7 otherprop Objects cooler 1 17 24 6 refridgerator refridgerator Furniture n03102654 cooler.n.01 kitchen island 1 456 38 7 kitchen island otherstructure Objects n03620600 kitchen_island.n.01 balcony 1 40 7 otherprop Objects escape door 1 28 8 12 door door Wall door hammer 1 883 40 7 hammer otherprop Objects n03481172 hammer.n.02 wall and paiting 1 40 7 otherprop Objects kitch shelf 1 40 7 otherprop Objects handwasher 1 40 7 otherprop Objects vanity top 1 40 7 otherprop Objects bodyboard 1 40 7 otherprop Objects messenger bag 1 55 37 7 bag bag Objects stationary bike 1 40 7 otherprop Objects cabinet countertop 1 40 7 otherprop Objects ping pong padle 1 40 7 otherprop Objects teapoy 1 40 7 otherprop Objects clothes basket 1 39 40 7 basket otherprop Objects basket 02801938 n03050864 clothes_hamper.n.01 xbox 1 628 40 7 xbox otherprop Objects xbox both 1 40 7 otherprop Objects foosball 1 40 7 otherprop Objects soad stand 1 50 39 6 stand otherfurniture Furniture prop 1 40 7 otherprop Objects n02692086 airplane_propeller.n.01 buddha 1 40 7 otherprop Objects reflection in a mirror 1 122 19 7 mirror mirror Objects bar stol 1 40 7 otherprop Objects oven/stove 1 40 7 otherprop Objects patterned rug 1 130 40 7 rug otherprop Objects window panel 1 40 7 otherprop Objects vault 1 40 7 otherprop Objects dust bin cover 1 40 7 otherprop Objects throw 1 872 40 7 throw otherprop Objects n04429971 throw.n.04 painting and frame 1 40 7 otherprop Objects covered piano 1 298 39 6 piano otherfurniture Furniture piano piano 03928116 n03928116 piano.n.01 drawer unit 1 40 7 otherprop Objects aircon 1 40 7 otherprop Objects package 1 40 7 otherprop Objects n03871083 package.n.02 gas vent 1 40 7 otherprop Objects block 1 40 7 otherprop Objects cloth container 1 140 40 7 container otherprop Objects additional printer 1 66 40 7 printer otherprop Objects printer 04004475 n04004475 printer.n.03 danger sign 1 208 40 7 sign otherprop Objects game machine 1 220 40 7 machine otherprop Objects light fixture 1 40 7 otherprop Objects utility 1 40 7 otherprop Objects n04516874 utility.n.06 base rack 1 40 7 otherprop Objects staircase landing 1 40 7 otherprop Objects szll 1 40 7 otherprop Objects piano note 1 40 7 otherprop Objects bboks 1 40 7 otherprop Objects cabord 1 40 7 otherprop Objects central table 1 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 splash 1 40 7 otherprop Objects n04682319 splash.n.04 suit 1 40 7 otherprop Objects n04350905 suit.n.01 cook top 1 40 7 otherprop Objects jug 1 687 40 7 jug otherprop Objects bottle bottle 02876657 n03603722 jug.n.01 stepstool 1 276 40 7 step stool otherprop Objects tripod 1 50 39 6 stand otherfurniture Furniture n04485082 tripod.n.01 cover box 1 26 29 7 box box Objects baby crib 1 485 39 6 crib otherfurniture Furniture air condisnor 1 40 7 otherprop Objects water softner 1 40 7 otherprop Objects chandelier 1 342 38 7 chandelier otherstructure Objects n03005285 chandelier.n.01 floor patterning 1 40 7 otherprop Objects tablet top 1 40 7 otherprop Objects smoke detector 1 40 7 otherprop Objects baseball cap 1 40 7 otherprop Objects cap 02954340 n02799323 baseball_cap.n.01 tissue roll holder 1 40 7 otherprop Objects case of water 1 40 7 otherprop Objects wall-organizer 1 40 7 otherprop Objects piece 1 40 7 otherprop Objects n03343853 firearm.n.01 wheelbarrel 1 40 7 otherprop Objects desktop item 1 40 7 otherprop Objects tv showcase 1 40 7 otherprop Objects chelves 1 40 7 otherprop Objects toothbrush 1 127 40 7 toothbrush otherprop Objects n04453156 toothbrush.n.01 chiffonière 1 40 7 otherprop Objects leg towel 1 135 27 7 towel towel Objects flowers/decorations 1 40 7 otherprop Objects snake toy 1 389 40 7 toy otherprop Objects cabinet's side 1 40 7 otherprop Objects bedroom chair 1 5 5 4 chair chair Chair chair chair chair 03001627 n03001627 chair.n.01 drum 1 145 40 7 drum otherprop Objects n03249569 drum.n.01 liquid soap 1 133 40 7 soap otherprop Objects set of bedding 1 40 7 otherprop Objects night lamp 1 144 35 7 lamp lamp Objects lamp lamp 03636649 n03636649 lamp.n.02 post board 1 408 38 7 board otherstructure Objects measuring cup 1 730 40 7 measuring cup otherprop Objects cup n03733805 measuring_cup.n.01 baseboard heater 1 111 39 6 heater otherfurniture Furniture paper shelf 1 40 7 otherprop Objects alert sheet 1 559 40 7 sheet otherprop Objects duster 1 115 40 7 duster otherprop Objects n03258330 dustcloth.n.01 snooker table 1 19 7 10 table table Table table table table 04379243 n03982430 pool_table.n.01 leg rest 1 40 7 otherprop Objects wall storage 1 40 7 otherprop Objects office board 1 408 38 7 board otherstructure Objects bathroom counter 1 7 12 6 counter counter Furniture table table table 04379243 n03116530 counter.n.01 table sofa 1 83 6 9 sofa sofa Sofa sofa sofa sofa 04256520 n04256520 sofa.n.01 glass-topped table 1 19 7 10 table table Table table table table 04379243 n04379243 table.n.02 racket bat 1 40 7 otherprop Objects fridge handle 1 758 40 7 handle otherprop Objects stove top 1 40 7 otherprop Objects monitor from pc 1 40 7 otherprop Objects stick 1 529 40 7 stick otherprop Objects ================================================ FILE: pointnet2_tf/scannet/preprocessing/scannet_util.py ================================================ g_label_names = ['unannotated', 'wall', 'floor', 'chair', 'table', 'desk', 'bed', 'bookshelf', 'sofa', 'sink', 'bathtub', 'toilet', 'curtain', 'counter', 'door', 'window', 'shower curtain', 'refridgerator', 'picture', 'cabinet', 'otherfurniture'] def get_raw2scannet_label_map(): lines = [line.rstrip() for line in open('scannet-labels.combined.tsv')] lines = lines[1:] raw2scannet = {} for i in range(len(lines)): label_classes_set = set(g_label_names) elements = lines[i].split('\t') raw_name = elements[0] nyu40_name = elements[6] if nyu40_name not in label_classes_set: raw2scannet[raw_name] = 'unannotated' else: raw2scannet[raw_name] = nyu40_name return raw2scannet g_raw2scannet = get_raw2scannet_label_map() ================================================ FILE: pointnet2_tf/scannet/scannet_dataset.py ================================================ import pickle import os import sys import numpy as np import pc_util import scene_util class ScannetDataset(): def __init__(self, root, npoints=8192, split='train'): self.npoints = npoints self.root = root self.split = split self.data_filename = os.path.join(self.root, 'scannet_%s.pickle'%(split)) with open(self.data_filename,'rb') as fp: self.scene_points_list = pickle.load(fp) self.semantic_labels_list = pickle.load(fp) if split=='train': labelweights = np.zeros(21) for seg in self.semantic_labels_list: tmp,_ = np.histogram(seg,range(22)) labelweights += tmp labelweights = labelweights.astype(np.float32) labelweights = labelweights/np.sum(labelweights) self.labelweights = 1/np.log(1.2+labelweights) elif split=='test': self.labelweights = np.ones(21) def __getitem__(self, index): point_set = self.scene_points_list[index] semantic_seg = self.semantic_labels_list[index].astype(np.int32) coordmax = np.max(point_set,axis=0) coordmin = np.min(point_set,axis=0) smpmin = np.maximum(coordmax-[1.5,1.5,3.0], coordmin) smpmin[2] = coordmin[2] smpsz = np.minimum(coordmax-smpmin,[1.5,1.5,3.0]) smpsz[2] = coordmax[2]-coordmin[2] isvalid = False for i in range(10): curcenter = point_set[np.random.choice(len(semantic_seg),1)[0],:] curmin = curcenter-[0.75,0.75,1.5] curmax = curcenter+[0.75,0.75,1.5] curmin[2] = coordmin[2] curmax[2] = coordmax[2] curchoice = np.sum((point_set>=(curmin-0.2))*(point_set<=(curmax+0.2)),axis=1)==3 cur_point_set = point_set[curchoice,:] cur_semantic_seg = semantic_seg[curchoice] if len(cur_semantic_seg)==0: continue mask = np.sum((cur_point_set>=(curmin-0.01))*(cur_point_set<=(curmax+0.01)),axis=1)==3 vidx = np.ceil((cur_point_set[mask,:]-curmin)/(curmax-curmin)*[31.0,31.0,62.0]) vidx = np.unique(vidx[:,0]*31.0*62.0+vidx[:,1]*62.0+vidx[:,2]) isvalid = np.sum(cur_semantic_seg>0)/len(cur_semantic_seg)>=0.7 and len(vidx)/31.0/31.0/62.0>=0.02 if isvalid: break choice = np.random.choice(len(cur_semantic_seg), self.npoints, replace=True) point_set = cur_point_set[choice,:] semantic_seg = cur_semantic_seg[choice] mask = mask[choice] sample_weight = self.labelweights[semantic_seg] sample_weight *= mask return point_set, semantic_seg, sample_weight def __len__(self): return len(self.scene_points_list) class ScannetDatasetWholeScene(): def __init__(self, root, npoints=8192, split='train'): self.npoints = npoints self.root = root self.split = split self.data_filename = os.path.join(self.root, 'scannet_%s.pickle'%(split)) with open(self.data_filename,'rb') as fp: self.scene_points_list = pickle.load(fp) self.semantic_labels_list = pickle.load(fp) if split=='train': labelweights = np.zeros(21) for seg in self.semantic_labels_list: tmp,_ = np.histogram(seg,range(22)) labelweights += tmp labelweights = labelweights.astype(np.float32) labelweights = labelweights/np.sum(labelweights) self.labelweights = 1/np.log(1.2+labelweights) elif split=='test': self.labelweights = np.ones(21) def __getitem__(self, index): point_set_ini = self.scene_points_list[index] semantic_seg_ini = self.semantic_labels_list[index].astype(np.int32) coordmax = np.max(point_set_ini,axis=0) coordmin = np.min(point_set_ini,axis=0) nsubvolume_x = np.ceil((coordmax[0]-coordmin[0])/1.5).astype(np.int32) nsubvolume_y = np.ceil((coordmax[1]-coordmin[1])/1.5).astype(np.int32) point_sets = list() semantic_segs = list() sample_weights = list() isvalid = False for i in range(nsubvolume_x): for j in range(nsubvolume_y): curmin = coordmin+[i*1.5,j*1.5,0] curmax = coordmin+[(i+1)*1.5,(j+1)*1.5,coordmax[2]-coordmin[2]] curchoice = np.sum((point_set_ini>=(curmin-0.2))*(point_set_ini<=(curmax+0.2)),axis=1)==3 cur_point_set = point_set_ini[curchoice,:] cur_semantic_seg = semantic_seg_ini[curchoice] if len(cur_semantic_seg)==0: continue mask = np.sum((cur_point_set>=(curmin-0.001))*(cur_point_set<=(curmax+0.001)),axis=1)==3 choice = np.random.choice(len(cur_semantic_seg), self.npoints, replace=True) point_set = cur_point_set[choice,:] # Nx3 semantic_seg = cur_semantic_seg[choice] # N mask = mask[choice] if sum(mask)/float(len(mask))<0.01: continue sample_weight = self.labelweights[semantic_seg] sample_weight *= mask # N point_sets.append(np.expand_dims(point_set,0)) # 1xNx3 semantic_segs.append(np.expand_dims(semantic_seg,0)) # 1xN sample_weights.append(np.expand_dims(sample_weight,0)) # 1xN point_sets = np.concatenate(tuple(point_sets),axis=0) semantic_segs = np.concatenate(tuple(semantic_segs),axis=0) sample_weights = np.concatenate(tuple(sample_weights),axis=0) return point_sets, semantic_segs, sample_weights def __len__(self): return len(self.scene_points_list) class ScannetDatasetVirtualScan(): def __init__(self, root, npoints=8192, split='train'): self.npoints = npoints self.root = root self.split = split self.data_filename = os.path.join(self.root, 'scannet_%s.pickle'%(split)) with open(self.data_filename,'rb') as fp: self.scene_points_list = pickle.load(fp) self.semantic_labels_list = pickle.load(fp) if split=='train': labelweights = np.zeros(21) for seg in self.semantic_labels_list: tmp,_ = np.histogram(seg,range(22)) labelweights += tmp labelweights = labelweights.astype(np.float32) labelweights = labelweights/np.sum(labelweights) self.labelweights = 1/np.log(1.2+labelweights) elif split=='test': self.labelweights = np.ones(21) def __getitem__(self, index): point_set_ini = self.scene_points_list[index] semantic_seg_ini = self.semantic_labels_list[index].astype(np.int32) sample_weight_ini = self.labelweights[semantic_seg_ini] point_sets = list() semantic_segs = list() sample_weights = list() for i in xrange(8): smpidx = scene_util.virtual_scan(point_set_ini,mode=i) if len(smpidx)<300: continue point_set = point_set_ini[smpidx,:] semantic_seg = semantic_seg_ini[smpidx] sample_weight = sample_weight_ini[smpidx] choice = np.random.choice(len(semantic_seg), self.npoints, replace=True) point_set = point_set[choice,:] # Nx3 semantic_seg = semantic_seg[choice] # N sample_weight = sample_weight[choice] # N point_sets.append(np.expand_dims(point_set,0)) # 1xNx3 semantic_segs.append(np.expand_dims(semantic_seg,0)) # 1xN sample_weights.append(np.expand_dims(sample_weight,0)) # 1xN point_sets = np.concatenate(tuple(point_sets),axis=0) semantic_segs = np.concatenate(tuple(semantic_segs),axis=0) sample_weights = np.concatenate(tuple(sample_weights),axis=0) return point_sets, semantic_segs, sample_weights def __len__(self): return len(self.scene_points_list) if __name__=='__main__': d = ScannetDatasetWholeScene(root = './data', split='test', npoints=8192) labelweights_vox = np.zeros(21) for ii in xrange(len(d)): print ii ps,seg,smpw = d[ii] for b in xrange(ps.shape[0]): _, uvlabel, _ = pc_util.point_cloud_label_to_surface_voxel_label_fast(ps[b,smpw[b,:]>0,:], seg[b,smpw[b,:]>0], res=0.02) tmp,_ = np.histogram(uvlabel,range(22)) labelweights_vox += tmp print labelweights_vox[1:].astype(np.float32)/np.sum(labelweights_vox[1:].astype(np.float32)) exit() ================================================ FILE: pointnet2_tf/scannet/scene_util.py ================================================ import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) import numpy as np from sklearn.neighbors import NearestNeighbors from numpy import linalg as la import scipy.io as sio def cart2sph(xyz): xy = xyz[:,0]**2+xyz[:,1]**2 aer = np.zeros(xyz.shape) aer[:,2] = np.sqrt(xy+xyz[:,2]**2) aer[:,1] = np.arctan2(xyz[:,2],np.sqrt(xy)) aer[:,0] = np.arctan2(xyz[:,1],xyz[:,0]) return aer # generate virtual scan of a scene by subsampling the point cloud def virtual_scan(xyz, mode=-1): camloc = np.mean(xyz,axis=0) camloc[2] = 1.5 # human height if mode==-1: view_dr = np.array([2*np.pi*np.random.random(), np.pi/10*(np.random.random()-0.75)]) camloc[:2] -= (0.8+0.7*np.random.random())*np.array([np.cos(view_dr[0]),np.sin(view_dr[0])]) else: view_dr = np.array([np.pi/4*mode, 0]) camloc[:2] -= np.array([np.cos(view_dr[0]),np.sin(view_dr[0])]) ct_ray_dr = np.array([np.cos(view_dr[1])*np.cos(view_dr[0]), np.cos(view_dr[1])*np.sin(view_dr[0]), np.sin(view_dr[1])]) hr_dr = np.cross(ct_ray_dr, np.array([0,0,1])) hr_dr /= la.norm(hr_dr) vt_dr = np.cross(hr_dr, ct_ray_dr) vt_dr /= la.norm(vt_dr) xx = np.linspace(-0.6,0.6,200) #200 yy = np.linspace(-0.45,0.45,150) #150 xx, yy = np.meshgrid(xx,yy) xx = xx.reshape(-1,1) yy = yy.reshape(-1,1) rays = xx*hr_dr.reshape(1,-1)+yy*vt_dr.reshape(1,-1)+ct_ray_dr.reshape(1,-1) rays_aer = cart2sph(rays) local_xyz = xyz-camloc.reshape(1,-1) local_aer = cart2sph(local_xyz) nbrs = NearestNeighbors(n_neighbors=1, algorithm='kd_tree').fit(rays_aer[:,:2]) mindd, minidx = nbrs.kneighbors(local_aer[:,:2]) mindd = mindd.reshape(-1) minidx = minidx.reshape(-1) sub_idx = mindd<0.01 if sum(sub_idx)<100: return np.ones(0) sub_r = local_aer[sub_idx,2] sub_minidx = minidx[sub_idx] min_r = float('inf')*np.ones(np.max(sub_minidx)+1) for i in xrange(len(sub_r)): if sub_r[i]min_r[sub_minidx[i]]: sub_smpidx[i] = 0 smpidx = np.where(sub_idx)[0] smpidx = smpidx[sub_smpidx==1] return smpidx if __name__=='__main__': pc = np.load('scannet_dataset/scannet_scenes/scene0015_00.npy') print pc.shape xyz = pc[:,:3] seg = pc[:,7] smpidx = virtual_scan(xyz,mode=2) xyz = xyz[smpidx,:] seg = seg[smpidx] sio.savemat('tmp.mat',{'pc':xyz,'seg':seg}) ================================================ FILE: pointnet2_tf/scannet/train.py ================================================ import argparse import math from datetime import datetime #import h5pyprovider import numpy as np import tensorflow as tf import socket import importlib import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = os.path.dirname(BASE_DIR) sys.path.append(BASE_DIR) # model sys.path.append(ROOT_DIR) # provider sys.path.append(os.path.join(ROOT_DIR, 'utils')) import provider import tf_util import pc_util sys.path.append(os.path.join(ROOT_DIR, 'data_prep')) import scannet_dataset parser = argparse.ArgumentParser() parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]') parser.add_argument('--model', default='model', help='Model name [default: model]') parser.add_argument('--log_dir', default='log', help='Log dir [default: log]') parser.add_argument('--num_point', type=int, default=8192, help='Point Number [default: 8192]') parser.add_argument('--max_epoch', type=int, default=201, help='Epoch to run [default: 201]') parser.add_argument('--batch_size', type=int, default=32, help='Batch Size during training [default: 32]') parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]') parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]') parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]') parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]') parser.add_argument('--decay_rate', type=float, default=0.7, help='Decay rate for lr decay [default: 0.7]') FLAGS = parser.parse_args() EPOCH_CNT = 0 BATCH_SIZE = FLAGS.batch_size NUM_POINT = FLAGS.num_point MAX_EPOCH = FLAGS.max_epoch BASE_LEARNING_RATE = FLAGS.learning_rate GPU_INDEX = FLAGS.gpu MOMENTUM = FLAGS.momentum OPTIMIZER = FLAGS.optimizer DECAY_STEP = FLAGS.decay_step DECAY_RATE = FLAGS.decay_rate MODEL = importlib.import_module(FLAGS.model) # import network module MODEL_FILE = os.path.join(BASE_DIR, FLAGS.model+'.py') LOG_DIR = FLAGS.log_dir if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR) os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w') LOG_FOUT.write(str(FLAGS)+'\n') BN_INIT_DECAY = 0.5 BN_DECAY_DECAY_RATE = 0.5 BN_DECAY_DECAY_STEP = float(DECAY_STEP) BN_DECAY_CLIP = 0.99 HOSTNAME = socket.gethostname() NUM_CLASSES = 21 # Shapenet official train/test split DATA_PATH = os.path.join(ROOT_DIR,'data','scannet_data_pointnet2') TRAIN_DATASET = scannet_dataset.ScannetDataset(root=DATA_PATH, npoints=NUM_POINT, split='train') TEST_DATASET = scannet_dataset.ScannetDataset(root=DATA_PATH, npoints=NUM_POINT, split='test') TEST_DATASET_WHOLE_SCENE = scannet_dataset.ScannetDatasetWholeScene(root=DATA_PATH, npoints=NUM_POINT, split='test') def log_string(out_str): LOG_FOUT.write(out_str+'\n') LOG_FOUT.flush() print(out_str) def get_learning_rate(batch): learning_rate = tf.train.exponential_decay( BASE_LEARNING_RATE, # Base learning rate. batch * BATCH_SIZE, # Current index into the dataset. DECAY_STEP, # Decay step. DECAY_RATE, # Decay rate. staircase=True) learing_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE! return learning_rate def get_bn_decay(batch): bn_momentum = tf.train.exponential_decay( BN_INIT_DECAY, batch*BATCH_SIZE, BN_DECAY_DECAY_STEP, BN_DECAY_DECAY_RATE, staircase=True) bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum) return bn_decay def train(): with tf.Graph().as_default(): with tf.device('/gpu:'+str(GPU_INDEX)): pointclouds_pl, labels_pl, smpws_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT) is_training_pl = tf.placeholder(tf.bool, shape=()) print is_training_pl # Note the global_step=batch parameter to minimize. # That tells the optimizer to helpfully increment the 'batch' parameter for you every time it trains. batch = tf.Variable(0) bn_decay = get_bn_decay(batch) tf.summary.scalar('bn_decay', bn_decay) print "--- Get model and loss" # Get model and loss pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl, NUM_CLASSES, bn_decay=bn_decay) loss = MODEL.get_loss(pred, labels_pl, smpws_pl) tf.summary.scalar('loss', loss) correct = tf.equal(tf.argmax(pred, 2), tf.to_int64(labels_pl)) accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE*NUM_POINT) tf.summary.scalar('accuracy', accuracy) print "--- Get training operator" # Get training operator learning_rate = get_learning_rate(batch) tf.summary.scalar('learning_rate', learning_rate) if OPTIMIZER == 'momentum': optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM) elif OPTIMIZER == 'adam': optimizer = tf.train.AdamOptimizer(learning_rate) train_op = optimizer.minimize(loss, global_step=batch) # Add ops to save and restore all the variables. saver = tf.train.Saver() # Create a session config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True config.log_device_placement = False sess = tf.Session(config=config) # Add summary writers merged = tf.summary.merge_all() train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'), sess.graph) test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'), sess.graph) # Init variables init = tf.global_variables_initializer() sess.run(init) #sess.run(init, {is_training_pl: True}) ops = {'pointclouds_pl': pointclouds_pl, 'labels_pl': labels_pl, 'smpws_pl': smpws_pl, 'is_training_pl': is_training_pl, 'pred': pred, 'loss': loss, 'train_op': train_op, 'merged': merged, 'step': batch, 'end_points': end_points} best_acc = -1 for epoch in range(MAX_EPOCH): log_string('**** EPOCH %03d ****' % (epoch)) sys.stdout.flush() train_one_epoch(sess, ops, train_writer) if epoch%5==0: acc = eval_one_epoch(sess, ops, test_writer) acc = eval_whole_scene_one_epoch(sess, ops, test_writer) if acc > best_acc: best_acc = acc save_path = saver.save(sess, os.path.join(LOG_DIR, "best_model_epoch_%03d.ckpt"%(epoch))) log_string("Model saved in file: %s" % save_path) # Save the variables to disk. if epoch % 10 == 0: save_path = saver.save(sess, os.path.join(LOG_DIR, "model.ckpt")) log_string("Model saved in file: %s" % save_path) def get_batch_wdp(dataset, idxs, start_idx, end_idx): bsize = end_idx-start_idx batch_data = np.zeros((bsize, NUM_POINT, 3)) batch_label = np.zeros((bsize, NUM_POINT), dtype=np.int32) batch_smpw = np.zeros((bsize, NUM_POINT), dtype=np.float32) for i in range(bsize): ps,seg,smpw = dataset[idxs[i+start_idx]] batch_data[i,...] = ps batch_label[i,:] = seg batch_smpw[i,:] = smpw dropout_ratio = np.random.random()*0.875 # 0-0.875 drop_idx = np.where(np.random.random((ps.shape[0]))<=dropout_ratio)[0] batch_data[i,drop_idx,:] = batch_data[i,0,:] batch_label[i,drop_idx] = batch_label[i,0] batch_smpw[i,drop_idx] *= 0 return batch_data, batch_label, batch_smpw def get_batch(dataset, idxs, start_idx, end_idx): bsize = end_idx-start_idx batch_data = np.zeros((bsize, NUM_POINT, 3)) batch_label = np.zeros((bsize, NUM_POINT), dtype=np.int32) batch_smpw = np.zeros((bsize, NUM_POINT), dtype=np.float32) for i in range(bsize): ps,seg,smpw = dataset[idxs[i+start_idx]] batch_data[i,...] = ps batch_label[i,:] = seg batch_smpw[i,:] = smpw return batch_data, batch_label, batch_smpw def train_one_epoch(sess, ops, train_writer): """ ops: dict mapping from string to tf ops """ is_training = True # Shuffle train samples train_idxs = np.arange(0, len(TRAIN_DATASET)) np.random.shuffle(train_idxs) num_batches = len(TRAIN_DATASET)/BATCH_SIZE log_string(str(datetime.now())) total_correct = 0 total_seen = 0 loss_sum = 0 for batch_idx in range(num_batches): start_idx = batch_idx * BATCH_SIZE end_idx = (batch_idx+1) * BATCH_SIZE batch_data, batch_label, batch_smpw = get_batch_wdp(TRAIN_DATASET, train_idxs, start_idx, end_idx) # Augment batched point clouds by rotation aug_data = provider.rotate_point_cloud_z(batch_data) feed_dict = {ops['pointclouds_pl']: aug_data, ops['labels_pl']: batch_label, ops['smpws_pl']:batch_smpw, ops['is_training_pl']: is_training,} summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'], ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict) train_writer.add_summary(summary, step) pred_val = np.argmax(pred_val, 2) correct = np.sum(pred_val == batch_label) total_correct += correct total_seen += (BATCH_SIZE*NUM_POINT) loss_sum += loss_val if (batch_idx+1)%10 == 0: log_string(' -- %03d / %03d --' % (batch_idx+1, num_batches)) log_string('mean loss: %f' % (loss_sum / 10)) log_string('accuracy: %f' % (total_correct / float(total_seen))) total_correct = 0 total_seen = 0 loss_sum = 0 # evaluate on randomly chopped scenes def eval_one_epoch(sess, ops, test_writer): """ ops: dict mapping from string to tf ops """ global EPOCH_CNT is_training = False test_idxs = np.arange(0, len(TEST_DATASET)) num_batches = len(TEST_DATASET)/BATCH_SIZE total_correct = 0 total_seen = 0 loss_sum = 0 total_seen_class = [0 for _ in range(NUM_CLASSES)] total_correct_class = [0 for _ in range(NUM_CLASSES)] total_correct_vox = 0 total_seen_vox = 0 total_seen_class_vox = [0 for _ in range(NUM_CLASSES)] total_correct_class_vox = [0 for _ in range(NUM_CLASSES)] log_string(str(datetime.now())) log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT)) labelweights = np.zeros(21) labelweights_vox = np.zeros(21) for batch_idx in range(num_batches): start_idx = batch_idx * BATCH_SIZE end_idx = (batch_idx+1) * BATCH_SIZE batch_data, batch_label, batch_smpw = get_batch(TEST_DATASET, test_idxs, start_idx, end_idx) aug_data = provider.rotate_point_cloud_z(batch_data) feed_dict = {ops['pointclouds_pl']: aug_data, ops['labels_pl']: batch_label, ops['smpws_pl']: batch_smpw, ops['is_training_pl']: is_training} summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'], ops['loss'], ops['pred']], feed_dict=feed_dict) test_writer.add_summary(summary, step) pred_val = np.argmax(pred_val, 2) # BxN correct = np.sum((pred_val == batch_label) & (batch_label>0) & (batch_smpw>0)) # evaluate only on 20 categories but not unknown total_correct += correct total_seen += np.sum((batch_label>0) & (batch_smpw>0)) loss_sum += loss_val tmp,_ = np.histogram(batch_label,range(22)) labelweights += tmp for l in range(NUM_CLASSES): total_seen_class[l] += np.sum((batch_label==l) & (batch_smpw>0)) total_correct_class[l] += np.sum((pred_val==l) & (batch_label==l) & (batch_smpw>0)) for b in xrange(batch_label.shape[0]): _, uvlabel, _ = pc_util.point_cloud_label_to_surface_voxel_label_fast(aug_data[b,batch_smpw[b,:]>0,:], np.concatenate((np.expand_dims(batch_label[b,batch_smpw[b,:]>0],1),np.expand_dims(pred_val[b,batch_smpw[b,:]>0],1)),axis=1), res=0.02) total_correct_vox += np.sum((uvlabel[:,0]==uvlabel[:,1])&(uvlabel[:,0]>0)) total_seen_vox += np.sum(uvlabel[:,0]>0) tmp,_ = np.histogram(uvlabel[:,0],range(22)) labelweights_vox += tmp for l in range(NUM_CLASSES): total_seen_class_vox[l] += np.sum(uvlabel[:,0]==l) total_correct_class_vox[l] += np.sum((uvlabel[:,0]==l) & (uvlabel[:,1]==l)) log_string('eval mean loss: %f' % (loss_sum / float(num_batches))) log_string('eval point accuracy vox: %f'% (total_correct_vox / float(total_seen_vox))) log_string('eval point avg class acc vox: %f' % (np.mean(np.array(total_correct_class_vox[1:])/(np.array(total_seen_class_vox[1:],dtype=np.float)+1e-6)))) log_string('eval point accuracy: %f'% (total_correct / float(total_seen))) log_string('eval point avg class acc: %f' % (np.mean(np.array(total_correct_class[1:])/(np.array(total_seen_class[1:],dtype=np.float)+1e-6)))) labelweights_vox = labelweights_vox[1:].astype(np.float32)/np.sum(labelweights_vox[1:].astype(np.float32)) caliweights = np.array([0.388,0.357,0.038,0.033,0.017,0.02,0.016,0.025,0.002,0.002,0.002,0.007,0.006,0.022,0.004,0.0004,0.003,0.002,0.024,0.029]) log_string('eval point calibrated average acc: %f' % (np.average(np.array(total_correct_class[1:])/(np.array(total_seen_class[1:],dtype=np.float)+1e-6),weights=caliweights))) per_class_str = 'vox based --------' for l in range(1,NUM_CLASSES): per_class_str += 'class %d weight: %f, acc: %f; ' % (l,labelweights_vox[l-1],total_correct_class[l]/float(total_seen_class[l])) log_string(per_class_str) EPOCH_CNT += 1 return total_correct/float(total_seen) # evaluate on whole scenes to generate numbers provided in the paper def eval_whole_scene_one_epoch(sess, ops, test_writer): """ ops: dict mapping from string to tf ops """ global EPOCH_CNT is_training = False test_idxs = np.arange(0, len(TEST_DATASET_WHOLE_SCENE)) num_batches = len(TEST_DATASET_WHOLE_SCENE) total_correct = 0 total_seen = 0 loss_sum = 0 total_seen_class = [0 for _ in range(NUM_CLASSES)] total_correct_class = [0 for _ in range(NUM_CLASSES)] total_correct_vox = 0 total_seen_vox = 0 total_seen_class_vox = [0 for _ in range(NUM_CLASSES)] total_correct_class_vox = [0 for _ in range(NUM_CLASSES)] log_string(str(datetime.now())) log_string('---- EPOCH %03d EVALUATION WHOLE SCENE----'%(EPOCH_CNT)) labelweights = np.zeros(21) labelweights_vox = np.zeros(21) is_continue_batch = False extra_batch_data = np.zeros((0,NUM_POINT,3)) extra_batch_label = np.zeros((0,NUM_POINT)) extra_batch_smpw = np.zeros((0,NUM_POINT)) for batch_idx in range(num_batches): if not is_continue_batch: batch_data, batch_label, batch_smpw = TEST_DATASET_WHOLE_SCENE[batch_idx] batch_data = np.concatenate((batch_data,extra_batch_data),axis=0) batch_label = np.concatenate((batch_label,extra_batch_label),axis=0) batch_smpw = np.concatenate((batch_smpw,extra_batch_smpw),axis=0) else: batch_data_tmp, batch_label_tmp, batch_smpw_tmp = TEST_DATASET_WHOLE_SCENE[batch_idx] batch_data = np.concatenate((batch_data,batch_data_tmp),axis=0) batch_label = np.concatenate((batch_label,batch_label_tmp),axis=0) batch_smpw = np.concatenate((batch_smpw,batch_smpw_tmp),axis=0) if batch_data.shape[0]0) & (batch_smpw>0)) # evaluate only on 20 categories but not unknown total_correct += correct total_seen += np.sum((batch_label>0) & (batch_smpw>0)) loss_sum += loss_val tmp,_ = np.histogram(batch_label,range(22)) labelweights += tmp for l in range(NUM_CLASSES): total_seen_class[l] += np.sum((batch_label==l) & (batch_smpw>0)) total_correct_class[l] += np.sum((pred_val==l) & (batch_label==l) & (batch_smpw>0)) for b in xrange(batch_label.shape[0]): _, uvlabel, _ = pc_util.point_cloud_label_to_surface_voxel_label_fast(aug_data[b,batch_smpw[b,:]>0,:], np.concatenate((np.expand_dims(batch_label[b,batch_smpw[b,:]>0],1),np.expand_dims(pred_val[b,batch_smpw[b,:]>0],1)),axis=1), res=0.02) total_correct_vox += np.sum((uvlabel[:,0]==uvlabel[:,1])&(uvlabel[:,0]>0)) total_seen_vox += np.sum(uvlabel[:,0]>0) tmp,_ = np.histogram(uvlabel[:,0],range(22)) labelweights_vox += tmp for l in range(NUM_CLASSES): total_seen_class_vox[l] += np.sum(uvlabel[:,0]==l) total_correct_class_vox[l] += np.sum((uvlabel[:,0]==l) & (uvlabel[:,1]==l)) log_string('eval whole scene mean loss: %f' % (loss_sum / float(num_batches))) log_string('eval whole scene point accuracy vox: %f'% (total_correct_vox / float(total_seen_vox))) log_string('eval whole scene point avg class acc vox: %f' % (np.mean(np.array(total_correct_class_vox[1:])/(np.array(total_seen_class_vox[1:],dtype=np.float)+1e-6)))) log_string('eval whole scene point accuracy: %f'% (total_correct / float(total_seen))) log_string('eval whole scene point avg class acc: %f' % (np.mean(np.array(total_correct_class[1:])/(np.array(total_seen_class[1:],dtype=np.float)+1e-6)))) labelweights = labelweights[1:].astype(np.float32)/np.sum(labelweights[1:].astype(np.float32)) labelweights_vox = labelweights_vox[1:].astype(np.float32)/np.sum(labelweights_vox[1:].astype(np.float32)) caliweights = np.array([0.388,0.357,0.038,0.033,0.017,0.02,0.016,0.025,0.002,0.002,0.002,0.007,0.006,0.022,0.004,0.0004,0.003,0.002,0.024,0.029]) caliacc = np.average(np.array(total_correct_class_vox[1:])/(np.array(total_seen_class_vox[1:],dtype=np.float)+1e-6),weights=caliweights) log_string('eval whole scene point calibrated average acc vox: %f' % caliacc) per_class_str = 'vox based --------' for l in range(1,NUM_CLASSES): per_class_str += 'class %d weight: %f, acc: %f; ' % (l,labelweights_vox[l-1],total_correct_class_vox[l]/float(total_seen_class_vox[l])) log_string(per_class_str) EPOCH_CNT += 1 return caliacc if __name__ == "__main__": log_string('pid: %s'%(str(os.getpid()))) train() LOG_FOUT.close() ================================================ FILE: pointnet2_tf/tf_ops/3d_interpolation/interpolate.cpp ================================================ #include #include #include // memset #include // rand, RAND_MAX #include // sqrtf #include #include using namespace std; float randomf(){ return (rand()+0.5)/(RAND_MAX+1.0); } static double get_time(){ timespec tp; clock_gettime(CLOCK_MONOTONIC,&tp); return tp.tv_sec+tp.tv_nsec*1e-9; } // Find three nearest neigbors with square distance // input: xyz1 (b,n,3), xyz2(b,m,3) // output: dist (b,n,3), idx (b,n,3) void threenn_cpu(int b, int n, int m, const float *xyz1, const float *xyz2, float *dist, int *idx) { for (int i=0;i #include #include // memset #include // rand, RAND_MAX #include // sqrtf #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/shape_inference.h" #include "tensorflow/core/framework/common_shape_fns.h" using namespace tensorflow; REGISTER_OP("ThreeNN") .Input("xyz1: float32") .Input("xyz2: float32") .Output("dist: float32") .Output("idx: int32") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { c->set_output(0, c->input(0)); c->set_output(1, c->input(0)); return Status::OK(); }); REGISTER_OP("ThreeInterpolate") .Input("points: float32") .Input("idx: int32") .Input("weight: float32") .Output("out: float32") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { ::tensorflow::shape_inference::ShapeHandle dims1; // (b,m,c) c->WithRank(c->input(0), 3, &dims1); ::tensorflow::shape_inference::ShapeHandle dims2; // (b,n,3) c->WithRank(c->input(1), 3, &dims2); // (b,n,c) ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims1, 0), c->Dim(dims2, 1), c->Dim(dims1, 2)}); c->set_output(0, output); return Status::OK(); }); REGISTER_OP("ThreeInterpolateGrad") .Input("points: float32") .Input("idx: int32") .Input("weight: float32") .Input("grad_out: float32") .Output("grad_points: float32") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { c->set_output(0, c->input(0)); return Status::OK(); }); float randomf(){ return (rand()+0.5)/(RAND_MAX+1.0); } static double get_time(){ timespec tp; clock_gettime(CLOCK_MONOTONIC,&tp); return tp.tv_sec+tp.tv_nsec*1e-9; } // Find three nearest neigbors with square distance // input: xyz1 (b,n,3), xyz2(b,m,3) // output: dist (b,n,3), idx (b,n,3) void threenn_cpu(int b, int n, int m, const float *xyz1, const float *xyz2, float *dist, int *idx) { for (int i=0;iinput(0); OP_REQUIRES(context, xyz1_tensor.dims()==3 && xyz1_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeNN expects (b,n,3) xyz1 shape.")); int b = xyz1_tensor.shape().dim_size(0); int n = xyz1_tensor.shape().dim_size(1); const Tensor& xyz2_tensor = context->input(1); OP_REQUIRES(context, xyz2_tensor.dims()==3 && xyz2_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeNN expects (b,m,3) xyz2 shape.")); int m = xyz2_tensor.shape().dim_size(1); Tensor *dist_tensor = nullptr; OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape{b,n,3}, &dist_tensor)); Tensor *idx_tensor = nullptr; OP_REQUIRES_OK(context, context->allocate_output(1, TensorShape{b,n,3}, &idx_tensor)); auto xyz1_flat = xyz1_tensor.flat(); const float *xyz1 = &(xyz1_flat(0)); auto xyz2_flat = xyz2_tensor.flat(); const float *xyz2 = &(xyz2_flat(0)); auto dist_flat = dist_tensor->flat(); float *dist = &(dist_flat(0)); auto idx_flat = idx_tensor->flat(); int *idx = &(idx_flat(0)); threenn_cpu(b,n,m,xyz1,xyz2,dist,idx); } }; REGISTER_KERNEL_BUILDER(Name("ThreeNN").Device(DEVICE_CPU), ThreeNNOp); class ThreeInterpolateOp: public OpKernel{ public: explicit ThreeInterpolateOp(OpKernelConstruction * context):OpKernel(context){} void Compute(OpKernelContext * context) override { const Tensor& points_tensor=context->input(0); OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("ThreeInterpolate expects (b,m,c) points shape")); int b = points_tensor.shape().dim_size(0); int m = points_tensor.shape().dim_size(1); int c = points_tensor.shape().dim_size(2); const Tensor& idx_tensor=context->input(1); OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b && idx_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolate expects (b,n,3) idx shape")); int n = idx_tensor.shape().dim_size(1); const Tensor& weight_tensor=context->input(2); OP_REQUIRES(context,weight_tensor.dims()==3 && weight_tensor.shape().dim_size(0)==b && weight_tensor.shape().dim_size(1)==n && weight_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolate expects (b,n,3) weight shape")); Tensor * out_tensor = nullptr; OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,n,c}, &out_tensor)); auto points_flat = points_tensor.flat(); const float *points = &(points_flat(0)); auto idx_flat = idx_tensor.flat(); const int *idx = &(idx_flat(0)); auto weight_flat = weight_tensor.flat(); const float *weight = &(weight_flat(0)); auto out_flat = out_tensor->flat(); float *out = &(out_flat(0)); threeinterpolate_cpu(b,m,c,n,points,idx,weight,out); } }; REGISTER_KERNEL_BUILDER(Name("ThreeInterpolate").Device(DEVICE_CPU),ThreeInterpolateOp); class ThreeInterpolateGradOp: public OpKernel{ public: explicit ThreeInterpolateGradOp(OpKernelConstruction * context):OpKernel(context){} void Compute(OpKernelContext * context) override { const Tensor& points_tensor=context->input(0); OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("ThreeInterpolateGrad expects (b,m,c) points shape")); int b = points_tensor.shape().dim_size(0); int m = points_tensor.shape().dim_size(1); int c = points_tensor.shape().dim_size(2); const Tensor& idx_tensor=context->input(1); OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,3) idx shape")); int n = idx_tensor.shape().dim_size(1); const Tensor& weight_tensor=context->input(2); OP_REQUIRES(context,weight_tensor.dims()==3 && weight_tensor.shape().dim_size(0)==b && weight_tensor.shape().dim_size(1)==n && weight_tensor.shape().dim_size(2)==3, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,3) weight shape")); const Tensor& grad_out_tensor=context->input(3); OP_REQUIRES(context,grad_out_tensor.dims()==3 && grad_out_tensor.shape().dim_size(0)==b && grad_out_tensor.shape().dim_size(1)==n && grad_out_tensor.shape().dim_size(2)==c, errors::InvalidArgument("ThreeInterpolateGrad expects (b,n,c) grad_out shape")); Tensor * grad_points_tensor = nullptr; OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,m,c}, &grad_points_tensor)); auto points_flat = points_tensor.flat(); const float *points = &(points_flat(0)); auto idx_flat = idx_tensor.flat(); const int *idx = &(idx_flat(0)); auto weight_flat = weight_tensor.flat(); const float *weight = &(weight_flat(0)); auto grad_out_flat = grad_out_tensor.flat(); const float *grad_out = &(grad_out_flat(0)); auto grad_points_flat = grad_points_tensor->flat(); float *grad_points = &(grad_points_flat(0)); memset(grad_points, 0, sizeof(float)*b*m*c); threeinterpolate_grad_cpu(b,n,c,m,grad_out,idx,weight,grad_points); } }; REGISTER_KERNEL_BUILDER(Name("ThreeInterpolateGrad").Device(DEVICE_CPU),ThreeInterpolateGradOp); ================================================ FILE: pointnet2_tf/tf_ops/3d_interpolation/tf_interpolate.py ================================================ import tensorflow as tf from tensorflow.python.framework import ops import sys import os BASE_DIR = os.path.dirname(__file__) sys.path.append(BASE_DIR) interpolate_module=tf.load_op_library(os.path.join(BASE_DIR, 'tf_interpolate_so.so')) def three_nn(xyz1, xyz2): ''' Input: xyz1: (b,n,3) float32 array, unknown points xyz2: (b,m,3) float32 array, known points Output: dist: (b,n,3) float32 array, distances to known points idx: (b,n,3) int32 array, indices to known points ''' return interpolate_module.three_nn(xyz1, xyz2) ops.NoGradient('ThreeNN') def three_interpolate(points, idx, weight): ''' Input: points: (b,m,c) float32 array, known points idx: (b,n,3) int32 array, indices to known points weight: (b,n,3) float32 array, weights on known points Output: out: (b,n,c) float32 array, interpolated point values ''' return interpolate_module.three_interpolate(points, idx, weight) @tf.RegisterGradient('ThreeInterpolate') def _three_interpolate_grad(op, grad_out): points = op.inputs[0] idx = op.inputs[1] weight = op.inputs[2] return [interpolate_module.three_interpolate_grad(points, idx, weight, grad_out), None, None] if __name__=='__main__': import numpy as np import time np.random.seed(100) pts = np.random.random((32,128,64)).astype('float32') tmp1 = np.random.random((32,512,3)).astype('float32') tmp2 = np.random.random((32,128,3)).astype('float32') with tf.device('/cpu:0'): points = tf.constant(pts) xyz1 = tf.constant(tmp1) xyz2 = tf.constant(tmp2) dist, idx = three_nn(xyz1, xyz2) weight = tf.ones_like(dist)/3.0 interpolated_points = three_interpolate(points, idx, weight) with tf.Session('') as sess: now = time.time() for _ in range(100): ret = sess.run(interpolated_points) print time.time() - now print ret.shape, ret.dtype #print ret ================================================ FILE: pointnet2_tf/tf_ops/3d_interpolation/tf_interpolate_compile.sh ================================================ # TF1.2 g++ -std=c++11 tf_interpolate.cpp -o tf_interpolate_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -lcudart -L /usr/local/cuda-8.0/lib64/ -O2 -D_GLIBCXX_USE_CXX11_ABI=0 # TF1.4 #g++ -std=c++11 tf_interpolate.cpp -o tf_interpolate_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -I /usr/local/lib/python2.7/dist-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-8.0/lib64/ -L/usr/local/lib/python2.7/dist-packages/tensorflow -ltensorflow_framework -O2 -D_GLIBCXX_USE_CXX11_ABI=0 ================================================ FILE: pointnet2_tf/tf_ops/3d_interpolation/tf_interpolate_op_test.py ================================================ import tensorflow as tf import numpy as np from tf_interpolate import three_nn, three_interpolate class GroupPointTest(tf.test.TestCase): def test(self): pass def test_grad(self): with self.test_session(): points = tf.constant(np.random.random((1,8,16)).astype('float32')) print points xyz1 = tf.constant(np.random.random((1,128,3)).astype('float32')) xyz2 = tf.constant(np.random.random((1,8,3)).astype('float32')) dist, idx = three_nn(xyz1, xyz2) weight = tf.ones_like(dist)/3.0 interpolated_points = three_interpolate(points, idx, weight) print interpolated_points err = tf.test.compute_gradient_error(points, (1,8,16), interpolated_points, (1,128,16)) print err self.assertLess(err, 1e-4) if __name__=='__main__': tf.test.main() ================================================ FILE: pointnet2_tf/tf_ops/3d_interpolation/visu_interpolation.py ================================================ ''' Visualize part segmentation ''' import os import sys ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) sys.path.append('/home/rqi/Projects/toolkits/visualization') from show3d_balls import showpoints import numpy as np from tf_interpolate import three_nn, three_interpolate import tensorflow as tf pts2 = np.array([[0,0,1],[1,0,0],[0,1,0],[1,1,0]]).astype('float32') xyz1 = np.random.random((100,3)).astype('float32') xyz2 = np.array([[0,0,0],[1,0,0],[0,1,0],[1,1,1]]).astype('float32') def fun(xyz1,xyz2,pts2): with tf.device('/cpu:0'): points = tf.constant(np.expand_dims(pts2,0)) xyz1 = tf.constant(np.expand_dims(xyz1,0)) xyz2 = tf.constant(np.expand_dims(xyz2,0)) dist, idx = three_nn(xyz1, xyz2) #weight = tf.ones_like(dist)/3.0 dist = tf.maximum(dist, 1e-10) norm = tf.reduce_sum((1.0/dist),axis=2,keep_dims=True) norm = tf.tile(norm, [1,1,3]) print norm weight = (1.0/dist) / norm interpolated_points = three_interpolate(points, idx, weight) with tf.Session('') as sess: tmp,pts1,d,w = sess.run([xyz1, interpolated_points, dist, weight]) #print w pts1 = pts1.squeeze() return pts1 pts1 = fun(xyz1,xyz2,pts2) all_pts = np.zeros((104,3)) all_pts[0:100,:] = pts1 all_pts[100:,:] = pts2 all_xyz = np.zeros((104,3)) all_xyz[0:100,:]=xyz1 all_xyz[100:,:]=xyz2 showpoints(xyz2, pts2, ballradius=8) showpoints(xyz1, pts1, ballradius=8) showpoints(all_xyz, all_pts, ballradius=8) ================================================ FILE: pointnet2_tf/tf_ops/grouping/.gitignore ================================================ a.out query_ball_point query_ball_point_block query_ball_point_cuda query_ball_point_grid tf_grouping_g.cu.o tf_grouping_so.so selection_sort selection_sort_cuda selection_sort_const_cuda ================================================ FILE: pointnet2_tf/tf_ops/grouping/test/compile.sh ================================================ g++ query_ball_point.cpp -o query_ball_point nvcc query_ball_point.cu -o query_ball_point_cuda nvcc query_ball_point_block.cu -o query_ball_point_block nvcc query_ball_point_grid.cu -o query_ball_point_grid g++ -Wall selection_sort.cpp -o selection_sort nvcc selection_sort.cu -o selection_sort_cuda ================================================ FILE: pointnet2_tf/tf_ops/grouping/test/query_ball_point.cpp ================================================ #include #include #include // memset #include // rand, RAND_MAX #include // sqrtf #include #include using namespace std; float randomf(){ return (rand()+0.5)/(RAND_MAX+1.0); } static double get_time(){ timespec tp; clock_gettime(CLOCK_MONOTONIC,&tp); return tp.tv_sec+tp.tv_nsec*1e-9; } // input: radius (1), nsample (1), xyz1 (b,n,3), xyz2 (b,m,3) // output: idx (b,m,nsample) void query_ball_point_cpu(int b, int n, int m, float radius, int nsample, const float *xyz1, const float *xyz2, int *idx) { for (int i=0;i #include #include // memset #include // rand, RAND_MAX #include // sqrtf #include #include using namespace std; float randomf(){ return (rand()+0.5)/(RAND_MAX+1.0); } static double get_time(){ timespec tp; clock_gettime(CLOCK_MONOTONIC,&tp); return tp.tv_sec+tp.tv_nsec*1e-9; } // input: radius (1), nsample (1), xyz1 (b,n,3), xyz2 (b,m,3) // output: idx (b,m,nsample) __global__ void query_ball_point_gpu(int b, int n, int m, float radius, int nsample, const float *xyz1, const float *xyz2, int *idx) { for (int i=0;i>>(b,n,m,radius,nsample,xyz1,xyz2,idx); cudaDeviceSynchronize(); printf("query_ball_point gpu time %f\n",get_time()-t0); t0=get_time(); group_point_gpu<<<1,1>>>(b,n,c,m,nsample,points,idx,out); cudaDeviceSynchronize(); printf("grou_point gpu time %f\n",get_time()-t0); t0=get_time(); group_point_grad_gpu<<<1,1>>>(b,n,c,m,nsample,grad_out,idx,grad_points); cudaDeviceSynchronize(); printf("grou_point_grad gpu time %f\n",get_time()-t0); cudaFree(xyz1); cudaFree(xyz2); cudaFree(points); cudaFree(idx); cudaFree(out); cudaFree(grad_out); cudaFree(grad_points); return 0; } ================================================ FILE: pointnet2_tf/tf_ops/grouping/test/query_ball_point_block.cu ================================================ #include #include #include // memset #include // rand, RAND_MAX #include // sqrtf #include #include using namespace std; float randomf(){ return (rand()+0.5)/(RAND_MAX+1.0); } static double get_time(){ timespec tp; clock_gettime(CLOCK_MONOTONIC,&tp); return tp.tv_sec+tp.tv_nsec*1e-9; } // input: radius (1), nsample (1), xyz1 (b,n,3), xyz2 (b,m,3) // output: idx (b,m,nsample) __global__ void query_ball_point_gpu(int b, int n, int m, float radius, int nsample, const float *xyz1, const float *xyz2, int *idx) { int index = threadIdx.x; xyz1 += n*3*index; xyz2 += m*3*index; idx += m*nsample*index; for (int j=0;j>>(b,n,m,radius,nsample,xyz1,xyz2,idx); cudaDeviceSynchronize(); printf("query_ball_point gpu time %f\n",get_time()-t0); t0=get_time(); group_point_gpu<<<1,b>>>(b,n,c,m,nsample,points,idx,out); cudaDeviceSynchronize(); printf("grou_point gpu time %f\n",get_time()-t0); t0=get_time(); group_point_grad_gpu<<<1,b>>>(b,n,c,m,nsample,grad_out,idx,grad_points); cudaDeviceSynchronize(); printf("grou_point_grad gpu time %f\n",get_time()-t0); cudaFree(xyz1); cudaFree(xyz2); cudaFree(points); cudaFree(idx); cudaFree(out); cudaFree(grad_out); cudaFree(grad_points); return 0; } ================================================ FILE: pointnet2_tf/tf_ops/grouping/test/query_ball_point_grid.cu ================================================ #include #include #include // memset #include // rand, RAND_MAX #include // sqrtf #include #include using namespace std; float randomf(){ return (rand()+0.5)/(RAND_MAX+1.0); } static double get_time(){ timespec tp; clock_gettime(CLOCK_MONOTONIC,&tp); return tp.tv_sec+tp.tv_nsec*1e-9; } // input: radius (1), nsample (1), xyz1 (b,n,3), xyz2 (b,m,3) // output: idx (b,m,nsample) __global__ void query_ball_point_gpu(int b, int n, int m, float radius, int nsample, const float *xyz1, const float *xyz2, int *idx) { int batch_index = blockIdx.x; xyz1 += n*3*batch_index; xyz2 += m*3*batch_index; idx += m*nsample*batch_index; int index = threadIdx.x; int stride = blockDim.x; for (int j=index;j>>(b,n,m,radius,nsample,xyz1,xyz2,idx); cudaDeviceSynchronize(); printf("query_ball_point gpu time %f\n",get_time()-t0); t0=get_time(); group_point_gpu<<>>(b,n,c,m,nsample,points,idx,out); cudaDeviceSynchronize(); printf("grou_point gpu time %f\n",get_time()-t0); t0=get_time(); group_point_grad_gpu<<>>(b,n,c,m,nsample,grad_out,idx,grad_points); cudaDeviceSynchronize(); printf("grou_point_grad gpu time %f\n",get_time()-t0); cudaFree(xyz1); cudaFree(xyz2); cudaFree(points); cudaFree(idx); cudaFree(out); cudaFree(grad_out); cudaFree(grad_points); return 0; } ================================================ FILE: pointnet2_tf/tf_ops/grouping/test/selection_sort.cpp ================================================ #include #include #include // memset #include // rand, RAND_MAX #include // sqrtf #include #include using namespace std; float randomf(){ return (rand()+0.5)/(RAND_MAX+1.0); } static double get_time(){ timespec tp; clock_gettime(CLOCK_MONOTONIC,&tp); return tp.tv_sec+tp.tv_nsec*1e-9; } // input: k (1), distance matrix dist (b,m,n) // output: idx (b,m,n), val (b,m,n) void selection_sort_cpu(int b, int n, int m, int k, const float *dist, int *idx, float *val) { float *p_dist; float tmp; int tmpi; for (int i=0;i #include #include // memset #include // rand, RAND_MAX #include // sqrtf #include #include using namespace std; float randomf(){ return (rand()+0.5)/(RAND_MAX+1.0); } static double get_time(){ timespec tp; clock_gettime(CLOCK_MONOTONIC,&tp); return tp.tv_sec+tp.tv_nsec*1e-9; } // input: k (1), distance matrix dist (b,m,n) // output: idx (b,m,k), val (b,m,k) __global__ void selection_sort_gpu(int b, int n, int m, int k, float *dist, int *idx, float *val) { int batch_index = blockIdx.x; dist+=m*n*batch_index; idx+=m*k*batch_index; val+=m*k*batch_index; int index = threadIdx.x; int stride = blockDim.x; float *p_dist; for (int j=index;j>>(b,n,m,k,dist,idx,val); cudaDeviceSynchronize(); printf("selection sort cpu time %f\n",get_time()-t0); return 0; } ================================================ FILE: pointnet2_tf/tf_ops/grouping/test/selection_sort_const.cu ================================================ #include #include #include // memset #include // rand, RAND_MAX #include // sqrtf #include #include using namespace std; float randomf(){ return (rand()+0.5)/(RAND_MAX+1.0); } static double get_time(){ timespec tp; clock_gettime(CLOCK_MONOTONIC,&tp); return tp.tv_sec+tp.tv_nsec*1e-9; } // input: k (1), distance matrix dist (b,m,n) // output: idx (b,m,n), dist_out (b,m,n) __global__ void selection_sort_gpu(int b, int n, int m, int k, const float *dist, int *outi, float *out) { int batch_index = blockIdx.x; dist+=m*n*batch_index; outi+=m*n*batch_index; out+=m*n*batch_index; int index = threadIdx.x; int stride = blockDim.x; // copy from dist to dist_out for (int j=index;j>>(b,n,m,k,dist,idx,dist_out); cudaDeviceSynchronize(); printf("selection sort cpu time %f\n",get_time()-t0); //for (int i=0;i #include #include // memset #include // rand, RAND_MAX #include // sqrtf #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/shape_inference.h" #include "tensorflow/core/framework/common_shape_fns.h" #include using namespace tensorflow; REGISTER_OP("QueryBallPoint") .Attr("radius: float") .Attr("nsample: int") .Input("xyz1: float32") .Input("xyz2: float32") .Output("idx: int32") .Output("pts_cnt: int32") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { ::tensorflow::shape_inference::ShapeHandle dims2; // batch_size * npoint * 3 c->WithRank(c->input(1), 3, &dims2); int nsample; TF_RETURN_IF_ERROR(c->GetAttr("nsample", &nsample)); ::tensorflow::shape_inference::ShapeHandle output1 = c->MakeShape({c->Dim(dims2, 0), c->Dim(dims2, 1), nsample}); c->set_output(0, output1); ::tensorflow::shape_inference::ShapeHandle output2 = c->MakeShape({c->Dim(dims2, 0), c->Dim(dims2, 1)}); c->set_output(1, output2); return Status::OK(); }); REGISTER_OP("SelectionSort") .Attr("k: int") .Input("dist: float32") .Output("outi: int32") .Output("out: float32") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { c->set_output(0, c->input(0)); c->set_output(1, c->input(0)); return Status::OK(); }); REGISTER_OP("GroupPoint") .Input("points: float32") .Input("idx: int32") .Output("out: float32") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { ::tensorflow::shape_inference::ShapeHandle dims1; // batch_size * ndataset * channels c->WithRank(c->input(0), 3, &dims1); ::tensorflow::shape_inference::ShapeHandle dims2; // batch_size * npoints * nsample c->WithRank(c->input(1), 3, &dims2); // batch_size * npoints * nsample * channels ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims2, 0), c->Dim(dims2, 1), c->Dim(dims2, 2), c->Dim(dims1, 2)}); c->set_output(0, output); return Status::OK(); }); REGISTER_OP("GroupPointGrad") .Input("points: float32") .Input("idx: int32") .Input("grad_out: float32") .Output("grad_points: float32") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { c->set_output(0, c->input(0)); return Status::OK(); }); void queryBallPointLauncher(int b, int n, int m, float radius, int nsample, const float *xyz1, const float *xyz2, int *idx, int *pts_cnt); class QueryBallPointGpuOp : public OpKernel { public: explicit QueryBallPointGpuOp(OpKernelConstruction* context) : OpKernel(context) { OP_REQUIRES_OK(context, context->GetAttr("radius", &radius_)); OP_REQUIRES(context, radius_ > 0, errors::InvalidArgument("QueryBallPoint expects positive radius")); OP_REQUIRES_OK(context, context->GetAttr("nsample", &nsample_)); OP_REQUIRES(context, nsample_ > 0, errors::InvalidArgument("QueryBallPoint expects positive nsample")); } void Compute(OpKernelContext* context) override { const Tensor& xyz1_tensor = context->input(0); OP_REQUIRES(context, xyz1_tensor.dims()==3 && xyz1_tensor.shape().dim_size(2)==3, errors::InvalidArgument("QueryBallPoint expects (batch_size, ndataset, 3) xyz1 shape.")); int b = xyz1_tensor.shape().dim_size(0); int n = xyz1_tensor.shape().dim_size(1); const Tensor& xyz2_tensor = context->input(1); OP_REQUIRES(context, xyz2_tensor.dims()==3 && xyz2_tensor.shape().dim_size(2)==3, errors::InvalidArgument("QueryBallPoint expects (batch_size, npoint, 3) xyz2 shape.")); int m = xyz2_tensor.shape().dim_size(1); Tensor *idx_tensor = nullptr; OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape{b,m,nsample_}, &idx_tensor)); Tensor *pts_cnt_tensor = nullptr; OP_REQUIRES_OK(context, context->allocate_output(1, TensorShape{b,m}, &pts_cnt_tensor)); auto xyz1_flat = xyz1_tensor.flat(); const float *xyz1 = &(xyz1_flat(0)); auto xyz2_flat = xyz2_tensor.flat(); const float *xyz2 = &(xyz2_flat(0)); auto idx_flat = idx_tensor->flat(); int *idx = &(idx_flat(0)); auto pts_cnt_flat = pts_cnt_tensor->flat(); int *pts_cnt = &(pts_cnt_flat(0)); queryBallPointLauncher(b,n,m,radius_,nsample_,xyz1,xyz2,idx,pts_cnt); } private: float radius_; int nsample_; }; REGISTER_KERNEL_BUILDER(Name("QueryBallPoint").Device(DEVICE_GPU), QueryBallPointGpuOp); void selectionSortLauncher(int b, int n, int m, int k, const float *dist, int *outi, float *out); class SelectionSortGpuOp : public OpKernel { public: explicit SelectionSortGpuOp(OpKernelConstruction* context) : OpKernel(context) { OP_REQUIRES_OK(context, context->GetAttr("k", &k_)); OP_REQUIRES(context, k_ > 0, errors::InvalidArgument("SelectionSort expects positive k")); } void Compute(OpKernelContext* context) override { const Tensor& dist_tensor = context->input(0); OP_REQUIRES(context, dist_tensor.dims()==3, errors::InvalidArgument("SelectionSort expects (b,m,n) dist shape.")); int b = dist_tensor.shape().dim_size(0); int m = dist_tensor.shape().dim_size(1); int n = dist_tensor.shape().dim_size(2); Tensor *outi_tensor = nullptr; OP_REQUIRES_OK(context, context->allocate_output(0, TensorShape{b,m,n}, &outi_tensor)); Tensor *out_tensor = nullptr; OP_REQUIRES_OK(context, context->allocate_output(1, TensorShape{b,m,n}, &out_tensor)); auto dist_flat = dist_tensor.flat(); const float *dist = &(dist_flat(0)); auto outi_flat = outi_tensor->flat(); int *outi = &(outi_flat(0)); auto out_flat = out_tensor->flat(); float *out = &(out_flat(0)); selectionSortLauncher(b,n,m,k_,dist,outi,out); } private: int k_; }; REGISTER_KERNEL_BUILDER(Name("SelectionSort").Device(DEVICE_GPU), SelectionSortGpuOp); void groupPointLauncher(int b, int n, int c, int m, int nsample, const float *points, const int *idx, float *out); class GroupPointGpuOp: public OpKernel{ public: explicit GroupPointGpuOp(OpKernelConstruction * context):OpKernel(context){} void Compute(OpKernelContext * context) override { const Tensor& points_tensor=context->input(0); OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("GroupPoint expects (batch_size, num_points, channel) points shape")); int b = points_tensor.shape().dim_size(0); int n = points_tensor.shape().dim_size(1); int c = points_tensor.shape().dim_size(2); const Tensor& idx_tensor=context->input(1); OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b, errors::InvalidArgument("GroupPoint expects (batch_size, npoints, nsample) idx shape")); int m = idx_tensor.shape().dim_size(1); int nsample = idx_tensor.shape().dim_size(2); Tensor * out_tensor = nullptr; OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,m,nsample,c}, &out_tensor)); auto points_flat = points_tensor.flat(); const float *points = &(points_flat(0)); auto idx_flat = idx_tensor.flat(); const int *idx = &(idx_flat(0)); auto out_flat = out_tensor->flat(); float *out = &(out_flat(0)); groupPointLauncher(b,n,c,m,nsample,points,idx,out); } }; REGISTER_KERNEL_BUILDER(Name("GroupPoint").Device(DEVICE_GPU),GroupPointGpuOp); void groupPointGradLauncher(int b, int n, int c, int m, int nsample, const float *grad_out, const int *idx, float *grad_points); class GroupPointGradGpuOp: public OpKernel{ public: explicit GroupPointGradGpuOp(OpKernelConstruction * context):OpKernel(context){} void Compute(OpKernelContext * context) override { const Tensor& points_tensor=context->input(0); OP_REQUIRES(context, points_tensor.dims()==3, errors::InvalidArgument("GroupPointGrad expects (batch_size, num_points, channel) points shape")); int b = points_tensor.shape().dim_size(0); int n = points_tensor.shape().dim_size(1); int c = points_tensor.shape().dim_size(2); const Tensor& idx_tensor=context->input(1); OP_REQUIRES(context,idx_tensor.dims()==3 && idx_tensor.shape().dim_size(0)==b, errors::InvalidArgument("GroupPointGrad expects (batch_size, npoints, nsample) idx shape")); int m = idx_tensor.shape().dim_size(1); int nsample = idx_tensor.shape().dim_size(2); const Tensor& grad_out_tensor=context->input(2); OP_REQUIRES(context,grad_out_tensor.dims()==4 && grad_out_tensor.shape().dim_size(0)==b && grad_out_tensor.shape().dim_size(1)==m && grad_out_tensor.shape().dim_size(2)==nsample && grad_out_tensor.shape().dim_size(3)==c, errors::InvalidArgument("GroupPointGrad expects (batch_size, npoints, nsample, channel) grad_out shape")); Tensor * grad_points_tensor = nullptr; OP_REQUIRES_OK(context, context->allocate_output(0,TensorShape{b,n,c}, &grad_points_tensor)); auto points_flat = points_tensor.flat(); const float *points = &(points_flat(0)); auto idx_flat = idx_tensor.flat(); const int *idx = &(idx_flat(0)); auto grad_out_flat = grad_out_tensor.flat(); const float *grad_out = &(grad_out_flat(0)); auto grad_points_flat = grad_points_tensor->flat(); float *grad_points = &(grad_points_flat(0)); cudaMemset(grad_points, 0, sizeof(float)*b*n*c); groupPointGradLauncher(b,n,c,m,nsample,grad_out,idx,grad_points); } }; REGISTER_KERNEL_BUILDER(Name("GroupPointGrad").Device(DEVICE_GPU),GroupPointGradGpuOp); ================================================ FILE: pointnet2_tf/tf_ops/grouping/tf_grouping.py ================================================ import tensorflow as tf from tensorflow.python.framework import ops import sys import os BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) grouping_module=tf.load_op_library(os.path.join(BASE_DIR, 'tf_grouping_so.so')) def query_ball_point(radius, nsample, xyz1, xyz2): ''' Input: radius: float32, ball search radius nsample: int32, number of points selected in each ball region xyz1: (batch_size, ndataset, 3) float32 array, input points xyz2: (batch_size, npoint, 3) float32 array, query points Output: idx: (batch_size, npoint, nsample) int32 array, indices to input points pts_cnt: (batch_size, npoint) int32 array, number of unique points in each local region ''' #return grouping_module.query_ball_point(radius, nsample, xyz1, xyz2) return grouping_module.query_ball_point(xyz1, xyz2, radius, nsample) ops.NoGradient('QueryBallPoint') def select_top_k(k, dist): ''' Input: k: int32, number of k SMALLEST elements selected dist: (b,m,n) float32 array, distance matrix, m query points, n dataset points Output: idx: (b,m,n) int32 array, first k in n are indices to the top k dist_out: (b,m,n) float32 array, first k in n are the top k ''' return grouping_module.selection_sort(dist, k) ops.NoGradient('SelectionSort') def group_point(points, idx): ''' Input: points: (batch_size, ndataset, channel) float32 array, points to sample from idx: (batch_size, npoint, nsample) int32 array, indices to points Output: out: (batch_size, npoint, nsample, channel) float32 array, values sampled from points ''' return grouping_module.group_point(points, idx) @tf.RegisterGradient('GroupPoint') def _group_point_grad(op, grad_out): points = op.inputs[0] idx = op.inputs[1] return [grouping_module.group_point_grad(points, idx, grad_out), None] def knn_point(k, xyz1, xyz2): ''' Input: k: int32, number of k in k-nn search xyz1: (batch_size, ndataset, c) float32 array, input points xyz2: (batch_size, npoint, c) float32 array, query points Output: val: (batch_size, npoint, k) float32 array, L2 distances idx: (batch_size, npoint, k) int32 array, indices to input points ''' b = xyz1.get_shape()[0].value n = xyz1.get_shape()[1].value c = xyz1.get_shape()[2].value m = xyz2.get_shape()[1].value print b, n, c, m print xyz1, (b,1,n,c) xyz1 = tf.tile(tf.reshape(xyz1, (b,1,n,c)), [1,m,1,1]) xyz2 = tf.tile(tf.reshape(xyz2, (b,m,1,c)), [1,1,n,1]) dist = tf.reduce_sum((xyz1-xyz2)**2, -1) print dist, k outi, out = select_top_k(k, dist) idx = tf.slice(outi, [0,0,0], [-1,-1,k]) val = tf.slice(out, [0,0,0], [-1,-1,k]) print idx, val #val, idx = tf.nn.top_k(-dist, k=k) # ONLY SUPPORT CPU return val, idx if __name__=='__main__': knn=True import numpy as np import time np.random.seed(100) pts = np.random.random((32,512,64)).astype('float32') tmp1 = np.random.random((32,512,3)).astype('float32') tmp2 = np.random.random((32,128,3)).astype('float32') with tf.device('/gpu:1'): points = tf.constant(pts) xyz1 = tf.constant(tmp1) xyz2 = tf.constant(tmp2) radius = 0.1 nsample = 64 if knn: _, idx = knn_point(nsample, xyz1, xyz2) grouped_points = group_point(points, idx) else: idx, _ = query_ball_point(radius, nsample, xyz1, xyz2) grouped_points = group_point(points, idx) #grouped_points_grad = tf.ones_like(grouped_points) #points_grad = tf.gradients(grouped_points, points, grouped_points_grad) with tf.Session('') as sess: now = time.time() for _ in range(100): ret = sess.run(grouped_points) print time.time() - now print ret.shape, ret.dtype print ret ================================================ FILE: pointnet2_tf/tf_ops/grouping/tf_grouping_compile.sh ================================================ #/bin/bash /usr/local/cuda-8.0/bin/nvcc tf_grouping_g.cu -o tf_grouping_g.cu.o -c -O2 -DGOOGLE_CUDA=1 -x cu -Xcompiler -fPIC # TF1.2 g++ -std=c++11 tf_grouping.cpp tf_grouping_g.cu.o -o tf_grouping_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -lcudart -L /usr/local/cuda-8.0/lib64/ -O2 -D_GLIBCXX_USE_CXX11_ABI=0 # TF1.4 #g++ -std=c++11 tf_grouping.cpp tf_grouping_g.cu.o -o tf_grouping_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -I /usr/local/lib/python2.7/dist-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-8.0/lib64/ -L/usr/local/lib/python2.7/dist-packages/tensorflow -ltensorflow_framework -O2 -D_GLIBCXX_USE_CXX11_ABI=0 ================================================ FILE: pointnet2_tf/tf_ops/grouping/tf_grouping_g.cu ================================================ // input: radius (1), nsample (1), xyz1 (b,n,3), xyz2 (b,m,3) // output: idx (b,m,nsample), pts_cnt (b,m) __global__ void query_ball_point_gpu(int b, int n, int m, float radius, int nsample, const float *xyz1, const float *xyz2, int *idx, int *pts_cnt) { int batch_index = blockIdx.x; xyz1 += n*3*batch_index; xyz2 += m*3*batch_index; idx += m*nsample*batch_index; pts_cnt += m*batch_index; // counting how many unique points selected in local region int index = threadIdx.x; int stride = blockDim.x; for (int j=index;j>>(b,n,m,radius,nsample,xyz1,xyz2,idx,pts_cnt); //cudaDeviceSynchronize(); } void selectionSortLauncher(int b, int n, int m, int k, const float *dist, int *outi, float *out) { selection_sort_gpu<<>>(b,n,m,k,dist,outi,out); //cudaDeviceSynchronize(); } void groupPointLauncher(int b, int n, int c, int m, int nsample, const float *points, const int *idx, float *out){ group_point_gpu<<>>(b,n,c,m,nsample,points,idx,out); //cudaDeviceSynchronize(); } void groupPointGradLauncher(int b, int n, int c, int m, int nsample, const float *grad_out, const int *idx, float *grad_points){ group_point_grad_gpu<<>>(b,n,c,m,nsample,grad_out,idx,grad_points); //group_point_grad_gpu<<<1,1>>>(b,n,c,m,nsample,grad_out,idx,grad_points); //cudaDeviceSynchronize(); } ================================================ FILE: pointnet2_tf/tf_ops/grouping/tf_grouping_op_test.py ================================================ import tensorflow as tf import numpy as np from tf_grouping import query_ball_point, group_point class GroupPointTest(tf.test.TestCase): def test(self): pass def test_grad(self): with tf.device('/gpu:0'): points = tf.constant(np.random.random((1,128,16)).astype('float32')) print points xyz1 = tf.constant(np.random.random((1,128,3)).astype('float32')) xyz2 = tf.constant(np.random.random((1,8,3)).astype('float32')) radius = 0.3 nsample = 32 idx, pts_cnt = query_ball_point(radius, nsample, xyz1, xyz2) grouped_points = group_point(points, idx) print grouped_points with self.test_session(): print "---- Going to compute gradient error" err = tf.test.compute_gradient_error(points, (1,128,16), grouped_points, (1,8,32,16)) print err self.assertLess(err, 1e-4) if __name__=='__main__': tf.test.main() ================================================ FILE: pointnet2_tf/tf_ops/sampling/.gitignore ================================================ *.o *.so ================================================ FILE: pointnet2_tf/tf_ops/sampling/tf_sampling.cpp ================================================ /* Furthest point sampling * Original author: Haoqiang Fan * Modified by Charles R. Qi * All Rights Reserved. 2017. */ #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/shape_inference.h" #include "tensorflow/core/framework/common_shape_fns.h" #include using namespace tensorflow; REGISTER_OP("ProbSample") .Input("inp: float32") .Input("inpr: float32") .Output("out: int32") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { ::tensorflow::shape_inference::ShapeHandle dims1; // batch_size * ncategory c->WithRank(c->input(0), 2, &dims1); ::tensorflow::shape_inference::ShapeHandle dims2; // batch_size * npoints c->WithRank(c->input(1), 2, &dims2); // batch_size * npoints ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims2, 0), c->Dim(dims2, 1)}); c->set_output(0, output); return Status::OK(); }); REGISTER_OP("FarthestPointSample") .Attr("npoint: int") .Input("inp: float32") .Output("out: int32") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { ::tensorflow::shape_inference::ShapeHandle dims1; // batch_size * npoint * 3 c->WithRank(c->input(0), 3, &dims1); int npoint; TF_RETURN_IF_ERROR(c->GetAttr("npoint", &npoint)); ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims1, 0), npoint}); c->set_output(0, output); return Status::OK(); }); REGISTER_OP("GatherPoint") .Input("inp: float32") .Input("idx: int32") .Output("out: float32") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { ::tensorflow::shape_inference::ShapeHandle dims1; // batch_size * ndataset * 3 c->WithRank(c->input(0), 3, &dims1); ::tensorflow::shape_inference::ShapeHandle dims2; // batch_size * npoints c->WithRank(c->input(1), 2, &dims2); // batch_size * npoints * 3 ::tensorflow::shape_inference::ShapeHandle output = c->MakeShape({c->Dim(dims1, 0), c->Dim(dims2, 1), c->Dim(dims1, 2)}); c->set_output(0, output); return Status::OK(); }); REGISTER_OP("GatherPointGrad") .Input("inp: float32") .Input("idx: int32") .Input("out_g: float32") .Output("inp_g: float32") .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) { c->set_output(0, c->input(0)); return Status::OK(); }); void probsampleLauncher(int b,int n,int m,const float * inp_p,const float * inp_r,float * temp,int * out); class ProbSampleGpuOp: public OpKernel{ public: explicit ProbSampleGpuOp(OpKernelConstruction* context):OpKernel(context){} void Compute(OpKernelContext * context)override{ const Tensor& inp_tensor=context->input(0); const Tensor& inpr_tensor=context->input(1); auto inp_flat=inp_tensor.flat(); auto inpr_flat=inpr_tensor.flat(); const float * inp=&(inp_flat(0)); const float * inpr=&(inpr_flat(0)); OP_REQUIRES(context,inp_tensor.dims()==2,errors::InvalidArgument("ProbSample expects (batch_size,num_choices) inp shape")); int b=inp_tensor.shape().dim_size(0); int n=inp_tensor.shape().dim_size(1); OP_REQUIRES(context,inpr_tensor.dims()==2 && inpr_tensor.shape().dim_size(0)==b,errors::InvalidArgument("ProbSample expects (batch_size,num_points) inpr shape")); int m=inpr_tensor.shape().dim_size(1); Tensor * out_tensor=NULL; OP_REQUIRES_OK(context,context->allocate_output(0,TensorShape{b,m},&out_tensor)); auto out_flat=out_tensor->flat(); int * out=&(out_flat(0)); Tensor temp_tensor; OP_REQUIRES_OK(context,context->allocate_temp(DataTypeToEnum::value,TensorShape{b,n},&temp_tensor)); auto temp_flat=temp_tensor.flat(); float * temp=&(temp_flat(0)); probsampleLauncher(b,n,m,inp,inpr,temp,out); } }; REGISTER_KERNEL_BUILDER(Name("ProbSample").Device(DEVICE_GPU), ProbSampleGpuOp); void farthestpointsamplingLauncher(int b,int n,int m,const float * inp,float * temp,int * out); class FarthestPointSampleGpuOp: public OpKernel{ public: explicit FarthestPointSampleGpuOp(OpKernelConstruction* context):OpKernel(context) { OP_REQUIRES_OK(context, context->GetAttr("npoint", &npoint_)); OP_REQUIRES(context, npoint_ > 0, errors::InvalidArgument("FarthestPointSample expects positive npoint")); } void Compute(OpKernelContext * context)override{ int m = npoint_; const Tensor& inp_tensor=context->input(0); OP_REQUIRES(context,inp_tensor.dims()==3 && inp_tensor.shape().dim_size(2)==3,errors::InvalidArgument("FarthestPointSample expects (batch_size,num_points,3) inp shape")); int b=inp_tensor.shape().dim_size(0); int n=inp_tensor.shape().dim_size(1); auto inp_flat=inp_tensor.flat(); const float * inp=&(inp_flat(0)); Tensor * out_tensor; OP_REQUIRES_OK(context,context->allocate_output(0,TensorShape{b,m},&out_tensor)); auto out_flat=out_tensor->flat(); int * out=&(out_flat(0)); Tensor temp_tensor; OP_REQUIRES_OK(context,context->allocate_temp(DataTypeToEnum::value,TensorShape{32,n},&temp_tensor)); auto temp_flat=temp_tensor.flat(); float * temp=&(temp_flat(0)); farthestpointsamplingLauncher(b,n,m,inp,temp,out); } private: int npoint_; }; REGISTER_KERNEL_BUILDER(Name("FarthestPointSample").Device(DEVICE_GPU),FarthestPointSampleGpuOp); void gatherpointLauncher(int b,int n,int m,const float * inp,const int * idx,float * out); class GatherPointGpuOp: public OpKernel{ public: explicit GatherPointGpuOp(OpKernelConstruction * context):OpKernel(context){} void Compute(OpKernelContext * context)override{ const Tensor& inp_tensor=context->input(0); OP_REQUIRES(context,inp_tensor.dims()==3 && inp_tensor.shape().dim_size(2)==3,errors::InvalidArgument("GatherPoint expects (batch_size,num_points,3) inp shape")); int b=inp_tensor.shape().dim_size(0); int n=inp_tensor.shape().dim_size(1); const Tensor& idx_tensor=context->input(1); OP_REQUIRES(context,idx_tensor.dims()==2 && idx_tensor.shape().dim_size(0)==b,errors::InvalidArgument("GatherPoint expects (batch_size,num_result) idx shape")); int m=idx_tensor.shape().dim_size(1); auto inp_flat=inp_tensor.flat(); const float * inp=&(inp_flat(0)); auto idx_flat=idx_tensor.flat(); const int * idx=&(idx_flat(0)); Tensor * out_tensor=NULL; OP_REQUIRES_OK(context,context->allocate_output(0,TensorShape{b,m,3},&out_tensor)); auto out_flat=out_tensor->flat(); float * out=&(out_flat(0)); gatherpointLauncher(b,n,m,inp,idx,out); } }; REGISTER_KERNEL_BUILDER(Name("GatherPoint").Device(DEVICE_GPU),GatherPointGpuOp); void scatteraddpointLauncher(int b,int n,int m,const float * out_g,const int * idx,float * inp_g); class GatherPointGradGpuOp: public OpKernel{ public: explicit GatherPointGradGpuOp(OpKernelConstruction * context):OpKernel(context){} void Compute(OpKernelContext * context)override{ const Tensor& inp_tensor=context->input(0); OP_REQUIRES(context,inp_tensor.dims()==3 && inp_tensor.shape().dim_size(2)==3,errors::InvalidArgument("GatherPointGradGpuOp expects (batch_size,num_points,3) inp")); int b=inp_tensor.shape().dim_size(0); int n=inp_tensor.shape().dim_size(1); const Tensor& idx_tensor=context->input(1); OP_REQUIRES(context,idx_tensor.dims()==2 && idx_tensor.shape().dim_size(0)==b,errors::InvalidArgument("GatherPointGradGpuOp expects (batch_size,num_result) idx shape")); int m=idx_tensor.shape().dim_size(1); auto inp_flat=inp_tensor.flat(); const float * inp=&(inp_flat(0)); auto idx_flat=idx_tensor.flat(); const int * idx=&(idx_flat(0)); const Tensor& out_g_tensor=context->input(2); OP_REQUIRES(context,out_g_tensor.dims()==3 && out_g_tensor.shape().dim_size(0)==b && out_g_tensor.shape().dim_size(1)==m && out_g_tensor.shape().dim_size(2)==3,errors::InvalidArgument("GatherPointGradGpuOp expects (batch_size,num_result,3) out_g shape")); auto out_g_flat=out_g_tensor.flat(); const float * out_g=&(out_g_flat(0)); Tensor * inp_g_tensor=NULL; OP_REQUIRES_OK(context,context->allocate_output(0,TensorShape{b,n,3},&inp_g_tensor)); auto inp_g_flat=inp_g_tensor->flat(); float * inp_g=&(inp_g_flat(0)); cudaMemset(inp_g,0,b*n*3*4); scatteraddpointLauncher(b,n,m,out_g,idx,inp_g); } }; REGISTER_KERNEL_BUILDER(Name("GatherPointGrad").Device(DEVICE_GPU),GatherPointGradGpuOp); ================================================ FILE: pointnet2_tf/tf_ops/sampling/tf_sampling.py ================================================ ''' Furthest point sampling Original author: Haoqiang Fan Modified by Charles R. Qi All Rights Reserved. 2017. ''' import tensorflow as tf from tensorflow.python.framework import ops import sys import os BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) sampling_module=tf.load_op_library(os.path.join(BASE_DIR, 'tf_sampling_so.so')) def prob_sample(inp,inpr): ''' input: batch_size * ncategory float32 batch_size * npoints float32 returns: batch_size * npoints int32 ''' return sampling_module.prob_sample(inp,inpr) ops.NoGradient('ProbSample') # TF1.0 API requires set shape in C++ #@tf.RegisterShape('ProbSample') #def _prob_sample_shape(op): # shape1=op.inputs[0].get_shape().with_rank(2) # shape2=op.inputs[1].get_shape().with_rank(2) # return [tf.TensorShape([shape2.dims[0],shape2.dims[1]])] def gather_point(inp,idx): ''' input: batch_size * ndataset * 3 float32 batch_size * npoints int32 returns: batch_size * npoints * 3 float32 ''' return sampling_module.gather_point(inp,idx) #@tf.RegisterShape('GatherPoint') #def _gather_point_shape(op): # shape1=op.inputs[0].get_shape().with_rank(3) # shape2=op.inputs[1].get_shape().with_rank(2) # return [tf.TensorShape([shape1.dims[0],shape2.dims[1],shape1.dims[2]])] @tf.RegisterGradient('GatherPoint') def _gather_point_grad(op,out_g): inp=op.inputs[0] idx=op.inputs[1] return [sampling_module.gather_point_grad(inp,idx,out_g),None] def farthest_point_sample(npoint,inp): ''' input: int32 batch_size * ndataset * 3 float32 returns: batch_size * npoint int32 ''' return sampling_module.farthest_point_sample(inp, npoint) ops.NoGradient('FarthestPointSample') if __name__=='__main__': import numpy as np np.random.seed(100) triangles=np.random.rand(1,5,3,3).astype('float32') with tf.device('/gpu:1'): inp=tf.constant(triangles) tria=inp[:,:,0,:] trib=inp[:,:,1,:] tric=inp[:,:,2,:] areas=tf.sqrt(tf.reduce_sum(tf.cross(trib-tria,tric-tria)**2,2)+1e-9) randomnumbers=tf.random_uniform((1,8192)) triids=prob_sample(areas,randomnumbers) tria_sample=gather_point(tria,triids) trib_sample=gather_point(trib,triids) tric_sample=gather_point(tric,triids) us=tf.random_uniform((1,8192)) vs=tf.random_uniform((1,8192)) uplusv=1-tf.abs(us+vs-1) uminusv=us-vs us=(uplusv+uminusv)*0.5 vs=(uplusv-uminusv)*0.5 pt_sample=tria_sample+(trib_sample-tria_sample)*tf.expand_dims(us,-1)+(tric_sample-tria_sample)*tf.expand_dims(vs,-1) print 'pt_sample: ', pt_sample reduced_sample=gather_point(pt_sample,farthest_point_sample(1024,pt_sample)) print reduced_sample with tf.Session('') as sess: ret=sess.run(reduced_sample) print ret.shape,ret.dtype import cPickle as pickle pickle.dump(ret,open('1.pkl','wb'),-1) ================================================ FILE: pointnet2_tf/tf_ops/sampling/tf_sampling_compile.sh ================================================ #/bin/bash /usr/local/cuda-8.0/bin/nvcc tf_sampling_g.cu -o tf_sampling_g.cu.o -c -O2 -DGOOGLE_CUDA=1 -x cu -Xcompiler -fPIC # TF1.2 g++ -std=c++11 tf_sampling.cpp tf_sampling_g.cu.o -o tf_sampling_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -lcudart -L /usr/local/cuda-8.0/lib64/ -O2 -D_GLIBCXX_USE_CXX11_ABI=0 # TF1.4 #g++ -std=c++11 tf_sampling.cpp tf_sampling_g.cu.o -o tf_sampling_so.so -shared -fPIC -I /usr/local/lib/python2.7/dist-packages/tensorflow/include -I /usr/local/cuda-8.0/include -I /usr/local/lib/python2.7/dist-packages/tensorflow/include/external/nsync/public -lcudart -L /usr/local/cuda-8.0/lib64/ -L/usr/local/lib/python2.7/dist-packages/tensorflow -ltensorflow_framework -O2 -D_GLIBCXX_USE_CXX11_ABI=0 ================================================ FILE: pointnet2_tf/tf_ops/sampling/tf_sampling_g.cu ================================================ /* Furthest point sampling GPU implementation * Original author: Haoqiang Fan * Modified by Charles R. Qi * All Rights Reserved. 2017. */ __global__ void cumsumKernel(int b,int n,const float * __restrict__ inp,float * __restrict__ out){ const int BlockSize=2048; const int paddingLevel=5; __shared__ float buffer4[BlockSize*4]; __shared__ float buffer[BlockSize+(BlockSize>>paddingLevel)]; for (int i=blockIdx.x;i>2; for (int k=threadIdx.x*4;k>2)+(k>>(2+paddingLevel))]=v4; }else{ float v=0; for (int k2=k;k2>2)+(k>>(2+paddingLevel))]=v; } } int u=0; for (;(2<>(u+1));k+=blockDim.x){ int i1=(((k<<1)+2)<>paddingLevel; i2+=i2>>paddingLevel; buffer[i1]+=buffer[i2]; } } u--; for (;u>=0;u--){ __syncthreads(); for (int k=threadIdx.x;k>(u+1));k+=blockDim.x){ int i1=(((k<<1)+3)<>paddingLevel; i2+=i2>>paddingLevel; buffer[i1]+=buffer[i2]; } } __syncthreads(); for (int k=threadIdx.x*4;k>2)-1)+(((k>>2)-1)>>paddingLevel); buffer4[k]+=buffer[k2]; buffer4[k+1]+=buffer[k2]; buffer4[k+2]+=buffer[k2]; buffer4[k+3]+=buffer[k2]; } } __syncthreads(); for (int k=threadIdx.x;k>paddingLevel)]+runningsum2; float r2=runningsum+t; runningsum2=t-(r2-runningsum); runningsum=r2; __syncthreads(); } } } __global__ void binarysearchKernel(int b,int n,int m,const float * __restrict__ dataset,const float * __restrict__ query, int * __restrict__ result){ int base=1; while (base=1;k>>=1) if (r>=k && dataset[i*n+r-k]>=q) r-=k; result[i*m+j]=r; } } } __global__ void farthestpointsamplingKernel(int b,int n,int m,const float * __restrict__ dataset,float * __restrict__ temp,int * __restrict__ idxs){ if (m<=0) return; const int BlockSize=512; __shared__ float dists[BlockSize]; __shared__ int dists_i[BlockSize]; const int BufferSize=3072; __shared__ float buf[BufferSize*3]; for (int i=blockIdx.x;ibest){ best=d2; besti=k; } } dists[threadIdx.x]=best; dists_i[threadIdx.x]=besti; for (int u=0;(1<>(u+1))){ int i1=(threadIdx.x*2)<>>(b,n,inp,out); } //require b*n working space void probsampleLauncher(int b,int n,int m,const float * inp_p,const float * inp_r,float * temp,int * out){ cumsumKernel<<<32,512>>>(b,n,inp_p,temp); binarysearchKernel<<>>(b,n,m,temp,inp_r,out); } //require 32*n working space void farthestpointsamplingLauncher(int b,int n,int m,const float * inp,float * temp,int * out){ farthestpointsamplingKernel<<<32,512>>>(b,n,m,inp,temp,out); } void gatherpointLauncher(int b,int n,int m,const float * inp,const int * idx,float * out){ gatherpointKernel<<>>(b,n,m,inp,idx,out); } void scatteraddpointLauncher(int b,int n,int m,const float * out_g,const int * idx,float * inp_g){ scatteraddpointKernel<<>>(b,n,m,out_g,idx,inp_g); } ================================================ FILE: pointnet2_tf/train.py ================================================ ''' Single-GPU training. Will use H5 dataset in default. If using normal, will shift to the normal dataset. ''' import argparse import math from datetime import datetime import h5py import numpy as np import tensorflow as tf import socket import importlib import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = BASE_DIR sys.path.append(BASE_DIR) sys.path.append(os.path.join(ROOT_DIR, 'models')) sys.path.append(os.path.join(ROOT_DIR, 'utils')) import provider import tf_util import modelnet_dataset import modelnet_h5_dataset parser = argparse.ArgumentParser() parser.add_argument('--gpu', type=int, default=0, help='GPU to use [default: GPU 0]') parser.add_argument('--model', default='pointnet2_cls_ssg', help='Model name [default: pointnet2_cls_ssg]') parser.add_argument('--log_dir', default='log', help='Log dir [default: log]') parser.add_argument('--num_point', type=int, default=1024, help='Point Number [default: 1024]') parser.add_argument('--max_epoch', type=int, default=251, help='Epoch to run [default: 251]') parser.add_argument('--batch_size', type=int, default=16, help='Batch Size during training [default: 16]') parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]') parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]') parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]') parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]') parser.add_argument('--decay_rate', type=float, default=0.7, help='Decay rate for lr decay [default: 0.7]') parser.add_argument('--normal', action='store_true', help='Whether to use normal information') FLAGS = parser.parse_args() EPOCH_CNT = 0 BATCH_SIZE = FLAGS.batch_size NUM_POINT = FLAGS.num_point MAX_EPOCH = FLAGS.max_epoch BASE_LEARNING_RATE = FLAGS.learning_rate GPU_INDEX = FLAGS.gpu MOMENTUM = FLAGS.momentum OPTIMIZER = FLAGS.optimizer DECAY_STEP = FLAGS.decay_step DECAY_RATE = FLAGS.decay_rate MODEL = importlib.import_module(FLAGS.model) # import network module MODEL_FILE = os.path.join(ROOT_DIR, 'models', FLAGS.model+'.py') LOG_DIR = FLAGS.log_dir if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR) os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w') LOG_FOUT.write(str(FLAGS)+'\n') BN_INIT_DECAY = 0.5 BN_DECAY_DECAY_RATE = 0.5 BN_DECAY_DECAY_STEP = float(DECAY_STEP) BN_DECAY_CLIP = 0.99 HOSTNAME = socket.gethostname() NUM_CLASSES = 40 # Shapenet official train/test split if FLAGS.normal: assert(NUM_POINT<=10000) DATA_PATH = os.path.join(ROOT_DIR, 'data/modelnet40_normal_resampled') TRAIN_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='train', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE) TEST_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='test', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE) else: assert(NUM_POINT<=2048) TRAIN_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/train_files.txt'), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=True) TEST_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/test_files.txt'), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=False) def log_string(out_str): LOG_FOUT.write(out_str+'\n') LOG_FOUT.flush() print(out_str) def get_learning_rate(batch): learning_rate = tf.train.exponential_decay( BASE_LEARNING_RATE, # Base learning rate. batch * BATCH_SIZE, # Current index into the dataset. DECAY_STEP, # Decay step. DECAY_RATE, # Decay rate. staircase=True) learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE! return learning_rate def get_bn_decay(batch): bn_momentum = tf.train.exponential_decay( BN_INIT_DECAY, batch*BATCH_SIZE, BN_DECAY_DECAY_STEP, BN_DECAY_DECAY_RATE, staircase=True) bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum) return bn_decay def train(): with tf.Graph().as_default(): with tf.device('/gpu:'+str(GPU_INDEX)): pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT) is_training_pl = tf.placeholder(tf.bool, shape=()) # Note the global_step=batch parameter to minimize. # That tells the optimizer to helpfully increment the 'batch' parameter # for you every time it trains. batch = tf.get_variable('batch', [], initializer=tf.constant_initializer(0), trainable=False) bn_decay = get_bn_decay(batch) tf.summary.scalar('bn_decay', bn_decay) # Get model and loss pred, end_points = MODEL.get_model(pointclouds_pl, is_training_pl, bn_decay=bn_decay) MODEL.get_loss(pred, labels_pl, end_points) losses = tf.get_collection('losses') total_loss = tf.add_n(losses, name='total_loss') tf.summary.scalar('total_loss', total_loss) for l in losses + [total_loss]: tf.summary.scalar(l.op.name, l) correct = tf.equal(tf.argmax(pred, 1), tf.to_int64(labels_pl)) accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE) tf.summary.scalar('accuracy', accuracy) print "--- Get training operator" # Get training operator learning_rate = get_learning_rate(batch) tf.summary.scalar('learning_rate', learning_rate) if OPTIMIZER == 'momentum': optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM) elif OPTIMIZER == 'adam': optimizer = tf.train.AdamOptimizer(learning_rate) train_op = optimizer.minimize(total_loss, global_step=batch) # Add ops to save and restore all the variables. saver = tf.train.Saver() # Create a session config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True config.log_device_placement = False sess = tf.Session(config=config) # Add summary writers merged = tf.summary.merge_all() train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'), sess.graph) test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'), sess.graph) # Init variables init = tf.global_variables_initializer() sess.run(init) ops = {'pointclouds_pl': pointclouds_pl, 'labels_pl': labels_pl, 'is_training_pl': is_training_pl, 'pred': pred, 'loss': total_loss, 'train_op': train_op, 'merged': merged, 'step': batch, 'end_points': end_points} best_acc = -1 for epoch in range(MAX_EPOCH): log_string('**** EPOCH %03d ****' % (epoch)) sys.stdout.flush() train_one_epoch(sess, ops, train_writer) eval_one_epoch(sess, ops, test_writer) # Save the variables to disk. if epoch % 10 == 0: save_path = saver.save(sess, os.path.join(LOG_DIR, "model.ckpt")) log_string("Model saved in file: %s" % save_path) def train_one_epoch(sess, ops, train_writer): """ ops: dict mapping from string to tf ops """ is_training = True log_string(str(datetime.now())) # Make sure batch data is of same size cur_batch_data = np.zeros((BATCH_SIZE,NUM_POINT,TRAIN_DATASET.num_channel())) cur_batch_label = np.zeros((BATCH_SIZE), dtype=np.int32) total_correct = 0 total_seen = 0 loss_sum = 0 batch_idx = 0 while TRAIN_DATASET.has_next_batch(): batch_data, batch_label = TRAIN_DATASET.next_batch(augment=True) #batch_data = provider.random_point_dropout(batch_data) bsize = batch_data.shape[0] cur_batch_data[0:bsize,...] = batch_data cur_batch_label[0:bsize] = batch_label feed_dict = {ops['pointclouds_pl']: cur_batch_data, ops['labels_pl']: cur_batch_label, ops['is_training_pl']: is_training,} summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'], ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict) train_writer.add_summary(summary, step) pred_val = np.argmax(pred_val, 1) correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize]) total_correct += correct total_seen += bsize loss_sum += loss_val if (batch_idx+1)%50 == 0: log_string(' ---- batch: %03d ----' % (batch_idx+1)) log_string('mean loss: %f' % (loss_sum / 50)) log_string('accuracy: %f' % (total_correct / float(total_seen))) total_correct = 0 total_seen = 0 loss_sum = 0 batch_idx += 1 TRAIN_DATASET.reset() def eval_one_epoch(sess, ops, test_writer): """ ops: dict mapping from string to tf ops """ global EPOCH_CNT is_training = False # Make sure batch data is of same size cur_batch_data = np.zeros((BATCH_SIZE,NUM_POINT,TEST_DATASET.num_channel())) cur_batch_label = np.zeros((BATCH_SIZE), dtype=np.int32) total_correct = 0 total_seen = 0 loss_sum = 0 batch_idx = 0 shape_ious = [] total_seen_class = [0 for _ in range(NUM_CLASSES)] total_correct_class = [0 for _ in range(NUM_CLASSES)] log_string(str(datetime.now())) log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT)) while TEST_DATASET.has_next_batch(): batch_data, batch_label = TEST_DATASET.next_batch(augment=False) bsize = batch_data.shape[0] # for the last batch in the epoch, the bsize:end are from last batch cur_batch_data[0:bsize,...] = batch_data cur_batch_label[0:bsize] = batch_label feed_dict = {ops['pointclouds_pl']: cur_batch_data, ops['labels_pl']: cur_batch_label, ops['is_training_pl']: is_training} summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'], ops['loss'], ops['pred']], feed_dict=feed_dict) test_writer.add_summary(summary, step) pred_val = np.argmax(pred_val, 1) correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize]) total_correct += correct total_seen += bsize loss_sum += loss_val batch_idx += 1 for i in range(0, bsize): l = batch_label[i] total_seen_class[l] += 1 total_correct_class[l] += (pred_val[i] == l) log_string('eval mean loss: %f' % (loss_sum / float(batch_idx))) log_string('eval accuracy: %f'% (total_correct / float(total_seen))) log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float)))) EPOCH_CNT += 1 TEST_DATASET.reset() return total_correct/float(total_seen) if __name__ == "__main__": log_string('pid: %s'%(str(os.getpid()))) train() LOG_FOUT.close() ================================================ FILE: pointnet2_tf/train_multi_gpu.py ================================================ ''' Multi-GPU training. Near linear scale acceleration for multi-gpus on a single machine. Will use H5 dataset in default. If using normal, will shift to the normal dataset. ''' import argparse import math from datetime import datetime import h5py import numpy as np import tensorflow as tf import socket import importlib import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = BASE_DIR sys.path.append(BASE_DIR) sys.path.append(os.path.join(ROOT_DIR, 'models')) sys.path.append(os.path.join(ROOT_DIR, 'utils')) import provider import tf_util import modelnet_dataset import modelnet_h5_dataset parser = argparse.ArgumentParser() parser.add_argument('--num_gpus', type=int, default=1, help='How many gpus to use [default: 1]') parser.add_argument('--model', default='pointnet2_cls_ssg', help='Model name [default: pointnet2_cls_ssg]') parser.add_argument('--log_dir', default='log', help='Log dir [default: log]') parser.add_argument('--num_point', type=int, default=1024, help='Point Number [default: 1024]') parser.add_argument('--max_epoch', type=int, default=251, help='Epoch to run [default: 251]') parser.add_argument('--batch_size', type=int, default=32, help='Batch Size during training [default: 32]') parser.add_argument('--learning_rate', type=float, default=0.001, help='Initial learning rate [default: 0.001]') parser.add_argument('--momentum', type=float, default=0.9, help='Initial learning rate [default: 0.9]') parser.add_argument('--optimizer', default='adam', help='adam or momentum [default: adam]') parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]') parser.add_argument('--decay_rate', type=float, default=0.7, help='Decay rate for lr decay [default: 0.7]') parser.add_argument('--normal', action='store_true', help='Whether to use normal information') FLAGS = parser.parse_args() EPOCH_CNT = 0 NUM_GPUS = FLAGS.num_gpus BATCH_SIZE = FLAGS.batch_size assert(BATCH_SIZE % NUM_GPUS == 0) DEVICE_BATCH_SIZE = BATCH_SIZE / NUM_GPUS NUM_POINT = FLAGS.num_point MAX_EPOCH = FLAGS.max_epoch BASE_LEARNING_RATE = FLAGS.learning_rate MOMENTUM = FLAGS.momentum OPTIMIZER = FLAGS.optimizer DECAY_STEP = FLAGS.decay_step DECAY_RATE = FLAGS.decay_rate MODEL = importlib.import_module(FLAGS.model) # import network module MODEL_FILE = os.path.join(ROOT_DIR, 'models', FLAGS.model+'.py') LOG_DIR = FLAGS.log_dir if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR) os.system('cp %s %s' % (MODEL_FILE, LOG_DIR)) # bkp of model def os.system('cp train.py %s' % (LOG_DIR)) # bkp of train procedure LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w') LOG_FOUT.write(str(FLAGS)+'\n') BN_INIT_DECAY = 0.5 BN_DECAY_DECAY_RATE = 0.5 BN_DECAY_DECAY_STEP = float(DECAY_STEP) BN_DECAY_CLIP = 0.99 HOSTNAME = socket.gethostname() NUM_CLASSES = 40 # Shapenet official train/test split if FLAGS.normal: assert(NUM_POINT<=10000) DATA_PATH = os.path.join(ROOT_DIR, 'data/modelnet40_normal_resampled') TRAIN_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='train', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE) TEST_DATASET = modelnet_dataset.ModelNetDataset(root=DATA_PATH, npoints=NUM_POINT, split='test', normal_channel=FLAGS.normal, batch_size=BATCH_SIZE) else: assert(NUM_POINT<=2048) TRAIN_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/train_files.txt'), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=True) TEST_DATASET = modelnet_h5_dataset.ModelNetH5Dataset(os.path.join(BASE_DIR, 'data/modelnet40_ply_hdf5_2048/test_files.txt'), batch_size=BATCH_SIZE, npoints=NUM_POINT, shuffle=False) def log_string(out_str): LOG_FOUT.write(out_str+'\n') LOG_FOUT.flush() print(out_str) def average_gradients(tower_grads): """Calculate the average gradient for each shared variable across all towers. Note that this function provides a synchronization point across all towers. From tensorflow tutorial: cifar10/cifar10_multi_gpu_train.py Args: tower_grads: List of lists of (gradient, variable) tuples. The outer list is over individual gradients. The inner list is over the gradient calculation for each tower. Returns: List of pairs of (gradient, variable) where the gradient has been averaged across all towers. """ average_grads = [] for grad_and_vars in zip(*tower_grads): # Note that each grad_and_vars looks like the following: # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) grads = [] #for g, _ in grad_and_vars: for g, v in grad_and_vars: # Add 0 dimension to the gradients to represent the tower. expanded_g = tf.expand_dims(g, 0) # Append on a 'tower' dimension which we will average over below. grads.append(expanded_g) # Average over the 'tower' dimension. grad = tf.concat(axis=0, values=grads) grad = tf.reduce_mean(grad, 0) # Keep in mind that the Variables are redundant because they are shared # across towers. So .. we will just return the first tower's pointer to # the Variable. v = grad_and_vars[0][1] grad_and_var = (grad, v) average_grads.append(grad_and_var) return average_grads def get_learning_rate(batch): learning_rate = tf.train.exponential_decay( BASE_LEARNING_RATE, # Base learning rate. batch * BATCH_SIZE, # Current index into the dataset. DECAY_STEP, # Decay step. DECAY_RATE, # Decay rate. staircase=True) learning_rate = tf.maximum(learning_rate, 0.00001) # CLIP THE LEARNING RATE! return learning_rate def get_bn_decay(batch): bn_momentum = tf.train.exponential_decay( BN_INIT_DECAY, batch*BATCH_SIZE, BN_DECAY_DECAY_STEP, BN_DECAY_DECAY_RATE, staircase=True) bn_decay = tf.minimum(BN_DECAY_CLIP, 1 - bn_momentum) return bn_decay def train(): with tf.Graph().as_default(): with tf.device('/cpu:0'): pointclouds_pl, labels_pl = MODEL.placeholder_inputs(BATCH_SIZE, NUM_POINT) is_training_pl = tf.placeholder(tf.bool, shape=()) # Note the global_step=batch parameter to minimize. # That tells the optimizer to helpfully increment the 'batch' parameter # for you every time it trains. batch = tf.get_variable('batch', [], initializer=tf.constant_initializer(0), trainable=False) bn_decay = get_bn_decay(batch) tf.summary.scalar('bn_decay', bn_decay) # Set learning rate and optimizer learning_rate = get_learning_rate(batch) tf.summary.scalar('learning_rate', learning_rate) if OPTIMIZER == 'momentum': optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=MOMENTUM) elif OPTIMIZER == 'adam': optimizer = tf.train.AdamOptimizer(learning_rate) # ------------------------------------------- # Get model and loss on multiple GPU devices # ------------------------------------------- # Allocating variables on CPU first will greatly accelerate multi-gpu training. # Ref: https://github.com/kuza55/keras-extras/issues/21 MODEL.get_model(pointclouds_pl, is_training_pl, bn_decay=bn_decay) tower_grads = [] pred_gpu = [] total_loss_gpu = [] for i in range(NUM_GPUS): with tf.variable_scope(tf.get_variable_scope(), reuse=True): with tf.device('/gpu:%d'%(i)), tf.name_scope('gpu_%d'%(i)) as scope: # Evenly split input data to each GPU pc_batch = tf.slice(pointclouds_pl, [i*DEVICE_BATCH_SIZE,0,0], [DEVICE_BATCH_SIZE,-1,-1]) label_batch = tf.slice(labels_pl, [i*DEVICE_BATCH_SIZE], [DEVICE_BATCH_SIZE]) pred, end_points = MODEL.get_model(pc_batch, is_training=is_training_pl, bn_decay=bn_decay) MODEL.get_loss(pred, label_batch, end_points) losses = tf.get_collection('losses', scope) total_loss = tf.add_n(losses, name='total_loss') for l in losses + [total_loss]: tf.summary.scalar(l.op.name, l) grads = optimizer.compute_gradients(total_loss) tower_grads.append(grads) pred_gpu.append(pred) total_loss_gpu.append(total_loss) # Merge pred and losses from multiple GPUs pred = tf.concat(pred_gpu, 0) total_loss = tf.reduce_mean(total_loss_gpu) # Get training operator grads = average_gradients(tower_grads) train_op = optimizer.apply_gradients(grads, global_step=batch) correct = tf.equal(tf.argmax(pred, 1), tf.to_int64(labels_pl)) accuracy = tf.reduce_sum(tf.cast(correct, tf.float32)) / float(BATCH_SIZE) tf.summary.scalar('accuracy', accuracy) # Add ops to save and restore all the variables. saver = tf.train.Saver() # Create a session config = tf.ConfigProto() config.gpu_options.allow_growth = True config.allow_soft_placement = True config.log_device_placement = False sess = tf.Session(config=config) # Add summary writers merged = tf.summary.merge_all() train_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'train'), sess.graph) test_writer = tf.summary.FileWriter(os.path.join(LOG_DIR, 'test'), sess.graph) # Init variables init = tf.global_variables_initializer() sess.run(init) ops = {'pointclouds_pl': pointclouds_pl, 'labels_pl': labels_pl, 'is_training_pl': is_training_pl, 'pred': pred, 'loss': total_loss, 'train_op': train_op, 'merged': merged, 'step': batch, 'end_points': end_points} best_acc = -1 for epoch in range(MAX_EPOCH): log_string('**** EPOCH %03d ****' % (epoch)) sys.stdout.flush() train_one_epoch(sess, ops, train_writer) eval_one_epoch(sess, ops, test_writer) # Save the variables to disk. if epoch % 10 == 0: save_path = saver.save(sess, os.path.join(LOG_DIR, "model.ckpt")) log_string("Model saved in file: %s" % save_path) def train_one_epoch(sess, ops, train_writer): """ ops: dict mapping from string to tf ops """ is_training = True log_string(str(datetime.now())) # Make sure batch data is of same size cur_batch_data = np.zeros((BATCH_SIZE,NUM_POINT,TRAIN_DATASET.num_channel())) cur_batch_label = np.zeros((BATCH_SIZE), dtype=np.int32) total_correct = 0 total_seen = 0 loss_sum = 0 batch_idx = 0 while TRAIN_DATASET.has_next_batch(): batch_data, batch_label = TRAIN_DATASET.next_batch(augment=True) #batch_data = provider.random_point_dropout(batch_data) bsize = batch_data.shape[0] cur_batch_data[0:bsize,...] = batch_data cur_batch_label[0:bsize] = batch_label feed_dict = {ops['pointclouds_pl']: cur_batch_data, ops['labels_pl']: cur_batch_label, ops['is_training_pl']: is_training,} summary, step, _, loss_val, pred_val = sess.run([ops['merged'], ops['step'], ops['train_op'], ops['loss'], ops['pred']], feed_dict=feed_dict) train_writer.add_summary(summary, step) pred_val = np.argmax(pred_val, 1) correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize]) total_correct += correct total_seen += bsize loss_sum += loss_val if (batch_idx+1)%50 == 0: log_string(' ---- batch: %03d ----' % (batch_idx+1)) log_string('mean loss: %f' % (loss_sum / 50)) log_string('accuracy: %f' % (total_correct / float(total_seen))) total_correct = 0 total_seen = 0 loss_sum = 0 batch_idx += 1 TRAIN_DATASET.reset() def eval_one_epoch(sess, ops, test_writer): """ ops: dict mapping from string to tf ops """ global EPOCH_CNT is_training = False # Make sure batch data is of same size cur_batch_data = np.zeros((BATCH_SIZE,NUM_POINT,TEST_DATASET.num_channel())) cur_batch_label = np.zeros((BATCH_SIZE), dtype=np.int32) total_correct = 0 total_seen = 0 loss_sum = 0 batch_idx = 0 shape_ious = [] total_seen_class = [0 for _ in range(NUM_CLASSES)] total_correct_class = [0 for _ in range(NUM_CLASSES)] log_string(str(datetime.now())) log_string('---- EPOCH %03d EVALUATION ----'%(EPOCH_CNT)) while TEST_DATASET.has_next_batch(): batch_data, batch_label = TEST_DATASET.next_batch(augment=False) bsize = batch_data.shape[0] # for the last batch in the epoch, the bsize:end are from last batch cur_batch_data[0:bsize,...] = batch_data cur_batch_label[0:bsize] = batch_label feed_dict = {ops['pointclouds_pl']: cur_batch_data, ops['labels_pl']: cur_batch_label, ops['is_training_pl']: is_training} summary, step, loss_val, pred_val = sess.run([ops['merged'], ops['step'], ops['loss'], ops['pred']], feed_dict=feed_dict) test_writer.add_summary(summary, step) pred_val = np.argmax(pred_val, 1) correct = np.sum(pred_val[0:bsize] == batch_label[0:bsize]) total_correct += correct total_seen += bsize loss_sum += loss_val batch_idx += 1 for i in range(0, bsize): l = batch_label[i] total_seen_class[l] += 1 total_correct_class[l] += (pred_val[i] == l) log_string('eval mean loss: %f' % (loss_sum / float(batch_idx))) log_string('eval accuracy: %f'% (total_correct / float(total_seen))) log_string('eval avg class acc: %f' % (np.mean(np.array(total_correct_class)/np.array(total_seen_class,dtype=np.float)))) EPOCH_CNT += 1 TEST_DATASET.reset() return total_correct/float(total_seen) if __name__ == "__main__": log_string('pid: %s'%(str(os.getpid()))) train() LOG_FOUT.close() ================================================ FILE: pointnet2_tf/utils/README.md ================================================ ## Utilility Functions for 3D Point Cloud Deep Learning ### visualization tool sh compile_render_balls_so.sh python show3d_balls.py ================================================ FILE: pointnet2_tf/utils/compile_render_balls_so.sh ================================================ g++ -std=c++11 render_balls_so.cpp -o render_balls_so.so -shared -fPIC -O2 -D_GLIBCXX_USE_CXX11_ABI=0 ================================================ FILE: pointnet2_tf/utils/pc_util.py ================================================ """ Utility functions for processing point clouds. Author: Charles R. Qi, Hao Su Date: November 2016 """ import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) # Draw point cloud from eulerangles import euler2mat # Point cloud IO import numpy as np from plyfile import PlyData, PlyElement # ---------------------------------------- # Point Cloud/Volume Conversions # ---------------------------------------- def point_cloud_to_volume_batch(point_clouds, vsize=12, radius=1.0, flatten=True): """ Input is BxNx3 batch of point cloud Output is Bx(vsize^3) """ vol_list = [] for b in range(point_clouds.shape[0]): vol = point_cloud_to_volume(np.squeeze(point_clouds[b,:,:]), vsize, radius) if flatten: vol_list.append(vol.flatten()) else: vol_list.append(np.expand_dims(np.expand_dims(vol, -1), 0)) if flatten: return np.vstack(vol_list) else: return np.concatenate(vol_list, 0) def point_cloud_to_volume(points, vsize, radius=1.0): """ input is Nx3 points. output is vsize*vsize*vsize assumes points are in range [-radius, radius] """ vol = np.zeros((vsize,vsize,vsize)) voxel = 2*radius/float(vsize) locations = (points + radius)/voxel locations = locations.astype(int) vol[locations[:,0],locations[:,1],locations[:,2]] = 1.0 return vol #a = np.zeros((16,1024,3)) #print point_cloud_to_volume_batch(a, 12, 1.0, False).shape def volume_to_point_cloud(vol): """ vol is occupancy grid (value = 0 or 1) of size vsize*vsize*vsize return Nx3 numpy array. """ vsize = vol.shape[0] assert(vol.shape[1] == vsize and vol.shape[1] == vsize) points = [] for a in range(vsize): for b in range(vsize): for c in range(vsize): if vol[a,b,c] == 1: points.append(np.array([a,b,c])) if len(points) == 0: return np.zeros((0,3)) points = np.vstack(points) return points def point_cloud_to_volume_v2_batch(point_clouds, vsize=12, radius=1.0, num_sample=128): """ Input is BxNx3 a batch of point cloud Output is BxVxVxVxnum_samplex3 Added on Feb 19 """ vol_list = [] for b in range(point_clouds.shape[0]): vol = point_cloud_to_volume_v2(point_clouds[b,:,:], vsize, radius, num_sample) vol_list.append(np.expand_dims(vol, 0)) return np.concatenate(vol_list, 0) def point_cloud_to_volume_v2(points, vsize, radius=1.0, num_sample=128): """ input is Nx3 points output is vsize*vsize*vsize*num_sample*3 assumes points are in range [-radius, radius] samples num_sample points in each voxel, if there are less than num_sample points, replicate the points Added on Feb 19 """ vol = np.zeros((vsize,vsize,vsize,num_sample,3)) voxel = 2*radius/float(vsize) locations = (points + radius)/voxel locations = locations.astype(int) loc2pc = {} for n in range(points.shape[0]): loc = tuple(locations[n,:]) if loc not in loc2pc: loc2pc[loc] = [] loc2pc[loc].append(points[n,:]) #print loc2pc for i in range(vsize): for j in range(vsize): for k in range(vsize): if (i,j,k) not in loc2pc: vol[i,j,k,:,:] = np.zeros((num_sample,3)) else: pc = loc2pc[(i,j,k)] # a list of (3,) arrays pc = np.vstack(pc) # kx3 # Sample/pad to num_sample points if pc.shape[0]>num_sample: choices = np.random.choice(pc.shape[0], num_sample, replace=False) pc = pc[choices,:] elif pc.shape[0]num_sample: choices = np.random.choice(pc.shape[0], num_sample, replace=False) pc = pc[choices,:] elif pc.shape[0] 0) dx = mask[:, 0] dy = mask[:, 1] dv = disk[disk > 0] # Order points by z-buffer zorder = np.argsort(points[:, 2]) points = points[zorder, :] points[:, 2] = (points[:, 2] - np.min(points[:, 2])) / (np.max(points[:, 2] - np.min(points[:, 2]))) max_depth = np.max(points[:, 2]) for i in range(points.shape[0]): j = points.shape[0] - i - 1 x = points[j, 0] y = points[j, 1] xc = canvasSize/2 + (x*space) yc = canvasSize/2 + (y*space) xc = int(np.round(xc)) yc = int(np.round(yc)) px = dx + xc py = dy + yc image[px, py] = image[px, py] * 0.7 + dv * (max_depth - points[j, 2]) * 0.3 image = image / np.max(image) return image def point_cloud_three_views(points): """ input points Nx3 numpy array (+y is up direction). return an numpy array gray image of size 500x1500. """ # +y is up direction # xrot is azimuth # yrot is in-plane # zrot is elevation img1 = draw_point_cloud(points, zrot=110/180.0*np.pi, xrot=45/180.0*np.pi, yrot=0/180.0*np.pi) img2 = draw_point_cloud(points, zrot=70/180.0*np.pi, xrot=135/180.0*np.pi, yrot=0/180.0*np.pi) img3 = draw_point_cloud(points, zrot=180.0/180.0*np.pi, xrot=90/180.0*np.pi, yrot=0/180.0*np.pi) image_large = np.concatenate([img1, img2, img3], 1) return image_large def point_cloud_three_views_demo(): """ Demo for draw_point_cloud function """ from PIL import Image points = read_ply('../third_party/mesh_sampling/piano.ply') im_array = point_cloud_three_views(points) img = Image.fromarray(np.uint8(im_array*255.0)) img.save('piano.jpg') if __name__=="__main__": point_cloud_three_views_demo() def pyplot_draw_point_cloud(points, output_filename): """ points is a Nx3 numpy array """ import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.scatter(points[:,0], points[:,1], points[:,2]) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') #savefig(output_filename) def pyplot_draw_volume(vol, output_filename): """ vol is of size vsize*vsize*vsize output an image to output_filename """ points = volume_to_point_cloud(vol) pyplot_draw_point_cloud(points, output_filename) def write_ply_color(points, labels, out_filename, num_classes=None): """ Color (N,3) points with labels (N) within range 0 ~ num_classes-1 as OBJ file """ import matplotlib.pyplot as pyplot labels = labels.astype(int) N = points.shape[0] if num_classes is None: num_classes = np.max(labels)+1 else: assert(num_classes>np.max(labels)) fout = open(out_filename, 'w') #colors = [pyplot.cm.hsv(i/float(num_classes)) for i in range(num_classes)] colors = [pyplot.cm.jet(i/float(num_classes)) for i in range(num_classes)] for i in range(N): c = colors[labels[i]] c = [int(x*255) for x in c] fout.write('v %f %f %f %d %d %d\n' % (points[i,0],points[i,1],points[i,2],c[0],c[1],c[2])) fout.close() ================================================ FILE: pointnet2_tf/utils/pointnet_util.py ================================================ """ PointNet++ Layers Author: Charles R. Qi Date: November 2017 """ import os import sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) ROOT_DIR = os.path.dirname(BASE_DIR) sys.path.append(os.path.join(ROOT_DIR, 'utils')) sys.path.append(os.path.join(ROOT_DIR, 'tf_ops/sampling')) sys.path.append(os.path.join(ROOT_DIR, 'tf_ops/grouping')) sys.path.append(os.path.join(ROOT_DIR, 'tf_ops/3d_interpolation')) from tf_sampling import farthest_point_sample, gather_point from tf_grouping import query_ball_point, group_point, knn_point from tf_interpolate import three_nn, three_interpolate import tensorflow as tf import numpy as np import tf_util def sample_and_group(npoint, radius, nsample, xyz, points, knn=False, use_xyz=True): ''' Input: npoint: int32 radius: float32 nsample: int32 xyz: (batch_size, ndataset, 3) TF tensor points: (batch_size, ndataset, channel) TF tensor, if None will just use xyz as points knn: bool, if True use kNN instead of radius search use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features Output: new_xyz: (batch_size, npoint, 3) TF tensor new_points: (batch_size, npoint, nsample, 3+channel) TF tensor idx: (batch_size, npoint, nsample) TF tensor, indices of local points as in ndataset points grouped_xyz: (batch_size, npoint, nsample, 3) TF tensor, normalized point XYZs (subtracted by seed point XYZ) in local regions ''' new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz)) # (batch_size, npoint, 3) if knn: _,idx = knn_point(nsample, xyz, new_xyz) else: idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz) grouped_xyz = group_point(xyz, idx) # (batch_size, npoint, nsample, 3) grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1,1,nsample,1]) # translation normalization if points is not None: grouped_points = group_point(points, idx) # (batch_size, npoint, nsample, channel) if use_xyz: new_points = tf.concat([grouped_xyz, grouped_points], axis=-1) # (batch_size, npoint, nample, 3+channel) else: new_points = grouped_points else: new_points = grouped_xyz return new_xyz, new_points, idx, grouped_xyz def sample_and_group_all(xyz, points, use_xyz=True): ''' Inputs: xyz: (batch_size, ndataset, 3) TF tensor points: (batch_size, ndataset, channel) TF tensor, if None will just use xyz as points use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features Outputs: new_xyz: (batch_size, 1, 3) as (0,0,0) new_points: (batch_size, 1, ndataset, 3+channel) TF tensor Note: Equivalent to sample_and_group with npoint=1, radius=inf, use (0,0,0) as the centroid ''' batch_size = xyz.get_shape()[0].value nsample = xyz.get_shape()[1].value new_xyz = tf.constant(np.tile(np.array([0,0,0]).reshape((1,1,3)), (batch_size,1,1)),dtype=tf.float32) # (batch_size, 1, 3) idx = tf.constant(np.tile(np.array(range(nsample)).reshape((1,1,nsample)), (batch_size,1,1))) grouped_xyz = tf.reshape(xyz, (batch_size, 1, nsample, 3)) # (batch_size, npoint=1, nsample, 3) if points is not None: if use_xyz: new_points = tf.concat([xyz, points], axis=2) # (batch_size, 16, 259) else: new_points = points new_points = tf.expand_dims(new_points, 1) # (batch_size, 1, 16, 259) else: new_points = grouped_xyz return new_xyz, new_points, idx, grouped_xyz def pointnet_sa_module(xyz, points, npoint, radius, nsample, mlp, mlp2, group_all, is_training, bn_decay, scope, bn=True, pooling='max', knn=False, use_xyz=True, use_nchw=False): ''' PointNet Set Abstraction (SA) Module Input: xyz: (batch_size, ndataset, 3) TF tensor points: (batch_size, ndataset, channel) TF tensor npoint: int32 -- #points sampled in farthest point sampling radius: float32 -- search radius in local region nsample: int32 -- how many points in each local region mlp: list of int32 -- output size for MLP on each point mlp2: list of int32 -- output size for MLP on each region group_all: bool -- group all points into one PC if set true, OVERRIDE npoint, radius and nsample settings use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features use_nchw: bool, if True, use NCHW data format for conv2d, which is usually faster than NHWC format Return: new_xyz: (batch_size, npoint, 3) TF tensor new_points: (batch_size, npoint, mlp[-1] or mlp2[-1]) TF tensor idx: (batch_size, npoint, nsample) int32 -- indices for local regions ''' data_format = 'NCHW' if use_nchw else 'NHWC' with tf.variable_scope(scope) as sc: # Sample and Grouping if group_all: nsample = xyz.get_shape()[1].value new_xyz, new_points, idx, grouped_xyz = sample_and_group_all(xyz, points, use_xyz) else: new_xyz, new_points, idx, grouped_xyz = sample_and_group(npoint, radius, nsample, xyz, points, knn, use_xyz) # Point Feature Embedding if use_nchw: new_points = tf.transpose(new_points, [0,3,1,2]) for i, num_out_channel in enumerate(mlp): new_points = tf_util.conv2d(new_points, num_out_channel, [1,1], padding='VALID', stride=[1,1], bn=bn, is_training=is_training, scope='conv%d'%(i), bn_decay=bn_decay, data_format=data_format) if use_nchw: new_points = tf.transpose(new_points, [0,2,3,1]) # Pooling in Local Regions if pooling=='max': new_points = tf.reduce_max(new_points, axis=[2], keep_dims=True, name='maxpool') elif pooling=='avg': new_points = tf.reduce_mean(new_points, axis=[2], keep_dims=True, name='avgpool') elif pooling=='weighted_avg': with tf.variable_scope('weighted_avg'): dists = tf.norm(grouped_xyz,axis=-1,ord=2,keep_dims=True) exp_dists = tf.exp(-dists * 5) weights = exp_dists/tf.reduce_sum(exp_dists,axis=2,keep_dims=True) # (batch_size, npoint, nsample, 1) new_points *= weights # (batch_size, npoint, nsample, mlp[-1]) new_points = tf.reduce_sum(new_points, axis=2, keep_dims=True) elif pooling=='max_and_avg': max_points = tf.reduce_max(new_points, axis=[2], keep_dims=True, name='maxpool') avg_points = tf.reduce_mean(new_points, axis=[2], keep_dims=True, name='avgpool') new_points = tf.concat([avg_points, max_points], axis=-1) # [Optional] Further Processing if mlp2 is not None: if use_nchw: new_points = tf.transpose(new_points, [0,3,1,2]) for i, num_out_channel in enumerate(mlp2): new_points = tf_util.conv2d(new_points, num_out_channel, [1,1], padding='VALID', stride=[1,1], bn=bn, is_training=is_training, scope='conv_post_%d'%(i), bn_decay=bn_decay, data_format=data_format) if use_nchw: new_points = tf.transpose(new_points, [0,2,3,1]) new_points = tf.squeeze(new_points, [2]) # (batch_size, npoints, mlp2[-1]) return new_xyz, new_points, idx def pointnet_sa_module_msg(xyz, points, npoint, radius_list, nsample_list, mlp_list, is_training, bn_decay, scope, bn=True, use_xyz=True, use_nchw=False): ''' PointNet Set Abstraction (SA) module with Multi-Scale Grouping (MSG) Input: xyz: (batch_size, ndataset, 3) TF tensor points: (batch_size, ndataset, channel) TF tensor npoint: int32 -- #points sampled in farthest point sampling radius: list of float32 -- search radius in local region nsample: list of int32 -- how many points in each local region mlp: list of list of int32 -- output size for MLP on each point use_xyz: bool, if True concat XYZ with local point features, otherwise just use point features use_nchw: bool, if True, use NCHW data format for conv2d, which is usually faster than NHWC format Return: new_xyz: (batch_size, npoint, 3) TF tensor new_points: (batch_size, npoint, \sum_k{mlp[k][-1]}) TF tensor ''' data_format = 'NCHW' if use_nchw else 'NHWC' with tf.variable_scope(scope) as sc: new_xyz = gather_point(xyz, farthest_point_sample(npoint, xyz)) new_points_list = [] for i in range(len(radius_list)): radius = radius_list[i] nsample = nsample_list[i] idx, pts_cnt = query_ball_point(radius, nsample, xyz, new_xyz) grouped_xyz = group_point(xyz, idx) grouped_xyz -= tf.tile(tf.expand_dims(new_xyz, 2), [1,1,nsample,1]) if points is not None: grouped_points = group_point(points, idx) if use_xyz: grouped_points = tf.concat([grouped_points, grouped_xyz], axis=-1) else: grouped_points = grouped_xyz if use_nchw: grouped_points = tf.transpose(grouped_points, [0,3,1,2]) for j,num_out_channel in enumerate(mlp_list[i]): grouped_points = tf_util.conv2d(grouped_points, num_out_channel, [1,1], padding='VALID', stride=[1,1], bn=bn, is_training=is_training, scope='conv%d_%d'%(i,j), bn_decay=bn_decay) if use_nchw: grouped_points = tf.transpose(grouped_points, [0,2,3,1]) new_points = tf.reduce_max(grouped_points, axis=[2]) new_points_list.append(new_points) new_points_concat = tf.concat(new_points_list, axis=-1) return new_xyz, new_points_concat def pointnet_fp_module(xyz1, xyz2, points1, points2, mlp, is_training, bn_decay, scope, bn=True): ''' PointNet Feature Propogation (FP) Module Input: xyz1: (batch_size, ndataset1, 3) TF tensor xyz2: (batch_size, ndataset2, 3) TF tensor, sparser than xyz1 points1: (batch_size, ndataset1, nchannel1) TF tensor points2: (batch_size, ndataset2, nchannel2) TF tensor mlp: list of int32 -- output size for MLP on each point Return: new_points: (batch_size, ndataset1, mlp[-1]) TF tensor ''' with tf.variable_scope(scope) as sc: dist, idx = three_nn(xyz1, xyz2) dist = tf.maximum(dist, 1e-10) norm = tf.reduce_sum((1.0/dist),axis=2,keep_dims=True) norm = tf.tile(norm,[1,1,3]) weight = (1.0/dist) / norm interpolated_points = three_interpolate(points2, idx, weight) if points1 is not None: new_points1 = tf.concat(axis=2, values=[interpolated_points, points1]) # B,ndataset1,nchannel1+nchannel2 else: new_points1 = interpolated_points new_points1 = tf.expand_dims(new_points1, 2) for i, num_out_channel in enumerate(mlp): new_points1 = tf_util.conv2d(new_points1, num_out_channel, [1,1], padding='VALID', stride=[1,1], bn=bn, is_training=is_training, scope='conv_%d'%(i), bn_decay=bn_decay) new_points1 = tf.squeeze(new_points1, [2]) # B,ndataset1,mlp[-1] return new_points1 ================================================ FILE: pointnet2_tf/utils/provider.py ================================================ import os import sys import numpy as np import h5py BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) def shuffle_data(data, labels): """ Shuffle data and labels. Input: data: B,N,... numpy array label: B,... numpy array Return: shuffled data, label and shuffle indices """ idx = np.arange(len(labels)) np.random.shuffle(idx) return data[idx, ...], labels[idx], idx def shuffle_points(batch_data): """ Shuffle orders of points in each point cloud -- changes FPS behavior. Use the same shuffling idx for the entire batch. Input: BxNxC array Output: BxNxC array """ idx = np.arange(batch_data.shape[1]) np.random.shuffle(idx) return batch_data[:,idx,:] def rotate_point_cloud(batch_data): """ Randomly rotate the point clouds to augument the dataset rotation is per shape based along up direction Input: BxNx3 array, original batch of point clouds Return: BxNx3 array, rotated batch of point clouds """ rotated_data = np.zeros(batch_data.shape, dtype=np.float32) for k in range(batch_data.shape[0]): rotation_angle = np.random.uniform() * 2 * np.pi cosval = np.cos(rotation_angle) sinval = np.sin(rotation_angle) rotation_matrix = np.array([[cosval, 0, sinval], [0, 1, 0], [-sinval, 0, cosval]]) shape_pc = batch_data[k, ...] rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) return rotated_data def rotate_point_cloud_z(batch_data): """ Randomly rotate the point clouds to augument the dataset rotation is per shape based along up direction Input: BxNx3 array, original batch of point clouds Return: BxNx3 array, rotated batch of point clouds """ rotated_data = np.zeros(batch_data.shape, dtype=np.float32) for k in range(batch_data.shape[0]): rotation_angle = np.random.uniform() * 2 * np.pi cosval = np.cos(rotation_angle) sinval = np.sin(rotation_angle) rotation_matrix = np.array([[cosval, sinval, 0], [-sinval, cosval, 0], [0, 0, 1]]) shape_pc = batch_data[k, ...] rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) return rotated_data def rotate_point_cloud_with_normal(batch_xyz_normal): ''' Randomly rotate XYZ, normal point cloud. Input: batch_xyz_normal: B,N,6, first three channels are XYZ, last 3 all normal Output: B,N,6, rotated XYZ, normal point cloud ''' for k in range(batch_xyz_normal.shape[0]): rotation_angle = np.random.uniform() * 2 * np.pi cosval = np.cos(rotation_angle) sinval = np.sin(rotation_angle) rotation_matrix = np.array([[cosval, 0, sinval], [0, 1, 0], [-sinval, 0, cosval]]) shape_pc = batch_xyz_normal[k,:,0:3] shape_normal = batch_xyz_normal[k,:,3:6] batch_xyz_normal[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) batch_xyz_normal[k,:,3:6] = np.dot(shape_normal.reshape((-1, 3)), rotation_matrix) return batch_xyz_normal def rotate_perturbation_point_cloud_with_normal(batch_data, angle_sigma=0.06, angle_clip=0.18): """ Randomly perturb the point clouds by small rotations Input: BxNx6 array, original batch of point clouds and point normals Return: BxNx3 array, rotated batch of point clouds """ rotated_data = np.zeros(batch_data.shape, dtype=np.float32) for k in range(batch_data.shape[0]): angles = np.clip(angle_sigma*np.random.randn(3), -angle_clip, angle_clip) Rx = np.array([[1,0,0], [0,np.cos(angles[0]),-np.sin(angles[0])], [0,np.sin(angles[0]),np.cos(angles[0])]]) Ry = np.array([[np.cos(angles[1]),0,np.sin(angles[1])], [0,1,0], [-np.sin(angles[1]),0,np.cos(angles[1])]]) Rz = np.array([[np.cos(angles[2]),-np.sin(angles[2]),0], [np.sin(angles[2]),np.cos(angles[2]),0], [0,0,1]]) R = np.dot(Rz, np.dot(Ry,Rx)) shape_pc = batch_data[k,:,0:3] shape_normal = batch_data[k,:,3:6] rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), R) rotated_data[k,:,3:6] = np.dot(shape_normal.reshape((-1, 3)), R) return rotated_data def rotate_point_cloud_by_angle(batch_data, rotation_angle): """ Rotate the point cloud along up direction with certain angle. Input: BxNx3 array, original batch of point clouds Return: BxNx3 array, rotated batch of point clouds """ rotated_data = np.zeros(batch_data.shape, dtype=np.float32) for k in range(batch_data.shape[0]): #rotation_angle = np.random.uniform() * 2 * np.pi cosval = np.cos(rotation_angle) sinval = np.sin(rotation_angle) rotation_matrix = np.array([[cosval, 0, sinval], [0, 1, 0], [-sinval, 0, cosval]]) shape_pc = batch_data[k,:,0:3] rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) return rotated_data def rotate_point_cloud_by_angle_with_normal(batch_data, rotation_angle): """ Rotate the point cloud along up direction with certain angle. Input: BxNx6 array, original batch of point clouds with normal scalar, angle of rotation Return: BxNx6 array, rotated batch of point clouds iwth normal """ rotated_data = np.zeros(batch_data.shape, dtype=np.float32) for k in range(batch_data.shape[0]): #rotation_angle = np.random.uniform() * 2 * np.pi cosval = np.cos(rotation_angle) sinval = np.sin(rotation_angle) rotation_matrix = np.array([[cosval, 0, sinval], [0, 1, 0], [-sinval, 0, cosval]]) shape_pc = batch_data[k,:,0:3] shape_normal = batch_data[k,:,3:6] rotated_data[k,:,0:3] = np.dot(shape_pc.reshape((-1, 3)), rotation_matrix) rotated_data[k,:,3:6] = np.dot(shape_normal.reshape((-1,3)), rotation_matrix) return rotated_data def rotate_perturbation_point_cloud(batch_data, angle_sigma=0.06, angle_clip=0.18): """ Randomly perturb the point clouds by small rotations Input: BxNx3 array, original batch of point clouds Return: BxNx3 array, rotated batch of point clouds """ rotated_data = np.zeros(batch_data.shape, dtype=np.float32) for k in range(batch_data.shape[0]): angles = np.clip(angle_sigma*np.random.randn(3), -angle_clip, angle_clip) Rx = np.array([[1,0,0], [0,np.cos(angles[0]),-np.sin(angles[0])], [0,np.sin(angles[0]),np.cos(angles[0])]]) Ry = np.array([[np.cos(angles[1]),0,np.sin(angles[1])], [0,1,0], [-np.sin(angles[1]),0,np.cos(angles[1])]]) Rz = np.array([[np.cos(angles[2]),-np.sin(angles[2]),0], [np.sin(angles[2]),np.cos(angles[2]),0], [0,0,1]]) R = np.dot(Rz, np.dot(Ry,Rx)) shape_pc = batch_data[k, ...] rotated_data[k, ...] = np.dot(shape_pc.reshape((-1, 3)), R) return rotated_data def jitter_point_cloud(batch_data, sigma=0.01, clip=0.05): """ Randomly jitter points. jittering is per point. Input: BxNx3 array, original batch of point clouds Return: BxNx3 array, jittered batch of point clouds """ B, N, C = batch_data.shape assert(clip > 0) jittered_data = np.clip(sigma * np.random.randn(B, N, C), -1*clip, clip) jittered_data += batch_data return jittered_data def shift_point_cloud(batch_data, shift_range=0.1): """ Randomly shift point cloud. Shift is per point cloud. Input: BxNx3 array, original batch of point clouds Return: BxNx3 array, shifted batch of point clouds """ B, N, C = batch_data.shape shifts = np.random.uniform(-shift_range, shift_range, (B,3)) for batch_index in range(B): batch_data[batch_index,:,:] += shifts[batch_index,:] return batch_data def random_scale_point_cloud(batch_data, scale_low=0.8, scale_high=1.25): """ Randomly scale the point cloud. Scale is per point cloud. Input: BxNx3 array, original batch of point clouds Return: BxNx3 array, scaled batch of point clouds """ B, N, C = batch_data.shape scales = np.random.uniform(scale_low, scale_high, B) for batch_index in range(B): batch_data[batch_index,:,:] *= scales[batch_index] return batch_data def random_point_dropout(batch_pc, max_dropout_ratio=0.875): ''' batch_pc: BxNx3 ''' for b in range(batch_pc.shape[0]): dropout_ratio = np.random.random()*max_dropout_ratio # 0~0.875 drop_idx = np.where(np.random.random((batch_pc.shape[1]))<=dropout_ratio)[0] if len(drop_idx)>0: batch_pc[b,drop_idx,:] = batch_pc[b,0,:] # set to the first point return batch_pc def getDataFiles(list_filename): return [line.rstrip() for line in open(list_filename)] def load_h5(h5_filename): f = h5py.File(h5_filename) data = f['data'][:] label = f['label'][:] return (data, label) def loadDataFile(filename): return load_h5(filename) ================================================ FILE: pointnet2_tf/utils/render_balls_so.cpp ================================================ #include #include #include #include using namespace std; struct PointInfo{ int x,y,z; float r,g,b; }; extern "C"{ void render_ball(int h,int w,unsigned char * show,int n,int * xyzs,float * c0,float * c1,float * c2,int r){ r=max(r,1); vector depth(h*w,-2100000000); vector pattern; for (int dx=-r;dx<=r;dx++) for (int dy=-r;dy<=r;dy++) if (dx*dx+dy*dy=h || y2<0 || y2>=w) && depth[x2*w+y2]0: show[:,:,0]=np.maximum(show[:,:,0],np.roll(show[:,:,0],1,axis=0)) if magnifyBlue>=2: show[:,:,0]=np.maximum(show[:,:,0],np.roll(show[:,:,0],-1,axis=0)) show[:,:,0]=np.maximum(show[:,:,0],np.roll(show[:,:,0],1,axis=1)) if magnifyBlue>=2: show[:,:,0]=np.maximum(show[:,:,0],np.roll(show[:,:,0],-1,axis=1)) if showrot: cv2.putText(show,'xangle %d'%(int(xangle/np.pi*180)),(30,showsz-30),0,0.5,cv2.cv.CV_RGB(255,0,0)) cv2.putText(show,'yangle %d'%(int(yangle/np.pi*180)),(30,showsz-50),0,0.5,cv2.cv.CV_RGB(255,0,0)) cv2.putText(show,'zoom %d%%'%(int(zoom*100)),(30,showsz-70),0,0.5,cv2.cv.CV_RGB(255,0,0)) changed=True while True: if changed: render() changed=False cv2.imshow('show3d',show) if waittime==0: cmd=cv2.waitKey(10)%256 else: cmd=cv2.waitKey(waittime)%256 if cmd==ord('q'): break elif cmd==ord('Q'): sys.exit(0) if cmd==ord('t') or cmd == ord('p'): if cmd == ord('t'): if c_gt is None: c0=np.zeros((len(xyz),),dtype='float32')+255 c1=np.zeros((len(xyz),),dtype='float32')+255 c2=np.zeros((len(xyz),),dtype='float32')+255 else: c0=c_gt[:,0] c1=c_gt[:,1] c2=c_gt[:,2] else: if c_pred is None: c0=np.zeros((len(xyz),),dtype='float32')+255 c1=np.zeros((len(xyz),),dtype='float32')+255 c2=np.zeros((len(xyz),),dtype='float32')+255 else: c0=c_pred[:,0] c1=c_pred[:,1] c2=c_pred[:,2] if normalizecolor: c0/=(c0.max()+1e-14)/255.0 c1/=(c1.max()+1e-14)/255.0 c2/=(c2.max()+1e-14)/255.0 c0=np.require(c0,'float32','C') c1=np.require(c1,'float32','C') c2=np.require(c2,'float32','C') changed = True if cmd==ord('n'): zoom*=1.1 changed=True elif cmd==ord('m'): zoom/=1.1 changed=True elif cmd==ord('r'): zoom=1.0 changed=True elif cmd==ord('s'): cv2.imwrite('show3d.png',show) if waittime!=0: break return cmd if __name__=='__main__': np.random.seed(100) showpoints(np.random.randn(2500,3)) ================================================ FILE: pointnet2_tf/utils/tf_util.py ================================================ """ Wrapper functions for TensorFlow layers. Author: Charles R. Qi Date: November 2017 """ import numpy as np import tensorflow as tf def _variable_on_cpu(name, shape, initializer, use_fp16=False): """Helper to create a Variable stored on CPU memory. Args: name: name of the variable shape: list of ints initializer: initializer for Variable Returns: Variable Tensor """ with tf.device("/cpu:0"): dtype = tf.float16 if use_fp16 else tf.float32 var = tf.get_variable(name, shape, initializer=initializer, dtype=dtype) return var def _variable_with_weight_decay(name, shape, stddev, wd, use_xavier=True): """Helper to create an initialized Variable with weight decay. Note that the Variable is initialized with a truncated normal distribution. A weight decay is added only if one is specified. Args: name: name of the variable shape: list of ints stddev: standard deviation of a truncated Gaussian wd: add L2Loss weight decay multiplied by this float. If None, weight decay is not added for this Variable. use_xavier: bool, whether to use xavier initializer Returns: Variable Tensor """ if use_xavier: initializer = tf.contrib.layers.xavier_initializer() else: initializer = tf.truncated_normal_initializer(stddev=stddev) var = _variable_on_cpu(name, shape, initializer) if wd is not None: weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss') tf.add_to_collection('losses', weight_decay) return var def conv1d(inputs, num_output_channels, kernel_size, scope, stride=1, padding='SAME', data_format='NHWC', use_xavier=True, stddev=1e-3, weight_decay=None, activation_fn=tf.nn.relu, bn=False, bn_decay=None, is_training=None): """ 1D convolution with non-linear operation. Args: inputs: 3-D tensor variable BxLxC num_output_channels: int kernel_size: int scope: string stride: int padding: 'SAME' or 'VALID' data_format: 'NHWC' or 'NCHW' use_xavier: bool, use xavier_initializer if true stddev: float, stddev for truncated_normal init weight_decay: float activation_fn: function bn: bool, whether to use batch norm bn_decay: float or float tensor variable in [0,1] is_training: bool Tensor variable Returns: Variable tensor """ with tf.variable_scope(scope) as sc: assert(data_format=='NHWC' or data_format=='NCHW') if data_format == 'NHWC': num_in_channels = inputs.get_shape()[-1].value elif data_format=='NCHW': num_in_channels = inputs.get_shape()[1].value kernel_shape = [kernel_size, num_in_channels, num_output_channels] kernel = _variable_with_weight_decay('weights', shape=kernel_shape, use_xavier=use_xavier, stddev=stddev, wd=weight_decay) outputs = tf.nn.conv1d(inputs, kernel, stride=stride, padding=padding, data_format=data_format) biases = _variable_on_cpu('biases', [num_output_channels], tf.constant_initializer(0.0)) outputs = tf.nn.bias_add(outputs, biases, data_format=data_format) if bn: outputs = batch_norm_for_conv1d(outputs, is_training, bn_decay=bn_decay, scope='bn', data_format=data_format) if activation_fn is not None: outputs = activation_fn(outputs) return outputs def conv2d(inputs, num_output_channels, kernel_size, scope, stride=[1, 1], padding='SAME', data_format='NHWC', use_xavier=True, stddev=1e-3, weight_decay=None, activation_fn=tf.nn.relu, bn=False, bn_decay=None, is_training=None): """ 2D convolution with non-linear operation. Args: inputs: 4-D tensor variable BxHxWxC num_output_channels: int kernel_size: a list of 2 ints scope: string stride: a list of 2 ints padding: 'SAME' or 'VALID' data_format: 'NHWC' or 'NCHW' use_xavier: bool, use xavier_initializer if true stddev: float, stddev for truncated_normal init weight_decay: float activation_fn: function bn: bool, whether to use batch norm bn_decay: float or float tensor variable in [0,1] is_training: bool Tensor variable Returns: Variable tensor """ with tf.variable_scope(scope) as sc: kernel_h, kernel_w = kernel_size assert(data_format=='NHWC' or data_format=='NCHW') if data_format == 'NHWC': num_in_channels = inputs.get_shape()[-1].value elif data_format=='NCHW': num_in_channels = inputs.get_shape()[1].value kernel_shape = [kernel_h, kernel_w, num_in_channels, num_output_channels] kernel = _variable_with_weight_decay('weights', shape=kernel_shape, use_xavier=use_xavier, stddev=stddev, wd=weight_decay) stride_h, stride_w = stride outputs = tf.nn.conv2d(inputs, kernel, [1, stride_h, stride_w, 1], padding=padding, data_format=data_format) biases = _variable_on_cpu('biases', [num_output_channels], tf.constant_initializer(0.0)) outputs = tf.nn.bias_add(outputs, biases, data_format=data_format) if bn: outputs = batch_norm_for_conv2d(outputs, is_training, bn_decay=bn_decay, scope='bn', data_format=data_format) if activation_fn is not None: outputs = activation_fn(outputs) return outputs def conv2d_transpose(inputs, num_output_channels, kernel_size, scope, stride=[1, 1], padding='SAME', use_xavier=True, stddev=1e-3, weight_decay=None, activation_fn=tf.nn.relu, bn=False, bn_decay=None, is_training=None): """ 2D convolution transpose with non-linear operation. Args: inputs: 4-D tensor variable BxHxWxC num_output_channels: int kernel_size: a list of 2 ints scope: string stride: a list of 2 ints padding: 'SAME' or 'VALID' use_xavier: bool, use xavier_initializer if true stddev: float, stddev for truncated_normal init weight_decay: float activation_fn: function bn: bool, whether to use batch norm bn_decay: float or float tensor variable in [0,1] is_training: bool Tensor variable Returns: Variable tensor Note: conv2d(conv2d_transpose(a, num_out, ksize, stride), a.shape[-1], ksize, stride) == a """ with tf.variable_scope(scope) as sc: kernel_h, kernel_w = kernel_size num_in_channels = inputs.get_shape()[-1].value kernel_shape = [kernel_h, kernel_w, num_output_channels, num_in_channels] # reversed to conv2d kernel = _variable_with_weight_decay('weights', shape=kernel_shape, use_xavier=use_xavier, stddev=stddev, wd=weight_decay) stride_h, stride_w = stride # from slim.convolution2d_transpose def get_deconv_dim(dim_size, stride_size, kernel_size, padding): dim_size *= stride_size if padding == 'VALID' and dim_size is not None: dim_size += max(kernel_size - stride_size, 0) return dim_size # caculate output shape batch_size = inputs.get_shape()[0].value height = inputs.get_shape()[1].value width = inputs.get_shape()[2].value out_height = get_deconv_dim(height, stride_h, kernel_h, padding) out_width = get_deconv_dim(width, stride_w, kernel_w, padding) output_shape = [batch_size, out_height, out_width, num_output_channels] outputs = tf.nn.conv2d_transpose(inputs, kernel, output_shape, [1, stride_h, stride_w, 1], padding=padding) biases = _variable_on_cpu('biases', [num_output_channels], tf.constant_initializer(0.0)) outputs = tf.nn.bias_add(outputs, biases) if bn: outputs = batch_norm_for_conv2d(outputs, is_training, bn_decay=bn_decay, scope='bn') if activation_fn is not None: outputs = activation_fn(outputs) return outputs def conv3d(inputs, num_output_channels, kernel_size, scope, stride=[1, 1, 1], padding='SAME', use_xavier=True, stddev=1e-3, weight_decay=None, activation_fn=tf.nn.relu, bn=False, bn_decay=None, is_training=None): """ 3D convolution with non-linear operation. Args: inputs: 5-D tensor variable BxDxHxWxC num_output_channels: int kernel_size: a list of 3 ints scope: string stride: a list of 3 ints padding: 'SAME' or 'VALID' use_xavier: bool, use xavier_initializer if true stddev: float, stddev for truncated_normal init weight_decay: float activation_fn: function bn: bool, whether to use batch norm bn_decay: float or float tensor variable in [0,1] is_training: bool Tensor variable Returns: Variable tensor """ with tf.variable_scope(scope) as sc: kernel_d, kernel_h, kernel_w = kernel_size num_in_channels = inputs.get_shape()[-1].value kernel_shape = [kernel_d, kernel_h, kernel_w, num_in_channels, num_output_channels] kernel = _variable_with_weight_decay('weights', shape=kernel_shape, use_xavier=use_xavier, stddev=stddev, wd=weight_decay) stride_d, stride_h, stride_w = stride outputs = tf.nn.conv3d(inputs, kernel, [1, stride_d, stride_h, stride_w, 1], padding=padding) biases = _variable_on_cpu('biases', [num_output_channels], tf.constant_initializer(0.0)) outputs = tf.nn.bias_add(outputs, biases) if bn: outputs = batch_norm_for_conv3d(outputs, is_training, bn_decay=bn_decay, scope='bn') if activation_fn is not None: outputs = activation_fn(outputs) return outputs def fully_connected(inputs, num_outputs, scope, use_xavier=True, stddev=1e-3, weight_decay=None, activation_fn=tf.nn.relu, bn=False, bn_decay=None, is_training=None): """ Fully connected layer with non-linear operation. Args: inputs: 2-D tensor BxN num_outputs: int Returns: Variable tensor of size B x num_outputs. """ with tf.variable_scope(scope) as sc: num_input_units = inputs.get_shape()[-1].value weights = _variable_with_weight_decay('weights', shape=[num_input_units, num_outputs], use_xavier=use_xavier, stddev=stddev, wd=weight_decay) outputs = tf.matmul(inputs, weights) biases = _variable_on_cpu('biases', [num_outputs], tf.constant_initializer(0.0)) outputs = tf.nn.bias_add(outputs, biases) if bn: outputs = batch_norm_for_fc(outputs, is_training, bn_decay, 'bn') if activation_fn is not None: outputs = activation_fn(outputs) return outputs def max_pool2d(inputs, kernel_size, scope, stride=[2, 2], padding='VALID'): """ 2D max pooling. Args: inputs: 4-D tensor BxHxWxC kernel_size: a list of 2 ints stride: a list of 2 ints Returns: Variable tensor """ with tf.variable_scope(scope) as sc: kernel_h, kernel_w = kernel_size stride_h, stride_w = stride outputs = tf.nn.max_pool(inputs, ksize=[1, kernel_h, kernel_w, 1], strides=[1, stride_h, stride_w, 1], padding=padding, name=sc.name) return outputs def avg_pool2d(inputs, kernel_size, scope, stride=[2, 2], padding='VALID'): """ 2D avg pooling. Args: inputs: 4-D tensor BxHxWxC kernel_size: a list of 2 ints stride: a list of 2 ints Returns: Variable tensor """ with tf.variable_scope(scope) as sc: kernel_h, kernel_w = kernel_size stride_h, stride_w = stride outputs = tf.nn.avg_pool(inputs, ksize=[1, kernel_h, kernel_w, 1], strides=[1, stride_h, stride_w, 1], padding=padding, name=sc.name) return outputs def max_pool3d(inputs, kernel_size, scope, stride=[2, 2, 2], padding='VALID'): """ 3D max pooling. Args: inputs: 5-D tensor BxDxHxWxC kernel_size: a list of 3 ints stride: a list of 3 ints Returns: Variable tensor """ with tf.variable_scope(scope) as sc: kernel_d, kernel_h, kernel_w = kernel_size stride_d, stride_h, stride_w = stride outputs = tf.nn.max_pool3d(inputs, ksize=[1, kernel_d, kernel_h, kernel_w, 1], strides=[1, stride_d, stride_h, stride_w, 1], padding=padding, name=sc.name) return outputs def avg_pool3d(inputs, kernel_size, scope, stride=[2, 2, 2], padding='VALID'): """ 3D avg pooling. Args: inputs: 5-D tensor BxDxHxWxC kernel_size: a list of 3 ints stride: a list of 3 ints Returns: Variable tensor """ with tf.variable_scope(scope) as sc: kernel_d, kernel_h, kernel_w = kernel_size stride_d, stride_h, stride_w = stride outputs = tf.nn.avg_pool3d(inputs, ksize=[1, kernel_d, kernel_h, kernel_w, 1], strides=[1, stride_d, stride_h, stride_w, 1], padding=padding, name=sc.name) return outputs def batch_norm_template_unused(inputs, is_training, scope, moments_dims, bn_decay): """ NOTE: this is older version of the util func. it is deprecated. Batch normalization on convolutional maps and beyond... Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow Args: inputs: Tensor, k-D input ... x C could be BC or BHWC or BDHWC is_training: boolean tf.Varialbe, true indicates training phase scope: string, variable scope moments_dims: a list of ints, indicating dimensions for moments calculation bn_decay: float or float tensor variable, controling moving average weight Return: normed: batch-normalized maps """ with tf.variable_scope(scope) as sc: num_channels = inputs.get_shape()[-1].value beta = _variable_on_cpu(name='beta',shape=[num_channels], initializer=tf.constant_initializer(0)) gamma = _variable_on_cpu(name='gamma',shape=[num_channels], initializer=tf.constant_initializer(1.0)) batch_mean, batch_var = tf.nn.moments(inputs, moments_dims, name='moments') decay = bn_decay if bn_decay is not None else 0.9 ema = tf.train.ExponentialMovingAverage(decay=decay) # Operator that maintains moving averages of variables. # Need to set reuse=False, otherwise if reuse, will see moments_1/mean/ExponentialMovingAverage/ does not exist # https://github.com/shekkizh/WassersteinGAN.tensorflow/issues/3 with tf.variable_scope(tf.get_variable_scope(), reuse=False): ema_apply_op = tf.cond(is_training, lambda: ema.apply([batch_mean, batch_var]), lambda: tf.no_op()) # Update moving average and return current batch's avg and var. def mean_var_with_update(): with tf.control_dependencies([ema_apply_op]): return tf.identity(batch_mean), tf.identity(batch_var) # ema.average returns the Variable holding the average of var. mean, var = tf.cond(is_training, mean_var_with_update, lambda: (ema.average(batch_mean), ema.average(batch_var))) normed = tf.nn.batch_normalization(inputs, mean, var, beta, gamma, 1e-3) return normed def batch_norm_template(inputs, is_training, scope, moments_dims_unused, bn_decay, data_format='NHWC'): """ Batch normalization on convolutional maps and beyond... Ref.: http://stackoverflow.com/questions/33949786/how-could-i-use-batch-normalization-in-tensorflow Args: inputs: Tensor, k-D input ... x C could be BC or BHWC or BDHWC is_training: boolean tf.Varialbe, true indicates training phase scope: string, variable scope moments_dims: a list of ints, indicating dimensions for moments calculation bn_decay: float or float tensor variable, controling moving average weight data_format: 'NHWC' or 'NCHW' Return: normed: batch-normalized maps """ bn_decay = bn_decay if bn_decay is not None else 0.9 return tf.contrib.layers.batch_norm(inputs, center=True, scale=True, is_training=is_training, decay=bn_decay,updates_collections=None, scope=scope, data_format=data_format) def batch_norm_for_fc(inputs, is_training, bn_decay, scope): """ Batch normalization on FC data. Args: inputs: Tensor, 2D BxC input is_training: boolean tf.Varialbe, true indicates training phase bn_decay: float or float tensor variable, controling moving average weight scope: string, variable scope Return: normed: batch-normalized maps """ return batch_norm_template(inputs, is_training, scope, [0,], bn_decay) def batch_norm_for_conv1d(inputs, is_training, bn_decay, scope, data_format): """ Batch normalization on 1D convolutional maps. Args: inputs: Tensor, 3D BLC input maps is_training: boolean tf.Varialbe, true indicates training phase bn_decay: float or float tensor variable, controling moving average weight scope: string, variable scope data_format: 'NHWC' or 'NCHW' Return: normed: batch-normalized maps """ return batch_norm_template(inputs, is_training, scope, [0,1], bn_decay, data_format) def batch_norm_for_conv2d(inputs, is_training, bn_decay, scope, data_format): """ Batch normalization on 2D convolutional maps. Args: inputs: Tensor, 4D BHWC input maps is_training: boolean tf.Varialbe, true indicates training phase bn_decay: float or float tensor variable, controling moving average weight scope: string, variable scope data_format: 'NHWC' or 'NCHW' Return: normed: batch-normalized maps """ return batch_norm_template(inputs, is_training, scope, [0,1,2], bn_decay, data_format) def batch_norm_for_conv3d(inputs, is_training, bn_decay, scope): """ Batch normalization on 3D convolutional maps. Args: inputs: Tensor, 5D BDHWC input maps is_training: boolean tf.Varialbe, true indicates training phase bn_decay: float or float tensor variable, controling moving average weight scope: string, variable scope Return: normed: batch-normalized maps """ return batch_norm_template(inputs, is_training, scope, [0,1,2,3], bn_decay) def dropout(inputs, is_training, scope, keep_prob=0.5, noise_shape=None): """ Dropout layer. Args: inputs: tensor is_training: boolean tf.Variable scope: string keep_prob: float in [0,1] noise_shape: list of ints Returns: tensor variable """ with tf.variable_scope(scope) as sc: outputs = tf.cond(is_training, lambda: tf.nn.dropout(inputs, keep_prob, noise_shape), lambda: inputs) return outputs ================================================ FILE: pointnet_pyt/.gitignore ================================================ .ipynb_checkpoints/ data *.pyc *.ipynb shapenetcore_partanno_segmentation_benchmark_v0/ *.so .idea* cls/ seg/ *.egg-info/ ================================================ FILE: pointnet_pyt/LICENSE ================================================ MIT License Copyright (c) 2017 Fei Xia Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: pointnet_pyt/README.md ================================================ # PointNet.pytorch This repo is implementation for PointNet(https://arxiv.org/abs/1612.00593) in pytorch. The model is in `pointnet/model.py`. It is tested with pytorch-1.0. # Download data and running ``` git clone https://github.com/fxia22/pointnet.pytorch cd pointnet.pytorch pip install -e . ``` Download and build visualization tool ``` cd script bash build.sh #build C++ code for visualization bash download.sh #download dataset ``` Training ``` cd utils python train_classification.py --dataset --nepoch= --dataset_type python train_segmentation.py --dataset --nepoch= ``` Use `--feature_transform` to use feature transform. # Performance ## Classification performance On ModelNet40: | | Overall Acc | | :---: | :---: | | Original implementation | 89.2 | | this implementation(w/o feature transform) | 86.4 | | this implementation(w/ feature transform) | 87.0 | On [A subset of shapenet](http://web.stanford.edu/~ericyi/project_page/part_annotation/index.html) | | Overall Acc | | :---: | :---: | | Original implementation | N/A | | this implementation(w/o feature transform) | 98.1 | | this implementation(w/ feature transform) | 97.7 | ## Segmentation performance Segmentation on [A subset of shapenet](http://web.stanford.edu/~ericyi/project_page/part_annotation/index.html). | Class(mIOU) | Airplane | Bag| Cap|Car|Chair|Earphone|Guitar|Knife|Lamp|Laptop|Motorbike|Mug|Pistol|Rocket|Skateboard|Table | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | Original implementation | 83.4 | 78.7 | 82.5| 74.9 |89.6| 73.0| 91.5| 85.9| 80.8| 95.3| 65.2| 93.0| 81.2| 57.9| 72.8| 80.6| | this implementation(w/o feature transform) | 73.5 | 71.3 | 64.3 | 61.1 | 87.2 | 69.5 | 86.1|81.6| 77.4|92.7|41.3|86.5|78.2|41.2|61.0|81.1| | this implementation(w/ feature transform) | | | | | 87.6 | | | | | | | | | | |81.0| Note that this implementation trains each class separately, so classes with fewer data will have slightly lower performance than reference implementation. Sample segmentation result: ![seg](https://raw.githubusercontent.com/fxia22/pointnet.pytorch/master/misc/show3d.png?token=AE638Oy51TL2HDCaeCF273X_-Bsy6-E2ks5Y_BUzwA%3D%3D) # Links - [Project Page](http://stanford.edu/~rqi/pointnet/) - [Tensorflow implementation](https://github.com/charlesq34/pointnet) ================================================ FILE: pointnet_pyt/misc/modelnet_id.txt ================================================ airplane 0 bathtub 1 bed 2 bench 3 bookshelf 4 bottle 5 bowl 6 car 7 chair 8 cone 9 cup 10 curtain 11 desk 12 door 13 dresser 14 flower_pot 15 glass_box 16 guitar 17 keyboard 18 lamp 19 laptop 20 mantel 21 monitor 22 night_stand 23 person 24 piano 25 plant 26 radio 27 range_hood 28 sink 29 sofa 30 stairs 31 stool 32 table 33 tent 34 toilet 35 tv_stand 36 vase 37 wardrobe 38 xbox 39 ================================================ FILE: pointnet_pyt/misc/num_seg_classes.txt ================================================ Airplane 4 Bag 2 Cap 2 Car 4 Chair 4 Earphone 3 Guitar 3 Knife 2 Lamp 4 Laptop 2 Motorbike 6 Mug 2 Pistol 3 Rocket 3 Skateboard 3 Table 3 ================================================ FILE: pointnet_pyt/pointnet/__init__.py ================================================ ================================================ FILE: pointnet_pyt/pointnet/dataset.py ================================================ from __future__ import print_function import torch.utils.data as data import os import os.path import torch import numpy as np import sys from tqdm import tqdm import json from plyfile import PlyData, PlyElement def get_segmentation_classes(root): catfile = os.path.join(root, 'synsetoffset2category.txt') cat = {} meta = {} with open(catfile, 'r') as f: for line in f: ls = line.strip().split() cat[ls[0]] = ls[1] for item in cat: dir_seg = os.path.join(root, cat[item], 'points_label') dir_point = os.path.join(root, cat[item], 'points') fns = sorted(os.listdir(dir_point)) meta[item] = [] for fn in fns: token = (os.path.splitext(os.path.basename(fn))[0]) meta[item].append((os.path.join(dir_point, token + '.pts'), os.path.join(dir_seg, token + '.seg'))) with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../misc/num_seg_classes.txt'), 'w') as f: for item in cat: datapath = [] num_seg_classes = 0 for fn in meta[item]: datapath.append((item, fn[0], fn[1])) for i in tqdm(range(len(datapath))): l = len(np.unique(np.loadtxt(datapath[i][-1]).astype(np.uint8))) if l > num_seg_classes: num_seg_classes = l print("category {} num segmentation classes {}".format(item, num_seg_classes)) f.write("{}\t{}\n".format(item, num_seg_classes)) def gen_modelnet_id(root): classes = [] with open(os.path.join(root, 'train.txt'), 'r') as f: for line in f: classes.append(line.strip().split('/')[0]) classes = np.unique(classes) with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../misc/modelnet_id.txt'), 'w') as f: for i in range(len(classes)): f.write('{}\t{}\n'.format(classes[i], i)) class ShapeNetDataset(data.Dataset): def __init__(self, root, npoints=2500, classification=False, class_choice=None, split='train', data_augmentation=True): self.npoints = npoints self.root = root self.catfile = os.path.join(self.root, 'synsetoffset2category.txt') self.cat = {} self.data_augmentation = data_augmentation self.classification = classification self.seg_classes = {} with open(self.catfile, 'r') as f: for line in f: ls = line.strip().split() self.cat[ls[0]] = ls[1] #print(self.cat) if not class_choice is None: self.cat = {k: v for k, v in self.cat.items() if k in class_choice} self.id2cat = {v: k for k, v in self.cat.items()} self.meta = {} splitfile = os.path.join(self.root, 'train_test_split', 'shuffled_{}_file_list.json'.format(split)) #from IPython import embed; embed() filelist = json.load(open(splitfile, 'r')) for item in self.cat: self.meta[item] = [] for file in filelist: _, category, uuid = file.split('/') if category in self.cat.values(): self.meta[self.id2cat[category]].append((os.path.join(self.root, category, 'points', uuid+'.pts'), os.path.join(self.root, category, 'points_label', uuid+'.seg'))) self.datapath = [] for item in self.cat: for fn in self.meta[item]: self.datapath.append((item, fn[0], fn[1])) self.classes = dict(zip(sorted(self.cat), range(len(self.cat)))) print(self.classes) with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../misc/num_seg_classes.txt'), 'r') as f: for line in f: ls = line.strip().split() self.seg_classes[ls[0]] = int(ls[1]) self.num_seg_classes = self.seg_classes[list(self.cat.keys())[0]] print(self.seg_classes, self.num_seg_classes) def __getitem__(self, index): fn = self.datapath[index] cls = self.classes[self.datapath[index][0]] point_set = np.loadtxt(fn[1]).astype(np.float32) seg = np.loadtxt(fn[2]).astype(np.int64) #print(point_set.shape, seg.shape) choice = np.random.choice(len(seg), self.npoints, replace=True) #resample point_set = point_set[choice, :] point_set = point_set - np.expand_dims(np.mean(point_set, axis = 0), 0) # center dist = np.max(np.sqrt(np.sum(point_set ** 2, axis = 1)),0) point_set = point_set / dist #scale if self.data_augmentation: theta = np.random.uniform(0,np.pi*2) rotation_matrix = np.array([[np.cos(theta), -np.sin(theta)],[np.sin(theta), np.cos(theta)]]) point_set[:,[0,2]] = point_set[:,[0,2]].dot(rotation_matrix) # random rotation point_set += np.random.normal(0, 0.02, size=point_set.shape) # random jitter seg = seg[choice] point_set = torch.from_numpy(point_set) seg = torch.from_numpy(seg) cls = torch.from_numpy(np.array([cls]).astype(np.int64)) if self.classification: return point_set, cls else: return point_set, seg def __len__(self): return len(self.datapath) class ModelNetDataset(data.Dataset): def __init__(self, root, npoints=2500, split='train', data_augmentation=True): self.npoints = npoints self.root = root self.split = split self.data_augmentation = data_augmentation self.fns = [] with open(os.path.join(root, '{}.txt'.format(self.split)), 'r') as f: for line in f: self.fns.append(line.strip()) self.cat = {} with open(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../misc/modelnet_id.txt'), 'r') as f: for line in f: ls = line.strip().split() self.cat[ls[0]] = int(ls[1]) print(self.cat) self.classes = list(self.cat.keys()) def __getitem__(self, index): fn = self.fns[index] cls = self.cat[fn.split('/')[0]] with open(os.path.join(self.root, fn), 'rb') as f: plydata = PlyData.read(f) pts = np.vstack([plydata['vertex']['x'], plydata['vertex']['y'], plydata['vertex']['z']]).T choice = np.random.choice(len(pts), self.npoints, replace=True) point_set = pts[choice, :] point_set = point_set - np.expand_dims(np.mean(point_set, axis=0), 0) # center dist = np.max(np.sqrt(np.sum(point_set ** 2, axis=1)), 0) point_set = point_set / dist # scale if self.data_augmentation: theta = np.random.uniform(0, np.pi * 2) rotation_matrix = np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]) point_set[:, [0, 2]] = point_set[:, [0, 2]].dot(rotation_matrix) # random rotation point_set += np.random.normal(0, 0.02, size=point_set.shape) # random jitter point_set = torch.from_numpy(point_set.astype(np.float32)) cls = torch.from_numpy(np.array([cls]).astype(np.int64)) return point_set, cls def __len__(self): return len(self.fns) if __name__ == '__main__': dataset = sys.argv[1] datapath = sys.argv[2] if dataset == 'shapenet': d = ShapeNetDataset(root = datapath, class_choice = ['Chair']) print(len(d)) ps, seg = d[0] print(ps.size(), ps.type(), seg.size(),seg.type()) d = ShapeNetDataset(root = datapath, classification = True) print(len(d)) ps, cls = d[0] print(ps.size(), ps.type(), cls.size(),cls.type()) # get_segmentation_classes(datapath) if dataset == 'modelnet': gen_modelnet_id(datapath) d = ModelNetDataset(root=datapath) print(len(d)) print(d[0]) ================================================ FILE: pointnet_pyt/pointnet/model.py ================================================ from __future__ import print_function import torch import torch.nn as nn import torch.nn.parallel import torch.utils.data from torch.autograd import Variable import numpy as np import torch.nn.functional as F class STN3d(nn.Module): def __init__(self): super(STN3d, self).__init__() self.conv1 = torch.nn.Conv1d(3, 64, 1) self.conv2 = torch.nn.Conv1d(64, 128, 1) self.conv3 = torch.nn.Conv1d(128, 1024, 1) self.fc1 = nn.Linear(1024, 512) self.fc2 = nn.Linear(512, 256) self.fc3 = nn.Linear(256, 9) self.relu = nn.ReLU() self.bn1 = nn.BatchNorm1d(64) self.bn2 = nn.BatchNorm1d(128) self.bn3 = nn.BatchNorm1d(1024) self.bn4 = nn.BatchNorm1d(512) self.bn5 = nn.BatchNorm1d(256) def forward(self, x): batchsize = x.size()[0] x = F.relu(self.bn1(self.conv1(x))) x = F.relu(self.bn2(self.conv2(x))) x = F.relu(self.bn3(self.conv3(x))) x = torch.max(x, 2, keepdim=True)[0] x = x.view(-1, 1024) x = F.relu(self.bn4(self.fc1(x))) x = F.relu(self.bn5(self.fc2(x))) x = self.fc3(x) iden = Variable(torch.from_numpy(np.array([1,0,0,0,1,0,0,0,1]).astype(np.float32))).view(1,9).repeat(batchsize,1) if x.is_cuda: iden = iden.cuda() x = x + iden x = x.view(-1, 3, 3) return x class STNkd(nn.Module): def __init__(self, k=64): super(STNkd, self).__init__() self.conv1 = torch.nn.Conv1d(k, 64, 1) self.conv2 = torch.nn.Conv1d(64, 128, 1) self.conv3 = torch.nn.Conv1d(128, 1024, 1) self.fc1 = nn.Linear(1024, 512) self.fc2 = nn.Linear(512, 256) self.fc3 = nn.Linear(256, k*k) self.relu = nn.ReLU() self.bn1 = nn.BatchNorm1d(64) self.bn2 = nn.BatchNorm1d(128) self.bn3 = nn.BatchNorm1d(1024) self.bn4 = nn.BatchNorm1d(512) self.bn5 = nn.BatchNorm1d(256) self.k = k def forward(self, x): batchsize = x.size()[0] x = F.relu(self.bn1(self.conv1(x))) x = F.relu(self.bn2(self.conv2(x))) x = F.relu(self.bn3(self.conv3(x))) x = torch.max(x, 2, keepdim=True)[0] x = x.view(-1, 1024) x = F.relu(self.bn4(self.fc1(x))) x = F.relu(self.bn5(self.fc2(x))) x = self.fc3(x) iden = Variable(torch.from_numpy(np.eye(self.k).flatten().astype(np.float32))).view(1,self.k*self.k).repeat(batchsize,1) if x.is_cuda: iden = iden.cuda() x = x + iden x = x.view(-1, self.k, self.k) return x class PointNetfeat(nn.Module): def __init__(self, global_feat = True, feature_transform = False): super(PointNetfeat, self).__init__() self.stn = STN3d() self.conv1 = torch.nn.Conv1d(3, 64, 1) self.conv2 = torch.nn.Conv1d(64, 128, 1) self.conv3 = torch.nn.Conv1d(128, 1024, 1) self.bn1 = nn.BatchNorm1d(64) self.bn2 = nn.BatchNorm1d(128) self.bn3 = nn.BatchNorm1d(1024) self.global_feat = global_feat self.feature_transform = feature_transform if self.feature_transform: self.fstn = STNkd(k=64) def forward(self, x): n_pts = x.size()[2] trans = self.stn(x) x = x.transpose(2, 1) x = torch.bmm(x, trans) x = x.transpose(2, 1) x = F.relu(self.bn1(self.conv1(x))) if self.feature_transform: trans_feat = self.fstn(x) x = x.transpose(2,1) x = torch.bmm(x, trans_feat) x = x.transpose(2,1) else: trans_feat = None pointfeat = x x = F.relu(self.bn2(self.conv2(x))) x = self.bn3(self.conv3(x)) x = torch.max(x, 2, keepdim=True)[0] x = x.view(-1, 1024) if self.global_feat: return x, trans, trans_feat else: x = x.view(-1, 1024, 1).repeat(1, 1, n_pts) return torch.cat([x, pointfeat], 1), trans, trans_feat class PointNetCls(nn.Module): def __init__(self, k=2, feature_transform=False): super(PointNetCls, self).__init__() self.feature_transform = feature_transform self.feat = PointNetfeat(global_feat=True, feature_transform=feature_transform) self.fc1 = nn.Linear(1024, 512) self.fc2 = nn.Linear(512, 256) self.fc3 = nn.Linear(256, k) self.dropout = nn.Dropout(p=0.3) self.bn1 = nn.BatchNorm1d(512) self.bn2 = nn.BatchNorm1d(256) self.relu = nn.ReLU() def forward(self, x): x, trans, trans_feat = self.feat(x) x = F.relu(self.bn1(self.fc1(x))) x = F.relu(self.bn2(self.dropout(self.fc2(x)))) x = self.fc3(x) return F.log_softmax(x, dim=1), trans, trans_feat class PointNetDenseCls(nn.Module): def __init__(self, k = 2, feature_transform=False): super(PointNetDenseCls, self).__init__() self.k = k self.feature_transform=feature_transform self.feat = PointNetfeat(global_feat=False, feature_transform=feature_transform) self.conv1 = torch.nn.Conv1d(1088, 512, 1) self.conv2 = torch.nn.Conv1d(512, 256, 1) self.conv3 = torch.nn.Conv1d(256, 128, 1) self.conv4 = torch.nn.Conv1d(128, self.k, 1) self.bn1 = nn.BatchNorm1d(512) self.bn2 = nn.BatchNorm1d(256) self.bn3 = nn.BatchNorm1d(128) def forward(self, x): batchsize = x.size()[0] n_pts = x.size()[2] x, trans, trans_feat = self.feat(x) x = F.relu(self.bn1(self.conv1(x))) x = F.relu(self.bn2(self.conv2(x))) x = F.relu(self.bn3(self.conv3(x))) x = self.conv4(x) x = x.transpose(2,1).contiguous() x = F.log_softmax(x.view(-1,self.k), dim=-1) x = x.view(batchsize, n_pts, self.k) return x, trans, trans_feat def feature_transform_regularizer(trans): d = trans.size()[1] batchsize = trans.size()[0] I = torch.eye(d)[None, :, :] if trans.is_cuda: I = I.cuda() loss = torch.mean(torch.norm(torch.bmm(trans, trans.transpose(2,1)) - I, dim=(1,2), p=2)) return loss if __name__ == '__main__': sim_data = Variable(torch.rand(32,3,2500)) trans = STN3d() out = trans(sim_data) print('stn', out.size()) print('loss', feature_transform_regularizer(out)) sim_data_64d = Variable(torch.rand(32, 64, 2500)) trans = STNkd(k=64) out = trans(sim_data_64d) print('stn64d', out.size()) print('loss', feature_transform_regularizer(out)) pointfeat = PointNetfeat(global_feat=True) out, _, _ = pointfeat(sim_data) print('global feat', out.size()) pointfeat = PointNetfeat(global_feat=False) out, _, _ = pointfeat(sim_data) print('point feat', out.size()) cls = PointNetCls(k = 5) out, _, _ = cls(sim_data) print('class', out.size()) seg = PointNetDenseCls(k = 3) out, _, _ = seg(sim_data) print('seg', out.size()) ================================================ FILE: pointnet_pyt/scripts/build.sh ================================================ SCRIPT=`realpath $0` SCRIPTPATH=`dirname $SCRIPT` echo $SCRIPTPATH g++ -std=c++11 $SCRIPTPATH/../utils/render_balls_so.cpp -o $SCRIPTPATH/../utils/render_balls_so.so -shared -fPIC -O2 -D_GLIBCXX_USE_CXX11_ABI=0 ================================================ FILE: pointnet_pyt/scripts/download.sh ================================================ SCRIPT=`realpath $0` SCRIPTPATH=`dirname $SCRIPT` cd $SCRIPTPATH/.. wget https://shapenet.cs.stanford.edu/ericyi/shapenetcore_partanno_segmentation_benchmark_v0.zip --no-check-certificate unzip shapenetcore_partanno_segmentation_benchmark_v0.zip rm shapenetcore_partanno_segmentation_benchmark_v0.zip cd - ================================================ FILE: pointnet_pyt/setup.py ================================================ # install using 'pip install -e .' from setuptools import setup setup(name='pointnet', packages=['pointnet'], package_dir={'pointnet': 'pointnet'}, install_requires=['torch', 'tqdm', 'plyfile'], version='0.0.1') ================================================ FILE: pointnet_pyt/utils/render_balls_so.cpp ================================================ #include #include #include #include using namespace std; struct PointInfo{ int x,y,z; float r,g,b; }; extern "C"{ void render_ball(int h,int w,unsigned char * show,int n,int * xyzs,float * c0,float * c1,float * c2,int r){ r=max(r,1); vector depth(h*w,-2100000000); vector pattern; for (int dx=-r;dx<=r;dx++) for (int dy=-r;dy<=r;dy++) if (dx*dx+dy*dy=h || y2<0 || y2>=w) && depth[x2*w+y2] 0: show[:, :, 0] = np.maximum(show[:, :, 0], np.roll( show[:, :, 0], 1, axis=0)) if magnifyBlue >= 2: show[:, :, 0] = np.maximum(show[:, :, 0], np.roll(show[:, :, 0], -1, axis=0)) show[:, :, 0] = np.maximum(show[:, :, 0], np.roll( show[:, :, 0], 1, axis=1)) if magnifyBlue >= 2: show[:, :, 0] = np.maximum(show[:, :, 0], np.roll(show[:, :, 0], -1, axis=1)) if showrot: cv2.putText(show, 'xangle %d' % (int(xangle / np.pi * 180)), (30, showsz - 30), 0, 0.5, cv2.cv.CV_RGB(255, 0, 0)) cv2.putText(show, 'yangle %d' % (int(yangle / np.pi * 180)), (30, showsz - 50), 0, 0.5, cv2.cv.CV_RGB(255, 0, 0)) cv2.putText(show, 'zoom %d%%' % (int(zoom * 100)), (30, showsz - 70), 0, 0.5, cv2.cv.CV_RGB(255, 0, 0)) changed = True while True: if changed: render() changed = False cv2.imshow('show3d', show) if waittime == 0: cmd = cv2.waitKey(10) % 256 else: cmd = cv2.waitKey(waittime) % 256 if cmd == ord('q'): break elif cmd == ord('Q'): sys.exit(0) if cmd == ord('t') or cmd == ord('p'): if cmd == ord('t'): if c_gt is None: c0 = np.zeros((len(xyz), ), dtype='float32') + 255 c1 = np.zeros((len(xyz), ), dtype='float32') + 255 c2 = np.zeros((len(xyz), ), dtype='float32') + 255 else: c0 = c_gt[:, 0] c1 = c_gt[:, 1] c2 = c_gt[:, 2] else: if c_pred is None: c0 = np.zeros((len(xyz), ), dtype='float32') + 255 c1 = np.zeros((len(xyz), ), dtype='float32') + 255 c2 = np.zeros((len(xyz), ), dtype='float32') + 255 else: c0 = c_pred[:, 0] c1 = c_pred[:, 1] c2 = c_pred[:, 2] if normalizecolor: c0 /= (c0.max() + 1e-14) / 255.0 c1 /= (c1.max() + 1e-14) / 255.0 c2 /= (c2.max() + 1e-14) / 255.0 c0 = np.require(c0, 'float32', 'C') c1 = np.require(c1, 'float32', 'C') c2 = np.require(c2, 'float32', 'C') changed = True if cmd==ord('n'): zoom*=1.1 changed=True elif cmd==ord('m'): zoom/=1.1 changed=True elif cmd==ord('r'): zoom=1.0 changed=True elif cmd==ord('s'): cv2.imwrite('show3d.png',show) if waittime!=0: break return cmd if __name__ == '__main__': np.random.seed(100) showpoints(np.random.randn(2500, 3)) ================================================ FILE: pointnet_pyt/utils/show_cls.py ================================================ from __future__ import print_function import argparse import torch import torch.nn.parallel import torch.utils.data from torch.autograd import Variable from pointnet.dataset import ShapeNetDataset from pointnet.model import PointNetCls import torch.nn.functional as F #showpoints(np.random.randn(2500,3), c1 = np.random.uniform(0,1,size = (2500))) parser = argparse.ArgumentParser() parser.add_argument('--model', type=str, default = '', help='model path') parser.add_argument('--num_points', type=int, default=2500, help='input batch size') opt = parser.parse_args() print(opt) test_dataset = ShapeNetDataset( root='shapenetcore_partanno_segmentation_benchmark_v0', split='test', classification=True, npoints=opt.num_points, data_augmentation=False) testdataloader = torch.utils.data.DataLoader( test_dataset, batch_size=32, shuffle=True) classifier = PointNetCls(k=len(test_dataset.classes)) classifier.cuda() classifier.load_state_dict(torch.load(opt.model)) classifier.eval() for i, data in enumerate(testdataloader, 0): points, target = data points, target = Variable(points), Variable(target[:, 0]) points = points.transpose(2, 1) points, target = points.cuda(), target.cuda() pred, _, _ = classifier(points) loss = F.nll_loss(pred, target) pred_choice = pred.data.max(1)[1] correct = pred_choice.eq(target.data).cpu().sum() print('i:%d loss: %f accuracy: %f' % (i, loss.data.item(), correct / float(32))) ================================================ FILE: pointnet_pyt/utils/show_seg.py ================================================ from __future__ import print_function from show3d_balls import showpoints import argparse import numpy as np import torch import torch.nn.parallel import torch.utils.data from torch.autograd import Variable from pointnet.dataset import ShapeNetDataset from pointnet.model import PointNetDenseCls import matplotlib.pyplot as plt #showpoints(np.random.randn(2500,3), c1 = np.random.uniform(0,1,size = (2500))) parser = argparse.ArgumentParser() parser.add_argument('--model', type=str, default='', help='model path') parser.add_argument('--idx', type=int, default=0, help='model index') parser.add_argument('--dataset', type=str, default='', help='dataset path') parser.add_argument('--class_choice', type=str, default='', help='class choice') opt = parser.parse_args() print(opt) d = ShapeNetDataset( root=opt.dataset, class_choice=[opt.class_choice], split='test', data_augmentation=False) idx = opt.idx print("model %d/%d" % (idx, len(d))) point, seg = d[idx] print(point.size(), seg.size()) point_np = point.numpy() cmap = plt.cm.get_cmap("hsv", 10) cmap = np.array([cmap(i) for i in range(10)])[:, :3] gt = cmap[seg.numpy() - 1, :] state_dict = torch.load(opt.model) classifier = PointNetDenseCls(k= state_dict['conv4.weight'].size()[0]) classifier.load_state_dict(state_dict) classifier.eval() point = point.transpose(1, 0).contiguous() point = Variable(point.view(1, point.size()[0], point.size()[1])) pred, _, _ = classifier(point) pred_choice = pred.data.max(2)[1] print(pred_choice) #print(pred_choice.size()) pred_color = cmap[pred_choice.numpy()[0], :] #print(pred_color.shape) showpoints(point_np, gt, pred_color) ================================================ FILE: pointnet_pyt/utils/train_classification.py ================================================ from __future__ import print_function import argparse import os import random import torch import torch.nn.parallel import torch.optim as optim import torch.utils.data from pointnet.dataset import ShapeNetDataset, ModelNetDataset from pointnet.model import PointNetCls, feature_transform_regularizer import torch.nn.functional as F from tqdm import tqdm parser = argparse.ArgumentParser() parser.add_argument( '--batchSize', type=int, default=32, help='input batch size') parser.add_argument( '--num_points', type=int, default=2500, help='input batch size') parser.add_argument( '--workers', type=int, help='number of data loading workers', default=4) parser.add_argument( '--nepoch', type=int, default=250, help='number of epochs to train for') parser.add_argument('--outf', type=str, default='cls', help='output folder') parser.add_argument('--model', type=str, default='', help='model path') parser.add_argument('--dataset', type=str, required=True, help="dataset path") parser.add_argument('--dataset_type', type=str, default='shapenet', help="dataset type shapenet|modelnet40") parser.add_argument('--feature_transform', action='store_true', help="use feature transform") opt = parser.parse_args() print(opt) blue = lambda x: '\033[94m' + x + '\033[0m' opt.manualSeed = random.randint(1, 10000) # fix seed print("Random Seed: ", opt.manualSeed) random.seed(opt.manualSeed) torch.manual_seed(opt.manualSeed) if opt.dataset_type == 'shapenet': dataset = ShapeNetDataset( root=opt.dataset, classification=True, npoints=opt.num_points) test_dataset = ShapeNetDataset( root=opt.dataset, classification=True, split='test', npoints=opt.num_points, data_augmentation=False) elif opt.dataset_type == 'modelnet40': dataset = ModelNetDataset( root=opt.dataset, npoints=opt.num_points, split='trainval') test_dataset = ModelNetDataset( root=opt.dataset, split='test', npoints=opt.num_points, data_augmentation=False) else: exit('wrong dataset type') dataloader = torch.utils.data.DataLoader( dataset, batch_size=opt.batchSize, shuffle=True, num_workers=int(opt.workers)) testdataloader = torch.utils.data.DataLoader( test_dataset, batch_size=opt.batchSize, shuffle=True, num_workers=int(opt.workers)) print(len(dataset), len(test_dataset)) num_classes = len(dataset.classes) print('classes', num_classes) try: os.makedirs(opt.outf) except OSError: pass classifier = PointNetCls(k=num_classes, feature_transform=opt.feature_transform) if opt.model != '': classifier.load_state_dict(torch.load(opt.model)) optimizer = optim.Adam(classifier.parameters(), lr=0.001, betas=(0.9, 0.999)) scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5) classifier.cuda() num_batch = len(dataset) / opt.batchSize for epoch in range(opt.nepoch): scheduler.step() for i, data in enumerate(dataloader, 0): points, target = data target = target[:, 0] points = points.transpose(2, 1) points, target = points.cuda(), target.cuda() optimizer.zero_grad() classifier = classifier.train() pred, trans, trans_feat = classifier(points) loss = F.nll_loss(pred, target) if opt.feature_transform: loss += feature_transform_regularizer(trans_feat) * 0.001 loss.backward() optimizer.step() pred_choice = pred.data.max(1)[1] correct = pred_choice.eq(target.data).cpu().sum() print('[%d: %d/%d] train loss: %f accuracy: %f' % (epoch, i, num_batch, loss.item(), correct.item() / float(opt.batchSize))) if i % 10 == 0: j, data = next(enumerate(testdataloader, 0)) points, target = data target = target[:, 0] points = points.transpose(2, 1) points, target = points.cuda(), target.cuda() classifier = classifier.eval() pred, _, _ = classifier(points) loss = F.nll_loss(pred, target) pred_choice = pred.data.max(1)[1] correct = pred_choice.eq(target.data).cpu().sum() print('[%d: %d/%d] %s loss: %f accuracy: %f' % (epoch, i, num_batch, blue('test'), loss.item(), correct.item()/float(opt.batchSize))) torch.save(classifier.state_dict(), '%s/cls_model_%d.pth' % (opt.outf, epoch)) total_correct = 0 total_testset = 0 for i,data in tqdm(enumerate(testdataloader, 0)): points, target = data target = target[:, 0] points = points.transpose(2, 1) points, target = points.cuda(), target.cuda() classifier = classifier.eval() pred, _, _ = classifier(points) pred_choice = pred.data.max(1)[1] correct = pred_choice.eq(target.data).cpu().sum() total_correct += correct.item() total_testset += points.size()[0] print("final accuracy {}".format(total_correct / float(total_testset))) ================================================ FILE: pointnet_pyt/utils/train_segmentation.py ================================================ from __future__ import print_function import argparse import os import random import torch import torch.nn.parallel import torch.optim as optim import torch.utils.data from pointnet.dataset import ShapeNetDataset from pointnet.model import PointNetDenseCls, feature_transform_regularizer import torch.nn.functional as F from tqdm import tqdm import numpy as np parser = argparse.ArgumentParser() parser.add_argument( '--batchSize', type=int, default=32, help='input batch size') parser.add_argument( '--workers', type=int, help='number of data loading workers', default=4) parser.add_argument( '--nepoch', type=int, default=25, help='number of epochs to train for') parser.add_argument('--outf', type=str, default='seg', help='output folder') parser.add_argument('--model', type=str, default='', help='model path') parser.add_argument('--dataset', type=str, required=True, help="dataset path") parser.add_argument('--class_choice', type=str, default='Chair', help="class_choice") parser.add_argument('--feature_transform', action='store_true', help="use feature transform") opt = parser.parse_args() print(opt) opt.manualSeed = random.randint(1, 10000) # fix seed print("Random Seed: ", opt.manualSeed) random.seed(opt.manualSeed) torch.manual_seed(opt.manualSeed) dataset = ShapeNetDataset( root=opt.dataset, classification=False, class_choice=[opt.class_choice]) dataloader = torch.utils.data.DataLoader( dataset, batch_size=opt.batchSize, shuffle=True, num_workers=int(opt.workers)) test_dataset = ShapeNetDataset( root=opt.dataset, classification=False, class_choice=[opt.class_choice], split='test', data_augmentation=False) testdataloader = torch.utils.data.DataLoader( test_dataset, batch_size=opt.batchSize, shuffle=True, num_workers=int(opt.workers)) print(len(dataset), len(test_dataset)) num_classes = dataset.num_seg_classes print('classes', num_classes) try: os.makedirs(opt.outf) except OSError: pass blue = lambda x: '\033[94m' + x + '\033[0m' classifier = PointNetDenseCls(k=num_classes, feature_transform=opt.feature_transform) if opt.model != '': classifier.load_state_dict(torch.load(opt.model)) optimizer = optim.Adam(classifier.parameters(), lr=0.001, betas=(0.9, 0.999)) scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5) classifier.cuda() num_batch = len(dataset) / opt.batchSize for epoch in range(opt.nepoch): scheduler.step() for i, data in enumerate(dataloader, 0): points, target = data points = points.transpose(2, 1) points, target = points.cuda(), target.cuda() optimizer.zero_grad() classifier = classifier.train() pred, trans, trans_feat = classifier(points) pred = pred.view(-1, num_classes) target = target.view(-1, 1)[:, 0] - 1 #print(pred.size(), target.size()) loss = F.nll_loss(pred, target) if opt.feature_transform: loss += feature_transform_regularizer(trans_feat) * 0.001 loss.backward() optimizer.step() pred_choice = pred.data.max(1)[1] correct = pred_choice.eq(target.data).cpu().sum() print('[%d: %d/%d] train loss: %f accuracy: %f' % (epoch, i, num_batch, loss.item(), correct.item()/float(opt.batchSize * 2500))) if i % 10 == 0: j, data = next(enumerate(testdataloader, 0)) points, target = data points = points.transpose(2, 1) points, target = points.cuda(), target.cuda() classifier = classifier.eval() pred, _, _ = classifier(points) pred = pred.view(-1, num_classes) target = target.view(-1, 1)[:, 0] - 1 loss = F.nll_loss(pred, target) pred_choice = pred.data.max(1)[1] correct = pred_choice.eq(target.data).cpu().sum() print('[%d: %d/%d] %s loss: %f accuracy: %f' % (epoch, i, num_batch, blue('test'), loss.item(), correct.item()/float(opt.batchSize * 2500))) torch.save(classifier.state_dict(), '%s/seg_model_%s_%d.pth' % (opt.outf, opt.class_choice, epoch)) ## benchmark mIOU shape_ious = [] for i,data in tqdm(enumerate(testdataloader, 0)): points, target = data points = points.transpose(2, 1) points, target = points.cuda(), target.cuda() classifier = classifier.eval() pred, _, _ = classifier(points) pred_choice = pred.data.max(2)[1] pred_np = pred_choice.cpu().data.numpy() target_np = target.cpu().data.numpy() - 1 for shape_idx in range(target_np.shape[0]): parts = range(num_classes)#np.unique(target_np[shape_idx]) part_ious = [] for part in parts: I = np.sum(np.logical_and(pred_np[shape_idx] == part, target_np[shape_idx] == part)) U = np.sum(np.logical_or(pred_np[shape_idx] == part, target_np[shape_idx] == part)) if U == 0: iou = 1 #If the union of groundtruth and prediction points is empty, then count part IoU as 1 else: iou = I / float(U) part_ious.append(iou) shape_ious.append(np.mean(part_ious)) print("mIOU for class {}: {}".format(opt.class_choice, np.mean(shape_ious))) ================================================ FILE: requirements.txt ================================================ git+https://github.com/imankgoyal/etw_pytorch_utils.git@v1.1.1#egg=etw_pytorch_utils enum34 future h5py==2.10.0 progressbar2==3.50.0 tensorboardX==2.0 -f https://download.pytorch.org/whl/torch_stable.html torch==1.4.0+cu100 -f https://download.pytorch.org/whl/torch_stable.html torchvision==0.5.0+cu100 yacs==0.1.6 open3d==0.13.0 ================================================ FILE: rs_cnn/.gitignore ================================================ cls/*.pth seg/*.pth ================================================ FILE: rs_cnn/CMakeLists.txt ================================================ project(PointNet2) cmake_minimum_required(VERSION 2.8) find_package(CUDA REQUIRED) include_directories("${CMAKE_CURRENT_SOURCE_DIR}/utils/cinclude") cuda_include_directories("${CMAKE_CURRENT_SOURCE_DIR}/utils/cinclude") file(GLOB cuda_kernels_src "${CMAKE_CURRENT_SOURCE_DIR}/utils/csrc/*.cu") cuda_compile(cuda_kernels SHARED ${cuda_kernels_src} OPTIONS -O3) set(BUILD_CMD python "${CMAKE_CURRENT_SOURCE_DIR}/utils/build_ffi.py") file(GLOB wrapper_headers "${CMAKE_CURRENT_SOURCE_DIR}/utils/cinclude/*wrapper.h") file(GLOB wrapper_sources "${CMAKE_CURRENT_SOURCE_DIR}/utils/csrs/*.c") add_custom_command(OUTPUT "${CMAKE_CURRENT_SOURCE_DIR}/utils/_ext/pointnet2/_pointnet2.so" WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/utils COMMAND ${BUILD_CMD} --build --objs ${cuda_kernels} DEPENDS ${cuda_kernels} DEPENDS ${wrapper_headers} DEPENDS ${wrapper_sources} VERBATIM) add_custom_target(pointnet2_ext ALL DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/utils/_ext/pointnet2/_pointnet2.so") ================================================ FILE: rs_cnn/LICENSE ================================================ MIT License Copyright (c) 2019 Yongcheng Liu Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: rs_cnn/README.md ================================================ Relation-Shape Convolutional Neural Network for Point Cloud Analysis === This repository contains the author's implementation in Pytorch for the paper: __Relation-Shape Convolutional Neural Network for Point Cloud Analysis__ [[arXiv](https://arxiv.org/abs/1904.07601)] [[CVF](http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Relation-Shape_Convolutional_Neural_Network_for_Point_Cloud_Analysis_CVPR_2019_paper.pdf)]
[Yongcheng Liu](https://yochengliu.github.io/), [Bin Fan](http://www.nlpr.ia.ac.cn/fanbin/), [Shiming Xiang](https://scholar.google.com/citations?user=0ggsACEAAAAJ&hl=zh-CN) and [Chunhong Pan](http://people.ucas.ac.cn/~0005314)
[__CVPR 2019 Oral & Best paper finalist__](http://cvpr2019.thecvf.com/)     __Project Page__: [https://yochengliu.github.io/Relation-Shape-CNN/](https://yochengliu.github.io/Relation-Shape-CNN/) ## Citation If our paper is helpful for your research, please consider citing: ```BibTex @inproceedings{liu2019rscnn, author = {Yongcheng Liu and Bin Fan and Shiming Xiang and Chunhong Pan}, title = {Relation-Shape Convolutional Neural Network for Point Cloud Analysis}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, pages = {8895--8904}, year = {2019} } ``` ## Usage: Preparation ### Requirement - Ubuntu 14.04 - Python 3 (recommend Anaconda3) - Pytorch 0.3.\*/0.4.\* - CMake > 2.8 - CUDA 8.0 + cuDNN 5.1 ### Building Kernel git clone https://github.com/Yochengliu/Relation-Shape-CNN.git cd Relation-Shape-CNN - mkdir build && cd build - cmake .. && make ### Dataset __Shape Classification__ Download and unzip [ModelNet40](https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip) (415M). Replace `$data_root$` in `cfgs/config_*_cls.yaml` with the dataset parent path. __ShapeNet Part Segmentation__ Download and unzip [ShapeNet Part](https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip) (674M). Replace `$data_root$` in `cfgs/config_*_partseg.yaml` with the dataset path. ## Usage: Training ### Shape Classification sh train_cls.sh You can modify `relation_prior` in `cfgs/config_*_cls.yaml`. We have trained a Single-Scale-Neighborhood classification model in `cls` folder, whose accuracy is 92.38%. ### Shape Part Segmentation sh train_partseg.sh We have trained a Multi-Scale-Neighborhood part segmentation model in `seg` folder, whose class mIoU and instance mIoU is 84.18% and 85.81% respectively. ## Usage: Evaluation ### Shape Classification Voting script: voting_evaluate_cls.py You can use our model `cls/model_cls_ssn_iter_16218_acc_0.923825.pth` as the checkpoint in `config_ssn_cls.yaml`, and after this voting you will get an accuracy of 92.71% if all things go right. ### Shape Part Segmentation Voting script: voting_evaluate_partseg.py You can use our model `seg/model_seg_msn_iter_57585_ins_0.858054_cls_0.841787.pth` as the checkpoint in `config_msn_partseg.yaml`. ## License The code is released under MIT License (see LICENSE file for details). ## Acknowledgement The code is heavily borrowed from [Pointnet2_PyTorch](https://github.com/erikwijmans/Pointnet2_PyTorch). ## Contact If you have some ideas or questions about our research to share with us, please contact ================================================ FILE: rs_cnn/cfgs/config_msn_partseg.yaml ================================================ common: workers: 4 num_points: 2048 num_classes: 50 batch_size: 28 base_lr: 0.001 lr_clip: 0.00001 lr_decay: 0.5 decay_step: 21 epochs: 200 weight_decay: 0 bn_momentum: 0.9 bnm_clip: 0.01 bn_decay: 0.5 evaluate: 1 # validation in training process val_freq_epoch: 0.7 # frequency in epoch for validation, can be decimal print_freq_iter: 20 # frequency in iteration for printing infomation input_channels: 0 # feature channels except (x, y, z) # h_ij: 0 for 3D Euclidean distance (3D Ed), channels = 1 # 1 for (3D Ed, x_i, x_j, x_j - x_i), channels = 10 # 2 for (2D Ed, x'_i, x'_j, x'_j - x'_i), channels = 10, x' indicates 2D coordinates relation_prior: 1 checkpoint: '' # the model to start from save_path: seg data_root: /u/agoyal/storage/view_point_cloud/Pytorch/data/shapenetcore_partanno_segmentation_benchmark_v0_normal ================================================ FILE: rs_cnn/cfgs/config_ssn_cls.yaml ================================================ common: workers: 4 num_points: 1024 num_classes: 40 batch_size: 32 base_lr: 0.001 lr_clip: 0.00001 lr_decay: 0.7 decay_step: 21 epochs: 200 weight_decay: 0 bn_momentum: 0.9 bnm_clip: 0.01 bn_decay: 0.5 evaluate: 1 val_freq_epoch: 0.5 # frequency in epoch for validation, can be decimal print_freq_iter: 20 # frequency in iteration for printing infomation input_channels: 0 # feature channels except (x, y, z) # h_ij: 0 for 3D Euclidean distance (3D Ed), channels = 1 # 1 for (3D Ed, x_i, x_j, x_j - x_i), channels = 10 # 2 for (2D Ed, x'_i, x'_j, x'_j - x'_i), channels = 10, x' indicates 2D coordinates relation_prior: 1 checkpoint: '' # the model to start from save_path: cls data_root: /u/agoyal/storage/view_point_cloud/data/ ================================================ FILE: rs_cnn/data/ModelNet40Loader.py ================================================ import torch import torch.utils.data as data import numpy as np import os, sys, h5py BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) def _get_data_files(list_filename): with open(list_filename) as f: return [line.rstrip()[5:] for line in f] def _load_data_file(name): f = h5py.File(name, 'r') data = f['data'][:] label = f['label'][:] return data, label class ModelNet40Cls(data.Dataset): def __init__(self, num_points, root, data_file, transforms=None, train=True): super().__init__() self.transforms = transforms root = os.path.abspath(root) self.folder = "modelnet40_ply_hdf5_2048" self.data_dir = os.path.join(root, self.folder) self.train, self.num_points = train, num_points self.files = _get_data_files(os.path.join(self.data_dir, data_file)) # if self.train: # self.files = _get_data_files( \ # os.path.join(self.data_dir, 'train_files.txt')) # else: # self.files = _get_data_files( \ # os.path.join(self.data_dir, 'test_files.txt')) point_list, label_list = [], [] for f in self.files: points, labels = _load_data_file(os.path.join(root, f)) point_list.append(points) label_list.append(labels) self.points = np.concatenate(point_list, 0) self.labels = np.concatenate(label_list, 0) def __getitem__(self, idx): pt_idxs = np.arange(0, self.points.shape[1]) # 2048 if self.train: np.random.shuffle(pt_idxs) current_points = self.points[idx, pt_idxs].copy() label = torch.from_numpy(self.labels[idx]).type(torch.LongTensor) if self.transforms is not None: current_points = self.transforms(current_points) return current_points, label def __len__(self): return self.points.shape[0] if __name__ == "__main__": from torchvision import transforms import data_utils as d_utils transforms = transforms.Compose([ d_utils.PointcloudToTensor(), d_utils.PointcloudRotate(axis=np.array([1,0,0])), d_utils.PointcloudScale(), d_utils.PointcloudTranslate(), d_utils.PointcloudJitter() ]) dset = ModelNet40Cls(16, "./", train=True, transforms=transforms) print(dset[0][0]) print(dset[0][1]) print(len(dset)) dloader = torch.utils.data.DataLoader(dset, batch_size=32, shuffle=True) ================================================ FILE: rs_cnn/data/ShapeNetPartLoader.py ================================================ import os import os.path import torch import json import pickle import numpy as np import sys import torchvision.transforms as transforms from progressbar import ProgressBar import pdb def pc_normalize(pc): l = pc.shape[0] centroid = np.mean(pc, axis=0) pc = pc - centroid m = np.max(np.sqrt(np.sum(pc**2, axis=1))) pc = pc / m return pc class ShapeNetPart(): def __init__(self, root, num_points = 2048, split='train', normalize=True, transforms = None, all_points=False): self.transforms = transforms self.num_points = num_points self.root = root self.catfile = os.path.join(self.root, 'synsetoffset2category.txt') self.normalize = normalize self.all_points = all_points self.cat = {} with open(self.catfile, 'r') as f: for line in f: ls = line.strip().split() self.cat[ls[0]] = ls[1] self.cat = {k:v for k,v in self.cat.items()} self.meta = {} with open(os.path.join(self.root, 'train_test_split', 'shuffled_train_file_list.json'), 'r') as f: train_ids = set([str(d.split('/')[2]) for d in json.load(f)]) with open(os.path.join(self.root, 'train_test_split', 'shuffled_val_file_list.json'), 'r') as f: val_ids = set([str(d.split('/')[2]) for d in json.load(f)]) with open(os.path.join(self.root, 'train_test_split', 'shuffled_test_file_list.json'), 'r') as f: test_ids = set([str(d.split('/')[2]) for d in json.load(f)]) for item in self.cat: self.meta[item] = [] dir_point = os.path.join(self.root, self.cat[item]) fns = sorted(os.listdir(dir_point)) if split=='trainval': fns = [fn for fn in fns if ((fn[0:-4] in train_ids) or (fn[0:-4] in val_ids))] elif split=='train': fns = [fn for fn in fns if fn[0:-4] in train_ids] elif split=='val': fns = [fn for fn in fns if fn[0:-4] in val_ids] elif split=='test': fns = [fn for fn in fns if fn[0:-4] in test_ids] else: print('Unknown split: %s. Exiting..'%(split)) exit(-1) for fn in fns: token = (os.path.splitext(os.path.basename(fn))[0]) self.meta[item].append(os.path.join(dir_point, token + '.txt')) self.datapath = [] for item in self.cat: for fn in self.meta[item]: self.datapath.append((item, fn)) self.classes = dict(zip(self.cat, range(len(self.cat)))) # Mapping from category ('Chair') to a list of int [10,11,12,13] as segmentation labels self.seg_classes = {'Earphone': [16, 17, 18], 'Motorbike': [30, 31, 32, 33, 34, 35], 'Rocket': [41, 42, 43], 'Car': [8, 9, 10, 11], 'Laptop': [28, 29], 'Cap': [6, 7], 'Skateboard': [44, 45, 46], 'Mug': [36, 37], 'Guitar': [19, 20, 21], 'Bag': [4, 5], 'Lamp': [24, 25, 26, 27], 'Table': [47, 48, 49], 'Airplane': [0, 1, 2, 3], 'Pistol': [38, 39, 40], 'Chair': [12, 13, 14, 15], 'Knife': [22, 23]} self.cache = {} # self.cache_size = self.__len__() preload_file = f"{self.root}/{split}_{self.normalize}_preload.pkl" if os.path.exists(preload_file): print(f"Preloading all data from {preload_file}") with open(preload_file, 'rb') as file: self.cache = pickle.load(file) else: print("Preloading data by reading individual files.") self.preload() with open(preload_file, 'wb') as file: print(f"Saving pre-loaded data at {preload_file}") pickle.dump(self.cache, file) def __getitem__(self, index): point_set, normal, seg, cls = self.cache[index] if self.all_points: pass else: choice = np.random.choice(len(seg), self.num_points, replace=True) #resample point_set = point_set[choice, :] seg = seg[choice] normal = normal[choice, :] if self.transforms is not None: point_set = self.transforms(point_set) return point_set, torch.from_numpy(normal), torch.from_numpy(seg), torch.from_numpy(cls) def __len__(self): return len(self.datapath) def preload(self): bar = ProgressBar(max_value=self.__len__()) for index in range(self.__len__()): fn = self.datapath[index] cat = self.datapath[index][0] cls = self.classes[cat] cls = np.array([cls]).astype(np.int64) data = np.loadtxt(fn[1]).astype(np.float32) point_set = data[:,0:3] # https://github.com/charlesq34/pointnet2/blob/master/part_seg/part_dataset_all_normal.py#L95 normal = data[:, 3:6] if self.normalize: point_set = pc_normalize(point_set) seg = data[:,-1].astype(np.int64) self.cache[index] = (point_set, normal, seg, cls) bar.update(index) ================================================ FILE: rs_cnn/data/__init__.py ================================================ from .ModelNet40Loader import ModelNet40Cls from .ShapeNetPartLoader import ShapeNetPart ================================================ FILE: rs_cnn/data/data_utils.py ================================================ import torch import numpy as np class PointcloudToTensor(object): def __call__(self, points): return torch.from_numpy(points).float() def angle_axis(angle: float, axis: np.ndarray): r"""Returns a 4x4 rotation matrix that performs a rotation around axis by angle Parameters ---------- angle : float Angle to rotate by axis: np.ndarray Axis to rotate about Returns ------- torch.Tensor 3x3 rotation matrix """ u = axis / np.linalg.norm(axis) cosval, sinval = np.cos(angle), np.sin(angle) # yapf: disable cross_prod_mat = np.array([[0.0, -u[2], u[1]], [u[2], 0.0, -u[0]], [-u[1], u[0], 0.0]]) R = torch.from_numpy( cosval * np.eye(3) + sinval * cross_prod_mat + (1.0 - cosval) * np.outer(u, u) ) # yapf: enable return R.float() class PointcloudRotatebyAngle(object): def __init__(self, rotation_angle = 0.0): self.rotation_angle = rotation_angle def __call__(self, pc): normals = pc.size(2) > 3 bsize = pc.size()[0] for i in range(bsize): cosval = np.cos(self.rotation_angle) sinval = np.sin(self.rotation_angle) rotation_matrix = np.array([[cosval, 0, sinval], [0, 1, 0], [-sinval, 0, cosval]]) rotation_matrix = torch.from_numpy(rotation_matrix).float().cuda() cur_pc = pc[i, :, :] if not normals: cur_pc = cur_pc @ rotation_matrix else: pc_xyz = cur_pc[:, 0:3] pc_normals = cur_pc[:, 3:] cur_pc[:, 0:3] = pc_xyz @ rotation_matrix cur_pc[:, 3:] = pc_normals @ rotation_matrix pc[i, :, :] = cur_pc return pc class PointcloudJitter(object): def __init__(self, std=0.01, clip=0.05): self.std, self.clip = std, clip def __call__(self, pc): bsize = pc.size()[0] for i in range(bsize): jittered_data = pc.new(pc.size(1), 3).normal_( mean=0.0, std=self.std ).clamp_(-self.clip, self.clip) pc[i, :, 0:3] += jittered_data return pc class PointcloudScaleAndTranslate(object): def __init__(self, scale_low=2. / 3., scale_high=3. / 2., translate_range=0.2): self.scale_low = scale_low self.scale_high = scale_high self.translate_range = translate_range def __call__(self, pc): bsize = pc.size()[0] for i in range(bsize): xyz1 = np.random.uniform(low=self.scale_low, high=self.scale_high, size=[3]) xyz2 = np.random.uniform(low=-self.translate_range, high=self.translate_range, size=[3]) pc[i, :, 0:3] = torch.mul(pc[i, :, 0:3], torch.from_numpy(xyz1).float().cuda()) + torch.from_numpy(xyz2).float().cuda() return pc class PointcloudScale(object): def __init__(self, scale_low=2. / 3., scale_high=3. / 2.): self.scale_low = scale_low self.scale_high = scale_high def __call__(self, pc): bsize = pc.size()[0] for i in range(bsize): xyz1 = np.random.uniform(low=self.scale_low, high=self.scale_high, size=[3]) pc[i, :, 0:3] = torch.mul(pc[i, :, 0:3], torch.from_numpy(xyz1).float().cuda()) return pc class PointcloudTranslate(object): def __init__(self, translate_range=0.2): self.translate_range = translate_range def __call__(self, pc): bsize = pc.size()[0] for i in range(bsize): xyz2 = np.random.uniform(low=-self.translate_range, high=self.translate_range, size=[3]) pc[i, :, 0:3] = pc[i, :, 0:3] + torch.from_numpy(xyz2).float().cuda() return pc class PointcloudRandomInputDropout(object): def __init__(self, max_dropout_ratio=0.875): assert max_dropout_ratio >= 0 and max_dropout_ratio < 1 self.max_dropout_ratio = max_dropout_ratio def __call__(self, pc): bsize = pc.size()[0] for i in range(bsize): dropout_ratio = np.random.random() * self.max_dropout_ratio # 0~0.875 drop_idx = np.where(np.random.random((pc.size()[1])) <= dropout_ratio)[0] if len(drop_idx) > 0: cur_pc = pc[i, :, :] cur_pc[drop_idx.tolist(), 0:3] = cur_pc[0, 0:3].repeat(len(drop_idx), 1) # set to the first point pc[i, :, :] = cur_pc return pc ================================================ FILE: rs_cnn/docs/_config.yml ================================================ theme: jekyll-theme-cayman title: Relation-Shape CNN (RS-CNN) description: ' ' show_downloads: true ================================================ FILE: rs_cnn/docs/index.md ================================================

Relation-Shape Convolutional Neural Network for Point Cloud Analysis

Yongcheng Liu   Bin Fan   Shiming Xiang   Chunhong Pan

CVPR 2019   Oral & Best paper finalist


partseg.jpg

Segmentation examples on ShapeNet part benchmark. Although the part shapes implied in irregular points are extremely diverse and they may be very confusing to recognize, our RS-CNN can also segment them out with decent accuracy.

Abstract

Point cloud analysis is very challenging, as the shape implied in irregular points is difficult to capture. In this paper, we propose RS-CNN, namely, Relation-Shape Convolutional Neural Network, which extends regular grid CNN to irregular configuration for point cloud analysis. ___The key to RS-CNN is learning from relation___, _i.e._, the geometric topology constraint among points. Specifically, the convolutional weight for local point set is forced to ___learn a high-level relation expression from predefined geometric priors___, between a sampled point from this point set and the others. In this way, an inductive local representation with ___explicit reasoning about the spatial layout of points___ can be obtained, which leads to much shape awareness and robustness. With this convolution as a basic operator, RS-CNN, a hierarchical architecture can be developed to achieve contextual shape-aware learning for point cloud analysis. Extensive experiments on challenging benchmarks across three tasks verify RS-CNN achieves the state of the arts.

Motivation

motivation.jpg

Left part: 3D Point cloud. Right part: Underlying shape formed by this point cloud.

- The geometric relation among points is an explicit expression about the spatial layout of points, further discriminatively reflecting the underlying shape. - CNN has demonstrated its powerful visual abstraction capability for 2D images that are in the format of a regular grid. - Can we extend 2D grid CNN to 3D irregular configuration for point cloud analysis, by learning expressive geometric relation encoding for discriminative shape awareness?

RS-Conv: Relation-Shape Convolution

[rsconv]: ./images/rsconv.jpg ![rsconv]

Overview of our relation-shape convolution (RS-Conv).

In this paper, we develop a hierarchical CNN-like architecture, _i.e._ RS-CNN. RS-CNN is equipped with a novel learn-from-relation convolution operator called relation-shape convolution (RS-Conv). As illustrated in the figure, the key to RS-CNN is learning from relation. To be specific: - The convolutional weight {\bm{\mathrm w}}_j for x_{j} is converted to {\bm{\mathrm w}}_{ij}, which learns a high-level mapping \mathcal{M}, _i.e._, {\bm{\mathrm w}}_{ij}=\mathcal{M}({\bm{\mathrm h}}_{ij}), on predefined geometric relation vector {\bm{\mathrm h}}_{ij}. - In this way, the inductive convolutional representation \sigma \big( \mathcal{A}(\{{\bm{\mathrm w}}_{ij} \cdot {\bm{\mathrm f}}_{x_j}, \hspace{0.1pt} \forall x_j\}) \big) can expressively reason the spatial layout of points, resulting in discriminative shape awareness. - As in image CNN, further channel-raising mapping is conducted for a more powerful shape-aware representation.

Revisiting 2D Grid Convolution

2dconv.jpg

Illustration of 2D grid convolution with a kernel of 3 x 3.

- The convolutional weight w_{j} for x_{j} always implies a fixed positional relation between x_{i} and its neighbor x_{j} in the regular grid. That is, w_{j} is actually constrained to encode one kind of regular grid relation in the learning process. - Therefore, our RS-Conv with relation learning is more general and can be applied to model 2D grid spatial relationship.

Experiment

### Shape Classification on ModelNet40 Benchmark
cls.jpg

Shape classification results (%) (nor: normal).

- Our RS-CNN outperforms the state-of-the-art point cloud-based methods with only \mathrm{xyz} as the input features. ### Normal Estimation
normal.jpg

Normal estimation examples. For clearness, we only show predictions with angle less than 30 degree in blue, and angle greater than 90 degree in red between the ground truth normals.

### Geometric Relation Definition
relation.jpg

The results (%) of five intuitive low-level relation. Model A applies only 3D Euclidean distance; Model B adds the coordinates difference to model A; Model C adds the coordinates of two points to model B; Model D utilizes the normals of two points and their cosine distance; Model E projects 3D points onto a 2D plane of XY, XZ and YZ.

- The low-level relation vector can be defined flexibly. - Using only 3D Euclidean distance as relation can result in an accuracy of 92.5%. - Even learning from the relation in 2D projections of points, a decent performance of 92.2% can also be achieved. ### Robustness to sampling density
density.jpg

Left part: Point cloud with random point dropout. Right part: Test results of using sparser points as the input to a model trained with 1024 points.

### Robustness to point permutation and rigid transformation (%)
rotation.jpg

All the models are trained without related data augmentations, e.g., translation or rotation, to avoid confusion. During testing, we perform random permutation (perm.) of points, add a small translation of 0.2 and rotate the input point cloud by 90 degree and 180 degree.

Visualization and Complexity

### Visualization
visualization.jpg

Visualization of the shape features learned by the first two layers of RS-CNN.

- The features learned by the first layer mostly respond to edges, corners and arcs, while the ones in the second layer capture more semantical shape parts like airfoils and heads. - As image CNN, our RS-CNN learns 3D shape semantics from point cloud in a local-to-global manner. ### Complexity
complexity.jpg

Complexity of RS-CNN in point cloud classification.

Publication

Yongcheng Liu, Bin Fan, Shiming Xiang and Chunhong Pan, "Relation-Shape Convolutional Neural Network for Point Cloud Analysis", in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. [[arXiv](https://arxiv.org/abs/1904.07601)] [[CVF](http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Relation-Shape_Convolutional_Neural_Network_for_Point_Cloud_Analysis_CVPR_2019_paper.pdf)] ``` @inproceedings{liu2019rscnn, author = {Yongcheng Liu and Bin Fan and Shiming Xiang and Chunhong Pan}, title = {Relation-Shape Convolutional Neural Network for Point Cloud Analysis}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, pages = {8895--8904}, year = {2019} } ``` ================================================ FILE: rs_cnn/models/__init__.py ================================================ from .rscnn_ssn_cls import RSCNN_SSN as RSCNN_SSN_Cls from .rscnn_msn_seg import RSCNN_MSN as RSCNN_MSN_Seg ================================================ FILE: rs_cnn/models/rscnn_msn_seg.py ================================================ import os, sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) sys.path.append(os.path.join(BASE_DIR, "../utils")) import torch import torch.nn as nn from torch.autograd import Variable import pytorch_utils as pt_utils from pointnet2_modules_updated import PointnetSAModule, PointnetFPModule, PointnetSAModuleMSG import numpy as np class RSCNN_MSN(nn.Module): r""" PointNet2 with multi-scale grouping Semantic segmentation network that uses feature propogation layers Parameters ---------- num_classes: int Number of semantics classes to predict over -- size of softmax classifier that run for each point input_channels: int = 6 Number of input channels in the feature descriptor for each point. If the point cloud is Nx9, this value should be 6 as in an Nx9 point cloud, 3 of the channels are xyz, and 6 are feature descriptors use_xyz: bool = True Whether or not to use the xyz position of a point as a feature """ def __init__(self, num_classes, input_channels=0, relation_prior=1, use_xyz=True): super().__init__() self.SA_modules = nn.ModuleList() c_in = input_channels self.SA_modules.append( # 0 PointnetSAModuleMSG( npoint=1024, radii=[0.075, 0.1, 0.125], nsamples=[16, 32, 48], mlps=[[c_in, 64], [c_in, 64], [c_in, 64]], first_layer=True, use_xyz=use_xyz, relation_prior=relation_prior ) ) c_out_0 = 64*3 c_in = c_out_0 self.SA_modules.append( # 1 PointnetSAModuleMSG( npoint=256, radii=[0.1, 0.15, 0.2], nsamples=[16, 48, 64], mlps=[[c_in, 128], [c_in, 128], [c_in, 128]], use_xyz=use_xyz, relation_prior=relation_prior ) ) c_out_1 = 128*3 c_in = c_out_1 self.SA_modules.append( # 2 PointnetSAModuleMSG( npoint=64, radii=[0.2, 0.3, 0.4], nsamples=[16, 32, 48], mlps=[[c_in, 256], [c_in, 256], [c_in, 256]], use_xyz=use_xyz, relation_prior=relation_prior ) ) c_out_2 = 256*3 c_in = c_out_2 self.SA_modules.append( # 3 PointnetSAModuleMSG( npoint=16, radii=[0.4, 0.6, 0.8], nsamples=[16, 24, 32], mlps=[[c_in, 512], [c_in, 512], [c_in, 512]], use_xyz=use_xyz, relation_prior=relation_prior ) ) c_out_3 = 512*3 self.SA_modules.append( # 4 global pooling PointnetSAModule( nsample = 16, mlp=[c_out_3, 128], use_xyz=use_xyz ) ) global_out = 128 self.SA_modules.append( # 5 global pooling PointnetSAModule( nsample = 64, mlp=[c_out_2, 128], use_xyz=use_xyz ) ) global_out2 = 128 self.FP_modules = nn.ModuleList() self.FP_modules.append( PointnetFPModule(mlp=[256 + input_channels, 128, 128]) ) self.FP_modules.append(PointnetFPModule(mlp=[512 + c_out_0, 256, 256])) self.FP_modules.append(PointnetFPModule(mlp=[512 + c_out_1, 512, 512])) self.FP_modules.append( PointnetFPModule(mlp=[c_out_3 + c_out_2, 512, 512]) ) self.FC_layer = nn.Sequential( pt_utils.Conv1d(128+global_out+global_out2+16, 128, bn=True), nn.Dropout(), pt_utils.Conv1d(128, num_classes, activation=None) ) def _break_up_pc(self, pc): xyz = pc[..., 0:3].contiguous() features = ( pc[..., 3:].transpose(1, 2).contiguous() if pc.size(-1) > 3 else None ) return xyz, features def forward(self, pointcloud: torch.cuda.FloatTensor, cls): r""" Forward pass of the network Parameters ---------- pointcloud: Variable(torch.cuda.FloatTensor) (B, N, 3 + input_channels) tensor Point cloud to run predicts on Each point in the point-cloud MUST be formated as (x, y, z, features...) """ xyz, features = self._break_up_pc(pointcloud) l_xyz, l_features = [xyz], [features] for i in range(len(self.SA_modules)): if i < 5: li_xyz, li_features = self.SA_modules[i](l_xyz[i], l_features[i]) if li_xyz is not None: random_index = np.arange(li_xyz.size()[1]) np.random.shuffle(random_index) li_xyz = li_xyz[:, random_index, :] li_features = li_features[:, :, random_index] l_xyz.append(li_xyz) l_features.append(li_features) _, global_out2_feat = self.SA_modules[5](l_xyz[3], l_features[3]) for i in range(-1, -(len(self.FP_modules) + 1), -1): l_features[i - 1 - 1] = self.FP_modules[i]( l_xyz[i - 1 - 1], l_xyz[i - 1], l_features[i - 1 - 1], l_features[i - 1] ) cls = cls.view(-1, 16, 1).repeat(1, 1, l_features[0].size()[2]) # object class one-hot-vector l_features[0] = torch.cat((l_features[0], l_features[-1].repeat(1, 1, l_features[0].size()[2]), global_out2_feat.repeat(1, 1, l_features[0].size()[2]), cls), 1) return self.FC_layer(l_features[0]).transpose(1, 2).contiguous() ================================================ FILE: rs_cnn/models/rscnn_ssn_cls.py ================================================ import os, sys BASE_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.append(BASE_DIR) sys.path.append(os.path.join(BASE_DIR, "../utils")) import torch import torch.nn as nn from torch.autograd import Variable import pytorch_utils as pt_utils from pointnet2_modules_updated import PointnetSAModule, PointnetSAModuleMSG import numpy as np # Relation-Shape CNN: Single-Scale Neighborhood class RSCNN_SSN(nn.Module): r""" PointNet2 with multi-scale grouping Semantic segmentation network that uses feature propogation layers Parameters ---------- num_classes: int Number of semantics classes to predict over -- size of softmax classifier that run for each point input_channels: int = 6 Number of input channels in the feature descriptor for each point. If the point cloud is Nx9, this value should be 6 as in an Nx9 point cloud, 3 of the channels are xyz, and 6 are feature descriptors use_xyz: bool = True Whether or not to use the xyz position of a point as a feature """ def __init__(self, num_classes, input_channels=0, relation_prior=1, use_xyz=True): super().__init__() self.SA_modules = nn.ModuleList() self.SA_modules.append( PointnetSAModuleMSG( npoint=512, radii=[0.23], nsamples=[48], mlps=[[input_channels, 128]], first_layer=True, use_xyz=use_xyz, relation_prior=relation_prior ) ) self.SA_modules.append( PointnetSAModuleMSG( npoint=128, radii=[0.32], nsamples=[64], mlps=[[128, 512]], use_xyz=use_xyz, relation_prior=relation_prior ) ) self.SA_modules.append( # global convolutional pooling PointnetSAModule( nsample = 128, mlp=[512, 1024], use_xyz=use_xyz ) ) self.FC_layer = nn.Sequential( pt_utils.FC(1024, 512, activation=nn.ReLU(inplace=True), bn=True), nn.Dropout(p=0.5), pt_utils.FC(512, 256, activation=nn.ReLU(inplace=True), bn=True), nn.Dropout(p=0.5), pt_utils.FC(256, num_classes, activation=None) ) def _break_up_pc(self, pc): xyz = pc[..., 0:3].contiguous() features = ( pc[..., 3:].transpose(1, 2).contiguous() if pc.size(-1) > 3 else None ) return xyz, features def forward(self, pointcloud: torch.cuda.FloatTensor): r""" Forward pass of the network Parameters ---------- pointcloud: Variable(torch.cuda.FloatTensor) (B, N, 3 + input_channels) tensor Point cloud to run predicts on Each point in the point-cloud MUST be formated as (x, y, z, features...) """ xyz, features = self._break_up_pc(pointcloud) for module in self.SA_modules: xyz, features = module(xyz, features) return self.FC_layer(features.squeeze(-1)) if __name__ == "__main__": sim_data = Variable(torch.rand(32, 2048, 6)) sim_data = sim_data.cuda() sim_cls = Variable(torch.ones(32, 16)) sim_cls = sim_cls.cuda() seg = RSCNN_SSN(num_classes=50, input_channels=3, use_xyz=True) seg = seg.cuda() out = seg(sim_data, sim_cls) print('seg', out.size()) ================================================ FILE: rs_cnn/train_cls.py ================================================ import torch import torch.optim as optim import torch.optim.lr_scheduler as lr_sched import torch.nn as nn from torch.utils.data import DataLoader from torch.autograd import Variable import numpy as np import os from torchvision import transforms from models import RSCNN_SSN_Cls as RSCNN_SSN from data import ModelNet40Cls import utils.pytorch_utils as pt_utils import pointnet2.utils.pointnet2_utils as pointnet2_utils import data.data_utils as d_utils import argparse import random import yaml import pdb torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = True torch.backends.cudnn.deterministic = True seed = 123 random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) parser = argparse.ArgumentParser(description='Relation-Shape CNN Shape Classification Training') parser.add_argument('--config', default='cfgs/config_ssn_cls.yaml', type=str) def main(): args = parser.parse_args() with open(args.config) as f: config = yaml.load(f) print("\n**************************") for k, v in config['common'].items(): setattr(args, k, v) print('\n[%s]:'%(k), v) print("\n**************************\n") try: os.makedirs(args.save_path) except OSError: pass train_transforms = transforms.Compose([ d_utils.PointcloudToTensor() ]) test_transforms = transforms.Compose([ d_utils.PointcloudToTensor() ]) train_dataset = ModelNet40Cls(num_points = args.num_points, root = args.data_root, transforms=train_transforms) train_dataloader = DataLoader( train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=int(args.workers), pin_memory=True ) test_dataset = ModelNet40Cls(num_points = args.num_points, root = args.data_root, transforms=test_transforms, train=False) test_dataloader = DataLoader( test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=int(args.workers), pin_memory=True ) model = RSCNN_SSN(num_classes = args.num_classes, input_channels = args.input_channels, relation_prior = args.relation_prior, use_xyz = True) model.cuda() optimizer = optim.Adam( model.parameters(), lr=args.base_lr, weight_decay=args.weight_decay) lr_lbmd = lambda e: max(args.lr_decay**(e // args.decay_step), args.lr_clip / args.base_lr) bnm_lmbd = lambda e: max(args.bn_momentum * args.bn_decay**(e // args.decay_step), args.bnm_clip) lr_scheduler = lr_sched.LambdaLR(optimizer, lr_lbmd) bnm_scheduler = pt_utils.BNMomentumScheduler(model, bnm_lmbd) if args.checkpoint is not '': model.load_state_dict(torch.load(args.checkpoint)) print('Load model successfully: %s' % (args.checkpoint)) criterion = nn.CrossEntropyLoss() num_batch = len(train_dataset)/args.batch_size # training train(train_dataloader, test_dataloader, model, criterion, optimizer, lr_scheduler, bnm_scheduler, args, num_batch) def train(train_dataloader, test_dataloader, model, criterion, optimizer, lr_scheduler, bnm_scheduler, args, num_batch): PointcloudScaleAndTranslate = d_utils.PointcloudScaleAndTranslate() # initialize augmentation global g_acc g_acc = 0.91 # only save the model whose acc > 0.91 batch_count = 0 model.train() for epoch in range(args.epochs): for i, data in enumerate(train_dataloader, 0): if lr_scheduler is not None: lr_scheduler.step(epoch) if bnm_scheduler is not None: bnm_scheduler.step(epoch-1) points, target = data points, target = points.cuda(), target.cuda() points, target = Variable(points), Variable(target) # fastest point sampling fps_idx = pointnet2_utils.furthest_point_sample(points, 1200) # (B, npoint) fps_idx = fps_idx[:, np.random.choice(1200, args.num_points, False)] points = pointnet2_utils.gather_operation(points.transpose(1, 2).contiguous(), fps_idx).transpose(1, 2).contiguous() # (B, N, 3) # augmentation points.data = PointcloudScaleAndTranslate(points.data) optimizer.zero_grad() pred = model(points) target = target.view(-1) loss = criterion(pred, target) loss.backward() optimizer.step() if i % args.print_freq_iter == 0: print('[epoch %3d: %3d/%3d] \t train loss: %0.6f \t lr: %0.5f' %(epoch+1, i, num_batch, loss.data.clone(), lr_scheduler.get_lr()[0])) batch_count += 1 # validation in between an epoch if args.evaluate and batch_count % int(args.val_freq_epoch * num_batch) == 0: validate(test_dataloader, model, criterion, args, batch_count) def validate(test_dataloader, model, criterion, args, iter): global g_acc model.eval() losses, preds, labels = [], [], [] with torch.no_grad(): for j, data in enumerate(test_dataloader, 0): points, target = data points, target = points.cuda(), target.cuda() # points, target = Variable(points, volatile=True), Variable(target, volatile=True) # fastest point sampling fps_idx = pointnet2_utils.furthest_point_sample(points, args.num_points) # (B, npoint) # fps_idx = fps_idx[:, np.random.choice(1200, args.num_points, False)] points = pointnet2_utils.gather_operation(points.transpose(1, 2).contiguous(), fps_idx).transpose(1, 2).contiguous() pred = model(points) target = target.view(-1) loss = criterion(pred, target) losses.append(loss.data.cpu()) _, pred_choice = torch.max(pred.data, -1) preds.append(pred_choice) labels.append(target.data) preds = torch.cat(preds, 0) labels = torch.cat(labels, 0) acc = (preds == labels).sum().float() / labels.numel() print('\nval loss: %0.6f \t acc: %0.6f\n' % (np.mean(np.array(losses)), acc)) if acc > g_acc: g_acc = acc torch.save(model.state_dict(), '%s/cls_ssn_iter_%d_acc_%0.6f.pth' % (args.save_path, iter, acc)) model.train() if __name__ == "__main__": main() ================================================ FILE: rs_cnn/train_cls.sh ================================================ #!/usr/bin/env sh mkdir -p log now=$(date +"%Y%m%d_%H%M%S") log_name="Cls_LOG_"$now"" export CUDA_VISIBLE_DEVICES=0 python -u train_cls.py \ --config cfgs/config_ssn_cls.yaml \ 2>&1|tee log/$log_name.log & ================================================ FILE: rs_cnn/train_partseg.py ================================================ import torch import torch.optim as optim import torch.optim.lr_scheduler as lr_sched import torch.nn as nn from torch.utils.data import DataLoader from torch.autograd import Variable import numpy as np import os from torchvision import transforms from models import RSCNN_MSN_Seg as RSCNN_MSN from data import ShapeNetPart import utils.pytorch_utils as pt_utils import data.data_utils as d_utils import argparse import random import yaml import pdb torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = True torch.backends.cudnn.deterministic = True seed = 123 random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) parser = argparse.ArgumentParser(description='Relation-Shape CNN Shape Part Segmentation Training') parser.add_argument('--config', default='cfgs/config_msn_partseg.yaml', type=str) def main(): args = parser.parse_args() with open(args.config) as f: config = yaml.load(f) print("\n**************************") for k, v in config['common'].items(): setattr(args, k, v) print('\n[%s]:'%(k), v) print("\n**************************\n") try: os.makedirs(args.save_path) except OSError: pass train_transforms = transforms.Compose([ d_utils.PointcloudToTensor() ]) test_transforms = transforms.Compose([ d_utils.PointcloudToTensor() ]) train_dataset = ShapeNetPart(root = args.data_root, num_points = args.num_points, split = 'trainval', normalize = True, transforms = train_transforms) train_dataloader = DataLoader( train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=int(args.workers), pin_memory=True ) global test_dataset test_dataset = ShapeNetPart(root = args.data_root, num_points = args.num_points, split = 'test', normalize = True, transforms = test_transforms) test_dataloader = DataLoader( test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=int(args.workers), pin_memory=True ) model = RSCNN_MSN(num_classes = args.num_classes, input_channels = args.input_channels, relation_prior = args.relation_prior, use_xyz = True) model.cuda() optimizer = optim.Adam( model.parameters(), lr=args.base_lr, weight_decay=args.weight_decay) lr_lbmd = lambda e: max(args.lr_decay**(e // args.decay_step), args.lr_clip / args.base_lr) bnm_lmbd = lambda e: max(args.bn_momentum * args.bn_decay**(e // args.decay_step), args.bnm_clip) lr_scheduler = lr_sched.LambdaLR(optimizer, lr_lbmd) bnm_scheduler = pt_utils.BNMomentumScheduler(model, bnm_lmbd) if args.checkpoint is not '': model.load_state_dict(torch.load(args.checkpoint)) print('Load model successfully: %s' % (args.checkpoint)) criterion = nn.CrossEntropyLoss() num_batch = len(train_dataset)/args.batch_size # training train(train_dataloader, test_dataloader, model, criterion, optimizer, lr_scheduler, bnm_scheduler, args, num_batch) def train(train_dataloader, test_dataloader, model, criterion, optimizer, lr_scheduler, bnm_scheduler, args, num_batch): PointcloudScaleAndTranslate = d_utils.PointcloudScaleAndTranslate() # initialize augmentation global Class_mIoU, Inst_mIoU Class_mIoU, Inst_mIoU = 0.83, 0.85 batch_count = 0 model.train() for epoch in range(args.epochs): for i, data in enumerate(train_dataloader, 0): if lr_scheduler is not None: lr_scheduler.step(epoch) if bnm_scheduler is not None: bnm_scheduler.step(epoch-1) points, target, cls = data points, target = points.cuda(), target.cuda() points, target = Variable(points), Variable(target) # augmentation points.data = PointcloudScaleAndTranslate(points.data) optimizer.zero_grad() batch_one_hot_cls = np.zeros((len(cls), 16)) # 16 object classes for b in range(len(cls)): batch_one_hot_cls[b, int(cls[b])] = 1 batch_one_hot_cls = torch.from_numpy(batch_one_hot_cls) batch_one_hot_cls = Variable(batch_one_hot_cls.float().cuda()) pred = model(points, batch_one_hot_cls) pred = pred.view(-1, args.num_classes) target = target.view(-1,1)[:,0] loss = criterion(pred, target) loss.backward() optimizer.step() if i % args.print_freq_iter == 0: print('[epoch %3d: %3d/%3d] \t train loss: %0.6f \t lr: %0.5f' %(epoch+1, i, num_batch, loss.data.clone(), lr_scheduler.get_lr()[0])) batch_count += 1 # validation in between an epoch if (epoch < 3 or epoch > 40) and args.evaluate and batch_count % int(args.val_freq_epoch * num_batch) == 0: validate(test_dataloader, model, criterion, args, batch_count) def validate(test_dataloader, model, criterion, args, iter): global Class_mIoU, Inst_mIoU, test_dataset model.eval() with torch.no_grad(): seg_classes = test_dataset.seg_classes shape_ious = {cat:[] for cat in seg_classes.keys()} seg_label_to_cat = {} # {0:Airplane, 1:Airplane, ...49:Table} for cat in seg_classes.keys(): for label in seg_classes[cat]: seg_label_to_cat[label] = cat losses = [] for _, data in enumerate(test_dataloader, 0): points, target, cls = data # points, target = Variable(points, volatile=True), Variable(target, volatile=True) points, target = points.cuda(), target.cuda() batch_one_hot_cls = np.zeros((len(cls), 16)) # 16 object classes for b in range(len(cls)): batch_one_hot_cls[b, int(cls[b])] = 1 batch_one_hot_cls = torch.from_numpy(batch_one_hot_cls) batch_one_hot_cls = Variable(batch_one_hot_cls.float().cuda()) pred = model(points, batch_one_hot_cls) loss = criterion(pred.view(-1, args.num_classes), target.view(-1,1)[:,0]) losses.append(loss.data.cpu()) pred = pred.data.cpu() target = target.data.cpu() pred_val = torch.zeros(len(cls), args.num_points).type(torch.LongTensor) # pred to the groundtruth classes (selected by seg_classes[cat]) for b in range(len(cls)): cat = seg_label_to_cat[target[b, 0].item()] logits = pred[b, :, :] # (num_points, num_classes) pred_val[b, :] = logits[:, seg_classes[cat]].max(1)[1] + seg_classes[cat][0] for b in range(len(cls)): segp = pred_val[b, :] segl = target[b, :] cat = seg_label_to_cat[segl[0].item()] part_ious = [0.0 for _ in range(len(seg_classes[cat]))] for l in seg_classes[cat]: if torch.sum((segl == l) | (segp == l)) == 0: # part is not present in this shape part_ious[l - seg_classes[cat][0]] = 1.0 else: part_ious[l - seg_classes[cat][0]] = torch.sum((segl == l) & (segp == l)) / float(torch.sum((segl == l) | (segp == l))) shape_ious[cat].append(np.mean(part_ious)) instance_ious = [] for cat in shape_ious.keys(): for iou in shape_ious[cat]: instance_ious.append(iou) shape_ious[cat] = np.mean(shape_ious[cat]) mean_class_ious = np.mean(list(shape_ious.values())) for cat in sorted(shape_ious.keys()): print('****** %s: %0.6f'%(cat, shape_ious[cat])) print('************ Test Loss: %0.6f' % (np.mean(np.array(losses)))) print('************ Class_mIoU: %0.6f' % (mean_class_ious)) print('************ Instance_mIoU: %0.6f' % (np.mean(instance_ious))) if mean_class_ious > Class_mIoU or np.mean(instance_ious) > Inst_mIoU: if mean_class_ious > Class_mIoU: Class_mIoU = mean_class_ious if np.mean(instance_ious) > Inst_mIoU: Inst_mIoU = np.mean(instance_ious) torch.save(model.state_dict(), '%s/seg_msn_iter_%d_ins_%0.6f_cls_%0.6f.pth' % (args.save_path, iter, np.mean(instance_ious), mean_class_ious)) model.train() if __name__ == "__main__": main() ================================================ FILE: rs_cnn/train_partseg.sh ================================================ #!/usr/bin/env sh mkdir -p log now=$(date +"%Y%m%d_%H%M%S") log_name="PartSeg_LOG_"$now"" export CUDA_VISIBLE_DEVICES=0 python -u train_partseg.py \ --config cfgs/config_msn_partseg.yaml \ 2>&1|tee log/$log_name.log & ================================================ FILE: rs_cnn/utils/__init__.py ================================================ ================================================ FILE: rs_cnn/utils/_ext/__init__.py ================================================ ================================================ FILE: rs_cnn/utils/_ext/pointnet2/__init__.py ================================================ from torch.utils.ffi import _wrap_function from ._pointnet2 import lib as _lib, ffi as _ffi __all__ = [] def _import_symbols(locals): for symbol in dir(_lib): fn = getattr(_lib, symbol) if callable(fn): locals[symbol] = _wrap_function(fn, _ffi) else: locals[symbol] = fn __all__.append(symbol) _import_symbols(locals()) ================================================ FILE: rs_cnn/utils/build_ffi.py ================================================ import glob import torch import os.path as osp from torch.utils.ffi import create_extension import sys, argparse, shutil base_dir = osp.dirname(osp.abspath(__file__)) def parse_args(): parser = argparse.ArgumentParser( description="Arguments for building pointnet2 ffi extension" ) parser.add_argument("--objs", nargs="*") clean_arg = parser.add_mutually_exclusive_group() clean_arg.add_argument("--build", dest='build', action="store_true") clean_arg.add_argument("--clean", dest='clean', action="store_true") parser.set_defaults(build=False, clean=False) args = parser.parse_args() assert args.build or args.clean return args def build(args): extra_objects = args.objs extra_objects += [a for a in glob.glob('/usr/local/cuda/lib64/*.a')] ffi = create_extension( '_ext.pointnet2', headers=[a for a in glob.glob("cinclude/*_wrapper.h")], sources=[a for a in glob.glob("csrc/*.c")], define_macros=[('WITH_CUDA', None)], relative_to=__file__, with_cuda=True, extra_objects=extra_objects, include_dirs=[osp.join(base_dir, 'cinclude')], verbose=False, package=False ) ffi.build() def clean(args): shutil.rmtree(osp.join(base_dir, "_ext")) if __name__ == "__main__": args = parse_args() if args.clean: clean(args) else: build(args) ================================================ FILE: rs_cnn/utils/cinclude/ball_query_gpu.h ================================================ #ifndef _BALL_QUERY_GPU #define _BALL_QUERY_GPU #ifdef __cplusplus extern "C" { #endif void query_ball_point_kernel_wrapper(int b, int n, int m, float radius, int nsample, const float *xyz, const float *new_xyz, const int *fps_idx, int *idx, cudaStream_t stream); #ifdef __cplusplus } #endif #endif ================================================ FILE: rs_cnn/utils/cinclude/ball_query_wrapper.h ================================================ int ball_query_wrapper(int b, int n, int m, float radius, int nsample, THCudaTensor *new_xyz_tensor, THCudaTensor *xyz_tensor, THCudaIntTensor *fps_idx_tensor, THCudaIntTensor *idx_tensor); ================================================ FILE: rs_cnn/utils/cinclude/cuda_utils.h ================================================ #ifndef _CUDA_UTILS_H #define _CUDA_UTILS_H #include #define TOTAL_THREADS 512 inline int opt_n_threads(int work_size) { const int pow_2 = std::log(static_cast(work_size)) / std::log(2.0); return max(min(1 << pow_2, TOTAL_THREADS), 1); } inline dim3 opt_block_config(int x, int y) { const int x_threads = opt_n_threads(x); const int y_threads = max(min(opt_n_threads(y), TOTAL_THREADS / x_threads), 1); dim3 block_config(x_threads, y_threads, 1); return block_config; } #endif ================================================ FILE: rs_cnn/utils/cinclude/group_points_gpu.h ================================================ #ifndef _BALL_QUERY_GPU #define _BALL_QUERY_GPU #ifdef __cplusplus extern "C" { #endif void group_points_kernel_wrapper(int b, int c, int n, int npoints, int nsample, const float *points, const int *idx, float *out, cudaStream_t stream); void group_points_grad_kernel_wrapper(int b, int c, int n, int npoints, int nsample, const float *grad_out, const int *idx, float *grad_points, cudaStream_t stream); #ifdef __cplusplus } #endif #endif ================================================ FILE: rs_cnn/utils/cinclude/group_points_wrapper.h ================================================ int group_points_wrapper(int b, int c, int n, int npoints, int nsample, THCudaTensor *points_tensor, THCudaIntTensor *idx_tensor, THCudaTensor *out); int group_points_grad_wrapper(int b, int c, int n, int npoints, int nsample, THCudaTensor *grad_out_tensor, THCudaIntTensor *idx_tensor, THCudaTensor *grad_points_tensor); ================================================ FILE: rs_cnn/utils/cinclude/interpolate_gpu.h ================================================ #ifndef _INTERPOLATE_GPU_H #define _INTERPOLATE_GPU_H #ifdef __cplusplus extern "C" { #endif void three_nn_kernel_wrapper(int b, int n, int m, const float *unknown, const float *known, float *dist2, int *idx, cudaStream_t stream); void three_interpolate_kernel_wrapper(int b, int c, int m, int n, const float *points, const int *idx, const float *weight, float *out, cudaStream_t stream); void three_interpolate_grad_kernel_wrapper(int b, int c, int n, int m, const float *grad_out, const int *idx, const float *weight, float *grad_points, cudaStream_t stream); #ifdef __cplusplus } #endif #endif ================================================ FILE: rs_cnn/utils/cinclude/interpolate_wrapper.h ================================================ void three_nn_wrapper(int b, int n, int m, THCudaTensor *unknown_tensor, THCudaTensor *known_tensor, THCudaTensor *dist2_tensor, THCudaIntTensor *idx_tensor); void three_interpolate_wrapper(int b, int c, int m, int n, THCudaTensor *points_tensor, THCudaIntTensor *idx_tensor, THCudaTensor *weight_tensor, THCudaTensor *out_tensor); void three_interpolate_grad_wrapper(int b, int c, int n, int m, THCudaTensor *grad_out_tensor, THCudaIntTensor *idx_tensor, THCudaTensor *weight_tensor, THCudaTensor *grad_points_tensor); ================================================ FILE: rs_cnn/utils/cinclude/sampling_gpu.h ================================================ #ifndef _SAMPLING_GPU_H #define _SAMPLING_GPU_H #ifdef __cplusplus extern "C" { #endif void gather_points_kernel_wrapper(int b, int c, int n, int npoints, const float *points, const int *idx, float *out, cudaStream_t stream); void gather_points_grad_kernel_wrapper(int b, int c, int n, int npoints, const float *grad_out, const int *idx, float *grad_points, cudaStream_t stream); void furthest_point_sampling_kernel_wrapper(int b, int n, int m, const float *dataset, float *temp, int *idxs, cudaStream_t stream); #ifdef __cplusplus } #endif #endif ================================================ FILE: rs_cnn/utils/cinclude/sampling_wrapper.h ================================================ int gather_points_wrapper(int b, int c, int n, int npoints, THCudaTensor *points_tensor, THCudaIntTensor *idx_tensor, THCudaTensor *out_tensor); int gather_points_grad_wrapper(int b, int c, int n, int npoints, THCudaTensor *grad_out_tensor, THCudaIntTensor *idx_tensor, THCudaTensor *grad_points_tensor); int furthest_point_sampling_wrapper(int b, int n, int m, THCudaTensor *points_tensor, THCudaTensor *temp_tensor, THCudaIntTensor *idx_tensor); ================================================ FILE: rs_cnn/utils/csrc/ball_query.c ================================================ #include #include "ball_query_gpu.h" extern THCState *state; int ball_query_wrapper(int b, int n, int m, float radius, int nsample, THCudaTensor *new_xyz_tensor, THCudaTensor *xyz_tensor, THCudaIntTensor *fps_idx_tensor, THCudaIntTensor *idx_tensor) { const float *new_xyz = THCudaTensor_data(state, new_xyz_tensor); const float *xyz = THCudaTensor_data(state, xyz_tensor); const int *fps_idx = THCudaIntTensor_data(state, fps_idx_tensor); int *idx = THCudaIntTensor_data(state, idx_tensor); cudaStream_t stream = THCState_getCurrentStream(state); query_ball_point_kernel_wrapper(b, n, m, radius, nsample, new_xyz, xyz, fps_idx, idx, stream); return 1; } ================================================ FILE: rs_cnn/utils/csrc/ball_query_gpu.cu ================================================ #include #include #include #include "ball_query_gpu.h" #include "cuda_utils.h" // input: new_xyz(b, m, 3) xyz(b, n, 3) // output: idx(b, m, nsample) __global__ void query_ball_point_kernel(int b, int n, int m, float radius, int nsample, const float *__restrict__ new_xyz, const float *__restrict__ xyz, const int *__restrict__ fps_idx, int *__restrict__ idx) { int batch_index = blockIdx.x; xyz += batch_index * n * 3; new_xyz += batch_index * m * 3; fps_idx += batch_index * m; idx += m * nsample * batch_index; int index = threadIdx.x; int stride = blockDim.x; float radius2 = radius * radius; for (int j = index; j < m; j += stride) { float new_x = new_xyz[j * 3 + 0]; float new_y = new_xyz[j * 3 + 1]; float new_z = new_xyz[j * 3 + 2]; for (int l = 0; l < nsample; ++l) { idx[j * nsample + l] = fps_idx[j]; } for (int k = 0, cnt = 0; k < n && cnt < nsample; ++k) { float x = xyz[k * 3 + 0]; float y = xyz[k * 3 + 1]; float z = xyz[k * 3 + 2]; float d2 = (new_x - x) * (new_x - x) + (new_y - y) * (new_y - y) + (new_z - z) * (new_z - z); if (d2 < radius2 && d2 > 0) { idx[j * nsample + cnt] = k; ++cnt; } } } } void query_ball_point_kernel_wrapper(int b, int n, int m, float radius, int nsample, const float *new_xyz, const float *xyz, const int *fps_idx, int *idx, cudaStream_t stream) { cudaError_t err; query_ball_point_kernel<<>>( b, n, m, radius, nsample, new_xyz, xyz, fps_idx, idx); err = cudaGetLastError(); if (cudaSuccess != err) { fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); exit(-1); } } ================================================ FILE: rs_cnn/utils/csrc/group_points.c ================================================ #include #include "group_points_gpu.h" extern THCState *state; int group_points_wrapper(int b, int c, int n, int npoints, int nsample, THCudaTensor *points_tensor, THCudaIntTensor *idx_tensor, THCudaTensor *out_tensor) { const float *points = THCudaTensor_data(state, points_tensor); const int *idx = THCudaIntTensor_data(state, idx_tensor); float *out = THCudaTensor_data(state, out_tensor); cudaStream_t stream = THCState_getCurrentStream(state); group_points_kernel_wrapper(b, c, n, npoints, nsample, points, idx, out, stream); return 1; } int group_points_grad_wrapper(int b, int c, int n, int npoints, int nsample, THCudaTensor *grad_out_tensor, THCudaIntTensor *idx_tensor, THCudaTensor *grad_points_tensor) { float *grad_points = THCudaTensor_data(state, grad_points_tensor); const int *idx = THCudaIntTensor_data(state, idx_tensor); const float *grad_out = THCudaTensor_data(state, grad_out_tensor); cudaStream_t stream = THCState_getCurrentStream(state); group_points_grad_kernel_wrapper(b, c, n, npoints, nsample, grad_out, idx, grad_points, stream); return 1; } ================================================ FILE: rs_cnn/utils/csrc/group_points_gpu.cu ================================================ #include #include #include "cuda_utils.h" #include "group_points_gpu.h" // input: points(b, c, n) idx(b, npoints, nsample) // output: out(b, c, npoints, nsample) __global__ void group_points_kernel(int b, int c, int n, int npoints, int nsample, const float *__restrict__ points, const int *__restrict__ idx, float *__restrict__ out) { int batch_index = blockIdx.x; points += batch_index * n * c; idx += batch_index * npoints * nsample; out += batch_index * npoints * nsample * c; const int index = threadIdx.y * blockDim.x + threadIdx.x; const int stride = blockDim.y * blockDim.x; for (int i = index; i < c * npoints; i += stride) { const int l = i / npoints; const int j = i % npoints; for (int k = 0; k < nsample; ++k) { int ii = idx[j * nsample + k]; out[(l * npoints + j) * nsample + k] = points[l * n + ii]; } } } void group_points_kernel_wrapper(int b, int c, int n, int npoints, int nsample, const float *points, const int *idx, float *out, cudaStream_t stream) { cudaError_t err; group_points_kernel<<>>( b, c, n, npoints, nsample, points, idx, out); err = cudaGetLastError(); if (cudaSuccess != err) { fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); exit(-1); } } // input: grad_out(b, c, npoints, nsample), idx(b, npoints, nsample) // output: grad_points(b, c, n) __global__ void group_points_grad_kernel(int b, int c, int n, int npoints, int nsample, const float *__restrict__ grad_out, const int *__restrict__ idx, float *__restrict__ grad_points) { int batch_index = blockIdx.x; grad_out += batch_index * npoints * nsample * c; idx += batch_index * npoints * nsample; grad_points += batch_index * n * c; const int index = threadIdx.y * blockDim.x + threadIdx.x; const int stride = blockDim.y * blockDim.x; for (int i = index; i < c * npoints; i += stride) { const int l = i / npoints; const int j = i % npoints; for (int k = 0; k < nsample; ++k) { int ii = idx[j * nsample + k]; atomicAdd(grad_points + l * n + ii, grad_out[(l * npoints + j) * nsample + k]); } } } void group_points_grad_kernel_wrapper(int b, int c, int n, int npoints, int nsample, const float *grad_out, const int *idx, float *grad_points, cudaStream_t stream) { cudaError_t err; group_points_grad_kernel<<>>( b, c, n, npoints, nsample, grad_out, idx, grad_points); err = cudaGetLastError(); if (cudaSuccess != err) { fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); exit(-1); } } ================================================ FILE: rs_cnn/utils/csrc/interpolate.c ================================================ #include #include #include #include #include "interpolate_gpu.h" extern THCState *state; void three_nn_wrapper(int b, int n, int m, THCudaTensor *unknown_tensor, THCudaTensor *known_tensor, THCudaTensor *dist2_tensor, THCudaIntTensor *idx_tensor) { const float *unknown = THCudaTensor_data(state, unknown_tensor); const float *known = THCudaTensor_data(state, known_tensor); float *dist2 = THCudaTensor_data(state, dist2_tensor); int *idx = THCudaIntTensor_data(state, idx_tensor); cudaStream_t stream = THCState_getCurrentStream(state); three_nn_kernel_wrapper(b, n, m, unknown, known, dist2, idx, stream); } void three_interpolate_wrapper(int b, int c, int m, int n, THCudaTensor *points_tensor, THCudaIntTensor *idx_tensor, THCudaTensor *weight_tensor, THCudaTensor *out_tensor) { const float *points = THCudaTensor_data(state, points_tensor); const float *weight = THCudaTensor_data(state, weight_tensor); float *out = THCudaTensor_data(state, out_tensor); const int *idx = THCudaIntTensor_data(state, idx_tensor); cudaStream_t stream = THCState_getCurrentStream(state); three_interpolate_kernel_wrapper(b, c, m, n, points, idx, weight, out, stream); } void three_interpolate_grad_wrapper(int b, int c, int n, int m, THCudaTensor *grad_out_tensor, THCudaIntTensor *idx_tensor, THCudaTensor *weight_tensor, THCudaTensor *grad_points_tensor) { const float *grad_out = THCudaTensor_data(state, grad_out_tensor); const float *weight = THCudaTensor_data(state, weight_tensor); float *grad_points = THCudaTensor_data(state, grad_points_tensor); const int *idx = THCudaIntTensor_data(state, idx_tensor); cudaStream_t stream = THCState_getCurrentStream(state); three_interpolate_grad_kernel_wrapper(b, c, n, m, grad_out, idx, weight, grad_points, stream); } ================================================ FILE: rs_cnn/utils/csrc/interpolate_gpu.cu ================================================ #include #include #include #include "cuda_utils.h" #include "interpolate_gpu.h" // input: unknown(b, n, 3) known(b, m, 3) // output: dist2(b, n, 3), idx(b, n, 3) __global__ void three_nn_kernel(int b, int n, int m, const float *__restrict__ unknown, const float *__restrict__ known, float *__restrict__ dist2, int *__restrict__ idx) { int batch_index = blockIdx.x; unknown += batch_index * n * 3; known += batch_index * m * 3; dist2 += batch_index * n * 3; idx += batch_index * n * 3; int index = threadIdx.x; int stride = blockDim.x; for (int j = index; j < n; j += stride) { float ux = unknown[j * 3 + 0]; float uy = unknown[j * 3 + 1]; float uz = unknown[j * 3 + 2]; double best1 = 1e40, best2 = 1e40, best3 = 1e40; int besti1 = 0, besti2 = 0, besti3 = 0; for (int k = 0; k < m; ++k) { float x = known[k * 3 + 0]; float y = known[k * 3 + 1]; float z = known[k * 3 + 2]; float d = (ux - x) * (ux - x) + (uy - y) * (uy - y) + (uz - z) * (uz - z); if (d < best1) { best3 = best2; besti3 = besti2; best2 = best1; besti2 = besti1; best1 = d; besti1 = k; } else if (d < best2) { best3 = best2; besti3 = besti2; best2 = d; besti2 = k; } else if (d < best3) { best3 = d; besti3 = k; } } dist2[j * 3 + 0] = best1; dist2[j * 3 + 1] = best2; dist2[j * 3 + 2] = best3; idx[j * 3 + 0] = besti1; idx[j * 3 + 1] = besti2; idx[j * 3 + 2] = besti3; } } void three_nn_kernel_wrapper(int b, int n, int m, const float *unknown, const float *known, float *dist2, int *idx, cudaStream_t stream) { cudaError_t err; three_nn_kernel<<>>(b, n, m, unknown, known, dist2, idx); err = cudaGetLastError(); if (cudaSuccess != err) { fprintf(stderr, "CUDA kernel " "failed : %s\n", cudaGetErrorString(err)); exit(-1); } } // input: points(b, c, m), idx(b, n, 3), weight(b, n, 3) // output: out(b, c, n) __global__ void three_interpolate_kernel(int b, int c, int m, int n, const float *__restrict__ points, const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ out) { int batch_index = blockIdx.x; points += batch_index * m * c; idx += batch_index * n * 3; weight += batch_index * n * 3; out += batch_index * n * c; const int index = threadIdx.y * blockDim.x + threadIdx.x; const int stride = blockDim.y * blockDim.x; for (int i = index; i < c * n; i += stride) { const int l = i / n; const int j = i % n; float w1 = weight[j * 3 + 0]; float w2 = weight[j * 3 + 1]; float w3 = weight[j * 3 + 2]; int i1 = idx[j * 3 + 0]; int i2 = idx[j * 3 + 1]; int i3 = idx[j * 3 + 2]; out[i] = points[l * m + i1] * w1 + points[l * m + i2] * w2 + points[l * m + i3] * w3; } } void three_interpolate_kernel_wrapper(int b, int c, int m, int n, const float *points, const int *idx, const float *weight, float *out, cudaStream_t stream) { cudaError_t err; three_interpolate_kernel<<>>( b, c, m, n, points, idx, weight, out); err = cudaGetLastError(); if (cudaSuccess != err) { fprintf(stderr, "CUDA kernel " "failed : %s\n", cudaGetErrorString(err)); exit(-1); } } // input: grad_out(b, c, n), idx(b, n, 3), weight(b, n, 3) // output: grad_points(b, c, m) __global__ void three_interpolate_grad_kernel( int b, int c, int n, int m, const float *__restrict__ grad_out, const int *__restrict__ idx, const float *__restrict__ weight, float *__restrict__ grad_points) { int batch_index = blockIdx.x; grad_out += batch_index * n * c; idx += batch_index * n * 3; weight += batch_index * n * 3; grad_points += batch_index * m * c; const int index = threadIdx.y * blockDim.x + threadIdx.x; const int stride = blockDim.y * blockDim.x; for (int i = index; i < c * n; i += stride) { const int l = i / n; const int j = i % n; float w1 = weight[j * 3 + 0]; float w2 = weight[j * 3 + 1]; float w3 = weight[j * 3 + 2]; int i1 = idx[j * 3 + 0]; int i2 = idx[j * 3 + 1]; int i3 = idx[j * 3 + 2]; atomicAdd(grad_points + l * m + i1, grad_out[i] * w1); atomicAdd(grad_points + l * m + i2, grad_out[i] * w2); atomicAdd(grad_points + l * m + i3, grad_out[i] * w3); } } void three_interpolate_grad_kernel_wrapper(int b, int n, int c, int m, const float *grad_out, const int *idx, const float *weight, float *grad_points, cudaStream_t stream) { cudaError_t err; three_interpolate_grad_kernel<<>>( b, n, c, m, grad_out, idx, weight, grad_points); err = cudaGetLastError(); if (cudaSuccess != err) { fprintf(stderr, "CUDA kernel " "failed : %s\n", cudaGetErrorString(err)); exit(-1); } } ================================================ FILE: rs_cnn/utils/csrc/sampling.c ================================================ #include #include "sampling_gpu.h" extern THCState *state; int gather_points_wrapper(int b, int c, int n, int npoints, THCudaTensor *points_tensor, THCudaIntTensor *idx_tensor, THCudaTensor *out_tensor) { const float *points = THCudaTensor_data(state, points_tensor); const int *idx = THCudaIntTensor_data(state, idx_tensor); float *out = THCudaTensor_data(state, out_tensor); cudaStream_t stream = THCState_getCurrentStream(state); gather_points_kernel_wrapper(b, c, n, npoints, points, idx, out, stream); return 1; } int gather_points_grad_wrapper(int b, int c, int n, int npoints, THCudaTensor *grad_out_tensor, THCudaIntTensor *idx_tensor, THCudaTensor *grad_points_tensor) { const float *grad_out = THCudaTensor_data(state, grad_out_tensor); const int *idx = THCudaIntTensor_data(state, idx_tensor); float *grad_points = THCudaTensor_data(state, grad_points_tensor); cudaStream_t stream = THCState_getCurrentStream(state); gather_points_grad_kernel_wrapper(b, c, n, npoints, grad_out, idx, grad_points, stream); return 1; } int furthest_point_sampling_wrapper(int b, int n, int m, THCudaTensor *points_tensor, THCudaTensor *temp_tensor, THCudaIntTensor *idx_tensor) { const float *points = THCudaTensor_data(state, points_tensor); float *temp = THCudaTensor_data(state, temp_tensor); int *idx = THCudaIntTensor_data(state, idx_tensor); cudaStream_t stream = THCState_getCurrentStream(state); furthest_point_sampling_kernel_wrapper(b, n, m, points, temp, idx, stream); return 1; } ================================================ FILE: rs_cnn/utils/csrc/sampling_gpu.cu ================================================ #include #include #include "cuda_utils.h" #include "sampling_gpu.h" // input: points(b, c, n) idx(b, m) // output: out(b, c, m) __global__ void gather_points_kernel(int b, int c, int n, int m, const float *__restrict__ points, const int *__restrict__ idx, float *__restrict__ out) { for (int i = blockIdx.x; i < b; i += gridDim.x) { for (int l = blockIdx.y; l < c; l += gridDim.y) { for (int j = threadIdx.x; j < m; j += blockDim.x) { int a = idx[i * m + j]; out[(i * c + l) * m + j] = points[(i * c + l) * n + a]; } } } } void gather_points_kernel_wrapper(int b, int c, int n, int npoints, const float *points, const int *idx, float *out, cudaStream_t stream) { cudaError_t err; gather_points_kernel<<>>( b, c, n, npoints, points, idx, out); err = cudaGetLastError(); if (cudaSuccess != err) { fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); exit(-1); } } // input: grad_out(b, c, m) idx(b, m) // output: grad_points(b, c, n) __global__ void gather_points_grad_kernel(int b, int c, int n, int m, const float *__restrict__ grad_out, const int *__restrict__ idx, float *__restrict__ grad_points) { for (int i = blockIdx.x; i < b; i += gridDim.x) { for (int l = blockIdx.y; l < c; l += gridDim.y) { for (int j = threadIdx.x; j < m; j += blockDim.x) { int a = idx[i * m + j]; atomicAdd(grad_points + (i * c + l) * n + a, grad_out[(i * c + l) * m + j]); } } } } void gather_points_grad_kernel_wrapper(int b, int c, int n, int npoints, const float *grad_out, const int *idx, float *grad_points, cudaStream_t stream) { cudaError_t err; gather_points_grad_kernel<<>>(b, c, n, npoints, grad_out, idx, grad_points); err = cudaGetLastError(); if (cudaSuccess != err) { fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); exit(-1); } } __device__ void __update(float *__restrict__ dists, int *__restrict__ dists_i, int idx1, int idx2) { const float v1 = dists[idx1], v2 = dists[idx2]; const int i1 = dists_i[idx1], i2 = dists_i[idx2]; dists[idx1] = max(v1, v2); dists_i[idx1] = v2 > v1 ? i2 : i1; } // Input dataset: (b, n, 3), tmp: (b, n) // Ouput idxs (b, m) template __global__ void furthest_point_sampling_kernel( int b, int n, int m, const float *__restrict__ dataset, float *__restrict__ temp, int *__restrict__ idxs) { if (m <= 0) return; __shared__ float dists[block_size]; __shared__ int dists_i[block_size]; int batch_index = blockIdx.x; dataset += batch_index * n * 3; temp += batch_index * n; idxs += batch_index * m; int tid = threadIdx.x; const int stride = block_size; int old = 0; if (threadIdx.x == 0) idxs[0] = old; __syncthreads(); for (int j = 1; j < m; j++) { int besti = 0; float best = -1; float x1 = dataset[old * 3 + 0]; float y1 = dataset[old * 3 + 1]; float z1 = dataset[old * 3 + 2]; for (int k = tid; k < n; k += stride) { float x2, y2, z2; x2 = dataset[k * 3 + 0]; y2 = dataset[k * 3 + 1]; z2 = dataset[k * 3 + 2]; float mag = (x2 * x2) + (y2 * y2) + (z2 * z2); if (mag <= 1e-3) continue; float d = (x2 - x1) * (x2 - x1) + (y2 - y1) * (y2 - y1) + (z2 - z1) * (z2 - z1); float d2 = min(d, temp[k]); temp[k] = d2; besti = d2 > best ? k : besti; best = d2 > best ? d2 : best; } dists[tid] = best; dists_i[tid] = besti; __syncthreads(); if (block_size >= 512) { if (tid < 256) { __update(dists, dists_i, tid, tid + 256); } __syncthreads(); } if (block_size >= 256) { if (tid < 128) { __update(dists, dists_i, tid, tid + 128); } __syncthreads(); } if (block_size >= 128) { if (tid < 64) { __update(dists, dists_i, tid, tid + 64); } __syncthreads(); } if (block_size >= 64) { if (tid < 32) { __update(dists, dists_i, tid, tid + 32); } __syncthreads(); } if (block_size >= 32) { if (tid < 16) { __update(dists, dists_i, tid, tid + 16); } __syncthreads(); } if (block_size >= 16) { if (tid < 8) { __update(dists, dists_i, tid, tid + 8); } __syncthreads(); } if (block_size >= 8) { if (tid < 4) { __update(dists, dists_i, tid, tid + 4); } __syncthreads(); } if (block_size >= 4) { if (tid < 2) { __update(dists, dists_i, tid, tid + 2); } __syncthreads(); } if (block_size >= 2) { if (tid < 1) { __update(dists, dists_i, tid, tid + 1); } __syncthreads(); } old = dists_i[0]; if (tid == 0) idxs[j] = old; } } void furthest_point_sampling_kernel_wrapper(int b, int n, int m, const float *dataset, float *temp, int *idxs, cudaStream_t stream) { cudaError_t err; unsigned int n_threads = opt_n_threads(n); switch (n_threads) { case 512: furthest_point_sampling_kernel<512><<>>( b, n, m, dataset, temp, idxs); break; case 256: furthest_point_sampling_kernel<256><<>>( b, n, m, dataset, temp, idxs); break; case 128: furthest_point_sampling_kernel<128><<>>( b, n, m, dataset, temp, idxs); break; case 64: furthest_point_sampling_kernel<64><<>>( b, n, m, dataset, temp, idxs); break; case 32: furthest_point_sampling_kernel<32><<>>( b, n, m, dataset, temp, idxs); break; case 16: furthest_point_sampling_kernel<16><<>>( b, n, m, dataset, temp, idxs); break; case 8: furthest_point_sampling_kernel<8><<>>( b, n, m, dataset, temp, idxs); break; case 4: furthest_point_sampling_kernel<4><<>>( b, n, m, dataset, temp, idxs); break; case 2: furthest_point_sampling_kernel<2><<>>( b, n, m, dataset, temp, idxs); break; case 1: furthest_point_sampling_kernel<1><<>>( b, n, m, dataset, temp, idxs); break; default: furthest_point_sampling_kernel<512><<>>( b, n, m, dataset, temp, idxs); } err = cudaGetLastError(); if (cudaSuccess != err) { fprintf(stderr, "CUDA kernel failed : %s\n", cudaGetErrorString(err)); exit(-1); } } ================================================ FILE: rs_cnn/utils/linalg_utils.py ================================================ import torch from enum import Enum PDist2Order = Enum('PDist2Order', 'd_first d_second') def pdist2( X: torch.Tensor, Z: torch.Tensor = None, order: PDist2Order = PDist2Order.d_second ) -> torch.Tensor: r""" Calculates the pairwise distance between X and Z D[b, i, j] = l2 distance X[b, i] and Z[b, j] Parameters --------- X : torch.Tensor X is a (B, N, d) tensor. There are B batches, and N vectors of dimension d Z: torch.Tensor Z is a (B, M, d) tensor. If Z is None, then Z = X Returns ------- torch.Tensor Distance matrix is size (B, N, M) """ if order == PDist2Order.d_second: if X.dim() == 2: X = X.unsqueeze(0) if Z is None: Z = X G = X @ Z.transpose(-2, -1) S = (X * X).sum(-1, keepdim=True) R = S.transpose(-2, -1) else: if Z.dim() == 2: Z = Z.unsqueeze(0) G = X @ Z.transpose(-2, -1) S = (X * X).sum(-1, keepdim=True) R = (Z * Z).sum(-1, keepdim=True).transpose(-2, -1) else: if X.dim() == 2: X = X.unsqueeze(0) if Z is None: Z = X G = X.transpose(-2, -1) @ Z R = (X * X).sum(-2, keepdim=True) S = R.transpose(-2, -1) else: if Z.dim() == 2: Z = Z.unsqueeze(0) G = X.transpose(-2, -1) @ Z S = (X * X).sum(-2, keepdim=True).transpose(-2, -1) R = (Z * Z).sum(-2, keepdim=True) return torch.abs(R + S - 2 * G).squeeze(0) def pdist2_slow(X, Z=None): if Z is None: Z = X D = torch.zeros(X.size(0), X.size(2), Z.size(2)) for b in range(D.size(0)): for i in range(D.size(1)): for j in range(D.size(2)): D[b, i, j] = torch.dist(X[b, :, i], Z[b, :, j]) return D if __name__ == "__main__": X = torch.randn(2, 3, 5) Z = torch.randn(2, 3, 3) print(pdist2(X, order=PDist2Order.d_first)) print(pdist2_slow(X)) print(torch.dist(pdist2(X, order=PDist2Order.d_first), pdist2_slow(X))) ================================================ FILE: rs_cnn/utils/pointnet2_modules.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F import pointnet2_utils import pytorch_utils as pt_utils from typing import List import numpy as np import time import math class _PointnetSAModuleBase(nn.Module): def __init__(self): super().__init__() self.npoint = None self.groupers = None self.mlps = None def forward(self, xyz: torch.Tensor, features: torch.Tensor = None) -> (torch.Tensor, torch.Tensor): r""" Parameters ---------- xyz : torch.Tensor (B, N, 3) tensor of the xyz coordinates of the points features : torch.Tensor (B, N, C) tensor of the descriptors of the the points Returns ------- new_xyz : torch.Tensor (B, npoint, 3) tensor of the new points' xyz new_features : torch.Tensor (B, npoint, \sum_k(mlps[k][-1])) tensor of the new_points descriptors """ new_features_list = [] xyz_flipped = xyz.transpose(1, 2).contiguous() if self.npoint is not None: fps_idx = pointnet2_utils.furthest_point_sample(xyz, self.npoint) # (B, npoint) new_xyz = pointnet2_utils.gather_operation(xyz_flipped, fps_idx).transpose(1, 2).contiguous() fps_idx = fps_idx.data else: new_xyz = None fps_idx = None for i in range(len(self.groupers)): new_features = self.groupers[i](xyz, new_xyz, features, fps_idx) if self.npoint is not None else self.groupers[i](xyz, new_xyz, features) # (B, C, npoint, nsample) new_features = self.mlps[i]( new_features ) # (B, mlp[-1], npoint) new_features_list.append(new_features) return new_xyz, torch.cat(new_features_list, dim=1) class PointnetSAModuleMSG(_PointnetSAModuleBase): r"""Pointnet set abstrction layer with multiscale grouping Parameters ---------- npoint : int Number of points radii : list of float32 list of radii to group with nsamples : list of int32 Number of samples in each ball query mlps : list of list of int32 Spec of the pointnet before the global max_pool for each scale bn : bool Use batchnorm """ def __init__( self, *, npoint: int, radii: List[float], nsamples: List[int], mlps: List[List[int]], use_xyz: bool = True, bias = True, init = nn.init.kaiming_normal, first_layer = False, relation_prior = 1 ): super().__init__() assert len(radii) == len(nsamples) == len(mlps) self.npoint = npoint self.groupers = nn.ModuleList() self.mlps = nn.ModuleList() # initialize shared mapping functions C_in = (mlps[0][0] + 3) if use_xyz else mlps[0][0] C_out = mlps[0][1] if relation_prior == 0: in_channels = 1 elif relation_prior == 1 or relation_prior == 2: in_channels = 10 else: assert False, "relation_prior can only be 0, 1, 2." if first_layer: mapping_func1 = nn.Conv2d(in_channels = in_channels, out_channels = math.floor(C_out / 2), kernel_size = (1, 1), stride = (1, 1), bias = bias) mapping_func2 = nn.Conv2d(in_channels = math.floor(C_out / 2), out_channels = 16, kernel_size = (1, 1), stride = (1, 1), bias = bias) xyz_raising = nn.Conv2d(in_channels = C_in, out_channels = 16, kernel_size = (1, 1), stride = (1, 1), bias = bias) init(xyz_raising.weight) if bias: nn.init.constant(xyz_raising.bias, 0) elif npoint is not None: mapping_func1 = nn.Conv2d(in_channels = in_channels, out_channels = math.floor(C_out / 4), kernel_size = (1, 1), stride = (1, 1), bias = bias) mapping_func2 = nn.Conv2d(in_channels = math.floor(C_out / 4), out_channels = C_in, kernel_size = (1, 1), stride = (1, 1), bias = bias) if npoint is not None: init(mapping_func1.weight) init(mapping_func2.weight) if bias: nn.init.constant(mapping_func1.bias, 0) nn.init.constant(mapping_func2.bias, 0) # channel raising mapping cr_mapping = nn.Conv1d(in_channels = C_in if not first_layer else 16, out_channels = C_out, kernel_size = 1, stride = 1, bias = bias) init(cr_mapping.weight) nn.init.constant(cr_mapping.bias, 0) if first_layer: mapping = [mapping_func1, mapping_func2, cr_mapping, xyz_raising] elif npoint is not None: mapping = [mapping_func1, mapping_func2, cr_mapping] for i in range(len(radii)): radius = radii[i] nsample = nsamples[i] self.groupers.append( pointnet2_utils.QueryAndGroup(radius, nsample, use_xyz=use_xyz) if npoint is not None else pointnet2_utils.GroupAll(use_xyz) ) mlp_spec = mlps[i] if use_xyz: mlp_spec[0] += 3 if npoint is not None: self.mlps.append(pt_utils.SharedRSConv(mlp_spec, mapping = mapping, relation_prior = relation_prior, first_layer = first_layer)) else: # global convolutional pooling self.mlps.append(pt_utils.GloAvgConv(C_in = C_in, C_out = C_out)) class PointnetSAModule(PointnetSAModuleMSG): r"""Pointnet set abstrction layer Parameters ---------- npoint : int Number of features radius : float Radius of ball nsample : int Number of samples in the ball query mlp : list Spec of the pointnet before the global max_pool bn : bool Use batchnorm """ def __init__( self, *, mlp: List[int], npoint: int = None, radius: float = None, nsample: int = None, use_xyz: bool = True, ): super().__init__( mlps=[mlp], npoint=npoint, radii=[radius], nsamples=[nsample], use_xyz=use_xyz ) class PointnetFPModule(nn.Module): r"""Propigates the features of one set to another Parameters ---------- mlp : list Pointnet module parameters bn : bool Use batchnorm """ def __init__(self, *, mlp: List[int], bn: bool = True): super().__init__() self.mlp = pt_utils.SharedMLP(mlp, bn=bn) def forward( self, unknown: torch.Tensor, known: torch.Tensor, unknow_feats: torch.Tensor, known_feats: torch.Tensor ) -> torch.Tensor: r""" Parameters ---------- unknown : torch.Tensor (B, n, 3) tensor of the xyz positions of the unknown features known : torch.Tensor (B, m, 3) tensor of the xyz positions of the known features unknow_feats : torch.Tensor (B, C1, n) tensor of the features to be propigated to known_feats : torch.Tensor (B, C2, m) tensor of features to be propigated Returns ------- new_features : torch.Tensor (B, mlp[-1], n) tensor of the features of the unknown features """ dist, idx = pointnet2_utils.three_nn(unknown, known) dist_recip = 1.0 / (dist + 1e-8) norm = torch.sum(dist_recip, dim=2, keepdim=True) weight = dist_recip / norm interpolated_feats = pointnet2_utils.three_interpolate( known_feats, idx, weight ) if unknow_feats is not None: new_features = torch.cat([interpolated_feats, unknow_feats], dim=1) #(B, C2 + C1, n) else: new_features = interpolated_feats new_features = new_features.unsqueeze(-1) new_features = self.mlp(new_features) return new_features.squeeze(-1) if __name__ == "__main__": from torch.autograd import Variable torch.manual_seed(1) torch.cuda.manual_seed_all(1) xyz = Variable(torch.randn(2, 9, 3).cuda(), requires_grad=True) xyz_feats = Variable(torch.randn(2, 9, 6).cuda(), requires_grad=True) test_module = PointnetSAModuleMSG( npoint=2, radii=[5.0, 10.0], nsamples=[6, 3], mlps=[[9, 3], [9, 6]] ) test_module.cuda() print(test_module(xyz, xyz_feats)) # test_module = PointnetFPModule(mlp=[6, 6]) # test_module.cuda() # from torch.autograd import gradcheck # inputs = (xyz, xyz, None, xyz_feats) # test = gradcheck(test_module, inputs, eps=1e-6, atol=1e-4) # print(test) for _ in range(1): _, new_features = test_module(xyz, xyz_feats) new_features.backward( torch.cuda.FloatTensor(*new_features.size()).fill_(1) ) print(new_features) print(xyz.grad) ================================================ FILE: rs_cnn/utils/pointnet2_modules_updated.py ================================================ from typing import * import torch import torch.nn as nn import torch.nn.functional as F import PCT_Pytorch.pointnet2_ops_lib.pointnet2_ops.pointnet2_utils as tp import pytorch_utils as pt_utils from typing import List import numpy as np import time import math class QueryAndGroup(nn.Module): r""" Groups with a ball query of radius Parameters --------- radius : float32 Radius of ball nsample : int32 Maximum number of points to gather in the ball """ def __init__(self, radius: float, nsample: int, use_xyz: bool = True): super().__init__() self.radius, self.nsample, self.use_xyz = radius, nsample, use_xyz def forward( self, xyz: torch.Tensor, new_xyz: torch.Tensor, features: torch.Tensor = None, fps_idx: torch.IntTensor = None ) -> Tuple[torch.Tensor]: r""" Parameters ---------- xyz : torch.Tensor xyz coordinates of the features (B, N, 3) new_xyz : torch.Tensor centriods (B, npoint, 3) features : torch.Tensor Descriptors of the features (B, C, N) Returns ------- new_features : torch.Tensor (B, 3 + C, npoint, nsample) tensor """ # idx = tp.ball_query(self.radius, self.nsample, xyz, new_xyz, mode='dense') idx = tp.ball_query(self.radius, self.nsample, xyz, new_xyz) xyz_trans = xyz.transpose(1, 2).contiguous() grouped_xyz = tp.grouping_operation( xyz_trans, idx ) # (B, 3, npoint, nsample) raw_grouped_xyz = grouped_xyz grouped_xyz -= new_xyz.transpose(1, 2).unsqueeze(-1) if features is not None: grouped_features = tp.grouping_operation(features, idx) if self.use_xyz: new_features = torch.cat([raw_grouped_xyz, grouped_xyz, grouped_features], dim=1) # (B, C + 3 + 3, npoint, nsample) else: new_features = grouped_features else: assert self.use_xyz, "Cannot have not features and not use xyz as a feature!" new_features = torch.cat([raw_grouped_xyz, grouped_xyz], dim = 1) return new_features class GroupAll(nn.Module): r""" Groups all features Parameters --------- """ def __init__(self, use_xyz: bool = True): super().__init__() self.use_xyz = use_xyz def forward( self, xyz: torch.Tensor, new_xyz: torch.Tensor, features: torch.Tensor = None ) -> Tuple[torch.Tensor]: r""" Parameters ---------- xyz : torch.Tensor xyz coordinates of the features (B, N, 3) new_xyz : torch.Tensor Ignored features : torch.Tensor Descriptors of the features (B, C, N) Returns ------- new_features : torch.Tensor (B, C + 3, 1, N) tensor """ grouped_xyz = xyz.transpose(1, 2).unsqueeze(2) if features is not None: grouped_features = features.unsqueeze(2) if self.use_xyz: new_features = torch.cat([grouped_xyz, grouped_features], dim=1) # (B, 3 + C, 1, N) else: new_features = grouped_features else: new_features = grouped_xyz return new_features class _PointnetSAModuleBase(nn.Module): def __init__(self): super().__init__() self.npoint = None self.groupers = None self.mlps = None def forward(self, xyz: torch.Tensor, features: torch.Tensor = None) -> (torch.Tensor, torch.Tensor): r""" Parameters ---------- xyz : torch.Tensor (B, N, 3) tensor of the xyz coordinates of the points features : torch.Tensor (B, N, C) tensor of the descriptors of the the points Returns ------- new_xyz : torch.Tensor (B, npoint, 3) tensor of the new points' xyz new_features : torch.Tensor (B, npoint, \sum_k(mlps[k][-1])) tensor of the new_points descriptors """ new_features_list = [] xyz_flipped = xyz.transpose(1, 2).contiguous() if self.npoint is not None: fps_idx = tp.furthest_point_sample(xyz, self.npoint) # (B, npoint) new_xyz = tp.gather_operation(xyz_flipped, fps_idx).transpose(1, 2).contiguous() fps_idx = fps_idx.data else: new_xyz = None fps_idx = None for i in range(len(self.groupers)): new_features = self.groupers[i](xyz, new_xyz, features, fps_idx) if self.npoint is not None else self.groupers[i](xyz, new_xyz, features) # (B, C, npoint, nsample) new_features = self.mlps[i]( new_features ) # (B, mlp[-1], npoint) new_features_list.append(new_features) return new_xyz, torch.cat(new_features_list, dim=1) class PointnetSAModuleMSG(_PointnetSAModuleBase): r"""Pointnet set abstrction layer with multiscale grouping Parameters ---------- npoint : int Number of points radii : list of float32 list of radii to group with nsamples : list of int32 Number of samples in each ball query mlps : list of list of int32 Spec of the pointnet before the global max_pool for each scale bn : bool Use batchnorm """ def __init__( self, *, npoint: int, radii: List[float], nsamples: List[int], mlps: List[List[int]], use_xyz: bool = True, bias = True, init = nn.init.kaiming_normal_, first_layer = False, relation_prior = 1 ): super().__init__() assert len(radii) == len(nsamples) == len(mlps) self.npoint = npoint self.groupers = nn.ModuleList() self.mlps = nn.ModuleList() # initialize shared mapping functions C_in = (mlps[0][0] + 3) if use_xyz else mlps[0][0] C_out = mlps[0][1] if relation_prior == 0: in_channels = 1 elif relation_prior == 1 or relation_prior == 2: in_channels = 10 else: assert False, "relation_prior can only be 0, 1, 2." if first_layer: mapping_func1 = nn.Conv2d(in_channels = in_channels, out_channels = math.floor(C_out / 2), kernel_size = (1, 1), stride = (1, 1), bias = bias) mapping_func2 = nn.Conv2d(in_channels = math.floor(C_out / 2), out_channels = 16, kernel_size = (1, 1), stride = (1, 1), bias = bias) xyz_raising = nn.Conv2d(in_channels = C_in, out_channels = 16, kernel_size = (1, 1), stride = (1, 1), bias = bias) init(xyz_raising.weight) if bias: nn.init.constant_(xyz_raising.bias, 0) elif npoint is not None: mapping_func1 = nn.Conv2d(in_channels = in_channels, out_channels = math.floor(C_out / 4), kernel_size = (1, 1), stride = (1, 1), bias = bias) mapping_func2 = nn.Conv2d(in_channels = math.floor(C_out / 4), out_channels = C_in, kernel_size = (1, 1), stride = (1, 1), bias = bias) if npoint is not None: init(mapping_func1.weight) init(mapping_func2.weight) if bias: nn.init.constant_(mapping_func1.bias, 0) nn.init.constant_(mapping_func2.bias, 0) # channel raising mapping cr_mapping = nn.Conv1d(in_channels = C_in if not first_layer else 16, out_channels = C_out, kernel_size = 1, stride = 1, bias = bias) init(cr_mapping.weight) nn.init.constant_(cr_mapping.bias, 0) if first_layer: mapping = [mapping_func1, mapping_func2, cr_mapping, xyz_raising] elif npoint is not None: mapping = [mapping_func1, mapping_func2, cr_mapping] for i in range(len(radii)): radius = radii[i] nsample = nsamples[i] self.groupers.append( QueryAndGroup(radius, nsample, use_xyz=use_xyz) if npoint is not None else GroupAll(use_xyz) ) mlp_spec = mlps[i] if use_xyz: mlp_spec[0] += 3 if npoint is not None: self.mlps.append(pt_utils.SharedRSConv(mlp_spec, mapping = mapping, relation_prior = relation_prior, first_layer = first_layer)) else: # global convolutional pooling self.mlps.append(pt_utils.GloAvgConv(C_in = C_in, C_out = C_out)) class PointnetSAModule(PointnetSAModuleMSG): r"""Pointnet set abstrction layer Parameters ---------- npoint : int Number of features radius : float Radius of ball nsample : int Number of samples in the ball query mlp : list Spec of the pointnet before the global max_pool bn : bool Use batchnorm """ def __init__( self, *, mlp: List[int], npoint: int = None, radius: float = None, nsample: int = None, use_xyz: bool = True, ): super().__init__( mlps=[mlp], npoint=npoint, radii=[radius], nsamples=[nsample], use_xyz=use_xyz ) class PointnetFPModule(nn.Module): r"""Propigates the features of one set to another Parameters ---------- mlp : list Pointnet module parameters bn : bool Use batchnorm """ def __init__(self, *, mlp: List[int], bn: bool = True): super().__init__() self.mlp = pt_utils.SharedMLP(mlp, bn=bn) def forward( self, unknown: torch.Tensor, known: torch.Tensor, unknow_feats: torch.Tensor, known_feats: torch.Tensor ) -> torch.Tensor: r""" Parameters ---------- unknown : torch.Tensor (B, n, 3) tensor of the xyz positions of the unknown features known : torch.Tensor (B, m, 3) tensor of the xyz positions of the known features unknow_feats : torch.Tensor (B, C1, n) tensor of the features to be propigated to known_feats : torch.Tensor (B, C2, m) tensor of features to be propigated Returns ------- new_features : torch.Tensor (B, mlp[-1], n) tensor of the features of the unknown features """ dist, idx = tp.three_nn(unknown, known) dist_recip = 1.0 / (dist + 1e-8) norm = torch.sum(dist_recip, dim=2, keepdim=True) weight = dist_recip / norm interpolated_feats = tp.three_interpolate( known_feats, idx, weight ) if unknow_feats is not None: new_features = torch.cat([interpolated_feats, unknow_feats], dim=1) #(B, C2 + C1, n) else: new_features = interpolated_feats new_features = new_features.unsqueeze(-1) new_features = self.mlp(new_features) return new_features.squeeze(-1) if __name__ == "__main__": from torch.autograd import Variable torch.manual_seed(1) torch.cuda.manual_seed_all(1) xyz = Variable(torch.randn(2, 9, 3).cuda(), requires_grad=True) xyz_feats = Variable(torch.randn(2, 9, 6).cuda(), requires_grad=True) test_module = PointnetSAModuleMSG( npoint=2, radii=[5.0, 10.0], nsamples=[6, 3], mlps=[[9, 3], [9, 6]] ) test_module.cuda() print(test_module(xyz, xyz_feats)) # test_module = PointnetFPModule(mlp=[6, 6]) # test_module.cuda() # from torch.autograd import gradcheck # inputs = (xyz, xyz, None, xyz_feats) # test = gradcheck(test_module, inputs, eps=1e-6, atol=1e-4) # print(test) for _ in range(1): _, new_features = test_module(xyz, xyz_feats) new_features.backward( torch.cuda.FloatTensor(*new_features.size()).fill_(1) ) print(new_features) print(xyz.grad) ================================================ FILE: rs_cnn/utils/pointnet2_utils.py ================================================ import torch from torch.autograd import Variable from torch.autograd import Function import torch.nn.functional as F import torch.nn as nn from linalg_utils import pdist2, PDist2Order from collections import namedtuple import pytorch_utils as pt_utils from typing import List, Tuple from _ext import pointnet2 class RandomDropout(nn.Module): def __init__(self, p=0.5, inplace=False): super().__init__() self.p = p self.inplace = inplace def forward(self, X): theta = torch.Tensor(1).uniform_(0, self.p)[0] return pt_utils.feature_dropout_no_scaling( X, theta, self.train, self.inplace ) class FurthestPointSampling(Function): @staticmethod def forward(ctx, xyz: torch.Tensor, npoint: int) -> torch.Tensor: r""" Uses iterative furthest point sampling to select a set of npoint features that have the largest minimum distance Parameters ---------- xyz : torch.Tensor (B, N, 3) tensor where N > npoint npoint : int32 number of features in the sampled set Returns ------- torch.Tensor (B, npoint) tensor containing the set """ assert xyz.is_contiguous() B, N, _ = xyz.size() output = torch.cuda.IntTensor(B, npoint) temp = torch.cuda.FloatTensor(B, N).fill_(1e10) pointnet2.furthest_point_sampling_wrapper( B, N, npoint, xyz, temp, output ) return output @staticmethod def backward(xyz, a=None): return None, None furthest_point_sample = FurthestPointSampling.apply class GatherOperation(Function): @staticmethod def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor: r""" Parameters ---------- features : torch.Tensor (B, C, N) tensor idx : torch.Tensor (B, npoint) tensor of the features to gather Returns ------- torch.Tensor (B, C, npoint) tensor """ assert features.is_contiguous() assert idx.is_contiguous() B, npoint = idx.size() _, C, N = features.size() output = torch.cuda.FloatTensor(B, C, npoint) pointnet2.gather_points_wrapper( B, C, N, npoint, features, idx, output ) ctx.for_backwards = (idx, C, N) return output @staticmethod def backward(ctx, grad_out): idx, C, N = ctx.for_backwards B, npoint = idx.size() grad_features = Variable(torch.cuda.FloatTensor(B, C, N).zero_()) grad_out_data = grad_out.data.contiguous() pointnet2.gather_points_grad_wrapper( B, C, N, npoint, grad_out_data, idx, grad_features.data ) return grad_features, None gather_operation = GatherOperation.apply class ThreeNN(Function): @staticmethod def forward(ctx, unknown: torch.Tensor, known: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: r""" Find the three nearest neighbors of unknown in known Parameters ---------- unknown : torch.Tensor (B, n, 3) tensor of known features known : torch.Tensor (B, m, 3) tensor of unknown features Returns ------- dist : torch.Tensor (B, n, 3) l2 distance to the three nearest neighbors idx : torch.Tensor (B, n, 3) index of 3 nearest neighbors """ assert unknown.is_contiguous() assert known.is_contiguous() B, N, _ = unknown.size() m = known.size(1) dist2 = torch.cuda.FloatTensor(B, N, 3) idx = torch.cuda.IntTensor(B, N, 3) pointnet2.three_nn_wrapper(B, N, m, unknown, known, dist2, idx) return torch.sqrt(dist2), idx @staticmethod def backward(ctx, a=None, b=None): return None, None three_nn = ThreeNN.apply class ThreeInterpolate(Function): @staticmethod def forward( ctx, features: torch.Tensor, idx: torch.Tensor, weight: torch.Tensor ) -> torch.Tensor: r""" Performs weight linear interpolation on 3 features Parameters ---------- features : torch.Tensor (B, c, m) Features descriptors to be interpolated from idx : torch.Tensor (B, n, 3) three nearest neighbors of the target features in features weight : torch.Tensor (B, n, 3) weights Returns ------- torch.Tensor (B, c, n) tensor of the interpolated features """ assert features.is_contiguous() assert idx.is_contiguous() assert weight.is_contiguous() B, c, m = features.size() n = idx.size(1) ctx.three_interpolate_for_backward = (idx, weight, m) output = torch.cuda.FloatTensor(B, c, n) pointnet2.three_interpolate_wrapper( B, c, m, n, features, idx, weight, output ) return output @staticmethod def backward(ctx, grad_out: torch.Tensor ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: r""" Parameters ---------- grad_out : torch.Tensor (B, c, n) tensor with gradients of ouputs Returns ------- grad_features : torch.Tensor (B, c, m) tensor with gradients of features None None """ idx, weight, m = ctx.three_interpolate_for_backward B, c, n = grad_out.size() grad_features = Variable(torch.cuda.FloatTensor(B, c, m).zero_()) grad_out_data = grad_out.data.contiguous() pointnet2.three_interpolate_grad_wrapper( B, c, n, m, grad_out_data, idx, weight, grad_features.data ) return grad_features, None, None three_interpolate = ThreeInterpolate.apply class GroupingOperation(Function): @staticmethod def forward(ctx, features: torch.Tensor, idx: torch.Tensor) -> torch.Tensor: r""" Parameters ---------- features : torch.Tensor (B, C, N) tensor of points to group idx : torch.Tensor (B, npoint, nsample) tensor containing the indicies of points to group with Returns ------- torch.Tensor (B, C, npoint, nsample) tensor """ assert features.is_contiguous() assert idx.is_contiguous() B, nfeatures, nsample = idx.size() _, C, N = features.size() output = torch.cuda.FloatTensor(B, C, nfeatures, nsample) pointnet2.group_points_wrapper( B, C, N, nfeatures, nsample, features, idx, output ) ctx.for_backwards = (idx, N) return output @staticmethod def backward(ctx, grad_out: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: r""" Parameters ---------- grad_out : torch.Tensor (B, C, npoint, nsample) tensor of the gradients of the output from forward Returns ------- torch.Tensor (B, C, N) gradient of the features None """ idx, N = ctx.for_backwards B, C, npoint, nsample = grad_out.size() grad_features = Variable(torch.cuda.FloatTensor(B, C, N).zero_()) grad_out_data = grad_out.data.contiguous() pointnet2.group_points_grad_wrapper( B, C, N, npoint, nsample, grad_out_data, idx, grad_features.data ) return grad_features, None grouping_operation = GroupingOperation.apply class BallQuery(Function): @staticmethod def forward( ctx, radius: float, nsample: int, xyz: torch.Tensor, new_xyz: torch.Tensor, fps_idx: torch.IntTensor ) -> torch.Tensor: r""" Parameters ---------- radius : float radius of the balls nsample : int maximum number of features in the balls xyz : torch.Tensor (B, N, 3) xyz coordinates of the features new_xyz : torch.Tensor (B, npoint, 3) centers of the ball query Returns ------- torch.Tensor (B, npoint, nsample) tensor with the indicies of the features that form the query balls """ assert new_xyz.is_contiguous() assert xyz.is_contiguous() B, N, _ = xyz.size() npoint = new_xyz.size(1) idx = torch.cuda.IntTensor(B, npoint, nsample).zero_() pointnet2.ball_query_wrapper( B, N, npoint, radius, nsample, new_xyz, xyz, fps_idx, idx ) return torch.cat([fps_idx.unsqueeze(2), idx], dim = 2) @staticmethod def backward(ctx, a=None): return None, None, None, None ball_query = BallQuery.apply class QueryAndGroup(nn.Module): r""" Groups with a ball query of radius Parameters --------- radius : float32 Radius of ball nsample : int32 Maximum number of points to gather in the ball """ def __init__(self, radius: float, nsample: int, use_xyz: bool = True): super().__init__() self.radius, self.nsample, self.use_xyz = radius, nsample, use_xyz def forward( self, xyz: torch.Tensor, new_xyz: torch.Tensor, features: torch.Tensor = None, fps_idx: torch.IntTensor = None ) -> Tuple[torch.Tensor]: r""" Parameters ---------- xyz : torch.Tensor xyz coordinates of the features (B, N, 3) new_xyz : torch.Tensor centriods (B, npoint, 3) features : torch.Tensor Descriptors of the features (B, C, N) Returns ------- new_features : torch.Tensor (B, 3 + C, npoint, nsample) tensor """ idx = ball_query(self.radius, self.nsample, xyz, new_xyz, fps_idx) xyz_trans = xyz.transpose(1, 2).contiguous() grouped_xyz = grouping_operation( xyz_trans, idx ) # (B, 3, npoint, nsample) raw_grouped_xyz = grouped_xyz grouped_xyz -= new_xyz.transpose(1, 2).unsqueeze(-1) if features is not None: grouped_features = grouping_operation(features, idx) if self.use_xyz: new_features = torch.cat([raw_grouped_xyz, grouped_xyz, grouped_features], dim=1) # (B, C + 3 + 3, npoint, nsample) else: new_features = grouped_features else: assert self.use_xyz, "Cannot have not features and not use xyz as a feature!" new_features = torch.cat([raw_grouped_xyz, grouped_xyz], dim = 1) return new_features class GroupAll(nn.Module): r""" Groups all features Parameters --------- """ def __init__(self, use_xyz: bool = True): super().__init__() self.use_xyz = use_xyz def forward( self, xyz: torch.Tensor, new_xyz: torch.Tensor, features: torch.Tensor = None ) -> Tuple[torch.Tensor]: r""" Parameters ---------- xyz : torch.Tensor xyz coordinates of the features (B, N, 3) new_xyz : torch.Tensor Ignored features : torch.Tensor Descriptors of the features (B, C, N) Returns ------- new_features : torch.Tensor (B, C + 3, 1, N) tensor """ grouped_xyz = xyz.transpose(1, 2).unsqueeze(2) if features is not None: grouped_features = features.unsqueeze(2) if self.use_xyz: new_features = torch.cat([grouped_xyz, grouped_features], dim=1) # (B, 3 + C, 1, N) else: new_features = grouped_features else: new_features = grouped_xyz return new_features ================================================ FILE: rs_cnn/utils/pytorch_utils/__init__.py ================================================ from .pytorch_utils import * ================================================ FILE: rs_cnn/utils/pytorch_utils/pytorch_utils.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable from torch.autograd.function import InplaceFunction from itertools import repeat import numpy as np import shutil, os from typing import List, Tuple from scipy.stats import t as student_t import statistics as stats import math ########## Relation-Shape Convolution begin ############ class RSConv(nn.Module): ''' Input shape: (B, C_in, npoint, nsample) Output shape: (B, C_out, npoint) ''' def __init__( self, C_in, C_out, activation = nn.ReLU(), mapping = None, relation_prior = 1, first_layer = False ): super(RSConv, self).__init__() self.bn_rsconv = nn.BatchNorm2d(C_in) if not first_layer else nn.BatchNorm2d(16) self.bn_channel_raising = nn.BatchNorm1d(C_out) self.bn_xyz_raising = nn.BatchNorm2d(16) if first_layer: self.bn_mapping = nn.BatchNorm2d(math.floor(C_out / 2)) else: self.bn_mapping = nn.BatchNorm2d(math.floor(C_out / 4)) self.activation = activation self.relation_prior = relation_prior self.first_layer = first_layer self.mapping_func1 = mapping[0] self.mapping_func2 = mapping[1] self.cr_mapping = mapping[2] if first_layer: self.xyz_raising = mapping[3] def forward(self, input): # input: (B, 3 + 3 + C_in, npoint, centroid + nsample) x = input[:, 3:, :, :] # (B, C_in, npoint, nsample+1), input features C_in = x.size()[1] nsample = x.size()[3] if self.relation_prior == 2: abs_coord = input[:, 0:2, :, :] delta_x = input[:, 3:5, :, :] zero_vec = Variable(torch.zeros(x.size()[0], 1, x.size()[2], nsample).cuda()) else: abs_coord = input[:, 0:3, :, :] # (B, 3, npoint, nsample+1), absolute coordinates delta_x = input[:, 3:6, :, :] # (B, 3, npoint, nsample+1), normalized coordinates coord_xi = abs_coord[:, :, :, 0:1].repeat(1, 1, 1, nsample) # (B, 3, npoint, nsample), centroid point h_xi_xj = torch.norm(delta_x, p = 2, dim = 1).unsqueeze(1) if self.relation_prior == 1: h_xi_xj = torch.cat((h_xi_xj, coord_xi, abs_coord, delta_x), dim = 1) elif self.relation_prior == 2: h_xi_xj = torch.cat((h_xi_xj, coord_xi, zero_vec, abs_coord, zero_vec, delta_x, zero_vec), dim = 1) h_xi_xj = self.mapping_func1(h_xi_xj) h_xi_xj = self.activation(self.bn_mapping(h_xi_xj)) h_xi_xj = self.mapping_func2(h_xi_xj) if self.first_layer: x = self.activation(self.bn_xyz_raising(self.xyz_raising(x))) x = F.max_pool2d(self.activation(self.bn_rsconv(torch.mul(h_xi_xj, x))), kernel_size = (1, nsample)).squeeze(3) # (B, C_in, npoint) x = self.activation(self.bn_channel_raising(self.cr_mapping(x))) return x class RSConvLayer(nn.Sequential): def __init__( self, in_size: int, out_size: int, activation=nn.ReLU(inplace=True), conv=RSConv, mapping = None, relation_prior = 1, first_layer = False ): super(RSConvLayer, self).__init__() conv_unit = conv( in_size, out_size, activation = activation, mapping = mapping, relation_prior = relation_prior, first_layer = first_layer ) self.add_module('RS_Conv', conv_unit) class SharedRSConv(nn.Sequential): def __init__( self, args: List[int], *, activation=nn.ReLU(inplace=True), mapping = None, relation_prior = 1, first_layer = False ): super().__init__() for i in range(len(args) - 1): self.add_module( 'RSConvLayer{}'.format(i), RSConvLayer( args[i], args[i + 1], activation = activation, mapping = mapping, relation_prior = relation_prior, first_layer = first_layer ) ) ########## Relation-Shape Convolution end ############ ########## global convolutional pooling begin ############ class GloAvgConv(nn.Module): ''' Input shape: (B, C_in, 1, nsample) Output shape: (B, C_out, npoint) ''' def __init__( self, C_in, C_out, init=nn.init.kaiming_normal_, bias = True, activation = nn.ReLU(inplace=True) ): super(GloAvgConv, self).__init__() self.conv_avg = nn.Conv2d(in_channels = C_in, out_channels = C_out, kernel_size = (1, 1), stride = (1, 1), bias = bias) self.bn_avg = nn.BatchNorm2d(C_out) self.activation = activation init(self.conv_avg.weight) if bias: nn.init.constant_(self.conv_avg.bias, 0) def forward(self, x): nsample = x.size()[3] x = self.activation(self.bn_avg(self.conv_avg(x))) x = F.max_pool2d(x, kernel_size = (1, nsample)).squeeze(3) return x ########## global convolutional pooling end ############ class SharedMLP(nn.Sequential): def __init__( self, args: List[int], *, bn: bool = False, activation=nn.ReLU(inplace=True), preact: bool = False, first: bool = False, name: str = "" ): super().__init__() for i in range(len(args) - 1): self.add_module( name + 'layer{}'.format(i), Conv2d( args[i], args[i + 1], bn=(not first or not preact or (i != 0)) and bn, activation=activation if (not first or not preact or (i != 0)) else None, preact=preact ) ) class _BNBase(nn.Sequential): def __init__(self, in_size, batch_norm=None, name=""): super().__init__() self.add_module(name + "bn", batch_norm(in_size)) nn.init.constant_(self[0].weight, 1.0) nn.init.constant_(self[0].bias, 0) class BatchNorm1d(_BNBase): def __init__(self, in_size: int, *, name: str = ""): super().__init__(in_size, batch_norm=nn.BatchNorm1d, name=name) class BatchNorm2d(_BNBase): def __init__(self, in_size: int, name: str = ""): super().__init__(in_size, batch_norm=nn.BatchNorm2d, name=name) class BatchNorm3d(_BNBase): def __init__(self, in_size: int, name: str = ""): super().__init__(in_size, batch_norm=nn.BatchNorm3d, name=name) class _ConvBase(nn.Sequential): def __init__( self, in_size, out_size, kernel_size, stride, padding, activation, bn, init, conv=None, batch_norm=None, bias=True, preact=False, name="" ): super().__init__() bias = bias and (not bn) conv_unit = conv( in_size, out_size, kernel_size=kernel_size, stride=stride, padding=padding, bias=bias ) init(conv_unit.weight) if bias: nn.init.constant_(conv_unit.bias, 0) if bn: if not preact: bn_unit = batch_norm(out_size) else: bn_unit = batch_norm(in_size) if preact: if bn: self.add_module(name + 'bn', bn_unit) if activation is not None: self.add_module(name + 'activation', activation) self.add_module(name + 'conv', conv_unit) if not preact: if bn: self.add_module(name + 'bn', bn_unit) if activation is not None: self.add_module(name + 'activation', activation) class Conv1d(_ConvBase): def __init__( self, in_size: int, out_size: int, *, kernel_size: int = 1, stride: int = 1, padding: int = 0, activation=nn.ReLU(inplace=True), bn: bool = False, init=nn.init.kaiming_normal_, bias: bool = True, preact: bool = False, name: str = "" ): super().__init__( in_size, out_size, kernel_size, stride, padding, activation, bn, init, conv=nn.Conv1d, batch_norm=BatchNorm1d, bias=bias, preact=preact, name=name ) class Conv2d(_ConvBase): def __init__( self, in_size: int, out_size: int, *, kernel_size: Tuple[int, int] = (1, 1), stride: Tuple[int, int] = (1, 1), padding: Tuple[int, int] = (0, 0), activation=nn.ReLU(inplace=True), bn: bool = False, init=nn.init.kaiming_normal_, bias: bool = True, preact: bool = False, name: str = "" ): super().__init__( in_size, out_size, kernel_size, stride, padding, activation, bn, init, conv=nn.Conv2d, batch_norm=BatchNorm2d, bias=bias, preact=preact, name=name ) class Conv3d(_ConvBase): def __init__( self, in_size: int, out_size: int, *, kernel_size: Tuple[int, int, int] = (1, 1, 1), stride: Tuple[int, int, int] = (1, 1, 1), padding: Tuple[int, int, int] = (0, 0, 0), activation=nn.ReLU(inplace=True), bn: bool = False, init=nn.init.kaiming_normal_, bias: bool = True, preact: bool = False, name: str = "" ): super().__init__( in_size, out_size, kernel_size, stride, padding, activation, bn, init, conv=nn.Conv3d, batch_norm=BatchNorm3d, bias=bias, preact=preact, name=name ) class FC(nn.Sequential): def __init__( self, in_size: int, out_size: int, *, activation=nn.ReLU(inplace=True), bn: bool = False, init=None, preact: bool = False, name: str = "" ): super().__init__() fc = nn.Linear(in_size, out_size, bias=not bn) if init is not None: init(fc.weight) if not bn: nn.init.constant_(fc.bias, 0) if preact: if bn: self.add_module(name + 'bn', BatchNorm1d(in_size)) if activation is not None: self.add_module(name + 'activation', activation) self.add_module(name + 'fc', fc) if not preact: if bn: self.add_module(name + 'bn', BatchNorm1d(out_size)) if activation is not None: self.add_module(name + 'activation', activation) class _DropoutNoScaling(InplaceFunction): @staticmethod def _make_noise(input): return input.new().resize_as_(input) @staticmethod def symbolic(g, input, p=0.5, train=False, inplace=False): if inplace: return None n = g.appendNode( g.create("Dropout", [input]).f_("ratio", p).i_("is_test", not train) ) real = g.appendNode(g.createSelect(n, 0)) g.appendNode(g.createSelect(n, 1)) return real @classmethod def forward(cls, ctx, input, p=0.5, train=False, inplace=False): if p < 0 or p > 1: raise ValueError( "dropout probability has to be between 0 and 1, " "but got {}".format(p) ) ctx.p = p ctx.train = train ctx.inplace = inplace if ctx.inplace: ctx.mark_dirty(input) output = input else: output = input.clone() if ctx.p > 0 and ctx.train: ctx.noise = cls._make_noise(input) if ctx.p == 1: ctx.noise.fill_(0) else: ctx.noise.bernoulli_(1 - ctx.p) ctx.noise = ctx.noise.expand_as(input) output.mul_(ctx.noise) return output @staticmethod def backward(ctx, grad_output): if ctx.p > 0 and ctx.train: return grad_output.mul(Variable(ctx.noise)), None, None, None else: return grad_output, None, None, None dropout_no_scaling = _DropoutNoScaling.apply class _FeatureDropoutNoScaling(_DropoutNoScaling): @staticmethod def symbolic(input, p=0.5, train=False, inplace=False): return None @staticmethod def _make_noise(input): return input.new().resize_( input.size(0), input.size(1), *repeat(1, input.dim() - 2) ) feature_dropout_no_scaling = _FeatureDropoutNoScaling.apply def group_model_params(model: nn.Module): decay_group = [] no_decay_group = [] for name, param in model.named_parameters(): if name.find("bn") != -1 or name.find("bias") != -1: no_decay_group.append(param) else: decay_group.append(param) assert len(list(model.parameters()) ) == len(decay_group) + len(no_decay_group) return [ dict(params=decay_group), dict(params=no_decay_group, weight_decay=0.0) ] def checkpoint_state(model=None, optimizer=None, best_prec=None, epoch=None): optim_state = optimizer.state_dict() if optimizer is not None else None if model is not None: if isinstance(model, torch.nn.DataParallel): model_state = model.module.state_dict() else: model_state = model.state_dict() else: model_state = None return { 'epoch': epoch, 'best_prec': best_prec, 'model_state': model_state, 'optimizer_state': optim_state } def save_checkpoint( state, is_best, filename='checkpoint', bestname='model_best' ): filename = '{}.pth.tar'.format(filename) torch.save(state, filename) if is_best: shutil.copyfile(filename, '{}.pth.tar'.format(bestname)) def load_checkpoint(model=None, optimizer=None, filename='checkpoint'): filename = "{}.pth.tar".format(filename) if os.path.isfile(filename): print("==> Loading from checkpoint '{}'".format(filename)) checkpoint = torch.load(filename) epoch = checkpoint['epoch'] best_prec = checkpoint['best_prec'] if model is not None and checkpoint['model_state'] is not None: model.load_state_dict(checkpoint['model_state']) if optimizer is not None and checkpoint['optimizer_state'] is not None: optimizer.load_state_dict(checkpoint['optimizer_state']) print("==> Done") else: print("==> Checkpoint '{}' not found".format(filename)) return epoch, best_prec def variable_size_collate(pad_val=0, use_shared_memory=True): import collections _numpy_type_map = { 'float64': torch.DoubleTensor, 'float32': torch.FloatTensor, 'float16': torch.HalfTensor, 'int64': torch.LongTensor, 'int32': torch.IntTensor, 'int16': torch.ShortTensor, 'int8': torch.CharTensor, 'uint8': torch.ByteTensor, } def wrapped(batch): "Puts each data field into a tensor with outer dimension batch size" error_msg = "batch must contain tensors, numbers, dicts or lists; found {}" elem_type = type(batch[0]) if torch.is_tensor(batch[0]): max_len = 0 for b in batch: max_len = max(max_len, b.size(0)) numel = sum([int(b.numel() / b.size(0) * max_len) for b in batch]) if use_shared_memory: # If we're in a background process, concatenate directly into a # shared memory tensor to avoid an extra copy storage = batch[0].storage()._new_shared(numel) out = batch[0].new(storage) else: out = batch[0].new(numel) out = out.view( len(batch), max_len, *[batch[0].size(i) for i in range(1, batch[0].dim())] ) out.fill_(pad_val) for i in range(len(batch)): out[i, 0:batch[i].size(0)] = batch[i] return out elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \ and elem_type.__name__ != 'string_': elem = batch[0] if elem_type.__name__ == 'ndarray': # array of string classes and object if re.search('[SaUO]', elem.dtype.str) is not None: raise TypeError(error_msg.format(elem.dtype)) return wrapped([torch.from_numpy(b) for b in batch]) if elem.shape == (): # scalars py_type = float if elem.dtype.name.startswith('float') else int return _numpy_type_map[elem.dtype.name]( list(map(py_type, batch)) ) elif isinstance(batch[0], int): return torch.LongTensor(batch) elif isinstance(batch[0], float): return torch.DoubleTensor(batch) elif isinstance(batch[0], collections.Mapping): return {key: wrapped([d[key] for d in batch]) for key in batch[0]} elif isinstance(batch[0], collections.Sequence): transposed = zip(*batch) return [wrapped(samples) for samples in transposed] raise TypeError((error_msg.format(type(batch[0])))) return wrapped class TrainValSplitter(): r""" Creates a training and validation split to be used as the sampler in a pytorch DataLoader Parameters --------- numel : int Number of elements in the entire training dataset percent_train : float Percentage of data in the training split shuffled : bool Whether or not shuffle which data goes to which split """ def __init__( self, *, numel: int, percent_train: float, shuffled: bool = False ): indicies = np.array([i for i in range(numel)]) if shuffled: np.random.shuffle(indicies) self.train = torch.utils.data.sampler.SubsetRandomSampler( indicies[0:int(percent_train * numel)] ) self.val = torch.utils.data.sampler.SubsetRandomSampler( indicies[int(percent_train * numel):-1] ) class CrossValSplitter(): r""" Class that creates cross validation splits. The train and val splits can be used in pytorch DataLoaders. The splits can be updated by calling next(self) or using a loop: for _ in self: .... Parameters --------- numel : int Number of elements in the training set k_folds : int Number of folds shuffled : bool Whether or not to shuffle which data goes in which fold """ def __init__(self, *, numel: int, k_folds: int, shuffled: bool = False): inidicies = np.array([i for i in range(numel)]) if shuffled: np.random.shuffle(inidicies) self.folds = np.array(np.array_split(inidicies, k_folds), dtype=object) self.current_v_ind = -1 self.val = torch.utils.data.sampler.SubsetRandomSampler(self.folds[0]) self.train = torch.utils.data.sampler.SubsetRandomSampler( np.concatenate(self.folds[1:], axis=0) ) self.metrics = {} def __iter__(self): self.current_v_ind = -1 return self def __len__(self): return len(self.folds) def __getitem__(self, idx): assert idx >= 0 and idx < len(self) self.val.inidicies = self.folds[idx] self.train.inidicies = np.concatenate( self.folds[np.arange(len(self)) != idx], axis=0 ) def __next__(self): self.current_v_ind += 1 if self.current_v_ind >= len(self): raise StopIteration self[self.current_v_ind] def update_metrics(self, to_post: dict): for k, v in to_post.items(): if k in self.metrics: self.metrics[k].append(v) else: self.metrics[k] = [v] def print_metrics(self): for name, samples in self.metrics.items(): xbar = stats.mean(samples) sx = stats.stdev(samples, xbar) tstar = student_t.ppf(1.0 - 0.025, len(samples) - 1) margin_of_error = tstar * sx / sqrt(len(samples)) print("{}: {} +/- {}".format(name, xbar, margin_of_error)) def set_bn_momentum_default(bn_momentum): def fn(m): if isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d)): m.momentum = bn_momentum return fn class BNMomentumScheduler(object): def __init__( self, model, bn_lambda, last_epoch=-1, setter=set_bn_momentum_default ): if not isinstance(model, nn.Module): raise RuntimeError( "Class '{}' is not a PyTorch nn Module".format( type(model).__name__ ) ) self.model = model self.setter = setter self.lmbd = bn_lambda self.step(last_epoch + 1) self.last_epoch = last_epoch def step(self, epoch=None): if epoch is None: epoch = self.last_epoch + 1 self.last_epoch = epoch self.model.apply(self.setter(self.lmbd(epoch))) def get_momentum(self, epoch=None): if epoch is None: epoch = self.last_epoch + 1 return self.lmbd(epoch) ================================================ FILE: rs_cnn/voting_evaluate_cls.py ================================================ import torch import torch.optim as optim import torch.optim.lr_scheduler as lr_sched import torch.nn as nn from torch.utils.data import DataLoader from torch.autograd import Variable import torch.nn.functional as F import numpy as np import os from torchvision import transforms from models import RSCNN_SSN_Cls as RSCNN_SSN from data import ModelNet40Cls import utils.pytorch_utils as pt_utils # import utils.pointnet2_utils as pointnet2_utils import pointnet2.utils.pointnet2_utils as pointnet2_utils import data.data_utils as d_utils import argparse import random import yaml torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = True torch.backends.cudnn.deterministic = True seed = 123 random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) parser = argparse.ArgumentParser(description='Relation-Shape CNN Shape Classification Voting Evaluation') parser.add_argument('--config', default='cfgs/config_ssn_cls.yaml', type=str) NUM_REPEAT = 300 NUM_VOTE = 10 def main(): args = parser.parse_args() with open(args.config) as f: config = yaml.load(f) for k, v in config['common'].items(): setattr(args, k, v) test_transforms = transforms.Compose([ d_utils.PointcloudToTensor() ]) test_dataset = ModelNet40Cls(num_points = args.num_points, root = args.data_root, transforms=test_transforms, train=False) test_dataloader = DataLoader( test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=int(args.workers), pin_memory=True ) model = RSCNN_SSN(num_classes = args.num_classes, input_channels = args.input_channels, relation_prior = args.relation_prior, use_xyz = True) model.cuda() if args.checkpoint is not '': model.load_state_dict(torch.load(args.checkpoint)) print('Load model successfully: %s' % (args.checkpoint)) # evaluate PointcloudScale = d_utils.PointcloudScale() # initialize random scaling model.eval() global_acc = 0 with torch.no_grad(): for i in range(NUM_REPEAT): preds = [] labels = [] for j, data in enumerate(test_dataloader, 0): points, target = data points, target = points.cuda(), target.cuda() # points, target = Variable(points, volatile=True), Variable(target, volatile=True) # fastest point sampling fps_idx = pointnet2_utils.furthest_point_sample(points, 1200) # (B, npoint) pred = 0 for v in range(NUM_VOTE): new_fps_idx = fps_idx[:, np.random.choice(1200, args.num_points, False)] new_points = pointnet2_utils.gather_operation(points.transpose(1, 2).contiguous(), new_fps_idx).transpose(1, 2).contiguous() if v > 0: new_points.data = PointcloudScale(new_points.data) pred += F.softmax(model(new_points), dim = 1) pred /= NUM_VOTE target = target.view(-1) _, pred_choice = torch.max(pred.data, -1) preds.append(pred_choice) labels.append(target.data) preds = torch.cat(preds, 0) labels = torch.cat(labels, 0) acc = (preds == labels).sum().float() / labels.numel() if acc > global_acc: global_acc = acc print('Repeat %3d \t Acc: %0.6f' % (i + 1, acc)) print('\nBest voting acc: %0.6f' % (global_acc)) if __name__ == '__main__': main() ================================================ FILE: rs_cnn/voting_evaluate_partseg.py ================================================ import torch import torch.optim as optim import torch.optim.lr_scheduler as lr_sched import torch.nn as nn from torch.utils.data import DataLoader from torch.autograd import Variable import torch.nn.functional as F import numpy as np import os from torchvision import transforms from models import RSCNN_MSN_Seg as RSCNN_MSN from data import ShapeNetPart import utils.pytorch_utils as pt_utils import data.data_utils as d_utils import argparse import random import yaml from progressbar import ProgressBar torch.backends.cudnn.enabled = True torch.backends.cudnn.benchmark = True torch.backends.cudnn.deterministic = True seed = 123 random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) parser = argparse.ArgumentParser(description='Relation-Shape CNN Shape Part Segmentation Voting Evaluate') parser.add_argument('--config', default='cfgs/config_msn_partseg.yaml', type=str) NUM_REPEAT = 300 NUM_VOTE = 10 def main(): args = parser.parse_args() with open(args.config) as f: config = yaml.load(f) for k, v in config['common'].items(): setattr(args, k, v) test_transforms = transforms.Compose([ d_utils.PointcloudToTensor() ]) test_dataset = ShapeNetPart(root = args.data_root, num_points = args.num_points, split = 'test', normalize = True, transforms = test_transforms) test_dataloader = DataLoader( test_dataset, batch_size=args.batch_size // 4, shuffle=False, num_workers=int(args.workers), pin_memory=True ) model = RSCNN_MSN(num_classes = args.num_classes, input_channels = args.input_channels, relation_prior = args.relation_prior, use_xyz = True) model.cuda() if args.checkpoint is not '': model.load_state_dict(torch.load(args.checkpoint)) print('Load model successfully: %s' % (args.checkpoint)) # evaluate PointcloudScale = d_utils.PointcloudScale(scale_low=0.87, scale_high=1.15) # initialize random scaling model.eval() global_Class_mIoU, global_Inst_mIoU = 0, 0 seg_classes = test_dataset.seg_classes seg_label_to_cat = {} # {0:Airplane, 1:Airplane, ...49:Table} for cat in seg_classes.keys(): for label in seg_classes[cat]: seg_label_to_cat[label] = cat with torch.no_grad(): for i in range(NUM_REPEAT): shape_ious = {cat:[] for cat in seg_classes.keys()} bar = ProgressBar(max_value=len(test_dataloader)) for i, data in enumerate(test_dataloader, 0): points, target, cls = data # points, target = Variable(points, volatile=True), Variable(target, volatile=True) points, target = points.cuda(), target.cuda() batch_one_hot_cls = np.zeros((len(cls), 16)) # 16 object classes for b in range(len(cls)): batch_one_hot_cls[b, int(cls[b])] = 1 batch_one_hot_cls = torch.from_numpy(batch_one_hot_cls) batch_one_hot_cls = Variable(batch_one_hot_cls.float().cuda()) pred = 0 new_points = torch.zeros(points.size()[0], points.size()[1], points.size()[2]).cuda() # new_points = Variable(torch.zeros(points.size()[0], points.size()[1], points.size()[2]).cuda(), volatile=True) for v in range(NUM_VOTE): if v > 0: new_points.data = PointcloudScale(points.data) pred += F.softmax(model(new_points, batch_one_hot_cls), dim = 2) pred /= NUM_VOTE pred = pred.data.cpu() target = target.data.cpu() pred_val = torch.zeros(len(cls), args.num_points).type(torch.LongTensor) # pred to the groundtruth classes (selected by seg_classes[cat]) for b in range(len(cls)): cat = seg_label_to_cat[target[b, 0].item()] logits = pred[b, :, :] # (num_points, num_classes) pred_val[b, :] = logits[:, seg_classes[cat]].max(1)[1] + seg_classes[cat][0] for b in range(len(cls)): segp = pred_val[b, :] segl = target[b, :] cat = seg_label_to_cat[segl[0].item()] part_ious = [0.0 for _ in range(len(seg_classes[cat]))] for l in seg_classes[cat]: if torch.sum((segl == l) | (segp == l)) == 0: # part is not present in this shape part_ious[l - seg_classes[cat][0]] = 1.0 else: part_ious[l - seg_classes[cat][0]] = torch.sum((segl == l) & (segp == l)) / float(torch.sum((segl == l) | (segp == l))) shape_ious[cat].append(np.mean(part_ious)) bar.update(i) instance_ious = [] for cat in shape_ious.keys(): for iou in shape_ious[cat]: instance_ious.append(iou) shape_ious[cat] = np.mean(shape_ious[cat]) mean_class_ious = np.mean(list(shape_ious.values())) print('\n------ Repeat %3d ------' % (i + 1)) for cat in sorted(shape_ious.keys()): print('%s: %0.6f'%(cat, shape_ious[cat])) print('Class_mIoU: %0.6f' % (mean_class_ious)) print('Instance_mIoU: %0.6f' % (np.mean(instance_ious))) if mean_class_ious > global_Class_mIoU: global_Class_mIoU = mean_class_ious global_Inst_mIoU = np.mean(instance_ious) print('\nBest voting Class_mIoU = %0.6f, Instance_mIoU = %0.6f' % (global_Class_mIoU, global_Inst_mIoU)) if __name__ == '__main__': main() ================================================ FILE: setup.sh ================================================ ### # @Description: # @Autor: Jiachen Sun # @Date: 2022-02-23 17:25:05 # @LastEditors: Jiachen Sun # @LastEditTime: 2022-02-24 23:20:04 ### set -e PYTHON_BIN=${PYTHON_BIN:-python} abspath=$(readlink -f "$0") root=$(dirname $abspath) export TORCH_CUDA_ARCH_LIST="6.0;6.1;6.2;7.0;7.5" cd ${root}/pointnet2_pyt && ${PYTHON_BIN} -m pip install -e . && cd - cd ${root}/PCT_Pytorch/pointnet2_ops_lib && ${PYTHON_BIN} setup.py install && cd - cd ${root}/emd && ${PYTHON_BIN} setup.py install && cd - cd ${root}/PyGeM && ${PYTHON_BIN} setup.py install && cd - ================================================ FILE: third_party/bn_helper.py ================================================ import torch import torch.nn as nn def configure_model(model, eps, momentum, reset_stats, no_stats): """Configure model for adaptation by test-time normalization.""" for m in model.modules(): if isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d): # use batch-wise statistics in forward m.train() # configure epsilon for stability, and momentum for updates m.eps = eps m.momentum = momentum if reset_stats: # reset state to estimate test stats without train stats m.reset_running_stats() if no_stats: # disable state entirely and use only batch stats m.track_running_stats = False m.running_mean = None m.running_var = None return model ================================================ FILE: third_party/tent_helper.py ================================================ import torch import torch.nn as nn import torch.jit def collect_params(model): """Collect the affine scale + shift parameters from batch norms. Walk the model's modules and collect all batch normalization parameters. Return the parameters and their names. Note: other choices of parameterization are possible! """ params = [] names = [] for nm, m in model.named_modules(): if isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d): for np, p in m.named_parameters(): if np in ['weight', 'bias']: # weight is scale, bias is shift params.append(p) names.append(f"{nm}.{np}") return params, names @torch.jit.script def softmax_entropy(x: torch.Tensor) -> torch.Tensor: """Entropy of softmax distribution from logits.""" return -(x.softmax(1) * x.log_softmax(1)).sum(1) def configure_model(model,eps, momentum): """Configure model for use with tent.""" # train mode, because tent optimizes the model to minimize entropy model.train() # disable grad, to (re-)enable only what tent updates model.requires_grad_(False) # configure norm for tent updates: enable grad + force batch statisics for m in model.modules(): if isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d): m.requires_grad_(True) m.eps = eps m.momentum = momentum # force use of batch stats in train and eval modes m.track_running_stats = True # m.running_mean -= m.running_mean # m.running_var /= m.running_var # m.reset_running_stats() return model @torch.enable_grad() # ensure grads in possible no grad context for testing def forward_and_adapt(x, model, optimizer): """Forward and adapt model on batch of data. Measure entropy of the model prediction, take gradients, and update params. """ # forward outputs = model(**x) # adapt loss = softmax_entropy(outputs['logit']).mean(0) loss.backward() optimizer.step() optimizer.zero_grad() return outputs ================================================ FILE: visualize/README.md ================================================ # Visualization of ModelNet40-C ## Requirements * Python3 * Python packages numpy, pandas, matplotlib, seaborn, and open3d (optional). * Github repository [mitsuba2](https://github.com/mitsuba-renderer/mitsuba2) (optional). ## Steps * Configuration file `config.py` records the path of ModelNet40-C dataset and the log files of experiments. Please make sure they are refering to right directories. * Script `main_results.py` processes the log files to collect accuracy results of all experiments. It also draws most of the figures and tables in the paper (Figure 1, 4, 9-16, Table 1-4). * Script `confusion_matrix.py` draws the confusion matrices in paper (Figure 3, 8). * Script `examples.py` draws the point cloud examples (Figure 2, 5-7). All results are saved to `figures` folder by default. ================================================ FILE: visualize/config.py ================================================ from collections import OrderedDict # Path of the dataset containing npy files. data_dir = "modelnet40_c" # Path of logs of experiments. result_dir = "output" # For each OrderedDict item, the first element is the name in log files while the # second item is the name to display on visualized figures. def_ranges = { 'model': OrderedDict([ ('pointnet', 'PointNet'), ('pointnet2', 'PointNet++'), ('dgcnn', 'DGCNN'), ('rscnn', 'RSCNN'), ('pct', 'PCT'), ('simpleview', 'SimpleView'), ('curvenet', 'CurveNet'), ("gdanet", "GDANet"), ('pointMLP', "PointMLP"), ("pointMLP2", "PointMLP-Elite") ]), 'train_mode': OrderedDict([ ('cutmix_r', 'PointCutMix-R'), ('cutmix_k', 'PointCutMix-K'), ('mixup', 'PointMixup'), ('rsmix', 'RSMix'), ('bn', 'BN'), ('tent', 'TENT'), ('pgd', 'PGD'), ('none', "Standard"), ('megamerger', "megamerger") ]), 'corruption': OrderedDict([ ("occlusion", "Occlusion"), ("lidar", "LiDAR"), ("density_inc", "Local_Density_Inc"), ("density", "Local_Density_Dec"), ("cutout", "Cutout"), ("uniform", "Uniform"), ("gaussian", "Gaussian"), ("impulse", "Impulse"), ("upsampling", "Upsampling"), ("background", "Background"), ("rotation", "Rotation"), ("shear", "Shear"), ("distortion", "FFD"), ("distortion_rbf", "RBF"), ("distortion_rbf_inv", "Inv_RBF"), ("none", "none"), ]), 'severity': [1, 2, 3, 4, 5], 'metric': ['acc', "class_acc", "err", "class_err"] } ================================================ FILE: visualize/confusion_matrix.py ================================================ import os import numpy as np import pandas as pd from matplotlib import pyplot as plt from config import result_dir from config import def_ranges from sklearn.metrics import confusion_matrix import seaborn as sns import math DATA_DIR = result_dir SHAPE = ["airplane", "bathtub", "bed", "bench", "bookshelf", "bottle", "bowl", "car", "chair", "cone", "cup", "curtain", "desk", "door", "dresser", "flower_pot", "glass_box", "guitar", "keyboard", "lamp", "laptop", "mantel", "monitor", "night_stand", "person", "piano", "plant", "radio", "range_hood", "sink", "sofa", "stairs", "stool", "table", "tent", "toilet", "tv_stand", "vase", "wardrobe", "xbox"] data = {} model_list = list(def_ranges["model"].keys()) corruption_list = list(def_ranges["corruption"].keys()) corruption_list.remove("none") severity_list = def_ranges["severity"] for model in model_list: data[model] = {"ground": [], "pred": []} for filename in os.listdir(DATA_DIR): if len(filename) < 4 or filename[-4:] != ".npy": continue tokens = filename.split('.')[0].split('_') gt = tokens[-1] severity = int(tokens[-2]) model = tokens[0] corruption = "_".join(tokens[1:-2]) print(model, corruption, severity, gt) filepath = os.path.join(DATA_DIR, filename) x = np.load(filepath) data[model][gt].append(x) matrixes = [] fig, axes = plt.subplots(3, 2, figsize=(20, 26)) for model_id, model in enumerate(model_list): ax = axes[model_id//2][model_id%2] total_number = len(data[model]["ground"]) * data[model]["ground"][0].shape[0] y_true = np.hstack(data[model]["ground"]).reshape(-1) y_pred = np.hstack(data[model]["pred"]).reshape(-1) m = confusion_matrix(y_true, y_pred) m = m / np.tile(np.sum(m, axis=1).reshape(40, 1), (1, 40)) matrixes.append(m) sns.heatmap(m, ax=ax, vmin=0, vmax=1, center=1) ax.set_xticks([i+0.5 for i in range(len(SHAPE))]) ax.set_xticklabels(SHAPE, rotation=45, ha="right") ax.set_yticks([i+0.5 for i in range(len(SHAPE))]) ax.set_yticklabels(SHAPE) ax.set_title(def_ranges["model"][model]) ax.set_xlabel("Prediction") ax.set_ylabel("Ground truth") ax.set_aspect('equal', adjustable='box') plt.tight_layout(pad=0, h_pad=0, w_pad=0) plt.savefig("figures/confusion_matrix_2.pdf") plt.clf() fig, ax = plt.subplots(figsize=(10,8)) m = np.sum(np.stack(matrixes, axis=0), axis=0) / 6 sns.heatmap(m, ax=ax, vmin=0, vmax=1, center=1) ax.set_xticks([i+0.5 for i in range(len(SHAPE))]) ax.set_xticklabels(SHAPE, rotation=45, ha="right") ax.set_yticks([i+0.5 for i in range(len(SHAPE))]) ax.set_yticklabels(SHAPE) ax.set_xlabel("Prediction") ax.set_ylabel("Ground truth") ax.set_aspect('equal', adjustable='box') plt.tight_layout(pad=0, h_pad=0, w_pad=0) plt.savefig("figures/confusion_matrix_average_2.pdf") ================================================ FILE: visualize/examples.py ================================================ import os import numpy as np import pickle from config import data_dir, def_ranges import matplotlib.pyplot as plt import matplotlib from PIL import Image example_ids = [6,7,32,54,85] severity = 4 examples_file = "examples.pkl" def build_examples(example_ids, severity): all_examples = {} for example_id in example_ids: examples = [] for corruption in list(def_ranges["corruption"].keys()): if corruption == "none": continue file_path = os.path.join(data_dir, "data_{}_{:d}.npy".format(corruption, severity)) examples.append(np.load(file_path)[example_id,:]) all_examples[example_id] = examples with open(examples_file, "wb") as f: pickle.dump(all_examples, f) return examples def load_examples(): with open(examples_file, 'rb') as f: all_examples = pickle.load(f) return all_examples def rotation_matrix(pitch, yaw, roll): R = np.array([[np.cos(yaw)*np.cos(pitch), np.cos(yaw)*np.sin(pitch)*np.sin(roll)-np.sin(yaw)*np.cos(roll), np.cos(yaw)*np.sin(pitch)*np.cos(roll)+np.sin(yaw)*np.sin(roll)], [np.sin(yaw)*np.cos(pitch), np.sin(yaw)*np.sin(pitch)*np.sin(roll)+np.cos(yaw)*np.cos(roll), np.sin(yaw)*np.sin(pitch)*np.cos(roll)-np.cos(yaw)*np.sin(roll)], [-np.sin(pitch), np.cos(pitch)*np.sin(roll), np.cos(pitch)*np.cos(roll)]]) return R def draw_one_example(example, rotate=[0, 0], scale=1, window_width=1080, window_height=720, show=False, save="test.png", flag=0): import open3d as o3d pcd = o3d.geometry.PointCloud() pcd.points = o3d.utility.Vector3dVector(example[:,:3]) meshes = [] for i in range(example.shape[0]): ball = o3d.geometry.TriangleMesh.create_sphere(radius=0.0125) ball.translate(example[i,:3]) ball.rotate(rotation_matrix(0, np.pi, np.pi), center=np.array([0,0,0])) meshes.append(ball) vis = o3d.visualization.Visualizer() vis.create_window(width=window_width, height=window_height, visible=True) for ball in meshes: vis.add_geometry(ball) opt = vis.get_render_option() opt.background_color = np.array([0.90, 0.90, 0.90]) opt.mesh_color_option = o3d.visualization.MeshColorOption.ZCoordinate control = vis.get_view_control() # control.convert_from_pinhole_camera_parameters(camera_parameters) control.rotate(400, 0) control.rotate(0, 100) if flag: control.rotate(0, -50) control.scale(6) vis.update_geometry(pcd) if show: vis.run() elif save is not None: vis.poll_events() vis.update_renderer() vis.capture_screen_image(save) vis.destroy_window() def draw_one_example_colorful(example, save="test.png"): from pointflow_fig_colorful import colorful_pcd import mitsuba mitsuba.set_variant('scalar_rgb') from mitsuba.core import Thread from mitsuba.core.xml import load_file xml_filename = "tmp.xml" colorful_pcd(example, xml_filename) Thread.thread().file_resolver().append(os.path.dirname(xml_filename)) scene = load_file(xml_filename) sensor = scene.sensors()[0] scene.integrator().render(scene, sensor) film = sensor.film() from mitsuba.core import Bitmap, Struct img = film.bitmap(raw=True).convert(Bitmap.PixelFormat.RGB, Struct.Type.UInt8, srgb_gamma=True) img.write(save) def draw_examples(tag, examples, colorful=False): os.makedirs("figures/{}".format(tag), exist_ok=True) for i, example in enumerate(examples): if not colorful: draw_one_example(example, window_width=720, window_height=600, show=False, save="figures/{}/example_{}.png".format(tag, i)) else: draw_one_example_colorful(example, save="figures/{}/example_{}.png".format(tag, i)) matplotlib.rcParams.update({'font.size': 13, 'font.weight': 'bold'}) fig, axes = plt.subplots(3, 5, figsize=(15, 9)) for i in range(15): ax = axes[i//5][i%5] im = Image.open("figures/{}/example_{}.png".format(tag, i)) w, h = im.size im = im.crop((w * 0.15, h * 0.25, w * 0.85, h * 0.95)) ax.set_xlim([0,1]) ax.set_ylim([0,1]) ax.imshow(im, extent=[0, 1, 0, 1]) ax.set_title(list(def_ranges["corruption"].values())[i], y=0) ax.axis('off') plt.tight_layout(pad=0, h_pad=0, w_pad=0) plt.savefig("figures/{}/examples.pdf".format(tag)) # Executes once is enough to build the npy file containing examples. if not os.path.isfile(examples_file): build_examples(example_ids) # Loads examples. all_examples = load_examples() # Draws the example demo (one object point cloud with different corruptions). for example_id in example_ids: # Sets `colorful` to True will draw colorful images but require mitsuba module. draw_examples(example_id, all_examples[example_id], colorful=False) ================================================ FILE: visualize/main_results.py ================================================ import os import statistics import pandas as pd import numpy as np from matplotlib import pyplot as plt import matplotlib import matplotlib.pylab as pylab from config import result_dir from config import def_ranges DATA_DIR = result_dir DATA_FILE = "data.csv" markers = ['o', 'v'] colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'dimgray', 'darkorange', 'seagreen', 'navy', 'purple', 'skyblue', 'olive', 'salmon', 'saddlebrown', 'slategray'] def format_data(): data = [] for filename in os.listdir(DATA_DIR): if len(filename) < 4 or filename[-4:] != ".txt": continue filepath = os.path.join(DATA_DIR, filename) with open(filepath, 'r') as f: log_str = f.readlines() if len(log_str) == 0: print("Error no content", filename) continue log_str = log_str[-1] if log_str[-1] == "\n": log_str = log_str[:-1] one_data = [] tokens = log_str.split(' ') next_value = False for token in tokens: if not next_value and token[-1] == ":": next_value = True elif next_value: if token.isnumeric(): one_data.append(int(token)) elif token.replace('.','',1).isdigit(): one_data.append(float(token)) else: if token == "nan": print("Error nan", filename) token = 0 one_data.append(token) next_value = False tokens = filename.split('.')[-2].split('_') if tokens[-1] == "clean": one_data[1] = "none" train_mode = '_'.join(tokens[1:len(tokens)-1]) else: for index in range(len(tokens)): if one_data[1].split('_')[0] == tokens[index]: break train_mode = '_'.join(tokens[1:index]) one_data.insert(1, train_mode if train_mode != "" else "none") one_data.insert(5, 100 - one_data[4] * 100) one_data.insert(7, 100 - one_data[6] * 100) if one_data[0] not in def_ranges["model"]: continue if one_data[1] not in def_ranges["train_mode"]: continue if one_data[2] not in def_ranges["corruption"]: continue data.append(one_data) df_data = pd.DataFrame(data, columns=['model', 'train_mode', 'corruption', 'severity', 'acc', 'err', 'class_acc', 'class_err']) df_data = df_data.sort_values(['model', 'train_mode', 'corruption', 'severity']) df_data.to_csv(DATA_FILE, index=False) def update_font(font): params = {'legend.fontsize': font, 'axes.labelsize': font, 'axes.titlesize': font, 'xtick.labelsize': font, 'ytick.labelsize': font} pylab.rcParams.update(params) def load_data(): return pd.read_csv(DATA_FILE) def draw_train_mode_comparison(df=None, figure_path=None): update_font('x-small') if df is None: df = load_data() corruption_list = list(def_ranges["corruption"].keys())[:-1] model_list = list(def_ranges["model"].keys()) train_mode_list = ["none"] + list(def_ranges["train_mode"].keys())[:4] metric = "err" dim0, dim1 = len(corruption_list), len(model_list) fig, ax = plt.subplots(dim0, dim1, figsize=(dim1 * 3, dim0 * 3)) for corruption_id, corruption in enumerate(corruption_list): for model_id, model in enumerate(model_list): for train_mode_id, train_mode in enumerate(train_mode_list): selected_data = df[(df["model"] == model) & (df["train_mode"] == train_mode) & (df["corruption"] == corruption)].sort_values("severity") label = "{}".format(def_ranges["train_mode"][train_mode]) if selected_data[metric].shape != 5: print(model, train_mode, corruption) continue ax[corruption_id, model_id].plot(def_ranges["severity"], selected_data[metric], "{}{}-".format(colors[train_mode_id], markers[0]), label=label) ax[corruption_id, model_id].set_title("Model {} and Corruption {}".format(def_ranges["model"][model], def_ranges["corruption"][corruption])) # ax[corruption_id, model_id].set_xlabel("Severity") # ax[corruption_id, model_id].set_ylabel("Accuracy") ax[corruption_id, model_id].legend() ax[corruption_id, model_id].set_xticks([1,2,3,4,5]) # ax[corruption_id, model_id].set_ylim([0,100]) plt.tight_layout(pad=0, w_pad=-2) if figure_path is None: plt.savefig("figures/train_mode_comparison.pdf") else: plt.savefig(figure_path) def draw_model_comparison(df=None, figure_path=None, mode="train", metric="err", split=0): update_font('x-small') if df is None: df = load_data() corruption_list = list(def_ranges["corruption"].keys())[:-1] if split == 1: corruption_list = corruption_list[:8] elif split == 2: corruption_list = corruption_list[8:] model_list = list(def_ranges["model"].keys()) if mode == "train": train_mode_list = ["none"] + list(def_ranges["train_mode"].keys())[:4] else: train_mode_list = ["none"] + list(def_ranges["train_mode"].keys())[4:6] dim0, dim1 = len(corruption_list), len(train_mode_list) fig, ax = plt.subplots(dim0, dim1, figsize=(dim1 * 3, dim0 * 2.3)) for corruption_id, corruption in enumerate(corruption_list): for train_mode_id, train_mode in enumerate(train_mode_list): for model_id, model in enumerate(model_list): selected_data = df[(df["model"] == model) & (df["train_mode"] == train_mode) & (df["corruption"] == corruption)].sort_values("severity") label = "{}".format(def_ranges["model"][model]) if selected_data[metric].shape[0] != 5: print(model, train_mode, corruption) continue ax[corruption_id, train_mode_id].plot(def_ranges["severity"], selected_data[metric], color=colors[model_id], marker=markers[0], linestyle='-', label=label) ax[corruption_id, train_mode_id].set_title("{} {} and {} corruption".format(def_ranges["train_mode"][train_mode], "training" if mode == "train" else "testing", def_ranges["corruption"][corruption])) # ax[corruption_id, train_mode_id].set_xlabel("Severity") # ax[corruption_id, train_mode_id].set_ylabel("Accuracy") ax[corruption_id, train_mode_id].legend() ax[corruption_id, train_mode_id].set_xticks([1,2,3,4,5]) if mode == "train" and split == 1: plt.tight_layout(pad=2, w_pad=-3) elif mode == "train" and split == 2: plt.tight_layout(pad=2, w_pad=-1) elif mode == "test" and split == 1: plt.tight_layout(pad=2, w_pad=0) else: plt.tight_layout(pad=2, w_pad=1) if figure_path is None: plt.savefig("figures/model_comparison_{}_{}_{}.pdf".format(mode, metric, split)) else: plt.savefig(figure_path) plt.clf() def draw_corruption_comparison(df=None, figure_path=None): if df is None: df = load_data() dim0, dim1 = len(def_ranges["model"]), len(def_ranges["train_mode"]) fig, ax = plt.subplots(dim0, dim1, figsize=(dim1 * 4, dim0 * 4)) for model_id, model in enumerate(list(def_ranges["model"].keys())): for train_mode_id, train_mode in enumerate(list(def_ranges["train_mode"].keys())): for corruption_id, corruption in enumerate(list(def_ranges["corruption"].keys())): if corruption == "none": continue selected_data = df[(df["corruption"] == corruption) & (df["train_mode"] == train_mode) & (df["model"] == model)].sort_values("severity") for metric_id, metric in enumerate(def_ranges["metric"]): label = "{}-{}".format(corruption, metric) ax[model_id, train_mode_id].plot(def_ranges["severity"], selected_data[metric], marker=markers[metric_id], color=colors[corruption_id], label=label if metric_id == 0 else None) ax[model_id, train_mode_id].set_title("Corruptions with Model {} and TrainMode {}".format(model, train_mode)) # ax[model_id, train_mode_id].set_xlabel("Severity") # ax[model_id, train_mode_id].set_ylabel("Accuracy") ax[model_id, train_mode_id].legend() ax[model_id, train_mode_id].set_xticks([1,2,3,4,5]) if figure_path is None: plt.savefig("figures/corruption_comparison.pdf") else: plt.savefig(figure_path) def get_best_model(df=None): if df is None: df = load_data() best_model = {} for metric_id, metric in enumerate(["acc", "class_acc"]): best_model[metric] = {} for corruption_id, corruption in enumerate(list(def_ranges["corruption"].keys())): best_model[metric][corruption] = [] for model_id, model in enumerate(list(def_ranges["model"].keys())): best_model[metric][corruption].append([]) for train_mode_id, train_mode in enumerate(list(def_ranges["train_mode"].keys())): selected_data = df[(df["corruption"] == corruption) & (df["train_mode"] == train_mode) & (df["model"] == model)][metric] best_model[metric][corruption][model_id].append(selected_data.sum()/5) best_model[metric][corruption][model_id] = float(np.mean(np.array(best_model[metric][corruption][model_id]))) best_model[metric][corruption] = list(def_ranges["model"].keys())[np.argmax(np.array(best_model[metric][corruption]))] data = [] for metric in best_model: for corruption in best_model[metric]: data.append([metric, corruption, best_model[metric][corruption]]) df_data = pd.DataFrame(data, columns=['metric', 'corruption', 'best_model']) df_data.to_csv("best_model.csv", index=False) def get_best_train_mode(df=None): if df is None: df = load_data() best_train_mode = {} for metric_id, metric in enumerate(["acc", "class_acc"]): best_train_mode[metric] = {} for corruption_id, corruption in enumerate(list(def_ranges["corruption"].keys())): best_train_mode[metric][corruption] = [] for train_mode_id, train_mode in enumerate(list(def_ranges["train_mode"].keys())): if train_mode in ["bn", "tent", "none", "pgd"]: continue best_train_mode[metric][corruption].append([]) for model_id, model in enumerate(list(def_ranges["model"].keys())): selected_data = df[(df["corruption"] == corruption) & (df["model"] == model) & (df["train_mode"] == train_mode)][metric] best_train_mode[metric][corruption][train_mode_id].append(selected_data.sum()/5) best_train_mode[metric][corruption][train_mode_id] = float(np.mean(np.array(best_train_mode[metric][corruption][train_mode_id]))) best_train_mode[metric][corruption] = list(def_ranges["train_mode"].keys())[np.argmax(np.array(best_train_mode[metric][corruption]))] data = [] for metric in best_train_mode: for corruption in best_train_mode[metric]: data.append([metric, corruption, best_train_mode[metric][corruption]]) df_data = pd.DataFrame(data, columns=['metric', 'corruption', 'best_train_mode']) print(df_data) df_data.to_csv("best_train_mode.csv", index=False) def get_corruption_tables(df=None, metric="acc"): if df is None: df = load_data() all_tables = "" for corruption_id, corruption in enumerate(list(def_ranges["corruption"].keys())): all_tables += "{}\n".format(corruption) data = [] for model_id, model in enumerate(list(def_ranges["model"].keys())): data.append([]) for train_mode_id, train_mode in enumerate(list(def_ranges["train_mode"].keys())): selected_data = df[(df["corruption"] == corruption) & (df["model"] == model) & (df["train_mode"] == train_mode)][metric].sum()/5 data[-1].append(selected_data) data[-1].append(sum(data[-1])/len(data[-1])) data[-1] = [model] + data[-1] data.append(["average"]) data[-1] += [statistics.mean([data[model_id][train_mode_id+1] for model_id, model in enumerate(def_ranges["model"])]) for train_mode_id, train_mode in enumerate(list(def_ranges["train_mode"].keys()))] df_data = pd.DataFrame(data, columns=["model"]+list(def_ranges["train_mode"].keys())+["average"]) df_data.to_csv("tables/{}_table.csv".format(corruption), index=False) with open("tables/{}_table.csv".format(corruption), 'r') as f: all_tables += f.read() all_tables += "\n\n" with open("corruption_tables.csv", 'w') as f: f.write(all_tables) def draw_teaser(df=None): update_font("xx-small") if df is None: df = load_data() model_dim = len(def_ranges["model"]) fig, ax = plt.subplots(figsize=(6, 1.2)) bar_width = 0.4 bar_params = { "edgecolor": None, } for bar_id, bar in enumerate(["Clean Inputs", "Corrupted Inputs"]): bar_mean = [] if bar == "Clean Inputs": bar_std = [0.1, 0.2, 0.1, 0.2, 0.1, 0.3, 0.2, 0.1, 0.2, 0.3] else: bar_std = [0.2, 0.2, 0.2, 0.3, 0.2, 0.2, 0.3, 0.2, 0.2, 0.3] for model_id, model in enumerate(list(def_ranges["model"].keys())): if bar == "Clean Inputs": one_bar_mean = (100 - df[(df["corruption"] == "none") & (df["model"] == model) & (df["train_mode"] == "none")]["acc"].mean() * 100) color = 'royalblue' else: one_bar_mean = (100 - df[(df["corruption"] != "none") & (df["model"] == model) & (df["train_mode"] == "none")]["acc"].mean() * 100) color = '#F15757' bar_mean.append(one_bar_mean) ax.bar(np.arange(model_dim) + (bar_id - 0.5) * bar_width, bar_mean, bar_width, color=color, label=bar, **bar_params) ax.set_ylabel("Error Rate (%)") ax.set_ylim([0, 40]) ax.set_xticks(np.arange(model_dim)) ax.set_xticklabels(list(def_ranges["model"].values())) ax.legend(ncol=2) for axis in ['top','bottom','left','right']: ax.spines[axis].set_linewidth(1) plt.tight_layout(pad=0.2) plt.savefig("figures/teaser.png") def get_table_1(df=None): if df is None: df = load_data() train_mode_list = ["cutmix_r", "cutmix_k", "mixup", "rsmix", "pgd"] format_string = " & ".join(["{: <10}"] + ["{:4.1f}" for i in range (len(train_mode_list))]) + " \\\\" print(" & ".join([""] + train_mode_list)) for model, model_name in def_ranges["model"].items(): row = [model_name] for train_mode in train_mode_list: data = (100 - df[(df["corruption"] == "none") & (df["model"] == model) & (df["train_mode"] == train_mode)]["acc"].mean() * 100) row.append(data) print(format_string.format(*row)) def get_table_2(): df = load_data() format_string = " & ".join(["{: <18}"] + ["{:4.1f}" for i in range (len(def_ranges["corruption"]))]) + " \\\\" print(" & ".join(["", "average"] + list(def_ranges["corruption"].values())[:-1])) rows = [] for model, model_name in def_ranges["model"].items(): row = [model_name] for corruption, corruption_name in def_ranges["corruption"].items(): if corruption == "none": continue data = df[(df["corruption"] == corruption) & (df["model"] == model) & (df["train_mode"] == "none")]["err"].mean() row.append(data) average = sum(row[1:]) / len(row[1:]) row.insert(1, average) print(format_string.format(*row)) rows.append(row) row = ["Average"] for col_id in range(1, len(def_ranges["corruption"])+1): row.append(np.mean([r[col_id] for r in rows])) print(format_string.format(*row)) def get_table_3(): df = load_data() train_mode_list = ["cutmix_r", "cutmix_k", "mixup", "rsmix", "pgd"] corruption_list = list(def_ranges["corruption"].keys()) corruption_groups = [corruption_list[:5], corruption_list[5:10], corruption_list[10:15]] format_string = " & ".join(["{: <10}"] + ["{:4.1f}" for i in range (20)]) + " \\\\" print(" & ".join(["", "average"] + ["Average", "density", "noise", "trans"] * 5)) rows = [] for model, model_name in def_ranges["model"].items(): if model_name == "PointMLP-Elite": continue row = [model_name] x = df[(df["model"] == model) & (df["train_mode"] == "none")]["err"].mean() row.append(x) for train_mode in train_mode_list: group_data = [] for corruption_group in corruption_groups: data = [] for corruption in corruption_group: x = df[(df["corruption"] == corruption) & (df["model"] == model) & (df["train_mode"] == train_mode)]["err"].mean() data.append(x) group_data.append(np.mean(data)) row.append(np.mean(group_data)) row += group_data print(format_string.format(*row)) rows.append(row) row = ["Average"] for col_id in range(1, 22): row.append(np.mean([r[col_id] for r in rows])) print(format_string.format(*row)) def get_table_4(): df = load_data() # train_mode_list = ["bn", "tent"] train_mode_list = ["megamerger"] corruption_list = list(def_ranges["corruption"].keys()) corruption_groups = [corruption_list[:5], corruption_list[5:10], corruption_list[10:15]] format_string = " & ".join(["{: <10}"] + ["{:6.3f}" for i in range (4 * len(train_mode_list))]) + " \\\\" print(" & ".join([""] + ["Average", "density", "noise", "trans"] * len(train_mode_list))) rows = [] for model, model_name in def_ranges["model"].items(): if model_name == "PointMLP-Elite": continue row = [model_name] for train_mode in train_mode_list: group_data = [] for corruption_group in corruption_groups: data = [] for corruption in corruption_group: x = df[(df["corruption"] == corruption) & (df["model"] == model) & (df["train_mode"] == train_mode)]["err"].mean() data.append(x) group_data.append(np.mean(data)) row.append(np.mean(group_data)) row += group_data print(format_string.format(*row)) rows.append(row) row = ["Average"] for col_id in range(1, 4 * len(train_mode_list)+1): row.append(np.mean([r[col_id] for r in rows])) print(format_string.format(*row)) def draw_severity_comparison(): df = load_data() model_list = list(def_ranges["model"].keys()) model_list.remove("pointMLP2") corruption_list = list(def_ranges["corruption"].keys()) corruption_list.remove("none") corruption_list.remove("lidar") corruption_list.remove("occlusion") train_mode_list = list(def_ranges["train_mode"].keys()) train_mode_list.remove("none") train_mode_list.remove("bn") train_mode_list.remove("tent") train_mode_list.remove("pgd") train_mode_list.remove("megamerger") train_mode_list = ["none"] + train_mode_list figure_dim = len(model_list)+1 bar_dim = len(train_mode_list) # fig, axes = plt.subplots(figure_dim, 1, figsize=(8, figure_dim * 4)) fig, ax = plt.subplots(figsize=(6.0, 2.4)) bar_width = 0.18 bar_params = { "edgecolor": None, } figure_data_all = [] for model_id, model in enumerate(model_list): # ax = axes[model_id] figure_data = [] for i, severity in enumerate(def_ranges["severity"]): bar_data = [] for train_mode_id, train_mode in enumerate(train_mode_list): x = df[(df["corruption"] != "none") & (df["model"] == model) & (df["train_mode"] == train_mode) & (df["severity"] == severity)]["err"].mean() bar_data.append(x) figure_data.append(bar_data) figure_data_all.append(figure_data) figure_data_all = np.array(figure_data_all) print(figure_data_all) for i, severity in enumerate(def_ranges["severity"]): bar_mean = np.mean(figure_data_all[:,i,:], axis=0) bar_std = np.std(figure_data_all[:,i,:], axis=0) ax.bar(np.arange(bar_dim) + (i - 2) * bar_width, bar_mean, bar_width, label="Severity-"+str(severity), **bar_params) ax.errorbar(np.arange(bar_dim) + (i - 2) * bar_width, bar_mean, yerr=bar_std, color="k", fmt='o', markersize=2, capsize=4) ax.set_ylim([0, 44]) ax.set_ylabel("Average Error Rate (%)") ax.set_xticks(np.arange(bar_dim)) ax.set_xticklabels([def_ranges["train_mode"][train_mode] for train_mode in train_mode_list]) # ax.set_title("All") ax.legend(ncol=3) for axis in ['top','bottom','left','right']: ax.spines[axis].set_linewidth(1) plt.tight_layout(pad=0.2) plt.savefig("figures/draw_severity_comparison.pdf") def draw_test_adaptation(): import copy from collections import OrderedDict df = load_data() fig, ax = plt.subplots(figsize=(6.0, 2.4)) best_train = OrderedDict([("occlusion", "cutmix_k"), ("lidar", "rsmix"), ("rotation", "mixup")]) test = ["tent", "bn"] data = {"train": {}, "bn": {}, "tent": {}} for severity in [1, 2, 3, 4, 5]: for key in data: data[key][severity] = [] for corruption, train_mode in best_train.items(): data["train"][severity].append(df[(df["corruption"] == corruption) & (df["train_mode"] == train_mode) & (df["severity"] == severity)]["err"].mean()) for test_mode in test: data[test_mode][severity].append(df[(df["corruption"] == corruption) & (df["train_mode"] == test_mode) & (df["severity"] == severity)]["err"].mean()) for mode in ["train", "tent", "bn"]: data[mode][0] = [0,0,0] bar_width = 0.06 default_bar_params = { "edgecolor": 'black', 'linewidth': 0.5, } for mode_id, mode in enumerate(["train", "tent", "bn"]): for severity in [1, 2, 3, 4, 5]: print(mode, severity, data[mode][severity]) color = [0.9 - 0.18 * severity] * 3 color[mode_id] = 1 bar_params = copy.deepcopy(default_bar_params) if severity == 5: bar_params["label"] = "Best augmentation" if mode == "train" else def_ranges["train_mode"][mode] ax.bar([i+(severity-0.5)*bar_width+5*mode_id*bar_width for i in range(3)], data[mode][severity], bar_width, color=tuple(color), **bar_params ) ax.set_xticks([i + 7.5 * bar_width for i in range(3)]) ax.set_xticklabels([def_ranges["corruption"][corruption] for corruption, train_mode in best_train.items()]) ax.legend() plt.tight_layout(pad=0) plt.savefig("figures/draw_test_adaptation.pdf") # Sets the font of matplotlib. update_font('xx-small') # Builds the csv data table once. if not os.path.isfile(DATA_FILE): format_data() # Figure 9-16. draw_model_comparison() draw_train_mode_comparison() draw_corruption_comparison() get_best_model() get_best_train_mode() get_corruption_tables() # Figure 1. draw_teaser() get_table_1() get_table_2() get_table_3() get_table_4() # Figure 4. draw_severity_comparison() draw_test_adaptation() ================================================ FILE: visualize/pointflow_fig_colorful.py ================================================ import numpy as np def standardize_bbox(pcl, points_per_object): # pt_indices = np.random.choice(pcl.shape[0], points_per_object, replace=False) # np.random.shuffle(pt_indices) # pcl = pcl[pt_indices] # n by 3 mins = np.amin(pcl, axis=0) maxs = np.amax(pcl, axis=0) center = ( mins + maxs ) / 2. scale = np.amax(maxs-mins) print("Center: {}, Scale: {}".format(center, scale)) result = ((pcl - center)/scale).astype(np.float32) # [-0.5, 0.5] return result xml_head = \ """ """ xml_ball_segment = \ """ """ xml_tail = \ """ """ def colormap(x,y,z): vec = np.array([x,y,z]) vec = np.clip(vec, 0.001,1.0) norm = np.sqrt(np.sum(vec**2)) vec /= norm return [vec[0], vec[1], vec[2]] def colorful_pcd(pcd_data, output_file): xml_segments = [xml_head] pcl = standardize_bbox(pcd_data, 2048) pcl = pcl[:,[2,0,1]] pcl[:,0] *= -1 pcl[:,2] += 0.0125 for i in range(pcl.shape[0]): color = colormap(pcl[i,0]+0.5,pcl[i,1]+0.5,pcl[i,2]+0.5-0.0125) xml_segments.append(xml_ball_segment.format(pcl[i,0],pcl[i,1],pcl[i,2], *color)) xml_segments.append(xml_tail) xml_content = str.join('', xml_segments) with open(output_file, 'w') as f: f.write(xml_content)