From 045522e464778e1869b344ee9b7fe170f8d99d65 Mon Sep 17 00:00:00 2001 From: v-zeliu1 Date: Thu, 1 Apr 2021 21:12:49 +0800 Subject: [PATCH] initial release --- .gitignore | 129 ++ LICENSE | 21 + README.md | 199 +++ doc/teaser.png | Bin 0 -> 3451459 bytes eval_avg.py | 363 ++++ init.sh | 7 + models/__init__.py | 3 + models/ap_helper.py | 297 ++++ models/backbone_module.py | 147 ++ models/detector.py | 232 +++ models/loss_helper.py | 306 ++++ models/losses.py | 81 + models/modules.py | 193 +++ models/multi_head_attention.py | 399 +++++ models/transformer.py | 87 + pointnet2/_ext_src/include/ball_query.h | 10 + pointnet2/_ext_src/include/cuda_utils.h | 46 + pointnet2/_ext_src/include/group_points.h | 10 + pointnet2/_ext_src/include/interpolate.h | 15 + pointnet2/_ext_src/include/sampling.h | 11 + pointnet2/_ext_src/include/utils.h | 30 + pointnet2/_ext_src/src/ball_query.cpp | 37 + pointnet2/_ext_src/src/ball_query_gpu.cu | 59 + pointnet2/_ext_src/src/bindings.cpp | 24 + pointnet2/_ext_src/src/group_points.cpp | 65 + pointnet2/_ext_src/src/group_points_gpu.cu | 80 + pointnet2/_ext_src/src/interpolate.cpp | 104 ++ pointnet2/_ext_src/src/interpolate_gpu.cu | 159 ++ pointnet2/_ext_src/src/sampling.cpp | 91 + pointnet2/_ext_src/src/sampling_gpu.cu | 234 +++ pointnet2/pointnet2_modules.py | 518 ++++++ pointnet2/pointnet2_test.py | 33 + pointnet2/pointnet2_utils.py | 425 +++++ pointnet2/pytorch_utils.py | 298 ++++ pointnet2/setup.py | 34 + scannet/README.md | 5 + scannet/batch_load_scannet_data.py | 83 + scannet/data_viz.py | 39 + scannet/load_scannet_data.py | 149 ++ scannet/meta_data/scannet_means.npz | Bin 0 -> 676 bytes scannet/meta_data/scannet_train.txt | 1513 +++++++++++++++++ .../meta_data/scannetv2-labels.combined.tsv | 608 +++++++ scannet/meta_data/scannetv2_test.txt | 100 ++ scannet/meta_data/scannetv2_train.txt | 1201 +++++++++++++ scannet/meta_data/scannetv2_val.txt | 312 ++++ scannet/model_util_scannet.py | 94 + scannet/scannet_detection_dataset.py | 218 +++ scannet/scannet_utils.py | 76 + scannet/scans/.gitignore | 2 + sunrgbd/OFFICIAL_SUNRGBD/.gitignore | 2 + sunrgbd/README.md | 11 + sunrgbd/matlab/extract_rgbd_data_v1.m | 55 + sunrgbd/matlab/extract_rgbd_data_v2.m | 86 + sunrgbd/matlab/extract_split.m | 52 + sunrgbd/model_util_sunrgbd.py | 88 + sunrgbd/sunrgbd_data.py | 439 +++++ sunrgbd/sunrgbd_detection_dataset.py | 218 +++ sunrgbd/sunrgbd_trainval/.gitignore | 2 + sunrgbd/sunrgbd_utils.py | 341 ++++ train_dist.py | 493 ++++++ utils/__init__.py | 2 + utils/box_util.py | 320 ++++ utils/eval_det.py | 274 +++ utils/logger.py | 94 + utils/lr_scheduler.py | 87 + utils/metric_util.py | 170 ++ utils/nms.py | 183 ++ utils/nn_distance.py | 96 ++ utils/pc_util.py | 560 ++++++ 69 files changed, 12720 insertions(+) create mode 100644 .gitignore create mode 100644 LICENSE create mode 100644 README.md create mode 100644 doc/teaser.png create mode 100644 eval_avg.py create mode 100644 init.sh create mode 100644 models/__init__.py create mode 100644 models/ap_helper.py create mode 100644 models/backbone_module.py create mode 100644 models/detector.py create mode 100644 models/loss_helper.py create mode 100644 models/losses.py create mode 100644 models/modules.py create mode 100644 models/multi_head_attention.py create mode 100644 models/transformer.py create mode 100644 pointnet2/_ext_src/include/ball_query.h create mode 100644 pointnet2/_ext_src/include/cuda_utils.h create mode 100644 pointnet2/_ext_src/include/group_points.h create mode 100644 pointnet2/_ext_src/include/interpolate.h create mode 100644 pointnet2/_ext_src/include/sampling.h create mode 100644 pointnet2/_ext_src/include/utils.h create mode 100644 pointnet2/_ext_src/src/ball_query.cpp create mode 100644 pointnet2/_ext_src/src/ball_query_gpu.cu create mode 100644 pointnet2/_ext_src/src/bindings.cpp create mode 100644 pointnet2/_ext_src/src/group_points.cpp create mode 100644 pointnet2/_ext_src/src/group_points_gpu.cu create mode 100644 pointnet2/_ext_src/src/interpolate.cpp create mode 100644 pointnet2/_ext_src/src/interpolate_gpu.cu create mode 100644 pointnet2/_ext_src/src/sampling.cpp create mode 100644 pointnet2/_ext_src/src/sampling_gpu.cu create mode 100644 pointnet2/pointnet2_modules.py create mode 100644 pointnet2/pointnet2_test.py create mode 100644 pointnet2/pointnet2_utils.py create mode 100644 pointnet2/pytorch_utils.py create mode 100644 pointnet2/setup.py create mode 100644 scannet/README.md create mode 100644 scannet/batch_load_scannet_data.py create mode 100644 scannet/data_viz.py create mode 100644 scannet/load_scannet_data.py create mode 100644 scannet/meta_data/scannet_means.npz create mode 100644 scannet/meta_data/scannet_train.txt create mode 100644 scannet/meta_data/scannetv2-labels.combined.tsv create mode 100644 scannet/meta_data/scannetv2_test.txt create mode 100644 scannet/meta_data/scannetv2_train.txt create mode 100644 scannet/meta_data/scannetv2_val.txt create mode 100644 scannet/model_util_scannet.py create mode 100644 scannet/scannet_detection_dataset.py create mode 100644 scannet/scannet_utils.py create mode 100644 scannet/scans/.gitignore create mode 100644 sunrgbd/OFFICIAL_SUNRGBD/.gitignore create mode 100644 sunrgbd/README.md create mode 100644 sunrgbd/matlab/extract_rgbd_data_v1.m create mode 100644 sunrgbd/matlab/extract_rgbd_data_v2.m create mode 100644 sunrgbd/matlab/extract_split.m create mode 100644 sunrgbd/model_util_sunrgbd.py create mode 100644 sunrgbd/sunrgbd_data.py create mode 100644 sunrgbd/sunrgbd_detection_dataset.py create mode 100644 sunrgbd/sunrgbd_trainval/.gitignore create mode 100644 sunrgbd/sunrgbd_utils.py create mode 100644 train_dist.py create mode 100644 utils/__init__.py create mode 100644 utils/box_util.py create mode 100644 utils/eval_det.py create mode 100644 utils/logger.py create mode 100644 utils/lr_scheduler.py create mode 100644 utils/metric_util.py create mode 100644 utils/nms.py create mode 100644 utils/nn_distance.py create mode 100644 utils/pc_util.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..b6e4761 --- /dev/null +++ b/.gitignore @@ -0,0 +1,129 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# 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/ diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..0c1502c --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2021 Ze 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. diff --git a/README.md b/README.md new file mode 100644 index 0000000..415f47f --- /dev/null +++ b/README.md @@ -0,0 +1,199 @@ +# Group-Free 3D Object Detection via Transformers + +By [Ze Liu](https://github.com/zeliu98), [Zheng Zhang](https://github.com/stupidZZ) +, [Yue Cao](https://github.com/caoyue10), [Han Hu](https://github.com/ancientmooner), [Xin Tong](http://www.xtong.info/) + +![teaser](doc/teaser.png) + +**Updates** + +- April 01, 2021: initial release. + +## Introduction + +This repo is the official implementation +of ["Group-Free 3D Object Detection via Transformers"](https://arxiv.org/abs/2104.). + +Recently, directly detecting 3D objects from 3D point clouds has received increasing attention. To extract object +representation from an irregular point cloud, existing methods usually take a point grouping step to assign the points +to an object candidate so that a PointNet-like network could be used to derive object features from the grouped points. +However, the inaccurate point assignments caused by the hand-crafted grouping scheme decrease the performance of 3D +object detection. In this paper, we present a simple yet effective method for directly detecting 3D objects from the 3D +point cloud. Instead of grouping local points to each object candidate, our method computes the feature of an object +from all the points in the point cloud with the help of an attention mechanism in the Transformers, where the +contribution of each point is automatically learned in the network training. With an improved attention stacking scheme, +our method fuses object features in different stages and generates more accurate object detection results. With few +bells and whistles, the proposed method achieves state-of-the-art 3D object detection performance on two widely used +benchmarks, ScanNet V2 and SUN RGB-D. + +In this repository, we provide model implementation (with Pytorch) as well as data preparation, training and evaluation +scripts on ScanNet and SUN RGB-D. + +## Citation + +``` +@article{liu2021, + title={Group-Free 3D Object Detection via Transformers}, + author={Liu, Ze and Zhang, Zheng and Cao, Yue and Hu, Han and Tong, Xin}, + journal={arXiv preprint arXiv:2104.}, + year={2021} +} +``` + +## Main Results + +### Scannet V2 + +|Method | backbone | mAP@0.25 | mAP@0.5 | Model | +|:---:|:---:|:---:|:---:|:---:| +|[HGNet](https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_A_Hierarchical_Graph_Network_for_3D_Object_Detection_on_Point_CVPR_2020_paper.pdf)| GU-net| 61.3 | 34.4 | - | +|[GSDN](https://arxiv.org/pdf/2006.12356.pdf)| MinkNet | 62.8 | 34.8 | [waiting for release](https://github.com/jgwak/GSDN) | +|[3D-MPA](https://arxiv.org/abs/2003.13867)| MinkNet | 64.2 | 49.2 | [waiting for release](https://github.com/francisengelmann/3D-MPA) | +|[VoteNet](https://arxiv.org/abs/1904.09664) | PointNet++ | 62.9 | 39.9 | [official repo](https://github.com/facebookresearch/votenet) | +|[MLCVNet](https://arxiv.org/abs/2004.05679) | PointNet++ | 64.5 | 41.4 | [official repo](https://github.com/NUAAXQ/MLCVNet) | +|[H3DNet](https://arxiv.org/abs/2006.05682) | PointNet++ | 64.4 | 43.4 | [official repo](https://github.com/zaiweizhang/H3DNet) | +|[H3DNet](https://arxiv.org/abs/2006.05682) | 4xPointNet++ | 67.2| 48.1 | [official repo](https://github.com/zaiweizhang/H3DNet) | +| Ours(L6, O256) | PointNet++ | 67.3 (66.2*) | 48.9 (48.4*) |[model](https://drive.google.com/file/d/1aS3vsHtg1QU0yFGPa_-kdBmfGR7VTvY0/view?usp=sharing)| +| Ours(L12, O256) | PointNet++ | 67.2 (66.6*) | 49.7 (49.3*) |[model](https://drive.google.com/file/d/1IMaSW3GbXSKdDRnO_r60AiJaDEKkqAv8/view?usp=sharing)| +| Ours(L12, O256) | PointNet++w2× |68.8 (68.3*) | 52.1 (51.1*) |[model](https://drive.google.com/file/d/1V6sFLFcqsp7YJ3-9AV2NqUhEGVkuNGWT/view?usp=sharing)| +| Ours(L12, O512) | PointNet++w2× | 69.1 (68.8*) |52.8 (52.3*) |[model](https://drive.google.com/file/d/16NAEZqxPdBkxW7GGKGHe4-nDtfqL1htE/view?usp=sharing)| + +### SUNRGBD + +|Method | backbone | inputs | mAP@0.25 | mAP@0.5 | Model | +|:---:|:---:|:---:|:---:|:---:|:---:| +|[VoteNet](https://arxiv.org/abs/1904.09664)| PointNet++ |point | 59.1 | 35.8 |[official repo](https://github.com/facebookresearch/votenet)| +|[MLCVNet](https://arxiv.org/abs/2004.05679)|PointNet++ | point | 59.8 | - | [official repo](https://github.com/NUAAXQ/MLCVNet) | +|[HGNet](https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_A_Hierarchical_Graph_Network_for_3D_Object_Detection_on_Point_CVPR_2020_paper.pdf)| GU-net |point | 61.6 |-|-| +|[H3DNet](https://arxiv.org/abs/2006.05682) | 4xPointNet++ |point | 60.1 | 39.0 | [official repo](https://github.com/zaiweizhang/H3DNet) | +|[imVoteNet](https://arxiv.org/abs/2001.10692)|PointNet++|point+RGB| 63.4 | - | [official repo](https://github.com/facebookresearch/imvotenet)| +| Ours(L6, O256)| PointNet++ | point | 62.8 (62.6*) | 42.3 (42.0*) |[model](https://drive.google.com/file/d/1uVQS3jtPQ6osZXPpydEcsoTt51TPqhMs/view?usp=sharing) | + +**Notes:** + +- `*` means the result is averaged over 5-times evaluation since the algorithm randomness is large. + +## Install + +### Requirements + +- `Ubuntu 16.04` +- `Anaconda` with `python=3.6` +- `pytorch>=1.3` +- `torchvision` with `pillow<7` +- `cuda=10.1` +- `trimesh>=2.35.39,<2.35.40` +- `'networkx>=2.2,<2.3'` +- compile the CUDA layers for [PointNet++](http://arxiv.org/abs/1706.02413), which we used in the backbone + network: `sh init.sh` +- others: `pip install termcolor opencv-python tensorboard` + +### Data preparation + +For SUN RGB-D, follow the [README](./sunrgbd/README.md) under the `sunrgbd` folder. + +For ScanNet, follow the [README](./scannet/README.md) under the `scannet` folder. + +## Usage + +### Scannet + +For `L6, O256` training: + +```bash +python -m torch.distributed.launch --master_port --nproc_per_node \ + train_dist.py --num_point 50000 --num_decoder_layers 6 \ + --size_delta 0.111111111111 --center_delta 0.04 \ + --learning_rate 0.006 --decoder_learning_rate 0.0006 --weight_decay 0.0005 \ + --dataset scannet --data_root [--log_dir ] +``` + +For `L6, O256` evaluation: + +```bash +python eval_avg.py --num_point 50000 --num_decoder_layers 6 \ + --checkpoint_path --avg_times 5 \ + --dataset scannet --data_root [--dump_dir ] +``` + +For `L12, O256` training: + +```bash +python -m torch.distributed.launch --master_port --nproc_per_node \ + train_dist.py --num_point 50000 --num_decoder_layers 12 \ + --size_delta 0.111111111111 --center_delta 0.04 \ + --learning_rate 0.006 --decoder_learning_rate 0.0006 --weight_decay 0.0005 \ + --dataset scannet --data_root [--log_dir ] +``` + +For `L6, O256` evaluation: + +```bash +python eval_avg.py --num_point 50000 --num_decoder_layers 12 \ + --checkpoint_path --avg_times 5 \ + --dataset scannet --data_root [--dump_dir ] +``` + +For `w2x, L12, O256` training: + +```bash +python -m torch.distributed.launch --master_port --nproc_per_node \ + train_dist.py --num_point 50000 --width 2 --num_decoder_layers 12 \ + --size_delta 0.111111111111 --center_delta 0.04 \ + --learning_rate 0.006 --decoder_learning_rate 0.0006 --weight_decay 0.0005 \ + --dataset scannet --data_root [--log_dir ] +``` + +For `w2x, L12, O256` evaluation: + +```bash +python eval_avg.py --num_point 50000 --width 2 --num_decoder_layers 12 \ + --checkpoint_path --avg_times 5 \ + --dataset scannet --data_root [--dump_dir ] +``` + +For `w2x, L12, O512` training: + +```bash +python -m torch.distributed.launch --master_port --nproc_per_node \ + train_dist.py --num_point 50000 --width 2 --num_decoder_layers 12 --num_target 512 \ + --size_delta 0.111111111111 --center_delta 0.04 \ + --learning_rate 0.006 --decoder_learning_rate 0.0006 --weight_decay 0.0005 \ + --dataset scannet --data_root [--log_dir ] +``` + +For `w2x, L12, O512` evaluation: + +```bash +python eval_avg.py --num_point 50000 --width 2 --num_decoder_layers 12 --num_target 512 \ + --checkpoint_path --avg_times 5 \ + --dataset scannet --data_root [--dump_dir ] +``` + +#### SUNRGBD + +For `L6, O256` training: + +```bash +python -m torch.distributed.launch --master_port --nproc_per_node \ + train_dist.py --max_epoch 600 --lr_decay_epochs 420 480 540 --num_point 20000 --num_decoder_layers 6 \ + --size_delta 0.0625 --heading_delta 0.04 --center_delta 0.1111111111111 \ + --learning_rate 0.004 --decoder_learning_rate 0.0002 --weight_decay 0.00000001 --query_points_generator_loss_coef 0.2 --obj_loss_coef 0.4 \ + --dataset sunrgbd --data_root [--log_dir ] +``` + +For `L6, O256` evaluation: + +```bash +python eval_avg.py --num_point 20000 --num_decoder_layers 6 \ + --checkpoint_path --avg_times 5 \ + --dataset sunrgbd --data_root [--dump_dir ] +``` + +## Acknowledgements + +We thank a lot for the flexible codebase of [votenet](https://github.com/facebookresearch/votenet). + +## License + +The code is released under MIT License (see LICENSE file for details). diff --git a/doc/teaser.png b/doc/teaser.png new file mode 100644 index 0000000000000000000000000000000000000000..24cab400145dd9060f767eda797b1de542697119 GIT binary patch literal 3451459 zcmeFZhdZ17`!}uwx2i6st=&cKE_+pV7)5Wj1+978BSul8T3z;v+8sfyq@hNv6eUJQ zYXvcD1VQW|#`D%2{d|AVas0lYKj66zJ$h8HT;qJ7<8{8y*E_H8Yu#lz#Bqp@j*dl5 z^_C7D-I*pjx`WWa7{G5%!7q7%zxF|O?%t$B;<+ZkKMvX|Ybw*xp(B{rAJKzDgXz7D?(S!rId*mi z95?0nS=``bI;$K_$MpLbGs~NvYdMX&)W^|kHvBCW1dQ+7>=|Sfvb&u~l5?}gC(9`R zee(AI=f`>`S{&{4fBpLJ_F7S;(z9jnFTMHjKYvQUjp8InB>(SEocZKvh(Du*+Xr6k ze?JC;3S?vsH~n`fBZOa!{_mIEKBJYp#{IuLb0Yka%zt%epaEBjF>7XPN#%;^jvTY;o^g=pcMq|*UA=XUKcENb{v&-v+pbT z4u$@;doEp;2fv~%6G?QGa!q3@rL`ftsG_@vYM` z6%j|Lm(mXIJ%?cakR*CTS|46@A+Z^8AHLePATuQMH8z3XoFb2U=? 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[default: 0.25]') + parser.add_argument('--conf_thresh', type=float, default=0.0, + help='Filter out predictions with obj prob less than it. [default: 0.05]') + parser.add_argument('--ap_iou_thresholds', type=float, default=[0.25, 0.5], nargs='+', + help='A list of AP IoU thresholds [default: 0.25,0.5]') + parser.add_argument('--faster_eval', action='store_true', + help='Faster evaluation by skippling empty bounding box removal.') + parser.add_argument('--shuffle_dataset', action='store_true', help='Shuffle the dataset (random order).') + + # Model + parser.add_argument('--width', default=1, type=int, help='backbone width') + parser.add_argument('--num_target', type=int, default=256, help='Proposal number [default: 256]') + parser.add_argument('--sampling', default='kps', type=str, help='Query points sampling method (kps, fps)') + + # Transformer + parser.add_argument('--nhead', default=8, type=int, help='multi-head number') + parser.add_argument('--num_decoder_layers', default=6, type=int, help='number of decoder layers') + parser.add_argument('--dim_feedforward', default=2048, type=int, help='dim_feedforward') + parser.add_argument('--transformer_dropout', default=0.1, type=float, help='transformer_dropout') + parser.add_argument('--transformer_activation', default='relu', type=str, help='transformer_activation') + parser.add_argument('--self_position_embedding', default='loc_learned', type=str, + help='position_embedding in self attention (none, xyz_learned, loc_learned)') + parser.add_argument('--cross_position_embedding', default='xyz_learned', type=str, + help='position embedding in cross attention (none, xyz_learned)') + + # Loss + parser.add_argument('--query_points_generator_loss_coef', default=0.8, type=float) + parser.add_argument('--obj_loss_coef', default=0.1, type=float, help='Loss weight for objectness loss') + parser.add_argument('--box_loss_coef', default=1, type=float, help='Loss weight for box loss') + parser.add_argument('--sem_cls_loss_coef', default=0.1, type=float, help='Loss weight for classification loss') + parser.add_argument('--center_loss_type', default='smoothl1', type=str, help='(smoothl1, l1)') + parser.add_argument('--center_delta', default=1.0, type=float, help='delta for smoothl1 loss in center loss') + parser.add_argument('--size_loss_type', default='smoothl1', type=str, help='(smoothl1, l1)') + parser.add_argument('--size_delta', default=1.0, type=float, help='delta for smoothl1 loss in size loss') + parser.add_argument('--heading_loss_type', default='smoothl1', type=str, help='(smoothl1, l1)') + parser.add_argument('--heading_delta', default=1.0, type=float, help='delta for smoothl1 loss in heading loss') + parser.add_argument('--query_points_obj_topk', default=4, type=int, help='query_points_obj_topk') + + # Data + parser.add_argument('--batch_size', type=int, default=16, help='Batch Size during training [default: 8]') + parser.add_argument('--dataset', default='scannet', help='Dataset name. sunrgbd or scannet. [default: scannet]') + parser.add_argument('--num_point', type=int, default=50000, help='Point Number [default: 50000]') + parser.add_argument('--data_root', default='data', help='data root path') + parser.add_argument('--use_height', action='store_true', help='Use height signal in input.') + parser.add_argument('--use_color', action='store_true', help='Use RGB color in input.') + parser.add_argument('--use_sunrgbd_v2', action='store_true', help='Use SUN RGB-D V2 box labels.') + + args, unparsed = parser.parse_known_args() + + return args + + +def get_loader(args): + # Init datasets and dataloaders + def my_worker_init_fn(worker_id): + np.random.seed(np.random.get_state()[1][0] + worker_id) + + # Create Dataset and Dataloader + if args.dataset == 'sunrgbd': + from sunrgbd.sunrgbd_detection_dataset import SunrgbdDetectionVotesDataset + from sunrgbd.model_util_sunrgbd import SunrgbdDatasetConfig + + DATASET_CONFIG = SunrgbdDatasetConfig() + TEST_DATASET = SunrgbdDetectionVotesDataset('val', num_points=args.num_point, + augment=False, + use_color=True if args.use_color else False, + use_height=True if args.use_height else False, + use_v1=(not args.use_sunrgbd_v2), + data_root=args.data_root) + elif args.dataset == 'scannet': + sys.path.append(os.path.join(ROOT_DIR, 'scannet')) + from scannet.scannet_detection_dataset import ScannetDetectionDataset + from scannet.model_util_scannet import ScannetDatasetConfig + + DATASET_CONFIG = ScannetDatasetConfig() + TEST_DATASET = ScannetDetectionDataset('val', num_points=args.num_point, + augment=False, + use_color=True if args.use_color else False, + use_height=True if args.use_height else False, + data_root=args.data_root) + else: + raise NotImplementedError(f'Unknown dataset {args.dataset}. Exiting...') + + logger.info(str(len(TEST_DATASET))) + + TEST_DATALOADER = DataLoader(TEST_DATASET, batch_size=args.batch_size * torch.cuda.device_count(), + shuffle=args.shuffle_dataset, + num_workers=4, + worker_init_fn=my_worker_init_fn) + return TEST_DATALOADER, DATASET_CONFIG + + +def get_model(args, DATASET_CONFIG): + if args.use_height: + num_input_channel = int(args.use_color) * 3 + 1 + else: + num_input_channel = int(args.use_color) * 3 + + model = GroupFreeDetector(num_class=DATASET_CONFIG.num_class, + num_heading_bin=DATASET_CONFIG.num_heading_bin, + num_size_cluster=DATASET_CONFIG.num_size_cluster, + mean_size_arr=DATASET_CONFIG.mean_size_arr, + input_feature_dim=num_input_channel, + width=args.width, + num_proposal=args.num_target, + sampling=args.sampling, + dropout=args.transformer_dropout, + activation=args.transformer_activation, + nhead=args.nhead, + num_decoder_layers=args.num_decoder_layers, + dim_feedforward=args.dim_feedforward, + self_position_embedding=args.self_position_embedding, + cross_position_embedding=args.cross_position_embedding) + + criterion = get_loss + return model, criterion + + +def load_checkpoint(args, model): + # Load checkpoint if there is any + if args.checkpoint_path is not None and os.path.isfile(args.checkpoint_path): + checkpoint = torch.load(args.checkpoint_path, map_location='cpu') + state_dict = checkpoint['model'] + save_path = checkpoint.get('save_path', 'none') + for k in list(state_dict.keys()): + state_dict[k[len("module."):]] = state_dict[k] + # delete renamed or unused k + del state_dict[k] + model.load_state_dict(state_dict) + logger.info(f"{args.checkpoint_path} loaded successfully!!!") + + del checkpoint + torch.cuda.empty_cache() + else: + raise FileNotFoundError + return save_path + + +def evaluate_one_time(test_loader, DATASET_CONFIG, CONFIG_DICT, AP_IOU_THRESHOLDS, model, criterion, args, time=0): + stat_dict = {} + if args.num_decoder_layers > 0: + if args.dataset == 'sunrgbd': + _prefixes = ['last_', 'proposal_'] + _prefixes += [f'{i}head_' for i in range(args.num_decoder_layers - 1)] + prefixes = _prefixes.copy() + ['all_layers_'] + elif args.dataset == 'scannet': + _prefixes = ['last_', 'proposal_'] + _prefixes += [f'{i}head_' for i in range(args.num_decoder_layers - 1)] + prefixes = _prefixes.copy() + ['last_three_'] + ['all_layers_'] + else: + prefixes = ['proposal_'] # only proposal + _prefixes = prefixes + + if args.num_decoder_layers >= 3: + last_three_prefixes = ['last_', f'{args.num_decoder_layers - 2}head_', f'{args.num_decoder_layers - 3}head_'] + elif args.num_decoder_layers == 2: + last_three_prefixes = ['last_', '0head_'] + elif args.num_decoder_layers == 1: + last_three_prefixes = ['last_'] + else: + last_three_prefixes = [] + + ap_calculator_list = [APCalculator(iou_thresh, DATASET_CONFIG.class2type) \ + for iou_thresh in AP_IOU_THRESHOLDS] + mAPs = [[iou_thresh, {k: 0 for k in prefixes}] for iou_thresh in AP_IOU_THRESHOLDS] + + model.eval() # set model to eval mode (for bn and dp) + + batch_pred_map_cls_dict = {k: [] for k in prefixes} + batch_gt_map_cls_dict = {k: [] for k in prefixes} + + for batch_idx, batch_data_label in enumerate(test_loader): + for key in batch_data_label: + batch_data_label[key] = batch_data_label[key].cuda(non_blocking=True) + + # Forward pass + inputs = {'point_clouds': batch_data_label['point_clouds']} + with torch.no_grad(): + end_points = model(inputs) + + # Compute loss + for key in batch_data_label: + assert (key not in end_points) + end_points[key] = batch_data_label[key] + loss, end_points = criterion(end_points, DATASET_CONFIG, + num_decoder_layers=args.num_decoder_layers, + query_points_generator_loss_coef=args.query_points_generator_loss_coef, + obj_loss_coef=args.obj_loss_coef, + box_loss_coef=args.box_loss_coef, + sem_cls_loss_coef=args.sem_cls_loss_coef, + query_points_obj_topk=args.query_points_obj_topk, + center_loss_type=args.center_loss_type, + center_delta=args.center_delta, + size_loss_type=args.size_loss_type, + size_delta=args.size_delta, + heading_loss_type=args.heading_loss_type, + heading_delta=args.heading_delta, + ) + + # Accumulate statistics and print out + for key in end_points: + if 'loss' in key or 'acc' in key or 'ratio' in key: + if key not in stat_dict: stat_dict[key] = 0 + if isinstance(end_points[key], float): + stat_dict[key] += end_points[key] + else: + stat_dict[key] += end_points[key].item() + + for prefix in prefixes: + if prefix == 'last_three_': + end_points[f'{prefix}center'] = torch.cat([end_points[f'{ppx}center'] + for ppx in last_three_prefixes], 1) + end_points[f'{prefix}heading_scores'] = torch.cat([end_points[f'{ppx}heading_scores'] + for ppx in last_three_prefixes], 1) + end_points[f'{prefix}heading_residuals'] = torch.cat([end_points[f'{ppx}heading_residuals'] + for ppx in last_three_prefixes], 1) + end_points[f'{prefix}size_scores'] = torch.cat([end_points[f'{ppx}size_scores'] + for ppx in last_three_prefixes], 1) + end_points[f'{prefix}size_residuals'] = torch.cat([end_points[f'{ppx}size_residuals'] + for ppx in last_three_prefixes], 1) + end_points[f'{prefix}sem_cls_scores'] = torch.cat([end_points[f'{ppx}sem_cls_scores'] + for ppx in last_three_prefixes], 1) + end_points[f'{prefix}objectness_scores'] = torch.cat([end_points[f'{ppx}objectness_scores'] + for ppx in last_three_prefixes], 1) + + elif prefix == 'all_layers_': + end_points[f'{prefix}center'] = torch.cat([end_points[f'{ppx}center'] + for ppx in _prefixes], 1) + end_points[f'{prefix}heading_scores'] = torch.cat([end_points[f'{ppx}heading_scores'] + for ppx in _prefixes], 1) + end_points[f'{prefix}heading_residuals'] = torch.cat([end_points[f'{ppx}heading_residuals'] + for ppx in _prefixes], 1) + end_points[f'{prefix}size_scores'] = torch.cat([end_points[f'{ppx}size_scores'] + for ppx in _prefixes], 1) + end_points[f'{prefix}size_residuals'] = torch.cat([end_points[f'{ppx}size_residuals'] + for ppx in _prefixes], 1) + end_points[f'{prefix}sem_cls_scores'] = torch.cat([end_points[f'{ppx}sem_cls_scores'] + for ppx in _prefixes], 1) + end_points[f'{prefix}objectness_scores'] = torch.cat([end_points[f'{ppx}objectness_scores'] + for ppx in _prefixes], 1) + + batch_pred_map_cls = parse_predictions(end_points, CONFIG_DICT, prefix) + batch_gt_map_cls = parse_groundtruths(end_points, CONFIG_DICT) + batch_pred_map_cls_dict[prefix].append(batch_pred_map_cls) + batch_gt_map_cls_dict[prefix].append(batch_gt_map_cls) + + if (batch_idx + 1) % 10 == 0: + logger.info(f'T[{time}] Eval: [{batch_idx + 1}/{len(test_loader)}] ' + ''.join( + [f'{key} {stat_dict[key] / (float(batch_idx + 1)):.4f} \t' + for key in sorted(stat_dict.keys()) if 'loss' not in key])) + logger.info(''.join([f'{key} {stat_dict[key] / (float(batch_idx + 1)):.4f} \t' + for key in sorted(stat_dict.keys()) if + 'loss' in key and 'proposal_' not in key and 'last_' not in key and 'head_' not in key])) + logger.info(''.join([f'{key} {stat_dict[key] / (float(batch_idx + 1)):.4f} \t' + for key in sorted(stat_dict.keys()) if 'last_' in key])) + logger.info(''.join([f'{key} {stat_dict[key] / (float(batch_idx + 1)):.4f} \t' + for key in sorted(stat_dict.keys()) if 'proposal_' in key])) + for ihead in range(args.num_decoder_layers - 2, -1, -1): + logger.info(''.join([f'{key} {stat_dict[key] / (float(batch_idx + 1)):.4f} \t' + for key in sorted(stat_dict.keys()) if f'{ihead}head_' in key])) + + for prefix in prefixes: + for (batch_pred_map_cls, batch_gt_map_cls) in zip(batch_pred_map_cls_dict[prefix], + batch_gt_map_cls_dict[prefix]): + for ap_calculator in ap_calculator_list: + ap_calculator.step(batch_pred_map_cls, batch_gt_map_cls) + # Evaluate average precision + for i, ap_calculator in enumerate(ap_calculator_list): + metrics_dict = ap_calculator.compute_metrics() + logger.info(f'===================>T{time} {prefix} IOU THRESH: {AP_IOU_THRESHOLDS[i]}<==================') + for key in metrics_dict: + logger.info(f'{key} {metrics_dict[key]}') + + mAPs[i][1][prefix] = metrics_dict['mAP'] + ap_calculator.reset() + for mAP in mAPs: + logger.info(f'T[{time}] IoU[{mAP[0]}]: ' + + ''.join([f'{key}: {mAP[1][key]:.4f} \t' for key in sorted(mAP[1].keys())])) + return mAPs + + +def eval(args, avg_times=5): + test_loader, DATASET_CONFIG = get_loader(args) + n_data = len(test_loader.dataset) + logger.info(f"length of testing dataset: {n_data}") + + model, criterion = get_model(args, DATASET_CONFIG) + logger.info(str(model)) + save_path = load_checkpoint(args, model) + model = model.cuda() + if torch.cuda.device_count() > 1: + logger.info("Let's use %d GPUs!" % (torch.cuda.device_count())) + model = torch.nn.DataParallel(model) + + # Used for AP calculation + CONFIG_DICT = {'remove_empty_box': (not args.faster_eval), 'use_3d_nms': True, 'nms_iou': args.nms_iou, + 'use_old_type_nms': args.use_old_type_nms, 'cls_nms': True, + 'per_class_proposal': True, + 'conf_thresh': args.conf_thresh, 'dataset_config': DATASET_CONFIG} + + logger.info(str(datetime.now())) + mAPs_times = [None for i in range(avg_times)] + for i in range(avg_times): + np.random.seed(i + args.rng_seed) + mAPs = evaluate_one_time(test_loader, DATASET_CONFIG, CONFIG_DICT, args.ap_iou_thresholds, + model, criterion, args, i) + mAPs_times[i] = mAPs + logger.info(f"checkpoint path {save_path}") + + mAPs_avg = mAPs.copy() + + for i, mAP in enumerate(mAPs_avg): + for key in mAP[1].keys(): + avg = 0 + for t in range(avg_times): + cur = mAPs_times[t][i][1][key] + avg += cur + avg /= avg_times + mAP[1][key] = avg + + for mAP in mAPs_avg: + logger.info(f'AVG IoU[{mAP[0]}]: \n' + + ''.join([f'{key}: {mAP[1][key]:.4f} \n' for key in sorted(mAP[1].keys())])) + + for mAP in mAPs_avg: + logger.info(f'AVG IoU[{mAP[0]}]: \t' + + ''.join([f'{key}: {mAP[1][key]:.4f} \t' for key in sorted(mAP[1].keys())])) + + logger.info(f"checkpoint path {save_path}") + + +if __name__ == '__main__': + opt = parse_option() + + opt.dump_dir = os.path.join(opt.dump_dir, f'eval_{opt.dataset}_{int(time.time())}_{np.random.randint(100000000)}') + logger = setup_logger(output=opt.dump_dir, name="eval") + + eval(opt, opt.avg_times) diff --git a/init.sh b/init.sh new file mode 100644 index 0000000..97ab5e2 --- /dev/null +++ b/init.sh @@ -0,0 +1,7 @@ +#!/bin/bash + +# compile custom operators +cd pointnet2 +python setup.py install --user +cd .. + diff --git a/models/__init__.py b/models/__init__.py new file mode 100644 index 0000000..0383a9d --- /dev/null +++ b/models/__init__.py @@ -0,0 +1,3 @@ +from .detector import GroupFreeDetector +from .loss_helper import get_loss +from .ap_helper import APCalculator, parse_predictions, parse_groundtruths \ No newline at end of file diff --git a/models/ap_helper.py b/models/ap_helper.py new file mode 100644 index 0000000..04a2103 --- /dev/null +++ b/models/ap_helper.py @@ -0,0 +1,297 @@ +""" Helper functions and class to calculate Average Precisions for 3D object detection. +""" +import os +import sys +import numpy as np +import torch + +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')) +from eval_det import eval_det_cls, eval_det_multiprocessing +from eval_det import get_iou_obb +from nms import nms_2d_faster, nms_3d_faster, nms_3d_faster_samecls +from box_util import get_3d_box + +sys.path.append(os.path.join(ROOT_DIR, 'sunrgbd')) +from sunrgbd_utils import extract_pc_in_box3d + + +def flip_axis_to_camera(pc): + ''' Flip X-right,Y-forward,Z-up to X-right,Y-down,Z-forward + Input and output are both (N,3) array + ''' + pc2 = np.copy(pc) + pc2[..., [0, 1, 2]] = pc2[..., [0, 2, 1]] # cam X,Y,Z = depth X,-Z,Y + pc2[..., 1] *= -1 + return pc2 + + +def flip_axis_to_depth(pc): + pc2 = np.copy(pc) + pc2[..., [0, 1, 2]] = pc2[..., [0, 2, 1]] # depth X,Y,Z = cam X,Z,-Y + pc2[..., 2] *= -1 + return pc2 + + +def softmax(x): + ''' Numpy function for softmax''' + shape = x.shape + probs = np.exp(x - np.max(x, axis=len(shape) - 1, keepdims=True)) + probs /= np.sum(probs, axis=len(shape) - 1, keepdims=True) + return probs + + +def sigmoid(x): + ''' Numpy function for softmax''' + s = 1 / (1 + np.exp(-x)) + return s + + +def parse_predictions(end_points, config_dict, prefix=""): + """ Parse predictions to OBB parameters and suppress overlapping boxes + + Args: + end_points: dict + {point_clouds, center, heading_scores, heading_residuals, + size_scores, size_residuals, sem_cls_scores} + config_dict: dict + {dataset_config, remove_empty_box, use_3d_nms, nms_iou, + use_old_type_nms, conf_thresh, per_class_proposal} + + Returns: + batch_pred_map_cls: a list of len == batch size (BS) + [pred_list_i], i = 0, 1, ..., BS-1 + where pred_list_i = [(pred_sem_cls, box_params, box_score)_j] + where j = 0, ..., num of valid detections - 1 from sample input i + """ + pred_center = end_points[f'{prefix}center'] # B,num_proposal,3 + pred_heading_class = torch.argmax(end_points[f'{prefix}heading_scores'], -1) # B,num_proposal + pred_heading_residual = torch.gather(end_points[f'{prefix}heading_residuals'], 2, + pred_heading_class.unsqueeze(-1)) # B,num_proposal,1 + pred_heading_residual.squeeze_(2) + pred_size_class = torch.argmax(end_points[f'{prefix}size_scores'], -1) # B,num_proposal + pred_size_residual = torch.gather(end_points[f'{prefix}size_residuals'], 2, + pred_size_class.unsqueeze(-1).unsqueeze(-1).repeat(1, 1, 1, + 3)) # B,num_proposal,1,3 + pred_size_residual.squeeze_(2) + pred_sem_cls = torch.argmax(end_points[f'{prefix}sem_cls_scores'], -1) # B,num_proposal + sem_cls_probs = softmax(end_points[f'{prefix}sem_cls_scores'].detach().cpu().numpy()) # B,num_proposal,10 + + num_proposal = pred_center.shape[1] + # Since we operate in upright_depth coord for points, while util functions + # assume upright_camera coord. + # pred_size_check = end_points[f'{prefix}pred_size'] # B,num_proposal,3 + # pred_bbox_check = end_points[f'{prefix}bbox_check'] # B,num_proposal,3 + + bsize = pred_center.shape[0] + pred_corners_3d_upright_camera = np.zeros((bsize, num_proposal, 8, 3)) + pred_center_upright_camera = flip_axis_to_camera(pred_center.detach().cpu().numpy()) + for i in range(bsize): + for j in range(num_proposal): + heading_angle = config_dict['dataset_config'].class2angle( \ + pred_heading_class[i, j].detach().cpu().numpy(), pred_heading_residual[i, j].detach().cpu().numpy()) + box_size = config_dict['dataset_config'].class2size( \ + int(pred_size_class[i, j].detach().cpu().numpy()), pred_size_residual[i, j].detach().cpu().numpy()) + corners_3d_upright_camera = get_3d_box(box_size, heading_angle, pred_center_upright_camera[i, j, :]) + pred_corners_3d_upright_camera[i, j] = corners_3d_upright_camera + + K = pred_center.shape[1] # K==num_proposal + nonempty_box_mask = np.ones((bsize, K)) + + if config_dict['remove_empty_box']: + # ------------------------------------- + # Remove predicted boxes without any point within them.. + batch_pc = end_points['point_clouds'].cpu().numpy()[:, :, 0:3] # B,N,3 + for i in range(bsize): + pc = batch_pc[i, :, :] # (N,3) + for j in range(K): + box3d = pred_corners_3d_upright_camera[i, j, :, :] # (8,3) + box3d = flip_axis_to_depth(box3d) + pc_in_box, inds = extract_pc_in_box3d(pc, box3d) + if len(pc_in_box) < 5: + nonempty_box_mask[i, j] = 0 + # ------------------------------------- + + obj_logits = end_points[f'{prefix}objectness_scores'].detach().cpu().numpy() + obj_prob = sigmoid(obj_logits)[:, :, 0] # (B,K) + if not config_dict['use_3d_nms']: + # ---------- NMS input: pred_with_prob in (B,K,7) ----------- + pred_mask = np.zeros((bsize, K)) + for i in range(bsize): + boxes_2d_with_prob = np.zeros((K, 5)) + for j in range(K): + boxes_2d_with_prob[j, 0] = np.min(pred_corners_3d_upright_camera[i, j, :, 0]) + boxes_2d_with_prob[j, 2] = np.max(pred_corners_3d_upright_camera[i, j, :, 0]) + boxes_2d_with_prob[j, 1] = np.min(pred_corners_3d_upright_camera[i, j, :, 2]) + boxes_2d_with_prob[j, 3] = np.max(pred_corners_3d_upright_camera[i, j, :, 2]) + boxes_2d_with_prob[j, 4] = obj_prob[i, j] + nonempty_box_inds = np.where(nonempty_box_mask[i, :] == 1)[0] + pick = nms_2d_faster(boxes_2d_with_prob[nonempty_box_mask[i, :] == 1, :], + config_dict['nms_iou'], config_dict['use_old_type_nms']) + assert (len(pick) > 0) + pred_mask[i, nonempty_box_inds[pick]] = 1 + # ---------- NMS output: pred_mask in (B,K) ----------- + elif config_dict['use_3d_nms'] and (not config_dict['cls_nms']): + # ---------- NMS input: pred_with_prob in (B,K,7) ----------- + pred_mask = np.zeros((bsize, K)) + for i in range(bsize): + boxes_3d_with_prob = np.zeros((K, 7)) + for j in range(K): + boxes_3d_with_prob[j, 0] = np.min(pred_corners_3d_upright_camera[i, j, :, 0]) + boxes_3d_with_prob[j, 1] = np.min(pred_corners_3d_upright_camera[i, j, :, 1]) + boxes_3d_with_prob[j, 2] = np.min(pred_corners_3d_upright_camera[i, j, :, 2]) + boxes_3d_with_prob[j, 3] = np.max(pred_corners_3d_upright_camera[i, j, :, 0]) + boxes_3d_with_prob[j, 4] = np.max(pred_corners_3d_upright_camera[i, j, :, 1]) + boxes_3d_with_prob[j, 5] = np.max(pred_corners_3d_upright_camera[i, j, :, 2]) + boxes_3d_with_prob[j, 6] = obj_prob[i, j] + nonempty_box_inds = np.where(nonempty_box_mask[i, :] == 1)[0] + pick = nms_3d_faster(boxes_3d_with_prob[nonempty_box_mask[i, :] == 1, :], + config_dict['nms_iou'], config_dict['use_old_type_nms']) + assert (len(pick) > 0) + pred_mask[i, nonempty_box_inds[pick]] = 1 + # ---------- NMS output: pred_mask in (B,K) ----------- + elif config_dict['use_3d_nms'] and config_dict['cls_nms']: + # ---------- NMS input: pred_with_prob in (B,K,8) ----------- + pred_mask = np.zeros((bsize, K)) + for i in range(bsize): + boxes_3d_with_prob = np.zeros((K, 8)) + for j in range(K): + boxes_3d_with_prob[j, 0] = np.min(pred_corners_3d_upright_camera[i, j, :, 0]) + boxes_3d_with_prob[j, 1] = np.min(pred_corners_3d_upright_camera[i, j, :, 1]) + boxes_3d_with_prob[j, 2] = np.min(pred_corners_3d_upright_camera[i, j, :, 2]) + boxes_3d_with_prob[j, 3] = np.max(pred_corners_3d_upright_camera[i, j, :, 0]) + boxes_3d_with_prob[j, 4] = np.max(pred_corners_3d_upright_camera[i, j, :, 1]) + boxes_3d_with_prob[j, 5] = np.max(pred_corners_3d_upright_camera[i, j, :, 2]) + boxes_3d_with_prob[j, 6] = obj_prob[i, j] + boxes_3d_with_prob[j, 7] = pred_sem_cls[i, j] # only suppress if the two boxes are of the same class!! + nonempty_box_inds = np.where(nonempty_box_mask[i, :] == 1)[0] + pick = nms_3d_faster_samecls(boxes_3d_with_prob[nonempty_box_mask[i, :] == 1, :], + config_dict['nms_iou'], config_dict['use_old_type_nms']) + # assert (len(pick) > 0) + if len(pick) > 0: + pred_mask[i, nonempty_box_inds[pick]] = 1 + # ---------- NMS output: pred_mask in (B,K) ----------- + + batch_pred_map_cls = [] # a list (len: batch_size) of list (len: num of predictions per sample) of tuples of pred_cls, pred_box and conf (0-1) + for i in range(bsize): + if config_dict['per_class_proposal']: + cur_list = [] + for ii in range(config_dict['dataset_config'].num_class): + cur_list += [(ii, pred_corners_3d_upright_camera[i, j], sem_cls_probs[i, j, ii] * obj_prob[i, j]) \ + for j in range(pred_center.shape[1]) if + pred_mask[i, j] == 1 and obj_prob[i, j] > config_dict['conf_thresh']] + batch_pred_map_cls.append(cur_list) + else: + batch_pred_map_cls.append([(pred_sem_cls[i, j].item(), pred_corners_3d_upright_camera[i, j], obj_prob[i, j]) \ + for j in range(pred_center.shape[1]) if + pred_mask[i, j] == 1 and obj_prob[i, j] > config_dict['conf_thresh']]) + + return batch_pred_map_cls + + +def parse_groundtruths(end_points, config_dict): + """ Parse groundtruth labels to OBB parameters. + + Args: + end_points: dict + {center_label, heading_class_label, heading_residual_label, + size_class_label, size_residual_label, sem_cls_label, + box_label_mask} + config_dict: dict + {dataset_config} + + Returns: + batch_gt_map_cls: a list of len == batch_size (BS) + [gt_list_i], i = 0, 1, ..., BS-1 + where gt_list_i = [(gt_sem_cls, gt_box_params)_j] + where j = 0, ..., num of objects - 1 at sample input i + """ + center_label = end_points['center_label'] + heading_class_label = end_points['heading_class_label'] + heading_residual_label = end_points['heading_residual_label'] + size_class_label = end_points['size_class_label'] + size_residual_label = end_points['size_residual_label'] + box_label_mask = end_points['box_label_mask'] + sem_cls_label = end_points['sem_cls_label'] + bsize = center_label.shape[0] + + K2 = center_label.shape[1] # K2==MAX_NUM_OBJ + gt_corners_3d_upright_camera = np.zeros((bsize, K2, 8, 3)) + gt_center_upright_camera = flip_axis_to_camera(center_label[:, :, 0:3].detach().cpu().numpy()) + for i in range(bsize): + for j in range(K2): + if box_label_mask[i, j] == 0: continue + heading_angle = config_dict['dataset_config'].class2angle(heading_class_label[i, j].detach().cpu().numpy(), + heading_residual_label[ + i, j].detach().cpu().numpy()) + box_size = config_dict['dataset_config'].class2size(int(size_class_label[i, j].detach().cpu().numpy()), + size_residual_label[i, j].detach().cpu().numpy()) + corners_3d_upright_camera = get_3d_box(box_size, heading_angle, gt_center_upright_camera[i, j, :]) + gt_corners_3d_upright_camera[i, j] = corners_3d_upright_camera + + batch_gt_map_cls = [] + for i in range(bsize): + batch_gt_map_cls.append([(sem_cls_label[i, j].item(), gt_corners_3d_upright_camera[i, j]) for j in + range(gt_corners_3d_upright_camera.shape[1]) if box_label_mask[i, j] == 1]) + end_points['batch_gt_map_cls'] = batch_gt_map_cls + + return batch_gt_map_cls + + +class APCalculator(object): + ''' Calculating Average Precision ''' + + def __init__(self, ap_iou_thresh=0.25, class2type_map=None): + """ + Args: + ap_iou_thresh: float between 0 and 1.0 + IoU threshold to judge whether a prediction is positive. + class2type_map: [optional] dict {class_int:class_name} + """ + self.ap_iou_thresh = ap_iou_thresh + self.class2type_map = class2type_map + self.reset() + + def step(self, batch_pred_map_cls, batch_gt_map_cls): + """ Accumulate one batch of prediction and groundtruth. + + Args: + batch_pred_map_cls: a list of lists [[(pred_cls, pred_box_params, score),...],...] + batch_gt_map_cls: a list of lists [[(gt_cls, gt_box_params),...],...] + should have the same length with batch_pred_map_cls (batch_size) + """ + + bsize = len(batch_pred_map_cls) + assert (bsize == len(batch_gt_map_cls)) + for i in range(bsize): + self.gt_map_cls[self.scan_cnt] = batch_gt_map_cls[i] + self.pred_map_cls[self.scan_cnt] = batch_pred_map_cls[i] + self.scan_cnt += 1 + + def compute_metrics(self): + """ Use accumulated predictions and groundtruths to compute Average Precision. + """ + rec, prec, ap = eval_det_multiprocessing(self.pred_map_cls, self.gt_map_cls, ovthresh=self.ap_iou_thresh, + get_iou_func=get_iou_obb) + ret_dict = {} + for key in sorted(ap.keys()): + clsname = self.class2type_map[key] if self.class2type_map else str(key) + ret_dict['%s Average Precision' % (clsname)] = ap[key] + ret_dict['mAP'] = np.mean(list(ap.values())) + rec_list = [] + for key in sorted(ap.keys()): + clsname = self.class2type_map[key] if self.class2type_map else str(key) + try: + ret_dict['%s Recall' % (clsname)] = rec[key][-1] + rec_list.append(rec[key][-1]) + except: + ret_dict['%s Recall' % (clsname)] = 0 + rec_list.append(0) + ret_dict['AR'] = np.mean(rec_list) + return ret_dict + + def reset(self): + self.gt_map_cls = {} # {scan_id: [(classname, bbox)]} + self.pred_map_cls = {} # {scan_id: [(classname, bbox, score)]} + self.scan_cnt = 0 diff --git a/models/backbone_module.py b/models/backbone_module.py new file mode 100644 index 0000000..3978816 --- /dev/null +++ b/models/backbone_module.py @@ -0,0 +1,147 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import sys +import os + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +ROOT_DIR = os.path.dirname(BASE_DIR) +sys.path.append(ROOT_DIR) +sys.path.append(os.path.join(ROOT_DIR, 'utils')) +sys.path.append(os.path.join(ROOT_DIR, 'pointnet2')) +sys.path.append(os.path.join(ROOT_DIR, 'ops', 'pt_custom_ops')) + +from pointnet2_modules import PointnetSAModuleVotes, PointnetFPModule + + +class Pointnet2Backbone(nn.Module): + r""" + Backbone network for point cloud feature learning. + Based on Pointnet++ single-scale grouping network. + + Parameters + ---------- + input_feature_dim: int + Number of input channels in the feature descriptor for each point. + e.g. 3 for RGB. + """ + + def __init__(self, input_feature_dim=0, width=1, depth=2): + super().__init__() + self.depth = depth + self.width = width + + self.sa1 = PointnetSAModuleVotes( + npoint=2048, + radius=0.2, + nsample=64, + mlp=[input_feature_dim] + [64 * width for i in range(depth)] + [128 * width], + use_xyz=True, + normalize_xyz=True + ) + + self.sa2 = PointnetSAModuleVotes( + npoint=1024, + radius=0.4, + nsample=32, + mlp=[128 * width] + [128 * width for i in range(depth)] + [256 * width], + use_xyz=True, + normalize_xyz=True + ) + + self.sa3 = PointnetSAModuleVotes( + npoint=512, + radius=0.8, + nsample=16, + mlp=[256 * width] + [128 * width for i in range(depth)] + [256 * width], + use_xyz=True, + normalize_xyz=True + ) + + self.sa4 = PointnetSAModuleVotes( + npoint=256, + radius=1.2, + nsample=16, + mlp=[256 * width] + [128 * width for i in range(depth)] + [256 * width], + use_xyz=True, + normalize_xyz=True + ) + + self.fp1 = PointnetFPModule(mlp=[256 * width + 256 * width, 256 * width, 256 * width]) + self.fp2 = PointnetFPModule(mlp=[256 * width + 256 * width, 256 * width, 288]) + + 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, end_points=None): + r""" + Forward pass of the network + + Parameters + ---------- + pointcloud: Variable(torch.cuda.FloatTensor) + (B, N, 3 + input_feature_dim) tensor + Point cloud to run predicts on + Each point in the point-cloud MUST + be formated as (x, y, z, features...) + + Returns + ---------- + end_points: {XXX_xyz, XXX_features, XXX_inds} + XXX_xyz: float32 Tensor of shape (B,K,3) + XXX_features: float32 Tensor of shape (B,K,D) + XXX-inds: int64 Tensor of shape (B,K) values in [0,N-1] + """ + if not end_points: end_points = {} + batch_size = pointcloud.shape[0] + + xyz, features = self._break_up_pc(pointcloud) + + # --------- 4 SET ABSTRACTION LAYERS --------- + xyz, features, fps_inds = self.sa1(xyz, features) + end_points['sa1_inds'] = fps_inds + end_points['sa1_xyz'] = xyz + end_points['sa1_features'] = features + + xyz, features, fps_inds = self.sa2(xyz, features) # this fps_inds is just 0,1,...,1023 + end_points['sa2_inds'] = fps_inds + end_points['sa2_xyz'] = xyz + end_points['sa2_features'] = features + + xyz, features, fps_inds = self.sa3(xyz, features) # this fps_inds is just 0,1,...,511 + end_points['sa3_xyz'] = xyz + end_points['sa3_features'] = features + + xyz, features, fps_inds = self.sa4(xyz, features) # this fps_inds is just 0,1,...,255 + end_points['sa4_xyz'] = xyz + end_points['sa4_features'] = features + + # --------- 2 FEATURE UPSAMPLING LAYERS -------- + features = self.fp1(end_points['sa3_xyz'], end_points['sa4_xyz'], end_points['sa3_features'], + end_points['sa4_features']) + features = self.fp2(end_points['sa2_xyz'], end_points['sa3_xyz'], end_points['sa2_features'], features) + end_points['fp2_features'] = features + end_points['fp2_xyz'] = end_points['sa2_xyz'] + num_seed = end_points['fp2_xyz'].shape[1] + end_points['fp2_inds'] = end_points['sa1_inds'][:, 0:num_seed] # indices among the entire input point clouds + + return end_points + + +if __name__ == '__main__': + backbone_net = Pointnet2Backbone(input_feature_dim=3).cuda() + print(backbone_net) + backbone_net.eval() + out = backbone_net(torch.rand(16, 20000, 6).cuda()) + for key in sorted(out.keys()): + print(key, '\t', out[key].shape) diff --git a/models/detector.py b/models/detector.py new file mode 100644 index 0000000..702f6df --- /dev/null +++ b/models/detector.py @@ -0,0 +1,232 @@ +import torch +import torch.nn as nn +import sys +import os + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +ROOT_DIR = os.path.dirname(BASE_DIR) +sys.path.append(BASE_DIR) + +from .backbone_module import Pointnet2Backbone +from .transformer import TransformerDecoderLayer +from .modules import PointsObjClsModule, FPSModule, GeneralSamplingModule, PositionEmbeddingLearned, PredictHead + + +class GroupFreeDetector(nn.Module): + r""" + A Group-Free detector for 3D object detection via Transformer. + + Parameters + ---------- + num_class: int + Number of semantics classes to predict over -- size of softmax classifier + num_heading_bin: int + num_size_cluster: int + input_feature_dim: (default: 0) + Input dim 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 + width: (default: 1) + PointNet backbone width ratio + num_proposal: int (default: 128) + Number of proposals/detections generated from the network. Each proposal is a 3D OBB with a semantic class. + sampling: (default: kps) + Initial object candidate sampling method + """ + + def __init__(self, num_class, num_heading_bin, num_size_cluster, mean_size_arr, + input_feature_dim=0, width=1, bn_momentum=0.1, sync_bn=False, num_proposal=128, sampling='kps', + dropout=0.1, activation="relu", nhead=8, num_decoder_layers=6, dim_feedforward=2048, + self_position_embedding='xyz_learned', cross_position_embedding='xyz_learned' + ): + super().__init__() + + self.num_class = num_class + self.num_heading_bin = num_heading_bin + self.num_size_cluster = num_size_cluster + self.mean_size_arr = mean_size_arr + assert (mean_size_arr.shape[0] == self.num_size_cluster) + self.input_feature_dim = input_feature_dim + self.num_proposal = num_proposal + self.bn_momentum = bn_momentum + self.sync_bn = sync_bn + self.width = width + self.nhead = nhead + self.sampling = sampling + self.num_decoder_layers = num_decoder_layers + self.dim_feedforward = dim_feedforward + self.self_position_embedding = self_position_embedding + self.cross_position_embedding = cross_position_embedding + + # Backbone point feature learning + self.backbone_net = Pointnet2Backbone(input_feature_dim=self.input_feature_dim, width=self.width) + + if self.sampling == 'fps': + self.fps_module = FPSModule(num_proposal) + elif self.sampling == 'kps': + self.points_obj_cls = PointsObjClsModule(288) + self.gsample_module = GeneralSamplingModule() + else: + raise NotImplementedError + # Proposal + self.proposal_head = PredictHead(num_class, num_heading_bin, num_size_cluster, + mean_size_arr, num_proposal, 288) + if self.num_decoder_layers <= 0: + # stop building if has no decoder layer + return + + # Transformer Decoder Projection + self.decoder_key_proj = nn.Conv1d(288, 288, kernel_size=1) + self.decoder_query_proj = nn.Conv1d(288, 288, kernel_size=1) + + # Position Embedding for Self-Attention + if self.self_position_embedding == 'none': + self.decoder_self_posembeds = [None for i in range(num_decoder_layers)] + elif self.self_position_embedding == 'xyz_learned': + self.decoder_self_posembeds = nn.ModuleList() + for i in range(self.num_decoder_layers): + self.decoder_self_posembeds.append(PositionEmbeddingLearned(3, 288)) + elif self.self_position_embedding == 'loc_learned': + self.decoder_self_posembeds = nn.ModuleList() + for i in range(self.num_decoder_layers): + self.decoder_self_posembeds.append(PositionEmbeddingLearned(6, 288)) + else: + raise NotImplementedError(f"self_position_embedding not supported {self.self_position_embedding}") + + # Position Embedding for Cross-Attention + if self.cross_position_embedding == 'none': + self.decoder_cross_posembeds = [None for i in range(num_decoder_layers)] + elif self.cross_position_embedding == 'xyz_learned': + self.decoder_cross_posembeds = nn.ModuleList() + for i in range(self.num_decoder_layers): + self.decoder_cross_posembeds.append(PositionEmbeddingLearned(3, 288)) + else: + raise NotImplementedError(f"cross_position_embedding not supported {self.cross_position_embedding}") + + # Transformer decoder layers + self.decoder = nn.ModuleList() + for i in range(self.num_decoder_layers): + self.decoder.append( + TransformerDecoderLayer( + 288, nhead, dim_feedforward, dropout, activation, + self_posembed=self.decoder_self_posembeds[i], + cross_posembed=self.decoder_cross_posembeds[i], + )) + + # Prediction Head + self.prediction_heads = nn.ModuleList() + for i in range(self.num_decoder_layers): + self.prediction_heads.append(PredictHead(num_class, num_heading_bin, num_size_cluster, + mean_size_arr, num_proposal, 288)) + + # Init + self.init_weights() + self.init_bn_momentum() + if self.sync_bn: + nn.SyncBatchNorm.convert_sync_batchnorm(self) + + def forward(self, inputs): + """ Forward pass of the network + + Args: + inputs: dict + {point_clouds} + + point_clouds: 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...) + Returns: + end_points: dict + """ + end_points = {} + + end_points = self.backbone_net(inputs['point_clouds'], end_points) + + # Query Points Generation + points_xyz = end_points['fp2_xyz'] + points_features = end_points['fp2_features'] + xyz = end_points['fp2_xyz'] + features = end_points['fp2_features'] + end_points['seed_inds'] = end_points['fp2_inds'] + end_points['seed_xyz'] = xyz + end_points['seed_features'] = features + if self.sampling == 'fps': + xyz, features, sample_inds = self.fps_module(xyz, features) + cluster_feature = features + cluster_xyz = xyz + end_points['query_points_xyz'] = xyz # (batch_size, num_proposal, 3) + end_points['query_points_feature'] = features # (batch_size, C, num_proposal) + end_points['query_points_sample_inds'] = sample_inds # (bsz, num_proposal) # should be 0,1,...,num_proposal + elif self.sampling == 'kps': + points_obj_cls_logits = self.points_obj_cls(features) # (batch_size, 1, num_seed) + end_points['seeds_obj_cls_logits'] = points_obj_cls_logits + points_obj_cls_scores = torch.sigmoid(points_obj_cls_logits).squeeze(1) + sample_inds = torch.topk(points_obj_cls_scores, self.num_proposal)[1].int() + xyz, features, sample_inds = self.gsample_module(xyz, features, sample_inds) + cluster_feature = features + cluster_xyz = xyz + end_points['query_points_xyz'] = xyz # (batch_size, num_proposal, 3) + end_points['query_points_feature'] = features # (batch_size, C, num_proposal) + end_points['query_points_sample_inds'] = sample_inds # (bsz, num_proposal) # should be 0,1,...,num_proposal + else: + raise NotImplementedError + + # Proposal + proposal_center, proposal_size = self.proposal_head(cluster_feature, + base_xyz=cluster_xyz, + end_points=end_points, + prefix='proposal_') # N num_proposal 3 + + base_xyz = proposal_center.detach().clone() + base_size = proposal_size.detach().clone() + + # Transformer Decoder and Prediction + if self.num_decoder_layers > 0: + query = self.decoder_query_proj(cluster_feature) + key = self.decoder_key_proj(points_features) if self.decoder_key_proj is not None else None + # Position Embedding for Cross-Attention + if self.cross_position_embedding == 'none': + key_pos = None + elif self.cross_position_embedding in ['xyz_learned']: + key_pos = points_xyz + else: + raise NotImplementedError(f"cross_position_embedding not supported {self.cross_position_embedding}") + + for i in range(self.num_decoder_layers): + prefix = 'last_' if (i == self.num_decoder_layers - 1) else f'{i}head_' + + # Position Embedding for Self-Attention + if self.self_position_embedding == 'none': + query_pos = None + elif self.self_position_embedding == 'xyz_learned': + query_pos = base_xyz + elif self.self_position_embedding == 'loc_learned': + query_pos = torch.cat([base_xyz, base_size], -1) + else: + raise NotImplementedError(f"self_position_embedding not supported {self.self_position_embedding}") + + # Transformer Decoder Layer + query = self.decoder[i](query, key, query_pos, key_pos) + + # Prediction + base_xyz, base_size = self.prediction_heads[i](query, + base_xyz=cluster_xyz, + end_points=end_points, + prefix=prefix) + + base_xyz = base_xyz.detach().clone() + base_size = base_size.detach().clone() + + return end_points + + def init_weights(self): + # initialize transformer + for m in self.decoder.parameters(): + if m.dim() > 1: + nn.init.xavier_uniform_(m) + + def init_bn_momentum(self): + for m in self.modules(): + if isinstance(m, (nn.BatchNorm2d, nn.BatchNorm1d)): + m.momentum = self.bn_momentum diff --git a/models/loss_helper.py b/models/loss_helper.py new file mode 100644 index 0000000..75873a0 --- /dev/null +++ b/models/loss_helper.py @@ -0,0 +1,306 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +import sys +import os + +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')) +from .losses import smoothl1_loss, l1_loss, SigmoidFocalClassificationLoss + + +def compute_points_obj_cls_loss_hard_topk(end_points, topk): + box_label_mask = end_points['box_label_mask'] + seed_inds = end_points['seed_inds'].long() # B, K + seed_xyz = end_points['seed_xyz'] # B, K, 3 + seeds_obj_cls_logits = end_points['seeds_obj_cls_logits'] # B, 1, K + gt_center = end_points['center_label'][:, :, 0:3] # B, K2, 3 + gt_size = end_points['size_gts'][:, :, 0:3] # B, K2, 3 + B = gt_center.shape[0] + K = seed_xyz.shape[1] + K2 = gt_center.shape[1] + + point_instance_label = end_points['point_instance_label'] # B, num_points + object_assignment = torch.gather(point_instance_label, 1, seed_inds) # B, num_seed + object_assignment[object_assignment < 0] = K2 - 1 # set background points to the last gt bbox + object_assignment_one_hot = torch.zeros((B, K, K2)).to(seed_xyz.device) + object_assignment_one_hot.scatter_(2, object_assignment.unsqueeze(-1), 1) # (B, K, K2) + delta_xyz = seed_xyz.unsqueeze(2) - gt_center.unsqueeze(1) # (B, K, K2, 3) + delta_xyz = delta_xyz / (gt_size.unsqueeze(1) + 1e-6) # (B, K, K2, 3) + new_dist = torch.sum(delta_xyz ** 2, dim=-1) + euclidean_dist1 = torch.sqrt(new_dist + 1e-6) # BxKxK2 + euclidean_dist1 = euclidean_dist1 * object_assignment_one_hot + 100 * (1 - object_assignment_one_hot) # BxKxK2 + euclidean_dist1 = euclidean_dist1.transpose(1, 2).contiguous() # BxK2xK + topk_inds = torch.topk(euclidean_dist1, topk, largest=False)[1] * box_label_mask[:, :, None] + \ + (box_label_mask[:, :, None] - 1) # BxK2xtopk + topk_inds = topk_inds.long() # BxK2xtopk + topk_inds = topk_inds.view(B, -1).contiguous() # B, K2xtopk + batch_inds = torch.arange(B).unsqueeze(1).repeat(1, K2 * topk).to(seed_xyz.device) + batch_topk_inds = torch.stack([batch_inds, topk_inds], -1).view(-1, 2).contiguous() + + objectness_label = torch.zeros((B, K + 1), dtype=torch.long).to(seed_xyz.device) + objectness_label[batch_topk_inds[:, 0], batch_topk_inds[:, 1]] = 1 + objectness_label = objectness_label[:, :K] + objectness_label_mask = torch.gather(point_instance_label, 1, seed_inds) # B, num_seed + objectness_label[objectness_label_mask < 0] = 0 + + total_num_points = B * K + end_points[f'points_hard_topk{topk}_pos_ratio'] = \ + torch.sum(objectness_label.float()) / float(total_num_points) + end_points[f'points_hard_topk{topk}_neg_ratio'] = 1 - end_points[f'points_hard_topk{topk}_pos_ratio'] + + # Compute objectness loss + criterion = SigmoidFocalClassificationLoss() + cls_weights = (objectness_label >= 0).float() + cls_normalizer = cls_weights.sum(dim=1, keepdim=True).float() + cls_weights /= torch.clamp(cls_normalizer, min=1.0) + cls_loss_src = criterion(seeds_obj_cls_logits.view(B, K, 1), objectness_label.unsqueeze(-1), weights=cls_weights) + objectness_loss = cls_loss_src.sum() / B + + # Compute recall upper bound + padding_array = torch.arange(0, B).to(point_instance_label.device) * 10000 + padding_array = padding_array.unsqueeze(1) # B,1 + point_instance_label_mask = (point_instance_label < 0) # B,num_points + point_instance_label = point_instance_label + padding_array # B,num_points + point_instance_label[point_instance_label_mask] = -1 + num_gt_bboxes = torch.unique(point_instance_label).shape[0] - 1 + seed_instance_label = torch.gather(point_instance_label, 1, seed_inds) # B,num_seed + pos_points_instance_label = seed_instance_label * objectness_label + (objectness_label - 1) + num_query_bboxes = torch.unique(pos_points_instance_label).shape[0] - 1 + if num_gt_bboxes > 0: + end_points[f'points_hard_topk{topk}_upper_recall_ratio'] = num_query_bboxes / num_gt_bboxes + + return objectness_loss + + +def compute_objectness_loss_based_on_query_points(end_points, num_decoder_layers): + """ Compute objectness loss for the proposals. + """ + + if num_decoder_layers > 0: + prefixes = ['proposal_'] + ['last_'] + [f'{i}head_' for i in range(num_decoder_layers - 1)] + else: + prefixes = ['proposal_'] # only proposal + + objectness_loss_sum = 0.0 + for prefix in prefixes: + # Associate proposal and GT objects + seed_inds = end_points['seed_inds'].long() # B,num_seed in [0,num_points-1] + gt_center = end_points['center_label'][:, :, 0:3] # B, K2, 3 + query_points_sample_inds = end_points['query_points_sample_inds'].long() + + B = seed_inds.shape[0] + K = query_points_sample_inds.shape[1] + K2 = gt_center.shape[1] + + seed_obj_gt = torch.gather(end_points['point_obj_mask'], 1, seed_inds) # B,num_seed + query_points_obj_gt = torch.gather(seed_obj_gt, 1, query_points_sample_inds) # B, query_points + + point_instance_label = end_points['point_instance_label'] # B, num_points + seed_instance_label = torch.gather(point_instance_label, 1, seed_inds) # B,num_seed + query_points_instance_label = torch.gather(seed_instance_label, 1, query_points_sample_inds) # B,query_points + + objectness_mask = torch.ones((B, K)).cuda() + + # Set assignment + object_assignment = query_points_instance_label # (B,K) with values in 0,1,...,K2-1 + object_assignment[object_assignment < 0] = K2 - 1 # set background points to the last gt bbox + + end_points[f'{prefix}objectness_label'] = query_points_obj_gt + end_points[f'{prefix}objectness_mask'] = objectness_mask + end_points[f'{prefix}object_assignment'] = object_assignment + total_num_proposal = query_points_obj_gt.shape[0] * query_points_obj_gt.shape[1] + end_points[f'{prefix}pos_ratio'] = \ + torch.sum(query_points_obj_gt.float().cuda()) / float(total_num_proposal) + end_points[f'{prefix}neg_ratio'] = \ + torch.sum(objectness_mask.float()) / float(total_num_proposal) - end_points[f'{prefix}pos_ratio'] + + # Compute objectness loss + objectness_scores = end_points[f'{prefix}objectness_scores'] + criterion = SigmoidFocalClassificationLoss() + cls_weights = objectness_mask.float() + cls_normalizer = cls_weights.sum(dim=1, keepdim=True).float() + cls_weights /= torch.clamp(cls_normalizer, min=1.0) + cls_loss_src = criterion(objectness_scores.transpose(2, 1).contiguous().view(B, K, 1), + query_points_obj_gt.unsqueeze(-1), + weights=cls_weights) + objectness_loss = cls_loss_src.sum() / B + + end_points[f'{prefix}objectness_loss'] = objectness_loss + objectness_loss_sum += objectness_loss + + return objectness_loss_sum, end_points + + +def compute_box_and_sem_cls_loss(end_points, config, num_decoder_layers, + center_loss_type='smoothl1', center_delta=1.0, + size_loss_type='smoothl1', size_delta=1.0, + heading_loss_type='smoothl1', heading_delta=1.0): + """ Compute 3D bounding box and semantic classification loss. + """ + + num_heading_bin = config.num_heading_bin + num_size_cluster = config.num_size_cluster + num_class = config.num_class + mean_size_arr = config.mean_size_arr + + if num_decoder_layers > 0: + prefixes = ['proposal_'] + ['last_'] + [f'{i}head_' for i in range(num_decoder_layers - 1)] + else: + prefixes = ['proposal_'] # only proposal + box_loss_sum = 0.0 + sem_cls_loss_sum = 0.0 + for prefix in prefixes: + object_assignment = end_points[f'{prefix}object_assignment'] + batch_size = object_assignment.shape[0] + # Compute center loss + pred_center = end_points[f'{prefix}center'] + gt_center = end_points['center_label'][:, :, 0:3] + + if center_loss_type == 'smoothl1': + objectness_label = end_points[f'{prefix}objectness_label'].float() + object_assignment_expand = object_assignment.unsqueeze(2).repeat(1, 1, 3) + assigned_gt_center = torch.gather(gt_center, 1, object_assignment_expand) # (B, K, 3) from (B, K2, 3) + center_loss = smoothl1_loss(assigned_gt_center - pred_center, delta=center_delta) # (B,K) + center_loss = torch.sum(center_loss * objectness_label.unsqueeze(2)) / (torch.sum(objectness_label) + 1e-6) + elif center_loss_type == 'l1': + objectness_label = end_points[f'{prefix}objectness_label'].float() + object_assignment_expand = object_assignment.unsqueeze(2).repeat(1, 1, 3) + assigned_gt_center = torch.gather(gt_center, 1, object_assignment_expand) # (B, K, 3) from (B, K2, 3) + center_loss = l1_loss(assigned_gt_center - pred_center) # (B,K) + center_loss = torch.sum(center_loss * objectness_label.unsqueeze(2)) / (torch.sum(objectness_label) + 1e-6) + else: + raise NotImplementedError + + # Compute heading loss + heading_class_label = torch.gather(end_points['heading_class_label'], 1, + object_assignment) # select (B,K) from (B,K2) + criterion_heading_class = nn.CrossEntropyLoss(reduction='none') + heading_class_loss = criterion_heading_class(end_points[f'{prefix}heading_scores'].transpose(2, 1), + heading_class_label) # (B,K) + heading_class_loss = torch.sum(heading_class_loss * objectness_label) / (torch.sum(objectness_label) + 1e-6) + + heading_residual_label = torch.gather(end_points['heading_residual_label'], 1, + object_assignment) # select (B,K) from (B,K2) + heading_residual_normalized_label = heading_residual_label / (np.pi / num_heading_bin) + + # Ref: https://discuss.pytorch.org/t/convert-int-into-one-hot-format/507/3 + heading_label_one_hot = torch.cuda.FloatTensor(batch_size, heading_class_label.shape[1], + num_heading_bin).zero_() + heading_label_one_hot.scatter_(2, heading_class_label.unsqueeze(-1), + 1) # src==1 so it's *one-hot* (B,K,num_heading_bin) + heading_residual_normalized_error = torch.sum( + end_points[f'{prefix}heading_residuals_normalized'] * heading_label_one_hot, + -1) - heading_residual_normalized_label + + if heading_loss_type == 'smoothl1': + heading_residual_normalized_loss = heading_delta * smoothl1_loss(heading_residual_normalized_error, + delta=heading_delta) # (B,K) + heading_residual_normalized_loss = torch.sum( + heading_residual_normalized_loss * objectness_label) / (torch.sum(objectness_label) + 1e-6) + elif heading_loss_type == 'l1': + heading_residual_normalized_loss = l1_loss(heading_residual_normalized_error) # (B,K) + heading_residual_normalized_loss = torch.sum( + heading_residual_normalized_loss * objectness_label) / (torch.sum(objectness_label) + 1e-6) + else: + raise NotImplementedError + + # Compute size loss + size_class_label = torch.gather(end_points['size_class_label'], 1, + object_assignment) # select (B,K) from (B,K2) + criterion_size_class = nn.CrossEntropyLoss(reduction='none') + size_class_loss = criterion_size_class(end_points[f'{prefix}size_scores'].transpose(2, 1), + size_class_label) # (B,K) + size_class_loss = torch.sum(size_class_loss * objectness_label) / (torch.sum(objectness_label) + 1e-6) + + size_residual_label = torch.gather( + end_points['size_residual_label'], 1, + object_assignment.unsqueeze(-1).repeat(1, 1, 3)) # select (B,K,3) from (B,K2,3) + + size_label_one_hot = torch.cuda.FloatTensor(batch_size, size_class_label.shape[1], num_size_cluster).zero_() + size_label_one_hot.scatter_(2, size_class_label.unsqueeze(-1), + 1) # src==1 so it's *one-hot* (B,K,num_size_cluster) + size_label_one_hot_tiled = size_label_one_hot.unsqueeze(-1).repeat(1, 1, 1, 3) # (B,K,num_size_cluster,3) + predicted_size_residual_normalized = torch.sum( + end_points[f'{prefix}size_residuals_normalized'] * size_label_one_hot_tiled, + 2) # (B,K,3) + + mean_size_arr_expanded = torch.from_numpy(mean_size_arr.astype(np.float32)).cuda().unsqueeze(0).unsqueeze( + 0) # (1,1,num_size_cluster,3) + mean_size_label = torch.sum(size_label_one_hot_tiled * mean_size_arr_expanded, 2) # (B,K,3) + size_residual_label_normalized = size_residual_label / mean_size_label # (B,K,3) + + size_residual_normalized_error = predicted_size_residual_normalized - size_residual_label_normalized + + if size_loss_type == 'smoothl1': + size_residual_normalized_loss = size_delta * smoothl1_loss(size_residual_normalized_error, + delta=size_delta) # (B,K,3) -> (B,K) + size_residual_normalized_loss = torch.sum(size_residual_normalized_loss * objectness_label.unsqueeze(2)) / ( + torch.sum(objectness_label) + 1e-6) + elif size_loss_type == 'l1': + size_residual_normalized_loss = l1_loss(size_residual_normalized_error) # (B,K,3) -> (B,K) + size_residual_normalized_loss = torch.sum(size_residual_normalized_loss * objectness_label.unsqueeze(2)) / ( + torch.sum(objectness_label) + 1e-6) + else: + raise NotImplementedError + + # 3.4 Semantic cls loss + sem_cls_label = torch.gather(end_points['sem_cls_label'], 1, object_assignment) # select (B,K) from (B,K2) + criterion_sem_cls = nn.CrossEntropyLoss(reduction='none') + sem_cls_loss = criterion_sem_cls(end_points[f'{prefix}sem_cls_scores'].transpose(2, 1), sem_cls_label) # (B,K) + sem_cls_loss = torch.sum(sem_cls_loss * objectness_label) / (torch.sum(objectness_label) + 1e-6) + + end_points[f'{prefix}center_loss'] = center_loss + end_points[f'{prefix}heading_cls_loss'] = heading_class_loss + end_points[f'{prefix}heading_reg_loss'] = heading_residual_normalized_loss + end_points[f'{prefix}size_cls_loss'] = size_class_loss + end_points[f'{prefix}size_reg_loss'] = size_residual_normalized_loss + box_loss = center_loss + 0.1 * heading_class_loss + heading_residual_normalized_loss + 0.1 * size_class_loss + size_residual_normalized_loss + end_points[f'{prefix}box_loss'] = box_loss + end_points[f'{prefix}sem_cls_loss'] = sem_cls_loss + + box_loss_sum += box_loss + sem_cls_loss_sum += sem_cls_loss + return box_loss_sum, sem_cls_loss_sum, end_points + + +def get_loss(end_points, config, num_decoder_layers, + query_points_generator_loss_coef, obj_loss_coef, box_loss_coef, sem_cls_loss_coef, + query_points_obj_topk=5, + center_loss_type='smoothl1', center_delta=1.0, + size_loss_type='smoothl1', size_delta=1.0, + heading_loss_type='smoothl1', heading_delta=1.0): + """ Loss functions + """ + if 'seeds_obj_cls_logits' in end_points.keys(): + query_points_generation_loss = compute_points_obj_cls_loss_hard_topk(end_points, query_points_obj_topk) + + end_points['query_points_generation_loss'] = query_points_generation_loss + else: + query_points_generation_loss = 0.0 + + # Obj loss + objectness_loss_sum, end_points = \ + compute_objectness_loss_based_on_query_points(end_points, num_decoder_layers) + + end_points['sum_heads_objectness_loss'] = objectness_loss_sum + + # Box loss and sem cls loss + box_loss_sum, sem_cls_loss_sum, end_points = compute_box_and_sem_cls_loss( + end_points, config, num_decoder_layers, + center_loss_type, center_delta=center_delta, + size_loss_type=size_loss_type, size_delta=size_delta, + heading_loss_type=heading_loss_type, heading_delta=heading_delta) + end_points['sum_heads_box_loss'] = box_loss_sum + end_points['sum_heads_sem_cls_loss'] = sem_cls_loss_sum + + # means average proposal with prediction loss + loss = query_points_generator_loss_coef * query_points_generation_loss + \ + 1.0 / (num_decoder_layers + 1) * ( + obj_loss_coef * objectness_loss_sum + box_loss_coef * box_loss_sum + sem_cls_loss_coef * sem_cls_loss_sum) + loss *= 10 + + end_points['loss'] = loss + return loss, end_points diff --git a/models/losses.py b/models/losses.py new file mode 100644 index 0000000..4a3ed3d --- /dev/null +++ b/models/losses.py @@ -0,0 +1,81 @@ +import torch +import torch.nn as nn + + +def smoothl1_loss(error, delta=1.0): + """Smooth L1 loss. + x = error = pred - gt or dist(pred,gt) + 0.5 * |x|^2 if |x|<=d + |x| - 0.5 * d if |x|>d + """ + diff = torch.abs(error) + loss = torch.where(diff < delta, 0.5 * diff * diff / delta, diff - 0.5 * delta) + return loss + + +def l1_loss(error): + loss = torch.abs(error) + return loss + + +class SigmoidFocalClassificationLoss(nn.Module): + """ + Sigmoid focal cross entropy loss. + """ + + def __init__(self, gamma: float = 2.0, alpha: float = 0.25): + """ + Args: + gamma: Weighting parameter to balance loss for hard and easy examples. + alpha: Weighting parameter to balance loss for positive and negative examples. + """ + super(SigmoidFocalClassificationLoss, self).__init__() + self.alpha = alpha + self.gamma = gamma + + @staticmethod + def sigmoid_cross_entropy_with_logits(input: torch.Tensor, target: torch.Tensor): + """ PyTorch Implementation for tf.nn.sigmoid_cross_entropy_with_logits: + max(x, 0) - x * z + log(1 + exp(-abs(x))) in + https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits + + Args: + input: (B, #proposals, #classes) float tensor. + Predicted logits for each class + target: (B, #proposals, #classes) float tensor. + One-hot encoded classification targets + + Returns: + loss: (B, #proposals, #classes) float tensor. + Sigmoid cross entropy loss without reduction + """ + loss = torch.clamp(input, min=0) - input * target + \ + torch.log1p(torch.exp(-torch.abs(input))) + return loss + + def forward(self, input: torch.Tensor, target: torch.Tensor, weights: torch.Tensor): + """ + Args: + input: (B, #proposals, #classes) float tensor. + Predicted logits for each class + target: (B, #proposals, #classes) float tensor. + One-hot encoded classification targets + weights: (B, #proposals) float tensor. + Anchor-wise weights. + + Returns: + weighted_loss: (B, #proposals, #classes) float tensor after weighting. + """ + pred_sigmoid = torch.sigmoid(input) + alpha_weight = target * self.alpha + (1 - target) * (1 - self.alpha) + pt = target * (1.0 - pred_sigmoid) + (1.0 - target) * pred_sigmoid + focal_weight = alpha_weight * torch.pow(pt, self.gamma) + + bce_loss = self.sigmoid_cross_entropy_with_logits(input, target) + + loss = focal_weight * bce_loss + + weights = weights.unsqueeze(-1) + assert weights.shape.__len__() == loss.shape.__len__() + + return loss * weights diff --git a/models/modules.py b/models/modules.py new file mode 100644 index 0000000..04db031 --- /dev/null +++ b/models/modules.py @@ -0,0 +1,193 @@ +import torch +import torch.nn as nn +import numpy as np +import sys +import os + +import torch.nn.functional as F + +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, 'pointnet2')) +import pointnet2_utils + + +class PointsObjClsModule(nn.Module): + def __init__(self, seed_feature_dim): + """ object candidate point prediction from seed point features. + + Args: + seed_feature_dim: int + number of channels of seed point features + """ + super().__init__() + self.in_dim = seed_feature_dim + self.conv1 = torch.nn.Conv1d(self.in_dim, self.in_dim, 1) + self.bn1 = torch.nn.BatchNorm1d(self.in_dim) + self.conv2 = torch.nn.Conv1d(self.in_dim, self.in_dim, 1) + self.bn2 = torch.nn.BatchNorm1d(self.in_dim) + self.conv3 = torch.nn.Conv1d(self.in_dim, 1, 1) + + def forward(self, seed_features): + """ Forward pass. + + Arguments: + seed_features: (batch_size, feature_dim, num_seed) Pytorch tensor + Returns: + logits: (batch_size, 1, num_seed) + """ + net = F.relu(self.bn1(self.conv1(seed_features))) + net = F.relu(self.bn2(self.conv2(net))) + logits = self.conv3(net) # (batch_size, 1, num_seed) + + return logits + + +class PositionEmbeddingLearned(nn.Module): + """ + Absolute pos embedding, learned. + """ + + def __init__(self, input_channel, num_pos_feats=288): + super().__init__() + self.position_embedding_head = nn.Sequential( + nn.Conv1d(input_channel, num_pos_feats, kernel_size=1), + nn.BatchNorm1d(num_pos_feats), + nn.ReLU(inplace=True), + nn.Conv1d(num_pos_feats, num_pos_feats, kernel_size=1)) + + def forward(self, xyz): + xyz = xyz.transpose(1, 2).contiguous() + position_embedding = self.position_embedding_head(xyz) + return position_embedding + + +class FPSModule(nn.Module): + def __init__(self, num_proposal): + super().__init__() + self.num_proposal = num_proposal + + def forward(self, xyz, features): + """ + Args: + xyz: (B,K,3) + features: (B,C,K) + """ + # Farthest point sampling (FPS) + sample_inds = pointnet2_utils.furthest_point_sample(xyz, self.num_proposal) + xyz_flipped = xyz.transpose(1, 2).contiguous() + new_xyz = pointnet2_utils.gather_operation(xyz_flipped, sample_inds).transpose(1, 2).contiguous() + new_features = pointnet2_utils.gather_operation(features, sample_inds).contiguous() + + return new_xyz, new_features, sample_inds + + +class GeneralSamplingModule(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, xyz, features, sample_inds): + """ + Args: + xyz: (B,K,3) + features: (B,C,K) + """ + xyz_flipped = xyz.transpose(1, 2).contiguous() + new_xyz = pointnet2_utils.gather_operation(xyz_flipped, sample_inds).transpose(1, 2).contiguous() + new_features = pointnet2_utils.gather_operation(features, sample_inds).contiguous() + + return new_xyz, new_features, sample_inds + + +class PredictHead(nn.Module): + def __init__(self, num_class, num_heading_bin, num_size_cluster, + mean_size_arr, num_proposal, seed_feat_dim=256): + super().__init__() + + self.num_class = num_class + self.num_heading_bin = num_heading_bin + self.num_size_cluster = num_size_cluster + self.mean_size_arr = mean_size_arr + self.num_proposal = num_proposal + self.seed_feat_dim = seed_feat_dim + + # Object proposal/detection + # Objectness scores (1), center residual (3), + # heading class+residual (num_heading_bin*2), size class+residual(num_size_cluster*4) + self.conv1 = torch.nn.Conv1d(seed_feat_dim, seed_feat_dim, 1) + self.bn1 = torch.nn.BatchNorm1d(seed_feat_dim) + self.conv2 = torch.nn.Conv1d(seed_feat_dim, seed_feat_dim, 1) + self.bn2 = torch.nn.BatchNorm1d(seed_feat_dim) + + self.objectness_scores_head = torch.nn.Conv1d(seed_feat_dim, 1, 1) + self.center_residual_head = torch.nn.Conv1d(seed_feat_dim, 3, 1) + self.heading_class_head = torch.nn.Conv1d(seed_feat_dim, num_heading_bin, 1) + self.heading_residual_head = torch.nn.Conv1d(seed_feat_dim, num_heading_bin, 1) + self.size_class_head = torch.nn.Conv1d(seed_feat_dim, num_size_cluster, 1) + self.size_residual_head = torch.nn.Conv1d(seed_feat_dim, num_size_cluster * 3, 1) + self.sem_cls_scores_head = torch.nn.Conv1d(seed_feat_dim, self.num_class, 1) + + def forward(self, features, base_xyz, end_points, prefix=''): + """ + Args: + features: (B,C,num_proposal) + Returns: + scores: (B,num_proposal,2+3+NH*2+NS*4) + """ + + batch_size = features.shape[0] + num_proposal = features.shape[-1] + net = F.relu(self.bn1(self.conv1(features))) + net = F.relu(self.bn2(self.conv2(net))) + # objectness + objectness_scores = self.objectness_scores_head(net).transpose(2, 1) # (batch_size, num_proposal, 1) + # center + center_residual = self.center_residual_head(net).transpose(2, 1) # (batch_size, num_proposal, 3) + center = base_xyz + center_residual # (batch_size, num_proposal, 3) + + # heading + heading_scores = self.heading_class_head(net).transpose(2, 1) # (batch_size, num_proposal, num_heading_bin) + # (batch_size, num_proposal, num_heading_bin) (should be -1 to 1) + heading_residuals_normalized = self.heading_residual_head(net).transpose(2, 1) + heading_residuals = heading_residuals_normalized * (np.pi / self.num_heading_bin) + + # size + mean_size_arr = torch.from_numpy(self.mean_size_arr.astype(np.float32)).cuda() # (num_size_cluster, 3) + mean_size_arr = mean_size_arr.unsqueeze(0).unsqueeze(0) # (1, 1, num_size_cluster, 3) + size_scores = self.size_class_head(net).transpose(2, 1) # (batch_size, num_proposal, num_size_cluster) + size_residuals_normalized = self.size_residual_head(net).transpose(2, 1).view( + [batch_size, num_proposal, self.num_size_cluster, 3]) # (batch_size, num_proposal, num_size_cluster, 3) + size_residuals = size_residuals_normalized * mean_size_arr # (batch_size, num_proposal, num_size_cluster, 3) + size_recover = size_residuals + mean_size_arr # (batch_size, num_proposal, num_size_cluster, 3) + pred_size_class = torch.argmax(size_scores, -1) # batch_size, num_proposal + pred_size_class = pred_size_class.unsqueeze(-1).unsqueeze(-1).repeat(1, 1, 1, 3) + pred_size = torch.gather(size_recover, 2, pred_size_class) # batch_size, num_proposal, 1, 3 + pred_size = pred_size.squeeze_(2) # batch_size, num_proposal, 3 + + # class + sem_cls_scores = self.sem_cls_scores_head(net).transpose(2, 1) # (batch_size, num_proposal, num_class) + + end_points[f'{prefix}base_xyz'] = base_xyz + end_points[f'{prefix}objectness_scores'] = objectness_scores + end_points[f'{prefix}center'] = center + end_points[f'{prefix}heading_scores'] = heading_scores + end_points[f'{prefix}heading_residuals_normalized'] = heading_residuals_normalized + end_points[f'{prefix}heading_residuals'] = heading_residuals + end_points[f'{prefix}size_scores'] = size_scores + end_points[f'{prefix}size_residuals_normalized'] = size_residuals_normalized + end_points[f'{prefix}size_residuals'] = size_residuals + end_points[f'{prefix}pred_size'] = pred_size + end_points[f'{prefix}sem_cls_scores'] = sem_cls_scores + + # # used to check bbox size + # l = pred_size[:, :, 0] + # h = pred_size[:, :, 1] + # w = pred_size[:, :, 2] + # x_corners = torch.stack([l / 2, l / 2, -l / 2, -l / 2, l / 2, l / 2, -l / 2, -l / 2], -1) # N Pq 8 + # y_corners = torch.stack([h / 2, h / 2, h / 2, h / 2, -h / 2, -h / 2, -h / 2, -h / 2], -1) # N Pq 8 + # z_corners = torch.stack([w / 2, -w / 2, -w / 2, w / 2, w / 2, -w / 2, -w / 2, w / 2], -1) # N Pq 8 + # corners = torch.stack([x_corners, y_corners, z_corners], -1) # N Pq 8 3 + # bbox = center.unsqueeze(2) + corners + # end_points[f'{prefix}bbox_check'] = bbox + return center, pred_size diff --git a/models/multi_head_attention.py b/models/multi_head_attention.py new file mode 100644 index 0000000..0b176c3 --- /dev/null +++ b/models/multi_head_attention.py @@ -0,0 +1,399 @@ +import warnings +import torch +from torch.nn import Linear +from torch.nn.init import xavier_uniform_ +from torch.nn.init import constant_ +from torch.nn.init import xavier_normal_ +from torch.nn.parameter import Parameter +from torch.nn import Module +from torch.nn import functional as F + + +class MultiheadAttention(Module): + r"""Allows the model to jointly attend to information + from different representation subspaces. + See reference: Attention Is All You Need + + .. math:: + \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O + \text{where} head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V) + + Args: + embed_dim: total dimension of the model. + num_heads: parallel attention heads. + dropout: a Dropout layer on attn_output_weights. Default: 0.0. + bias: add bias as module parameter. Default: True. + add_bias_kv: add bias to the key and value sequences at dim=0. + add_zero_attn: add a new batch of zeros to the key and + value sequences at dim=1. + kdim: total number of features in key. Default: None. + vdim: total number of features in key. Default: None. + + Note: if kdim and vdim are None, they will be set to embed_dim such that + query, key, and value have the same number of features. + + Examples:: + + >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads) + >>> attn_output, attn_output_weights = multihead_attn(query, key, value) + """ + + def __init__(self, embed_dim, num_heads, dropout=0., bias=True, add_bias_kv=False, add_zero_attn=False, kdim=None, + vdim=None): + super(MultiheadAttention, self).__init__() + self.embed_dim = embed_dim + self.kdim = kdim if kdim is not None else embed_dim + self.vdim = vdim if vdim is not None else embed_dim + self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim + + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads" + + self.in_proj_weight = Parameter(torch.empty(3 * embed_dim, embed_dim)) + + if self._qkv_same_embed_dim is False: + self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim)) + self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim)) + self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim)) + + if bias: + self.in_proj_bias = Parameter(torch.empty(3 * embed_dim)) + else: + self.register_parameter('in_proj_bias', None) + self.out_proj = Linear(embed_dim, embed_dim, bias=bias) + + if add_bias_kv: + self.bias_k = Parameter(torch.empty(1, 1, embed_dim)) + self.bias_v = Parameter(torch.empty(1, 1, embed_dim)) + else: + self.bias_k = self.bias_v = None + + self.add_zero_attn = add_zero_attn + + self._reset_parameters() + + def _reset_parameters(self): + if self._qkv_same_embed_dim: + xavier_uniform_(self.in_proj_weight) + else: + xavier_uniform_(self.q_proj_weight) + xavier_uniform_(self.k_proj_weight) + xavier_uniform_(self.v_proj_weight) + + if self.in_proj_bias is not None: + constant_(self.in_proj_bias, 0.) + constant_(self.out_proj.bias, 0.) + if self.bias_k is not None: + xavier_normal_(self.bias_k) + if self.bias_v is not None: + xavier_normal_(self.bias_v) + + def forward(self, query, key, value, key_padding_mask=None, need_weights=True, attn_mask=None): + r""" + Args: + query, key, value: map a query and a set of key-value pairs to an output. + See "Attention Is All You Need" for more details. + key_padding_mask: if provided, specified padding elements in the key will + be ignored by the attention. This is an binary mask. When the value is True, + the corresponding value on the attention layer will be filled with -inf. + need_weights: output attn_output_weights. + attn_mask: mask that prevents attention to certain positions. This is an additive mask + (i.e. the values will be added to the attention layer). + + Shape: + - Inputs: + - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is + the embedding dimension. + - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - key_padding_mask: :math:`(N, S)`, ByteTensor, where N is the batch size, S is the source sequence length. + - attn_mask: :math:`(L, S)` where L is the target sequence length, S is the source sequence length. + + - Outputs: + - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, + E is the embedding dimension. + - attn_output_weights: :math:`(N, L, S)` where N is the batch size, + L is the target sequence length, S is the source sequence length. + """ + if hasattr(self, '_qkv_same_embed_dim') and self._qkv_same_embed_dim is False: + return multi_head_attention_forward( + query, key, value, self.embed_dim, self.num_heads, + self.in_proj_weight, self.in_proj_bias, + self.bias_k, self.bias_v, self.add_zero_attn, + self.dropout, self.out_proj.weight, self.out_proj.bias, + training=self.training, + key_padding_mask=key_padding_mask, need_weights=need_weights, + attn_mask=attn_mask, use_separate_proj_weight=True, + q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight, + v_proj_weight=self.v_proj_weight) + else: + if not hasattr(self, '_qkv_same_embed_dim'): + warnings.warn('A new version of MultiheadAttention module has been implemented. \ + Please re-train your model with the new module', + UserWarning) + + return multi_head_attention_forward( + query, key, value, self.embed_dim, self.num_heads, + self.in_proj_weight, self.in_proj_bias, + self.bias_k, self.bias_v, self.add_zero_attn, + self.dropout, self.out_proj.weight, self.out_proj.bias, + training=self.training, + key_padding_mask=key_padding_mask, need_weights=need_weights, + attn_mask=attn_mask) + + +def multi_head_attention_forward(query, # type: Tensor + key, # type: Tensor + value, # type: Tensor + embed_dim_to_check, # type: int + num_heads, # type: int + in_proj_weight, # type: Tensor + in_proj_bias, # type: Tensor + bias_k, # type: Optional[Tensor] + bias_v, # type: Optional[Tensor] + add_zero_attn, # type: bool + dropout_p, # type: float + out_proj_weight, # type: Tensor + out_proj_bias, # type: Tensor + training=True, # type: bool + key_padding_mask=None, # type: Optional[Tensor] + need_weights=True, # type: bool + attn_mask=None, # type: Optional[Tensor] + use_separate_proj_weight=False, # type: bool + q_proj_weight=None, # type: Optional[Tensor] + k_proj_weight=None, # type: Optional[Tensor] + v_proj_weight=None, # type: Optional[Tensor] + static_k=None, # type: Optional[Tensor] + static_v=None, # type: Optional[Tensor] + ): + # type: (...) -> Tuple[Tensor, Optional[Tensor]] + r""" + Args: + query, key, value: map a query and a set of key-value pairs to an output. + See "Attention Is All You Need" for more details. + embed_dim_to_check: total dimension of the model. + num_heads: parallel attention heads. + in_proj_weight, in_proj_bias: input projection weight and bias. + bias_k, bias_v: bias of the key and value sequences to be added at dim=0. + add_zero_attn: add a new batch of zeros to the key and + value sequences at dim=1. + dropout_p: probability of an element to be zeroed. + out_proj_weight, out_proj_bias: the output projection weight and bias. + training: apply dropout if is ``True``. + key_padding_mask: if provided, specified padding elements in the key will + be ignored by the attention. This is an binary mask. When the value is True, + the corresponding value on the attention layer will be filled with -inf. + need_weights: output attn_output_weights. + attn_mask: mask that prevents attention to certain positions. This is an additive mask + (i.e. the values will be added to the attention layer). + use_separate_proj_weight: the function accept the proj. weights for query, key, + and value in differnt forms. If false, in_proj_weight will be used, which is + a combination of q_proj_weight, k_proj_weight, v_proj_weight. + q_proj_weight, k_proj_weight, v_proj_weight, in_proj_bias: input projection weight and bias. + static_k, static_v: static key and value used for attention operators. + + Shape: + Inputs: + - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is + the embedding dimension. + - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is + the embedding dimension. + - key_padding_mask: :math:`(N, S)`, ByteTensor, where N is the batch size, S is the source sequence length. + - attn_mask: :math:`(L, S)` where L is the target sequence length, S is the source sequence length. + - static_k: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length, + N is the batch size, E is the embedding dimension. E/num_heads is the head dimension. + - static_v: :math:`(N*num_heads, S, E/num_heads)`, where S is the source sequence length, + N is the batch size, E is the embedding dimension. E/num_heads is the head dimension. + Outputs: + - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, + E is the embedding dimension. + - attn_output_weights: :math:`(N, L, S)` where N is the batch size, + L is the target sequence length, S is the source sequence length. + """ + + qkv_same = torch.equal(query, key) and torch.equal(key, value) + kv_same = torch.equal(key, value) + + tgt_len, bsz, embed_dim = query.size() + assert embed_dim == embed_dim_to_check + assert list(query.size()) == [tgt_len, bsz, embed_dim] + assert key.size() == value.size() + + head_dim = embed_dim // num_heads + assert head_dim * num_heads == embed_dim, "embed_dim must be divisible by num_heads" + scaling = float(head_dim) ** -0.5 + + if use_separate_proj_weight is not True: + if qkv_same: + # self-attention + q, k, v = F.linear(query, in_proj_weight, in_proj_bias).chunk(3, dim=-1) + + elif kv_same: + # encoder-decoder attention + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = 0 + _end = embed_dim + _w = in_proj_weight[_start:_end, :] + if _b is not None: + _b = _b[_start:_end] + q = F.linear(query, _w, _b) + + if key is None: + assert value is None + k = None + v = None + else: + + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = embed_dim + _end = None + _w = in_proj_weight[_start:, :] + if _b is not None: + _b = _b[_start:] + k, v = F.linear(key, _w, _b).chunk(2, dim=-1) + + else: + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = 0 + _end = embed_dim + _w = in_proj_weight[_start:_end, :] + if _b is not None: + _b = _b[_start:_end] + q = F.linear(query, _w, _b) + + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = embed_dim + _end = embed_dim * 2 + _w = in_proj_weight[_start:_end, :] + if _b is not None: + _b = _b[_start:_end] + k = F.linear(key, _w, _b) + + # This is inline in_proj function with in_proj_weight and in_proj_bias + _b = in_proj_bias + _start = embed_dim * 2 + _end = None + _w = in_proj_weight[_start:, :] + if _b is not None: + _b = _b[_start:] + v = F.linear(value, _w, _b) + else: + q_proj_weight_non_opt = torch.jit._unwrap_optional(q_proj_weight) + len1, len2 = q_proj_weight_non_opt.size() + assert len1 == embed_dim and len2 == query.size(-1) + + k_proj_weight_non_opt = torch.jit._unwrap_optional(k_proj_weight) + len1, len2 = k_proj_weight_non_opt.size() + assert len1 == embed_dim and len2 == key.size(-1) + + v_proj_weight_non_opt = torch.jit._unwrap_optional(v_proj_weight) + len1, len2 = v_proj_weight_non_opt.size() + assert len1 == embed_dim and len2 == value.size(-1) + + if in_proj_bias is not None: + q = F.linear(query, q_proj_weight_non_opt, in_proj_bias[0:embed_dim]) + k = F.linear(key, k_proj_weight_non_opt, in_proj_bias[embed_dim:(embed_dim * 2)]) + v = F.linear(value, v_proj_weight_non_opt, in_proj_bias[(embed_dim * 2):]) + else: + q = F.linear(query, q_proj_weight_non_opt, in_proj_bias) + k = F.linear(key, k_proj_weight_non_opt, in_proj_bias) + v = F.linear(value, v_proj_weight_non_opt, in_proj_bias) + q = q * scaling + + if bias_k is not None and bias_v is not None: + if static_k is None and static_v is None: + k = torch.cat([k, bias_k.repeat(1, bsz, 1)]) + v = torch.cat([v, bias_v.repeat(1, bsz, 1)]) + if attn_mask is not None: + attn_mask = torch.cat([attn_mask, + torch.zeros((attn_mask.size(0), 1), + dtype=attn_mask.dtype, + device=attn_mask.device)], dim=1) + if key_padding_mask is not None: + key_padding_mask = torch.cat( + [key_padding_mask, torch.zeros((key_padding_mask.size(0), 1), + dtype=key_padding_mask.dtype, + device=key_padding_mask.device)], dim=1) + else: + assert static_k is None, "bias cannot be added to static key." + assert static_v is None, "bias cannot be added to static value." + else: + assert bias_k is None + assert bias_v is None + + q = q.contiguous().view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1) + if k is not None: + k = k.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1) + if v is not None: + v = v.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1) + + if static_k is not None: + assert static_k.size(0) == bsz * num_heads + assert static_k.size(2) == head_dim + k = static_k + + if static_v is not None: + assert static_v.size(0) == bsz * num_heads + assert static_v.size(2) == head_dim + v = static_v + + src_len = k.size(1) + + if key_padding_mask is not None: + assert key_padding_mask.size(0) == bsz + assert key_padding_mask.size(1) == src_len + + if add_zero_attn: + src_len += 1 + k = torch.cat([k, torch.zeros((k.size(0), 1) + k.size()[2:], dtype=k.dtype, device=k.device)], dim=1) + v = torch.cat([v, torch.zeros((v.size(0), 1) + v.size()[2:], dtype=v.dtype, device=v.device)], dim=1) + if attn_mask is not None: + attn_mask = torch.cat([attn_mask, torch.zeros((attn_mask.size(0), 1), + dtype=attn_mask.dtype, + device=attn_mask.device)], dim=1) + if key_padding_mask is not None: + key_padding_mask = torch.cat( + [key_padding_mask, torch.zeros((key_padding_mask.size(0), 1), + dtype=key_padding_mask.dtype, + device=key_padding_mask.device)], dim=1) + + attn_output_weights = torch.bmm(q, k.transpose(1, 2)) + assert list(attn_output_weights.size()) == [bsz * num_heads, tgt_len, src_len] + + if attn_mask is not None: + attn_mask = attn_mask.unsqueeze(0) + attn_output_weights += attn_mask + + if key_padding_mask is not None: + attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len) + attn_output_weights = attn_output_weights.masked_fill( + key_padding_mask.unsqueeze(1).unsqueeze(2), + float('-inf'), + ) + attn_output_weights = attn_output_weights.view(bsz * num_heads, tgt_len, src_len) + + attn_output_weights = F.softmax( + attn_output_weights, dim=-1) + attn_output_weights = F.dropout(attn_output_weights, p=dropout_p, training=training) + + attn_output = torch.bmm(attn_output_weights, v) + assert list(attn_output.size()) == [bsz * num_heads, tgt_len, head_dim] + attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim) + attn_output = F.linear(attn_output, out_proj_weight, out_proj_bias) + + if need_weights: + # average attention weights over heads + attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len) + return attn_output, attn_output_weights.sum(dim=1) / num_heads + else: + return attn_output, None diff --git a/models/transformer.py b/models/transformer.py new file mode 100644 index 0000000..5dfd304 --- /dev/null +++ b/models/transformer.py @@ -0,0 +1,87 @@ +import torch +import torch.nn as nn + +import torch.nn.functional as F +from torch import Tensor +from typing import Optional +from multi_head_attention import MultiheadAttention + + +class TransformerDecoderLayer(nn.Module): + def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation="relu", + self_posembed=None, cross_posembed=None): + super().__init__() + self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout) + self.multihead_attn = MultiheadAttention(d_model, nhead, dropout=dropout) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.norm3 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + self.dropout3 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + + self.self_posembed = self_posembed + self.cross_posembed = cross_posembed + + def with_pos_embed(self, tensor, pos_embed: Optional[Tensor]): + return tensor if pos_embed is None else tensor + pos_embed + + def forward(self, query, key, query_pos, key_pos): + """ + :param query: B C Pq + :param key: B C Pk + :param query_pos: B Pq 3/6 + :param key_pos: B Pk 3/6 + :param value_pos: [B Pq 3/6] + + :return: + """ + # NxCxP to PxNxC + if self.self_posembed is not None: + query_pos_embed = self.self_posembed(query_pos).permute(2, 0, 1) + else: + query_pos_embed = None + if self.cross_posembed is not None: + key_pos_embed = self.cross_posembed(key_pos).permute(2, 0, 1) + else: + key_pos_embed = None + + query = query.permute(2, 0, 1) + key = key.permute(2, 0, 1) + + q = k = v = self.with_pos_embed(query, query_pos_embed) + query2 = self.self_attn(q, k, value=v)[0] + query = query + self.dropout1(query2) + query = self.norm1(query) + + query2 = self.multihead_attn(query=self.with_pos_embed(query, query_pos_embed), + key=self.with_pos_embed(key, key_pos_embed), + value=self.with_pos_embed(key, key_pos_embed))[0] + query = query + self.dropout2(query2) + query = self.norm2(query) + + query2 = self.linear2(self.dropout(self.activation(self.linear1(query)))) + query = query + self.dropout3(query2) + query = self.norm3(query) + + # NxCxP to PxNxC + query = query.permute(1, 2, 0) + return query + + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(F"activation should be relu/gelu, not {activation}.") diff --git a/pointnet2/_ext_src/include/ball_query.h b/pointnet2/_ext_src/include/ball_query.h new file mode 100644 index 0000000..4a65b5a --- /dev/null +++ b/pointnet2/_ext_src/include/ball_query.h @@ -0,0 +1,10 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +#pragma once +#include + +at::Tensor ball_query(at::Tensor new_xyz, at::Tensor xyz, const float radius, + const int nsample); diff --git a/pointnet2/_ext_src/include/cuda_utils.h b/pointnet2/_ext_src/include/cuda_utils.h new file mode 100644 index 0000000..d4c4bb4 --- /dev/null +++ b/pointnet2/_ext_src/include/cuda_utils.h @@ -0,0 +1,46 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +#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 diff --git a/pointnet2/_ext_src/include/group_points.h b/pointnet2/_ext_src/include/group_points.h new file mode 100644 index 0000000..24e7cc7 --- /dev/null +++ b/pointnet2/_ext_src/include/group_points.h @@ -0,0 +1,10 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +#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); diff --git a/pointnet2/_ext_src/include/interpolate.h b/pointnet2/_ext_src/include/interpolate.h new file mode 100644 index 0000000..2af34c6 --- /dev/null +++ b/pointnet2/_ext_src/include/interpolate.h @@ -0,0 +1,15 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +#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); diff --git a/pointnet2/_ext_src/include/sampling.h b/pointnet2/_ext_src/include/sampling.h new file mode 100644 index 0000000..366ef31 --- /dev/null +++ b/pointnet2/_ext_src/include/sampling.h @@ -0,0 +1,11 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +#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); diff --git a/pointnet2/_ext_src/include/utils.h b/pointnet2/_ext_src/include/utils.h new file mode 100644 index 0000000..f730591 --- /dev/null +++ b/pointnet2/_ext_src/include/utils.h @@ -0,0 +1,30 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +#pragma once +#include +#include + +#define CHECK_CUDA(x) \ + do { \ + TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor"); \ + } while (0) + +#define CHECK_CONTIGUOUS(x) \ + do { \ + TORCH_CHECK(x.is_contiguous(), #x " must be a contiguous tensor"); \ + } while (0) + +#define CHECK_IS_INT(x) \ + do { \ + TORCH_CHECK(x.scalar_type() == at::ScalarType::Int, \ + #x " must be an int tensor"); \ + } while (0) + +#define CHECK_IS_FLOAT(x) \ + do { \ + TORCH_CHECK(x.scalar_type() == at::ScalarType::Float, \ + #x " must be a float tensor"); \ + } while (0) diff --git a/pointnet2/_ext_src/src/ball_query.cpp b/pointnet2/_ext_src/src/ball_query.cpp new file mode 100644 index 0000000..d486fb7 --- /dev/null +++ b/pointnet2/_ext_src/src/ball_query.cpp @@ -0,0 +1,37 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +#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.device().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.device().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 { + TORCH_CHECK(false, "CPU not supported"); + } + + return idx; +} diff --git a/pointnet2/_ext_src/src/ball_query_gpu.cu b/pointnet2/_ext_src/src/ball_query_gpu.cu new file mode 100644 index 0000000..cfc2eeb --- /dev/null +++ b/pointnet2/_ext_src/src/ball_query_gpu.cu @@ -0,0 +1,59 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +#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(); +} diff --git a/pointnet2/_ext_src/src/bindings.cpp b/pointnet2/_ext_src/src/bindings.cpp new file mode 100644 index 0000000..9783d87 --- /dev/null +++ b/pointnet2/_ext_src/src/bindings.cpp @@ -0,0 +1,24 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +#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); +} diff --git a/pointnet2/_ext_src/src/group_points.cpp b/pointnet2/_ext_src/src/group_points.cpp new file mode 100644 index 0000000..abe2352 --- /dev/null +++ b/pointnet2/_ext_src/src/group_points.cpp @@ -0,0 +1,65 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +#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.device().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.device().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 { + TORCH_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.device().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.device().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 { + TORCH_CHECK(false, "CPU not supported"); + } + + return output; +} diff --git a/pointnet2/_ext_src/src/group_points_gpu.cu b/pointnet2/_ext_src/src/group_points_gpu.cu new file mode 100644 index 0000000..98a3be1 --- /dev/null +++ b/pointnet2/_ext_src/src/group_points_gpu.cu @@ -0,0 +1,80 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +#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(); +} diff --git a/pointnet2/_ext_src/src/interpolate.cpp b/pointnet2/_ext_src/src/interpolate.cpp new file mode 100644 index 0000000..a92fa9c --- /dev/null +++ b/pointnet2/_ext_src/src/interpolate.cpp @@ -0,0 +1,104 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +#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.device().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.device().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 { + TORCH_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.device().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.device().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 { + TORCH_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.device().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.device().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 { + TORCH_CHECK(false, "CPU not supported"); + } + + return output; +} diff --git a/pointnet2/_ext_src/src/interpolate_gpu.cu b/pointnet2/_ext_src/src/interpolate_gpu.cu new file mode 100644 index 0000000..b13dbfa --- /dev/null +++ b/pointnet2/_ext_src/src/interpolate_gpu.cu @@ -0,0 +1,159 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +#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(); +} diff --git a/pointnet2/_ext_src/src/sampling.cpp b/pointnet2/_ext_src/src/sampling.cpp new file mode 100644 index 0000000..30e62e7 --- /dev/null +++ b/pointnet2/_ext_src/src/sampling.cpp @@ -0,0 +1,91 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +#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.device().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.device().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 { + TORCH_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.device().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.device().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 { + TORCH_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.device().is_cuda()) { + furthest_point_sampling_kernel_wrapper( + points.size(0), points.size(1), nsamples, points.data_ptr(), + tmp.data_ptr(), output.data_ptr()); + } else { + TORCH_CHECK(false, "CPU not supported"); + } + + return output; +} diff --git a/pointnet2/_ext_src/src/sampling_gpu.cu b/pointnet2/_ext_src/src/sampling_gpu.cu new file mode 100644 index 0000000..e2f5806 --- /dev/null +++ b/pointnet2/_ext_src/src/sampling_gpu.cu @@ -0,0 +1,234 @@ +// Copyright (c) Facebook, Inc. and its affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +#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(); +} diff --git a/pointnet2/pointnet2_modules.py b/pointnet2/pointnet2_modules.py new file mode 100644 index 0000000..bfb4c3e --- /dev/null +++ b/pointnet2/pointnet2_modules.py @@ -0,0 +1,518 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +''' Pointnet2 layers. +Modified based on: https://github.com/erikwijmans/Pointnet2_PyTorch +Extended with the following: +1. Uniform sampling in each local region (sample_uniformly) +2. Return sampled points indices to support votenet. +''' +import torch +import torch.nn as nn +import torch.nn.functional as F + +import os +import sys +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(BASE_DIR) + +import pointnet2_utils +import pytorch_utils as pt_utils +from typing import List + + +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 features + features : torch.Tensor + (B, N, C) 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, npoint, \sum_k(mlps[k][-1])) 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: int, + radii: List[float], + nsamples: List[int], + mlps: List[List[int]], + bn: bool = True, + use_xyz: bool = True, + sample_uniformly: bool = False + ): + super().__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, sample_uniformly=sample_uniformly) + 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: List[int], + npoint: int = None, + radius: float = None, + nsample: int = None, + bn: bool = True, + use_xyz: bool = True + ): + super().__init__( + mlps=[mlp], + npoint=npoint, + radii=[radius], + nsamples=[nsample], + bn=bn, + use_xyz=use_xyz + ) + + +class PointnetSAModuleVotes(nn.Module): + ''' Modified based on _PointnetSAModuleBase and PointnetSAModuleMSG + with extra support for returning point indices for getting their GT votes ''' + + def __init__( + self, + *, + mlp: List[int], + npoint: int = None, + radius: float = None, + nsample: int = None, + bn: bool = True, + use_xyz: bool = True, + pooling: str = 'max', + sigma: float = None, # for RBF pooling + normalize_xyz: bool = False, # noramlize local XYZ with radius + sample_uniformly: bool = False, + ret_unique_cnt: bool = False + ): + super().__init__() + + self.npoint = npoint + self.radius = radius + self.nsample = nsample + self.pooling = pooling + self.mlp_module = None + self.use_xyz = use_xyz + self.sigma = sigma + if self.sigma is None: + self.sigma = self.radius/2 + self.normalize_xyz = normalize_xyz + self.ret_unique_cnt = ret_unique_cnt + + if npoint is not None: + self.grouper = pointnet2_utils.QueryAndGroup(radius, nsample, + use_xyz=use_xyz, ret_grouped_xyz=True, normalize_xyz=normalize_xyz, + sample_uniformly=sample_uniformly, ret_unique_cnt=ret_unique_cnt) + else: + self.grouper = pointnet2_utils.GroupAll(use_xyz, ret_grouped_xyz=True) + + mlp_spec = mlp + if use_xyz and len(mlp_spec)>0: + mlp_spec[0] += 3 + self.mlp_module = pt_utils.SharedMLP(mlp_spec, bn=bn) + + + def forward(self, xyz: torch.Tensor, + features: torch.Tensor = None, + inds: torch.Tensor = None) -> (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 + inds : torch.Tensor + (B, npoint) tensor that stores index to the xyz points (values in 0-N-1) + + 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 + inds: torch.Tensor + (B, npoint) tensor of the inds + """ + + xyz_flipped = xyz.transpose(1, 2).contiguous() + if inds is None: + inds = pointnet2_utils.furthest_point_sample(xyz, self.npoint) + else: + assert(inds.shape[1] == self.npoint) + new_xyz = pointnet2_utils.gather_operation( + xyz_flipped, inds + ).transpose(1, 2).contiguous() if self.npoint is not None else None + + if not self.ret_unique_cnt: + grouped_features, grouped_xyz = self.grouper( + xyz, new_xyz, features + ) # (B, C, npoint, nsample) + else: + grouped_features, grouped_xyz, unique_cnt = self.grouper( + xyz, new_xyz, features + ) # (B, C, npoint, nsample), (B,3,npoint,nsample), (B,npoint) + + new_features = self.mlp_module( + grouped_features + ) # (B, mlp[-1], npoint, nsample) + if self.pooling == 'max': + new_features = F.max_pool2d( + new_features, kernel_size=[1, new_features.size(3)] + ) # (B, mlp[-1], npoint, 1) + elif self.pooling == 'avg': + new_features = F.avg_pool2d( + new_features, kernel_size=[1, new_features.size(3)] + ) # (B, mlp[-1], npoint, 1) + elif self.pooling == 'rbf': + # Use radial basis function kernel for weighted sum of features (normalized by nsample and sigma) + # Ref: https://en.wikipedia.org/wiki/Radial_basis_function_kernel + rbf = torch.exp(-1 * grouped_xyz.pow(2).sum(1,keepdim=False) / (self.sigma**2) / 2) # (B, npoint, nsample) + new_features = torch.sum(new_features * rbf.unsqueeze(1), -1, keepdim=True) / float(self.nsample) # (B, mlp[-1], npoint, 1) + new_features = new_features.squeeze(-1) # (B, mlp[-1], npoint) + + if not self.ret_unique_cnt: + return new_xyz, new_features, inds + else: + return new_xyz, new_features, inds, unique_cnt + +class PointnetSAModuleMSGVotes(nn.Module): + ''' Modified based on _PointnetSAModuleBase and PointnetSAModuleMSG + with extra support for returning point indices for getting their GT votes ''' + + def __init__( + self, + *, + mlps: List[List[int]], + npoint: int, + radii: List[float], + nsamples: List[int], + bn: bool = True, + use_xyz: bool = True, + sample_uniformly: bool = False + ): + super().__init__() + + assert(len(mlps) == len(nsamples) == len(radii)) + + 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, sample_uniformly=sample_uniformly) + 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)) + + def forward(self, xyz: torch.Tensor, + features: torch.Tensor = None, inds: torch.Tensor = None) -> (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, C) tensor of the descriptors of the the features + inds : torch.Tensor + (B, npoint) tensor that stores index to the xyz points (values in 0-N-1) + + 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 + inds: torch.Tensor + (B, npoint) tensor of the inds + """ + new_features_list = [] + + xyz_flipped = xyz.transpose(1, 2).contiguous() + if inds is None: + inds = pointnet2_utils.furthest_point_sample(xyz, self.npoint) + new_xyz = pointnet2_utils.gather_operation( + xyz_flipped, inds + ).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), inds + + +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 + """ + + 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) + +class PointnetLFPModuleMSG(nn.Module): + ''' Modified based on _PointnetSAModuleBase and PointnetSAModuleMSG + learnable feature propagation layer.''' + + def __init__( + self, + *, + mlps: List[List[int]], + radii: List[float], + nsamples: List[int], + post_mlp: List[int], + bn: bool = True, + use_xyz: bool = True, + sample_uniformly: bool = False + ): + super().__init__() + + assert(len(mlps) == len(nsamples) == len(radii)) + + self.post_mlp = pt_utils.SharedMLP(post_mlp, bn=bn) + + 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, + sample_uniformly=sample_uniformly) + ) + mlp_spec = mlps[i] + if use_xyz: + mlp_spec[0] += 3 + + self.mlps.append(pt_utils.SharedMLP(mlp_spec, bn=bn)) + + def forward(self, xyz2: torch.Tensor, xyz1: torch.Tensor, + features2: torch.Tensor, features1: torch.Tensor) -> torch.Tensor: + r""" Propagate features from xyz1 to xyz2. + Parameters + ---------- + xyz2 : torch.Tensor + (B, N2, 3) tensor of the xyz coordinates of the features + xyz1 : torch.Tensor + (B, N1, 3) tensor of the xyz coordinates of the features + features2 : torch.Tensor + (B, C2, N2) tensor of the descriptors of the the features + features1 : torch.Tensor + (B, C1, N1) tensor of the descriptors of the the features + + Returns + ------- + new_features1 : torch.Tensor + (B, \sum_k(mlps[k][-1]), N1) tensor of the new_features descriptors + """ + new_features_list = [] + + for i in range(len(self.groupers)): + new_features = self.groupers[i]( + xyz1, xyz2, features1 + ) # (B, C1, N2, nsample) + new_features = self.mlps[i]( + new_features + ) # (B, mlp[-1], N2, nsample) + new_features = F.max_pool2d( + new_features, kernel_size=[1, new_features.size(3)] + ) # (B, mlp[-1], N2, 1) + new_features = new_features.squeeze(-1) # (B, mlp[-1], N2) + + if features2 is not None: + new_features = torch.cat([new_features, features2], + dim=1) #(B, mlp[-1] + C2, N2) + + new_features = new_features.unsqueeze(-1) + new_features = self.post_mlp(new_features) + + new_features_list.append(new_features) + + return torch.cat(new_features_list, dim=1).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)) + + 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) diff --git a/pointnet2/pointnet2_test.py b/pointnet2/pointnet2_test.py new file mode 100644 index 0000000..be60b28 --- /dev/null +++ b/pointnet2/pointnet2_test.py @@ -0,0 +1,33 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +''' Testing customized ops. ''' + +import torch +from torch.autograd import gradcheck +import numpy as np + +import os +import sys +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(BASE_DIR) +import pointnet2_utils + +def test_interpolation_grad(): + batch_size = 1 + feat_dim = 2 + m = 4 + feats = torch.randn(batch_size, feat_dim, m, requires_grad=True).float().cuda() + + def interpolate_func(inputs): + idx = torch.from_numpy(np.array([[[0,1,2],[1,2,3]]])).int().cuda() + weight = torch.from_numpy(np.array([[[1,1,1],[2,2,2]]])).float().cuda() + interpolated_feats = pointnet2_utils.three_interpolate(inputs, idx, weight) + return interpolated_feats + + assert (gradcheck(interpolate_func, feats, atol=1e-1, rtol=1e-1)) + +if __name__=='__main__': + test_interpolation_grad() diff --git a/pointnet2/pointnet2_utils.py b/pointnet2/pointnet2_utils.py new file mode 100644 index 0000000..7ade909 --- /dev/null +++ b/pointnet2/pointnet2_utils.py @@ -0,0 +1,425 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +''' Modified based on: https://github.com/erikwijmans/Pointnet2_PyTorch ''' +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 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 + """ + fps_inds = _ext.furthest_point_sampling(xyz, npoint) + ctx.mark_non_differentiable(fps_inds) + return fps_inds + + @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 + """ + inds = _ext.ball_query(new_xyz, xyz, radius, nsample) + ctx.mark_non_differentiable(inds) + return inds + + @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, ret_grouped_xyz=False, normalize_xyz=False, sample_uniformly=False, ret_unique_cnt=False): + # type: (QueryAndGroup, float, int, bool) -> None + super(QueryAndGroup, self).__init__() + self.radius, self.nsample, self.use_xyz = radius, nsample, use_xyz + self.ret_grouped_xyz = ret_grouped_xyz + self.normalize_xyz = normalize_xyz + self.sample_uniformly = sample_uniformly + self.ret_unique_cnt = ret_unique_cnt + if self.ret_unique_cnt: + assert(self.sample_uniformly) + + 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) + + if self.sample_uniformly: + unique_cnt = torch.zeros((idx.shape[0], idx.shape[1])) + for i_batch in range(idx.shape[0]): + for i_region in range(idx.shape[1]): + unique_ind = torch.unique(idx[i_batch, i_region, :]) + num_unique = unique_ind.shape[0] + unique_cnt[i_batch, i_region] = num_unique + sample_ind = torch.randint(0, num_unique, (self.nsample - num_unique,), dtype=torch.long) + all_ind = torch.cat((unique_ind, unique_ind[sample_ind])) + idx[i_batch, i_region, :] = all_ind + + + 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 self.normalize_xyz: + grouped_xyz /= self.radius + + 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 + + ret = [new_features] + if self.ret_grouped_xyz: + ret.append(grouped_xyz) + if self.ret_unique_cnt: + ret.append(unique_cnt) + if len(ret) == 1: + return ret[0] + else: + return tuple(ret) + + +class GroupAll(nn.Module): + r""" + Groups all features + + Parameters + --------- + """ + + def __init__(self, use_xyz=True, ret_grouped_xyz=False): + # 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 + + if self.ret_grouped_xyz: + return new_features, grouped_xyz + else: + return new_features diff --git a/pointnet2/pytorch_utils.py b/pointnet2/pytorch_utils.py new file mode 100644 index 0000000..b9c9263 --- /dev/null +++ b/pointnet2/pytorch_utils.py @@ -0,0 +1,298 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +''' Modified based on Ref: https://github.com/erikwijmans/Pointnet2_PyTorch ''' +import torch +import torch.nn as nn +from typing import List, Tuple + +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) + +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))) + + diff --git a/pointnet2/setup.py b/pointnet2/setup.py new file mode 100644 index 0000000..2430d8f --- /dev/null +++ b/pointnet2/setup.py @@ -0,0 +1,34 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +from setuptools import setup +from torch.utils.cpp_extension import BuildExtension, CUDAExtension +import glob +import os.path as osp + +_ext_src_root = "_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)) +this_dir = osp.dirname(osp.abspath(__file__)) + +setup( + name='pointnet2', + 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))], + }, + include_dirs=[osp.join(this_dir, _ext_src_root, "include")], + ) + ], + cmdclass={ + 'build_ext': BuildExtension + } +) diff --git a/scannet/README.md b/scannet/README.md new file mode 100644 index 0000000..d0c96a0 --- /dev/null +++ b/scannet/README.md @@ -0,0 +1,5 @@ +### Prepare ScanNet Data + +1. Download ScanNet v2 data [HERE](https://github.com/ScanNet/ScanNet). Move/link the `scans` folder such that under `scans` there should be folders with names such as `scene0001_01`. + +2. Extract point clouds and annotations (semantic seg, instance seg etc.) by running `python batch_load_scannet_data.py`, which will create a folder named `scannet_train_detection_data` here. diff --git a/scannet/batch_load_scannet_data.py b/scannet/batch_load_scannet_data.py new file mode 100644 index 0000000..7315765 --- /dev/null +++ b/scannet/batch_load_scannet_data.py @@ -0,0 +1,83 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" Batch mode in loading Scannet scenes with vertices and ground truth labels +for semantic and instance segmentations + +Usage example: python ./batch_load_scannet_data.py +""" +import os +import sys +import datetime +import numpy as np +from load_scannet_data import export +import pdb + +SCANNET_DIR = 'scans' +TRAIN_SCAN_NAMES = [line.rstrip() for line in open('meta_data/scannet_train.txt')] +LABEL_MAP_FILE = 'meta_data/scannetv2-labels.combined.tsv' +DONOTCARE_CLASS_IDS = np.array([]) +OBJ_CLASS_IDS = np.array([3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28, 33, 34, 36, 39]) +MAX_NUM_POINT = 50000 +OUTPUT_FOLDER = './scannet_train_detection_data' + + +def export_one_scan(scan_name, output_filename_prefix): + mesh_file = os.path.join(SCANNET_DIR, scan_name, scan_name + '_vh_clean_2.ply') + agg_file = os.path.join(SCANNET_DIR, scan_name, scan_name + '.aggregation.json') + seg_file = os.path.join(SCANNET_DIR, scan_name, scan_name + '_vh_clean_2.0.010000.segs.json') + meta_file = os.path.join(SCANNET_DIR, scan_name, + scan_name + '.txt') # includes axisAlignment info for the train set scans. + mesh_vertices, semantic_labels, instance_labels, instance_bboxes, instance2semantic = \ + export(mesh_file, agg_file, seg_file, meta_file, LABEL_MAP_FILE, None) + + mask = np.logical_not(np.in1d(semantic_labels, DONOTCARE_CLASS_IDS)) + mesh_vertices = mesh_vertices[mask, :] + semantic_labels = semantic_labels[mask] + instance_labels = instance_labels[mask] + + num_instances = len(np.unique(instance_labels)) + print('Num of instances: ', num_instances) + + bbox_mask = np.in1d(instance_bboxes[:, -1], OBJ_CLASS_IDS) + instance_bboxes = instance_bboxes[bbox_mask, :] + print('Num of care instances: ', instance_bboxes.shape[0]) + + N = mesh_vertices.shape[0] + if N > MAX_NUM_POINT: + choices = np.random.choice(N, MAX_NUM_POINT, replace=False) + mesh_vertices = mesh_vertices[choices, :] + semantic_labels = semantic_labels[choices] + instance_labels = instance_labels[choices] + + np.save(output_filename_prefix + '_vert.npy', mesh_vertices) + np.save(output_filename_prefix + '_sem_label.npy', semantic_labels) + np.save(output_filename_prefix + '_ins_label.npy', instance_labels) + np.save(output_filename_prefix + '_bbox.npy', instance_bboxes) + + +def batch_export(): + if not os.path.exists(OUTPUT_FOLDER): + print('Creating new data folder: {}'.format(OUTPUT_FOLDER)) + os.mkdir(OUTPUT_FOLDER) + + for scan_name in TRAIN_SCAN_NAMES: + print('-' * 20 + 'begin') + print(datetime.datetime.now()) + print(scan_name) + output_filename_prefix = os.path.join(OUTPUT_FOLDER, scan_name) + if os.path.isfile(output_filename_prefix + '_vert.npy'): + print('File already exists. skipping.') + print('-' * 20 + 'done') + continue + try: + export_one_scan(scan_name, output_filename_prefix) + except: + print('Failed export scan: %s' % (scan_name)) + print('-' * 20 + 'done') + + +if __name__ == '__main__': + batch_export() diff --git a/scannet/data_viz.py b/scannet/data_viz.py new file mode 100644 index 0000000..e783044 --- /dev/null +++ b/scannet/data_viz.py @@ -0,0 +1,39 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import sys +import os + +BASE_DIR = os.path.dirname(__file__) +sys.path.append(BASE_DIR) + +import numpy as np +import pc_util + +scene_name = 'scannet_train_detection_data/scene0002_00' +output_folder = 'data_viz_dump' + +data = np.load(scene_name + '_vert.npy') +scene_points = data[:, 0:3] +colors = data[:, 3:] +instance_labels = np.load(scene_name + '_ins_label.npy') +semantic_labels = np.load(scene_name + '_sem_label.npy') +instance_bboxes = np.load(scene_name + '_bbox.npy') + +print(np.unique(instance_labels)) +print(np.unique(semantic_labels)) +input() +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')) + +from model_util_scannet import ScannetDatasetConfig + +DC = ScannetDatasetConfig() +print(instance_bboxes.shape) diff --git a/scannet/load_scannet_data.py b/scannet/load_scannet_data.py new file mode 100644 index 0000000..93476ea --- /dev/null +++ b/scannet/load_scannet_data.py @@ -0,0 +1,149 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" Load Scannet scenes with vertices and ground truth labels +for semantic and instance segmentations +""" + +# python imports +import math +import os, sys, argparse +import inspect +import json +import pdb + +try: + import numpy as np +except: + print("Failed to import numpy package.") + sys.exit(-1) + +currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) +import scannet_utils + + +def read_aggregation(filename): + assert os.path.isfile(filename) + object_id_to_segs = {} + label_to_segs = {} + with open(filename) as f: + data = json.load(f) + num_objects = len(data['segGroups']) + for i in range(num_objects): + object_id = data['segGroups'][i]['objectId'] + 1 # instance ids should be 1-indexed + label = data['segGroups'][i]['label'] + segs = data['segGroups'][i]['segments'] + object_id_to_segs[object_id] = segs + if label in label_to_segs: + label_to_segs[label].extend(segs) + else: + label_to_segs[label] = segs + return object_id_to_segs, label_to_segs + + +def read_segmentation(filename): + assert os.path.isfile(filename) + seg_to_verts = {} + with open(filename) as f: + data = json.load(f) + num_verts = len(data['segIndices']) + for i in range(num_verts): + seg_id = data['segIndices'][i] + if seg_id in seg_to_verts: + seg_to_verts[seg_id].append(i) + else: + seg_to_verts[seg_id] = [i] + return seg_to_verts, num_verts + + +def export(mesh_file, agg_file, seg_file, meta_file, label_map_file, output_file=None): + """ points are XYZ RGB (RGB in 0-255), + semantic label as nyu40 ids, + instance label as 1-#instance, + box as (cx,cy,cz,dx,dy,dz,semantic_label) + """ + label_map = scannet_utils.read_label_mapping(label_map_file, + label_from='raw_category', label_to='nyu40id') + mesh_vertices = scannet_utils.read_mesh_vertices_rgb(mesh_file) + + # Load scene axis alignment matrix + lines = open(meta_file).readlines() + for line in lines: + if 'axisAlignment' in line: + axis_align_matrix = [float(x) \ + for x in line.rstrip().strip('axisAlignment = ').split(' ')] + break + axis_align_matrix = np.array(axis_align_matrix).reshape((4, 4)) + pts = np.ones((mesh_vertices.shape[0], 4)) + pts[:, 0:3] = mesh_vertices[:, 0:3] + pts = np.dot(pts, axis_align_matrix.transpose()) # Nx4 + mesh_vertices[:, 0:3] = pts[:, 0:3] + + # Load semantic and instance labels + object_id_to_segs, label_to_segs = read_aggregation(agg_file) + seg_to_verts, num_verts = read_segmentation(seg_file) + label_ids = np.zeros(shape=(num_verts), dtype=np.uint32) # 0: unannotated + object_id_to_label_id = {} + for label, segs in label_to_segs.items(): + label_id = label_map[label] + for seg in segs: + verts = seg_to_verts[seg] + label_ids[verts] = label_id + instance_ids = np.zeros(shape=(num_verts), dtype=np.uint32) # 0: unannotated + num_instances = len(np.unique(list(object_id_to_segs.keys()))) + for object_id, segs in object_id_to_segs.items(): + for seg in segs: + verts = seg_to_verts[seg] + instance_ids[verts] = object_id + if object_id not in object_id_to_label_id: + object_id_to_label_id[object_id] = label_ids[verts][0] + instance_bboxes = np.zeros((num_instances, 7)) + for obj_id in object_id_to_segs: + label_id = object_id_to_label_id[obj_id] + obj_pc = mesh_vertices[instance_ids == obj_id, 0:3] + if len(obj_pc) == 0: continue + # Compute axis aligned box + # An axis aligned bounding box is parameterized by + # (cx,cy,cz) and (dx,dy,dz) and label id + # where (cx,cy,cz) is the center point of the box, + # dx is the x-axis length of the box. + xmin = np.min(obj_pc[:, 0]) + ymin = np.min(obj_pc[:, 1]) + zmin = np.min(obj_pc[:, 2]) + xmax = np.max(obj_pc[:, 0]) + ymax = np.max(obj_pc[:, 1]) + zmax = np.max(obj_pc[:, 2]) + bbox = np.array([(xmin + xmax) / 2, (ymin + ymax) / 2, (zmin + zmax) / 2, + xmax - xmin, ymax - ymin, zmax - zmin, label_id]) + # NOTE: this assumes obj_id is in 1,2,3,.,,,.NUM_INSTANCES + instance_bboxes[obj_id - 1, :] = bbox + + if output_file is not None: + np.save(output_file + '_vert.npy', mesh_vertices) + np.save(output_file + '_sem_label.npy', label_ids) + np.save(output_file + '_ins_label.npy', instance_ids) + np.save(output_file + '_bbox.npy', instance_bboxes) + + return mesh_vertices, label_ids, instance_ids, \ + instance_bboxes, object_id_to_label_id + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--scan_path', required=True, help='path to scannet scene (e.g., data/ScanNet/v2/scene0000_00') + parser.add_argument('--output_file', required=True, help='output file') + parser.add_argument('--label_map_file', required=True, help='path to scannetv2-labels.combined.tsv') + opt = parser.parse_args() + + scan_name = os.path.split(opt.scan_path)[-1] + mesh_file = os.path.join(opt.scan_path, scan_name + '_vh_clean_2.ply') + agg_file = os.path.join(opt.scan_path, scan_name + '.aggregation.json') + seg_file = os.path.join(opt.scan_path, scan_name + '_vh_clean_2.0.010000.segs.json') + meta_file = os.path.join(opt.scan_path, scan_name + '.txt') # includes axisAlignment info for the train set scans. + export(mesh_file, agg_file, seg_file, meta_file, opt.label_map_file, opt.output_file) + + +if __name__ == '__main__': + main() diff --git a/scannet/meta_data/scannet_means.npz b/scannet/meta_data/scannet_means.npz new file mode 100644 index 0000000000000000000000000000000000000000..e57647c9a3553ca4653a9d1e53ed4a2a58def822 GIT binary patch literal 676 zcmWIWW@Zs#fB;2?qMfn(4VV}hK$sIKm{?R4Z=jb~P&wHz)HfiKk)e#CT0JGTIJrpO zNVaPx7@2E}8$<-lIU?aSr=G`p*2`D_wHFi=4Ad6?Z2wg7 zT-4wBkL}%0{@(Oq&0~8dx7n)%|2(tb8@ImvNANfMQmeV|P8j~M=W9QlxM}k%d#067 zUa&{JxBoLix428>qy623b*tSSpV$lOrYR*cKeT7o{Lg!y@3DP|USJPD!yo(h`{xDw z&%CprZTw4G*5JAQt*;YQe#U*W=YO@prfcCV`xdJe0b(aV*c%%g_q#Iuwy*#AW9BTL z)ApCn)_;q8earr|Y)f&z;Y0i57fRxnOMSNohP6OR)Ia;sqByq**NgVcY$r}^ulQi! zH{+X)O!+sExn{*IZ|vWC+dbI9_RT)O$!tdV_b2wkTMovrc=pTwslw+=kseR&J0DEc zt3CM4-oiOsUFyUa`~8c<*;F6ivQO-)nYPC1hW(a21J<3af9!2LeYE%ddt~pgbh)GX z^&@*-e 0.5: + # Flipping along the YZ plane + point_cloud[:, 0] = -1 * point_cloud[:, 0] + target_bboxes[:, 0] = -1 * target_bboxes[:, 0] + + if np.random.random() > 0.5: + # Flipping along the XZ plane + point_cloud[:, 1] = -1 * point_cloud[:, 1] + target_bboxes[:, 1] = -1 * target_bboxes[:, 1] + + # Rotation along up-axis/Z-axis + rot_angle = (np.random.random() * np.pi / 18) - np.pi / 36 # -5 ~ +5 degree + rot_mat = pc_util.rotz(rot_angle) + point_cloud[:, 0:3] = np.dot(point_cloud[:, 0:3], np.transpose(rot_mat)) + target_bboxes = rotate_aligned_boxes(target_bboxes, rot_mat) + + gt_centers = target_bboxes[:, 0:3] + gt_centers[instance_bboxes.shape[0]:, :] += 1000.0 # padding centers with a large number + # compute GT Centers *AFTER* augmentation + # generate gt centers + # Note: since there's no map between bbox instance labels and + # pc instance_labels (it had been filtered + # in the data preparation step) we'll compute the instance bbox + # from the points sharing the same instance label. + point_obj_mask = np.zeros(self.num_points) + point_instance_label = np.zeros(self.num_points) - 1 + for i_instance in np.unique(instance_labels): + # find all points belong to that instance + ind = np.where(instance_labels == i_instance)[0] + # find the semantic label + if semantic_labels[ind[0]] in DC.nyu40ids: + x = point_cloud[ind, :3] + center = 0.5 * (x.min(0) + x.max(0)) + ilabel = np.argmin(((center - gt_centers) ** 2).sum(-1)) + point_instance_label[ind] = ilabel + point_obj_mask[ind] = 1.0 + + class_ind = [np.where(DC.nyu40ids == x)[0][0] for x in instance_bboxes[:, -1]] + # NOTE: set size class as semantic class. Consider use size2class. + size_classes[0:instance_bboxes.shape[0]] = class_ind + size_residuals[0:instance_bboxes.shape[0], :] = \ + target_bboxes[0:instance_bboxes.shape[0], 3:6] - DC.mean_size_arr[class_ind, :] + size_gts[0:instance_bboxes.shape[0], :] = target_bboxes[0:instance_bboxes.shape[0], 3:6] + + ret_dict = {} + ret_dict['point_clouds'] = point_cloud.astype(np.float32) + ret_dict['center_label'] = gt_centers.astype(np.float32) + ret_dict['heading_class_label'] = angle_classes.astype(np.int64) + ret_dict['heading_residual_label'] = angle_residuals.astype(np.float32) + ret_dict['size_class_label'] = size_classes.astype(np.int64) + ret_dict['size_residual_label'] = size_residuals.astype(np.float32) + ret_dict['size_gts'] = size_gts.astype(np.float32) + target_bboxes_semcls = np.zeros((MAX_NUM_OBJ)) + target_bboxes_semcls[0:instance_bboxes.shape[0]] = \ + [DC.nyu40id2class[x] for x in instance_bboxes[:, -1][0:instance_bboxes.shape[0]]] + ret_dict['sem_cls_label'] = target_bboxes_semcls.astype(np.int64) + ret_dict['box_label_mask'] = target_bboxes_mask.astype(np.float32) + ret_dict['point_obj_mask'] = point_obj_mask.astype(np.int64) + ret_dict['point_instance_label'] = point_instance_label.astype(np.int64) + ret_dict['scan_idx'] = np.array(idx).astype(np.int64) + ret_dict['pcl_color'] = pcl_color + return ret_dict diff --git a/scannet/scannet_utils.py b/scannet/scannet_utils.py new file mode 100644 index 0000000..647458d --- /dev/null +++ b/scannet/scannet_utils.py @@ -0,0 +1,76 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +''' Ref: https://github.com/ScanNet/ScanNet/blob/master/BenchmarkScripts ''' +import os +import sys +import json +import csv + +try: + import numpy as np +except: + print("Failed to import numpy package.") + sys.exit(-1) + +try: + from plyfile import PlyData, PlyElement +except: + print("Please install the module 'plyfile' for PLY i/o, e.g.") + print("pip install plyfile") + sys.exit(-1) + + +def represents_int(s): + ''' if string s represents an int. ''' + try: + int(s) + return True + except ValueError: + return False + + +def read_label_mapping(filename, label_from='raw_category', label_to='nyu40id'): + assert os.path.isfile(filename) + mapping = dict() + with open(filename) as csvfile: + reader = csv.DictReader(csvfile, delimiter='\t') + for row in reader: + mapping[row[label_from]] = int(row[label_to]) + if represents_int(list(mapping.keys())[0]): + mapping = {int(k): v for k, v in mapping.items()} + return mapping + + +def read_mesh_vertices(filename): + """ read XYZ for each vertex. + """ + assert os.path.isfile(filename) + with open(filename, 'rb') as f: + plydata = PlyData.read(f) + num_verts = plydata['vertex'].count + vertices = np.zeros(shape=[num_verts, 3], dtype=np.float32) + vertices[:, 0] = plydata['vertex'].data['x'] + vertices[:, 1] = plydata['vertex'].data['y'] + vertices[:, 2] = plydata['vertex'].data['z'] + return vertices + + +def read_mesh_vertices_rgb(filename): + """ read XYZ RGB for each vertex. + Note: RGB values are in 0-255 + """ + assert os.path.isfile(filename) + with open(filename, 'rb') as f: + plydata = PlyData.read(f) + num_verts = plydata['vertex'].count + vertices = np.zeros(shape=[num_verts, 6], dtype=np.float32) + vertices[:, 0] = plydata['vertex'].data['x'] + vertices[:, 1] = plydata['vertex'].data['y'] + vertices[:, 2] = plydata['vertex'].data['z'] + vertices[:, 3] = plydata['vertex'].data['red'] + vertices[:, 4] = plydata['vertex'].data['green'] + vertices[:, 5] = plydata['vertex'].data['blue'] + return vertices diff --git a/scannet/scans/.gitignore b/scannet/scans/.gitignore new file mode 100644 index 0000000..d6b7ef3 --- /dev/null +++ b/scannet/scans/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore diff --git a/sunrgbd/OFFICIAL_SUNRGBD/.gitignore b/sunrgbd/OFFICIAL_SUNRGBD/.gitignore new file mode 100644 index 0000000..d6b7ef3 --- /dev/null +++ b/sunrgbd/OFFICIAL_SUNRGBD/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore diff --git a/sunrgbd/README.md b/sunrgbd/README.md new file mode 100644 index 0000000..d8afec5 --- /dev/null +++ b/sunrgbd/README.md @@ -0,0 +1,11 @@ +### Prepare SUN RGB-D Data + +1. Download SUNRGBD v2 data [HERE](http://rgbd.cs.princeton.edu/data/) (SUNRGBD.zip, SUNRGBDMeta2DBB_v2.mat, SUNRGBDMeta3DBB_v2.mat) and the toolkits (SUNRGBDtoolbox.zip). Move all the downloaded files under OFFICIAL_SUNRGBD. Unzip the zip files. + +2. Extract point clouds and annotations (class, v2 2D -- xmin,ymin,xmax,ymax, and 3D bounding boxes -- centroids, size, 2D heading) by running `extract_split.m`, `extract_rgbd_data_v2.m` and `extract_rgbd_data_v1.m` under the `matlab` folder. + +3. Prepare data by running `python sunrgbd_data.py --gen_v1_data` + +You can also examine and visualize the data with `python sunrgbd_data.py --viz` and use MeshLab to view the generated PLY files at `data_viz_dump`. + +NOTE: SUNRGBDtoolbox.zip should have MD5 hash `18d22e1761d36352f37232cba102f91f` (you can check the hash with `md5 SUNRGBDtoolbox.zip` on Mac OS or `md5sum SUNRGBDtoolbox.zip` on Linux) diff --git a/sunrgbd/matlab/extract_rgbd_data_v1.m b/sunrgbd/matlab/extract_rgbd_data_v1.m new file mode 100644 index 0000000..5ee2c1d --- /dev/null +++ b/sunrgbd/matlab/extract_rgbd_data_v1.m @@ -0,0 +1,55 @@ +% Copyright (c) Facebook, Inc. and its affiliates. +% +% This source code is licensed under the MIT license found in the +% LICENSE file in the root directory of this source tree. + +%% Dump SUNRGBD data to our format +% for each sample, we have RGB image, 2d boxes. +% point cloud (in camera coordinate), calibration and 3d boxes. +% +% Extract using V1 labels. +% +% Author: Charles R. Qi +% +clear; close all; clc; +addpath(genpath('.')) +addpath('../OFFICIAL_SUNRGBD/SUNRGBDtoolbox') +%% V1 2D&3D BB and Seg masks +load('../OFFICIAL_SUNRGBD/SUNRGBDtoolbox/Metadata/SUNRGBDMeta.mat') +% load('./Metadata/SUNRGBD2Dseg.mat') + +%% Create folders +det_label_folder = '../sunrgbd_trainval/label_v1/'; +mkdir(det_label_folder); +%% Read +for imageId = 1:10335 + imageId +try +data = SUNRGBDMeta(imageId); +data.depthpath(1:16) = ''; +data.depthpath = strcat('../OFFICIAL_SUNRGBD/SUNRGBD', data.depthpath); +data.rgbpath(1:16) = ''; +data.rgbpath = strcat('../OFFICIAL_SUNRGBD/SUNRGBD', data.rgbpath); + +% MAT files are 3x smaller than TXT files. In Python we can use +% scipy.io.loadmat('xxx.mat')['points3d_rgb'] to load the data. +mat_filename = strcat(num2str(imageId,'%06d'), '.mat'); +txt_filename = strcat(num2str(imageId,'%06d'), '.txt'); + +% Write 2D and 3D box label +data2d = data; +fid = fopen(strcat(det_label_folder, txt_filename), 'w'); +for j = 1:length(data.groundtruth3DBB) + centroid = data.groundtruth3DBB(j).centroid; + classname = data.groundtruth3DBB(j).classname; + orientation = data.groundtruth3DBB(j).orientation; + coeffs = abs(data.groundtruth3DBB(j).coeffs); + box2d = data2d.groundtruth2DBB(j).gtBb2D; + fprintf(fid, '%s %d %d %d %d %f %f %f %f %f %f %f %f\n', classname, box2d(1), box2d(2), box2d(3), box2d(4), centroid(1), centroid(2), centroid(3), coeffs(1), coeffs(2), coeffs(3), orientation(1), orientation(2)); +end +fclose(fid); + +catch +end + +end diff --git a/sunrgbd/matlab/extract_rgbd_data_v2.m b/sunrgbd/matlab/extract_rgbd_data_v2.m new file mode 100644 index 0000000..9a81e0d --- /dev/null +++ b/sunrgbd/matlab/extract_rgbd_data_v2.m @@ -0,0 +1,86 @@ +% Copyright (c) Facebook, Inc. and its affiliates. +% +% This source code is licensed under the MIT license found in the +% LICENSE file in the root directory of this source tree. + +%% Dump SUNRGBD data to our format +% for each sample, we have RGB image, 2d boxes. +% point cloud (in camera coordinate), calibration and 3d boxes. +% +% Compared to extract_rgbd_data.m in frustum_pointents, use v2 2D and 3D +% bboxes. +% +% Author: Charles R. Qi +% +clear; close all; clc; +addpath(genpath('.')) +addpath('../OFFICIAL_SUNRGBD/SUNRGBDtoolbox/readData') +%% V1 2D&3D BB and Seg masks +% load('./Metadata/SUNRGBDMeta.mat') +% load('./Metadata/SUNRGBD2Dseg.mat') + +%% V2 3DBB annotations (overwrites SUNRGBDMeta) +load('../OFFICIAL_SUNRGBD/SUNRGBDMeta3DBB_v2.mat'); +load('../OFFICIAL_SUNRGBD/SUNRGBDMeta2DBB_v2.mat'); +%% Create folders +depth_folder = '../sunrgbd_trainval/depth/'; +image_folder = '../sunrgbd_trainval/image/'; +calib_folder = '../sunrgbd_trainval/calib/'; +det_label_folder = '../sunrgbd_trainval/label/'; +seg_label_folder = '../sunrgbd_trainval/seg_label/'; +mkdir(depth_folder); +mkdir(image_folder); +mkdir(calib_folder); +mkdir(det_label_folder); +mkdir(seg_label_folder); +%% Read +parfor imageId = 1:10335 + imageId +try +data = SUNRGBDMeta(imageId); +data.depthpath(1:16) = ''; +data.depthpath = strcat('../OFFICIAL_SUNRGBD', data.depthpath); +data.rgbpath(1:16) = ''; +data.rgbpath = strcat('../OFFICIAL_SUNRGBD', data.rgbpath); + +% Write point cloud in depth map +[rgb,points3d,depthInpaint,imsize]=read3dPoints(data); +rgb(isnan(points3d(:,1)),:) = []; +points3d(isnan(points3d(:,1)),:) = []; +points3d_rgb = [points3d, rgb]; + +% MAT files are 3x smaller than TXT files. In Python we can use +% scipy.io.loadmat('xxx.mat')['points3d_rgb'] to load the data. +mat_filename = strcat(num2str(imageId,'%06d'), '.mat'); +txt_filename = strcat(num2str(imageId,'%06d'), '.txt'); +parsave(strcat(depth_folder, mat_filename), points3d_rgb); + +% Write images +copyfile(data.rgbpath, sprintf('%s/%06d.jpg', image_folder, imageId)); + +% Write calibration +dlmwrite(strcat(calib_folder, txt_filename), data.Rtilt(:)', 'delimiter', ' '); +dlmwrite(strcat(calib_folder, txt_filename), data.K(:)', 'delimiter', ' ', '-append'); + +% Write 2D and 3D box label +data2d = SUNRGBDMeta2DBB(imageId); +fid = fopen(strcat(det_label_folder, txt_filename), 'w'); +for j = 1:length(data.groundtruth3DBB) + centroid = data.groundtruth3DBB(j).centroid; + classname = data.groundtruth3DBB(j).classname; + orientation = data.groundtruth3DBB(j).orientation; + coeffs = abs(data.groundtruth3DBB(j).coeffs); + box2d = data2d.groundtruth2DBB(j).gtBb2D; + assert(strcmp(data2d.groundtruth2DBB(j).classname, classname)); + fprintf(fid, '%s %d %d %d %d %f %f %f %f %f %f %f %f\n', classname, box2d(1), box2d(2), box2d(3), box2d(4), centroid(1), centroid(2), centroid(3), coeffs(1), coeffs(2), coeffs(3), orientation(1), orientation(2)); +end +fclose(fid); + +catch +end + +end + +function parsave(filename, instance) +save(filename, 'instance'); +end diff --git a/sunrgbd/matlab/extract_split.m b/sunrgbd/matlab/extract_split.m new file mode 100644 index 0000000..f3c81fd --- /dev/null +++ b/sunrgbd/matlab/extract_split.m @@ -0,0 +1,52 @@ +% Copyright (c) Facebook, Inc. and its affiliates. +% +% This source code is licensed under the MIT license found in the +% LICENSE file in the root directory of this source tree. + +%% Dump train/val split. +% Author: Charles R. Qi + +addpath('../OFFICIAL_SUNRGBD/SUNRGBDtoolbox') + +%% Construct Hash Map +hash_train = java.util.Hashtable; +hash_val = java.util.Hashtable; + +split = load('../OFFICIAL_SUNRGBD/SUNRGBDtoolbox/traintestSUNRGBD/allsplit.mat'); + +N_train = length(split.alltrain); +N_val = length(split.alltest); + +for i = 1:N_train + folder_path = split.alltrain{i}; + folder_path(1:16) = ''; + hash_train.put(folder_path,0); +end +for i = 1:N_val + folder_path = split.alltest{i}; + folder_path(1:16) = ''; + hash_val.put(folder_path,0); +end + +%% Map data to train or val set. +load('../OFFICIAL_SUNRGBD/SUNRGBDMeta3DBB_v2.mat'); + +fid_train = fopen('../sunrgbd_trainval/train_data_idx.txt', 'w'); +fid_val = fopen('../sunrgbd_trainval/val_data_idx.txt', 'w'); + +for imageId = 1:10335 + data = SUNRGBDMeta(imageId); + depthpath = data.depthpath; + depthpath(1:16) = ''; + [filepath,name,ext] = fileparts(depthpath); + [filepath,name,ext] = fileparts(filepath); + if hash_train.containsKey(filepath) + fprintf(fid_train, '%d\n', imageId); + elseif hash_val.containsKey(filepath) + fprintf(fid_val, '%d\n', imageId); + else + a = 1; + end +end +fclose(fid_train); +fclose(fid_val); diff --git a/sunrgbd/model_util_sunrgbd.py b/sunrgbd/model_util_sunrgbd.py new file mode 100644 index 0000000..a76fa1a --- /dev/null +++ b/sunrgbd/model_util_sunrgbd.py @@ -0,0 +1,88 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +import sys +import os + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(BASE_DIR) +ROOT_DIR = os.path.dirname(BASE_DIR) +sys.path.append(os.path.join(ROOT_DIR, 'utils')) + + +class SunrgbdDatasetConfig(object): + def __init__(self): + self.num_class = 10 + self.num_heading_bin = 12 + self.num_size_cluster = 10 + + self.type2class = {'bed': 0, 'table': 1, 'sofa': 2, 'chair': 3, 'toilet': 4, 'desk': 5, 'dresser': 6, + 'night_stand': 7, 'bookshelf': 8, 'bathtub': 9} + self.class2type = {self.type2class[t]: t for t in self.type2class} + self.type2onehotclass = {'bed': 0, 'table': 1, 'sofa': 2, 'chair': 3, 'toilet': 4, 'desk': 5, 'dresser': 6, + 'night_stand': 7, 'bookshelf': 8, 'bathtub': 9} + self.type_mean_size = {'bathtub': np.array([0.765840, 1.398258, 0.472728]), + 'bed': np.array([2.114256, 1.620300, 0.927272]), + 'bookshelf': np.array([0.404671, 1.071108, 1.688889]), + 'chair': np.array([0.591958, 0.552978, 0.827272]), + 'desk': np.array([0.695190, 1.346299, 0.736364]), + 'dresser': np.array([0.528526, 1.002642, 1.172878]), + 'night_stand': np.array([0.500618, 0.632163, 0.683424]), + 'sofa': np.array([0.923508, 1.867419, 0.845495]), + 'table': np.array([0.791118, 1.279516, 0.718182]), + 'toilet': np.array([0.699104, 0.454178, 0.756250])} + + self.mean_size_arr = np.zeros((self.num_size_cluster, 3)) + for i in range(self.num_size_cluster): + self.mean_size_arr[i, :] = self.type_mean_size[self.class2type[i]] + + def size2class(self, size, type_name): + ''' Convert 3D box size (l,w,h) to size class and size residual ''' + size_class = self.type2class[type_name] + size_residual = size - self.type_mean_size[type_name] + return size_class, size_residual + + def class2size(self, pred_cls, residual): + ''' Inverse function to size2class ''' + mean_size = self.type_mean_size[self.class2type[pred_cls]] + return mean_size + residual + + def angle2class(self, angle): + ''' Convert continuous angle to discrete class + [optinal] also small regression number from + class center angle to current angle. + + angle is from 0-2pi (or -pi~pi), class center at 0, 1*(2pi/N), 2*(2pi/N) ... (N-1)*(2pi/N) + return is class of int32 of 0,1,...,N-1 and a number such that + class*(2pi/N) + number = angle + ''' + num_class = self.num_heading_bin + angle = angle % (2 * np.pi) + assert (angle >= 0 and angle <= 2 * np.pi) + angle_per_class = 2 * np.pi / float(num_class) + shifted_angle = (angle + angle_per_class / 2) % (2 * np.pi) + class_id = int(shifted_angle / angle_per_class) + residual_angle = shifted_angle - (class_id * angle_per_class + angle_per_class / 2) + return class_id, residual_angle + + def class2angle(self, pred_cls, residual, to_label_format=True): + ''' Inverse function to angle2class ''' + num_class = self.num_heading_bin + angle_per_class = 2 * np.pi / float(num_class) + angle_center = pred_cls * angle_per_class + angle = angle_center + residual + if to_label_format and angle > np.pi: + angle = angle - 2 * np.pi + return angle + + def param2obb(self, center, heading_class, heading_residual, size_class, size_residual): + heading_angle = self.class2angle(heading_class, heading_residual) + box_size = self.class2size(int(size_class), size_residual) + obb = np.zeros((7,)) + obb[0:3] = center + obb[3:6] = box_size + obb[6] = heading_angle * -1 + return obb diff --git a/sunrgbd/sunrgbd_data.py b/sunrgbd/sunrgbd_data.py new file mode 100644 index 0000000..6b7c472 --- /dev/null +++ b/sunrgbd/sunrgbd_data.py @@ -0,0 +1,439 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +''' Helper class and functions for loading SUN RGB-D objects + +Author: Charles R. Qi +Date: December, 2018 + +Note: removed unused code for frustum preparation. +Changed a way for data visualization (removed depdency on mayavi). +Load depth with scipy.io + +Modified by Ze to support Certain GT Center Assign +''' + +import os +import sys +import numpy as np +import sys +import cv2 +import argparse +from PIL import Image + +import pickle + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(BASE_DIR) +sys.path.append(os.path.join(BASE_DIR, '../utils/')) +import pc_util +import sunrgbd_utils + +DEFAULT_TYPE_WHITELIST = ['bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser', 'night_stand', 'bookshelf', + 'bathtub'] + + +class sunrgbd_object(object): + ''' Load and parse object data ''' + + def __init__(self, root_dir, split='training', use_v1=False): + self.root_dir = root_dir + self.split = split + assert (self.split == 'training') + self.split_dir = os.path.join(root_dir) + + if split == 'training': + self.num_samples = 10335 + elif split == 'testing': + self.num_samples = 2860 + else: + print('Unknown split: %s' % (split)) + exit(-1) + + self.image_dir = os.path.join(self.split_dir, 'image') + self.calib_dir = os.path.join(self.split_dir, 'calib') + self.depth_dir = os.path.join(self.split_dir, 'depth') + if use_v1: + self.label_dir = os.path.join(self.split_dir, 'label_v1') + else: + self.label_dir = os.path.join(self.split_dir, 'label') + + def __len__(self): + return self.num_samples + + def get_image(self, idx): + img_filename = os.path.join(self.image_dir, '%06d.jpg' % (idx)) + return sunrgbd_utils.load_image(img_filename) + + def get_depth(self, idx): + depth_filename = os.path.join(self.depth_dir, '%06d.mat' % (idx)) + return sunrgbd_utils.load_depth_points_mat(depth_filename) + + def get_calibration(self, idx): + calib_filename = os.path.join(self.calib_dir, '%06d.txt' % (idx)) + return sunrgbd_utils.SUNRGBD_Calibration(calib_filename) + + def get_label_objects(self, idx): + label_filename = os.path.join(self.label_dir, '%06d.txt' % (idx)) + return sunrgbd_utils.read_sunrgbd_label(label_filename) + + +def data_viz(data_dir, dump_dir=os.path.join(BASE_DIR, 'data_viz_dump')): + ''' Examine and visualize SUN RGB-D data. ''' + sunrgbd = sunrgbd_object(data_dir) + idxs = np.array(range(1, len(sunrgbd) + 1)) + np.random.seed(0) + np.random.shuffle(idxs) + for idx in range(len(sunrgbd)): + data_idx = idxs[idx] + print('-' * 10, 'data index: ', data_idx) + pc = sunrgbd.get_depth(data_idx) + print('Point cloud shape:', pc.shape) + + # Project points to image + calib = sunrgbd.get_calibration(data_idx) + uv, d = calib.project_upright_depth_to_image(pc[:, 0:3]) + print('Point UV:', uv) + print('Point depth:', d) + + import matplotlib.pyplot as plt + cmap = plt.cm.get_cmap('hsv', 256) + cmap = np.array([cmap(i) for i in range(256)])[:, :3] * 255 + + img = sunrgbd.get_image(data_idx) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + for i in range(uv.shape[0]): + depth = d[i] + color = cmap[int(120.0 / depth), :] + cv2.circle(img, (int(np.round(uv[i, 0])), int(np.round(uv[i, 1]))), 2, + color=tuple(color), thickness=-1) + if not os.path.exists(dump_dir): + os.mkdir(dump_dir) + Image.fromarray(img).save(os.path.join(dump_dir, 'img_depth.jpg')) + + # Load box labels + objects = sunrgbd.get_label_objects(data_idx) + print('Objects:', objects) + + # Draw 2D boxes on image + img = sunrgbd.get_image(data_idx) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + for i, obj in enumerate(objects): + cv2.rectangle(img, (int(obj.xmin), int(obj.ymin)), + (int(obj.xmax), int(obj.ymax)), (0, 255, 0), 2) + cv2.putText(img, '%d %s' % (i, obj.classname), (max(int(obj.xmin), 15), + max(int(obj.ymin), 15)), cv2.FONT_HERSHEY_SIMPLEX, 0.8, + (255, 0, 0), 2) + Image.fromarray(img).save(os.path.join(dump_dir, 'img_box2d.jpg')) + + # Dump OBJ files for the colored point cloud + for num_point in [10000, 20000, 40000, 80000]: + sampled_pcrgb = pc_util.random_sampling(pc, num_point) + pc_util.write_ply_rgb(sampled_pcrgb[:, 0:3], + (sampled_pcrgb[:, 3:] * 256).astype(np.int8), + os.path.join(dump_dir, 'pcrgb_%dk.obj' % (num_point // 1000))) + # Dump OBJ files for 3D bounding boxes + # l,w,h correspond to dx,dy,dz + # heading angle is from +X rotating towards -Y + # (+X is degree, -Y is 90 degrees) + oriented_boxes = [] + for obj in objects: + obb = np.zeros((7)) + obb[0:3] = obj.centroid + # Some conversion to map with default setting of w,l,h + # and angle in box dumping + obb[3:6] = np.array([obj.l, obj.w, obj.h]) * 2 + obb[6] = -1 * obj.heading_angle + print('Object cls, heading, l, w, h:', \ + obj.classname, obj.heading_angle, obj.l, obj.w, obj.h) + oriented_boxes.append(obb) + if len(oriented_boxes) > 0: + oriented_boxes = np.vstack(tuple(oriented_boxes)) + pc_util.write_oriented_bbox(oriented_boxes, + os.path.join(dump_dir, 'obbs.ply')) + else: + print('-' * 30) + continue + + # Draw 3D boxes on depth points + box3d = [] + ori3d = [] + for obj in objects: + corners_3d_image, corners_3d = sunrgbd_utils.compute_box_3d(obj, calib) + ori_3d_image, ori_3d = sunrgbd_utils.compute_orientation_3d(obj, calib) + print('Corners 3D: ', corners_3d) + box3d.append(corners_3d) + ori3d.append(ori_3d) + pc_box3d = np.concatenate(box3d, 0) + pc_ori3d = np.concatenate(ori3d, 0) + print(pc_box3d.shape) + print(pc_ori3d.shape) + pc_util.write_ply(pc_box3d, os.path.join(dump_dir, 'box3d_corners.ply')) + pc_util.write_ply(pc_ori3d, os.path.join(dump_dir, 'box3d_ori.ply')) + print('-' * 30) + print('Point clouds and bounding boxes saved to PLY files under %s' % (dump_dir)) + print('Type anything to continue to the next sample...') + input() + + +def extract_sunrgbd_data(idx_filename, split, output_folder, num_point=20000, + type_whitelist=DEFAULT_TYPE_WHITELIST, + save_votes=False, use_v1=False, skip_empty_scene=True): + """ Extract scene point clouds and + bounding boxes (centroids, box sizes, heading angles, semantic classes). + Dumped point clouds and boxes are in upright depth coord. + + Args: + idx_filename: a TXT file where each line is an int number (index) + split: training or testing + save_votes: whether to compute and save Ground truth votes. + use_v1: use the SUN RGB-D V1 data + skip_empty_scene: if True, skip scenes that contain no object (no objet in whitelist) + + Dumps: + _pc.npz of (N,6) where N is for number of subsampled points and 6 is + for XYZ and RGB (in 0~1) in upright depth coord + _bbox.npy of (K,8) where K is the number of objects, 8 is for + centroids (cx,cy,cz), dimension (l,w,h), heanding_angle and semantic_class + _votes.npz of (N,10) with 0/1 indicating whether the point belongs to an object, + then three sets of GT votes for up to three objects. If the point is only in one + object's OBB, then the three GT votes are the same. + """ + dataset = sunrgbd_object('./sunrgbd_trainval', split, use_v1=use_v1) + data_idx_list = [int(line.rstrip()) for line in open(idx_filename)] + + if not os.path.exists(output_folder): + os.mkdir(output_folder) + + all_obbs = [] + all_pc_upright_depth_subsampled = [] + all_point_votes = [] + for data_idx in data_idx_list: + print('------------- ', data_idx) + objects = dataset.get_label_objects(data_idx) + + # Skip scenes with 0 object + if skip_empty_scene and (len(objects) == 0 or + len([obj for obj in objects if obj.classname in type_whitelist]) == 0): + continue + + object_list = [] + for obj in objects: + if obj.classname not in type_whitelist: continue + obb = np.zeros((8)) + obb[0:3] = obj.centroid + # Note that compared with that in data_viz, we do not time 2 to l,w.h + # neither do we flip the heading angle + obb[3:6] = np.array([obj.l, obj.w, obj.h]) + obb[6] = obj.heading_angle + obb[7] = sunrgbd_utils.type2class[obj.classname] + object_list.append(obb) + if len(object_list) == 0: + obbs = np.zeros((0, 8)) + else: + obbs = np.vstack(object_list) # (K,8) + print(f"{data_idx} has {obbs.shape[0]} gt bboxes") + + pc_upright_depth = dataset.get_depth(data_idx) + pc_upright_depth_subsampled = pc_util.random_sampling(pc_upright_depth, num_point) + + np.savez_compressed(os.path.join(output_folder, '%06d_pc.npz' % (data_idx)), + pc=pc_upright_depth_subsampled) + np.save(os.path.join(output_folder, '%06d_bbox.npy' % (data_idx)), obbs) + # pickle save + with open(os.path.join(output_folder, '%06d_pc.pkl' % (data_idx)), 'wb') as f: + pickle.dump(pc_upright_depth_subsampled, f) + print(f"{os.path.join(output_folder, '%06d_pc.pkl' % (data_idx))} saved successfully !!") + with open(os.path.join(output_folder, '%06d_bbox.pkl' % (data_idx)), 'wb') as f: + pickle.dump(obbs, f) + print(f"{os.path.join(output_folder, '%06d_bbox.pkl' % (data_idx))} saved successfully !!") + # add to collection + all_pc_upright_depth_subsampled.append(pc_upright_depth_subsampled) + all_obbs.append(obbs) + + N = pc_upright_depth_subsampled.shape[0] + point_votes = np.zeros((N, 13)) # 1 vote mask + 3 votes and + 3 votes gt ind + point_votes[:, 10:13] = -1 + point_vote_idx = np.zeros((N)).astype(np.int32) # in the range of [0,2] + indices = np.arange(N) + i_obj = 0 + for obj in objects: + if obj.classname not in type_whitelist: continue + try: + # Find all points in this object's OBB + box3d_pts_3d = sunrgbd_utils.my_compute_box_3d(obj.centroid, + np.array([obj.l, obj.w, obj.h]), obj.heading_angle) + pc_in_box3d, inds = sunrgbd_utils.extract_pc_in_box3d( \ + pc_upright_depth_subsampled, box3d_pts_3d) + # Assign first dimension to indicate it is in an object box + point_votes[inds, 0] = 1 + # Add the votes (all 0 if the point is not in any object's OBB) + votes = np.expand_dims(obj.centroid, 0) - pc_in_box3d[:, 0:3] + sparse_inds = indices[inds] # turn dense True,False inds to sparse number-wise inds + for i in range(len(sparse_inds)): + j = sparse_inds[i] + point_votes[j, int(point_vote_idx[j] * 3 + 1):int((point_vote_idx[j] + 1) * 3 + 1)] = votes[i, + :] + point_votes[j, point_vote_idx[j] + 10] = i_obj + # Populate votes with the fisrt vote + if point_vote_idx[j] == 0: + point_votes[j, 4:7] = votes[i, :] + point_votes[j, 7:10] = votes[i, :] + point_votes[j, 10] = i_obj + point_votes[j, 11] = i_obj + point_votes[j, 12] = i_obj + point_vote_idx[inds] = np.minimum(2, point_vote_idx[inds] + 1) + i_obj += 1 + except: + print('ERROR ----', data_idx, obj.classname) + + # choose the nearest as the first gt for each point + for ip in range(N): + is_pos = (point_votes[ip, 0] > 0) + if is_pos: + vote_delta1 = point_votes[ip, 1:4].copy() + vote_delta2 = point_votes[ip, 4:7].copy() + vote_delta3 = point_votes[ip, 7:10].copy() + dist1 = np.sum(vote_delta1 ** 2) + dist2 = np.sum(vote_delta2 ** 2) + dist3 = np.sum(vote_delta3 ** 2) + + gt_ind1 = int(point_votes[ip, 10].copy()) + # gt_ind2 = int(point_votes[ip, 11].copy()) + # gt_ind3 = int(point_votes[ip, 12].copy()) + # gt1 = obbs[gt_ind1] + # gt2 = obbs[gt_ind2] + # gt3 = obbs[gt_ind3] + # size_norm_vote_delta1 = vote_delta1 / gt1[3:6] + # size_norm_vote_delta2 = vote_delta2 / gt2[3:6] + # size_norm_vote_delta3 = vote_delta3 / gt3[3:6] + # size_norm_dist1 = np.sum(size_norm_vote_delta1 ** 2) + # size_norm_dist2 = np.sum(size_norm_vote_delta2 ** 2) + # size_norm_dist3 = np.sum(size_norm_vote_delta3 ** 2) + + near_ind = np.argmin([dist1, dist2, dist3]) + # near_ind = np.argmin([size_norm_dist1, size_norm_dist2, size_norm_dist3]) + + point_votes[ip, 10] = point_votes[ip, 10 + near_ind].copy() + point_votes[ip, 10 + near_ind] = gt_ind1 + point_votes[ip, 1:4] = point_votes[ip, int(near_ind * 3 + 1):int((near_ind + 1) * 3 + 1)].copy() + point_votes[ip, int(near_ind * 3 + 1):int((near_ind + 1) * 3 + 1)] = vote_delta1 + else: + assert point_votes[ip, 10] == -1, "error" + assert point_votes[ip, 11] == -1, "error" + assert point_votes[ip, 12] == -1, "error" + + print(f"{data_idx}_votes.npz has {i_obj} gt bboxes") + np.savez_compressed(os.path.join(output_folder, '%06d_votes.npz' % (data_idx)), + point_votes=point_votes) + with open(os.path.join(output_folder, '%06d_votes.pkl' % (data_idx)), 'wb') as f: + pickle.dump(point_votes, f) + print(f"{os.path.join(output_folder, '%06d_votes.pkl' % (data_idx))} saved successfully !!") + all_point_votes.append(point_votes) + + pickle_filename = os.path.join(output_folder, 'all_obbs_modified_nearest_has_empty.pkl') + with open(pickle_filename, 'wb') as f: + pickle.dump(all_obbs, f) + print(f"{pickle_filename} saved successfully !!") + + pickle_filename = os.path.join(output_folder, 'all_pc_modified_nearest_has_empty.pkl') + with open(pickle_filename, 'wb') as f: + pickle.dump(all_pc_upright_depth_subsampled, f) + print(f"{pickle_filename} saved successfully !!") + + pickle_filename = os.path.join(output_folder, 'all_point_votes_nearest_has_empty.pkl') + with open(pickle_filename, 'wb') as f: + pickle.dump(all_point_votes, f) + print(f"{pickle_filename} saved successfully !!") + + all_point_labels = [] + for point_votes in all_point_votes: + point_labels = point_votes[:, [0, 10]] + all_point_labels.append(point_labels) + pickle_filename = os.path.join(output_folder, 'all_point_labels_nearest_has_empty.pkl') + with open(pickle_filename, 'wb') as f: + pickle.dump(all_point_labels, f) + print(f"{pickle_filename} saved successfully !!") + + +def get_box3d_dim_statistics(idx_filename, + type_whitelist=DEFAULT_TYPE_WHITELIST, + save_path=None): + """ Collect 3D bounding box statistics. + Used for computing mean box sizes. """ + dataset = sunrgbd_object('./sunrgbd_trainval') + dimension_list = [] + type_list = [] + ry_list = [] + data_idx_list = [int(line.rstrip()) for line in open(idx_filename)] + for data_idx in data_idx_list: + print('------------- ', data_idx) + calib = dataset.get_calibration(data_idx) # 3 by 4 matrix + objects = dataset.get_label_objects(data_idx) + for obj_idx in range(len(objects)): + obj = objects[obj_idx] + if obj.classname not in type_whitelist: continue + heading_angle = -1 * np.arctan2(obj.orientation[1], obj.orientation[0]) + dimension_list.append(np.array([obj.l, obj.w, obj.h])) + type_list.append(obj.classname) + ry_list.append(heading_angle) + + import cPickle as pickle + if save_path is not None: + with open(save_path, 'wb') as fp: + pickle.dump(type_list, fp) + pickle.dump(dimension_list, fp) + pickle.dump(ry_list, fp) + + # Get average box size for different catgories + box3d_pts = np.vstack(dimension_list) + for class_type in sorted(set(type_list)): + cnt = 0 + box3d_list = [] + for i in range(len(dimension_list)): + if type_list[i] == class_type: + cnt += 1 + box3d_list.append(dimension_list[i]) + median_box3d = np.median(box3d_list, 0) + print("\'%s\': np.array([%f,%f,%f])," % \ + (class_type, median_box3d[0] * 2, median_box3d[1] * 2, median_box3d[2] * 2)) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--viz', action='store_true', help='Run data visualization.') + parser.add_argument('--compute_median_size', action='store_true', + help='Compute median 3D bounding box sizes for each class.') + parser.add_argument('--gen_v1_data', action='store_true', help='Generate V1 dataset.') + parser.add_argument('--gen_v2_data', action='store_true', help='Generate V2 dataset.') + args = parser.parse_args() + + if args.viz: + data_viz(os.path.join(BASE_DIR, 'sunrgbd_trainval')) + exit() + + if args.compute_median_size: + get_box3d_dim_statistics(os.path.join(BASE_DIR, 'sunrgbd_trainval/train_data_idx.txt')) + exit() + + if args.gen_v1_data: + extract_sunrgbd_data(os.path.join(BASE_DIR, 'sunrgbd_trainval/train_data_idx.txt'), + split='training', + output_folder=os.path.join(BASE_DIR, 'sunrgbd_pc_bbox_votes_50k_v1_train'), + save_votes=True, num_point=50000, use_v1=True, skip_empty_scene=False) + extract_sunrgbd_data(os.path.join(BASE_DIR, 'sunrgbd_trainval/val_data_idx.txt'), + split='training', + output_folder=os.path.join(BASE_DIR, 'sunrgbd_pc_bbox_votes_50k_v1_val'), + save_votes=True, num_point=50000, use_v1=True, skip_empty_scene=False) + + if args.gen_v2_data: + extract_sunrgbd_data(os.path.join(BASE_DIR, 'sunrgbd_trainval/train_data_idx.txt'), + split='training', + output_folder=os.path.join(BASE_DIR, 'sunrgbd_pc_bbox_votes_50k_v2_train'), + save_votes=True, num_point=50000, use_v1=False, skip_empty_scene=False) + extract_sunrgbd_data(os.path.join(BASE_DIR, 'sunrgbd_trainval/val_data_idx.txt'), + split='training', + output_folder=os.path.join(BASE_DIR, 'sunrgbd_pc_bbox_votes_50k_v2_val'), + save_votes=True, num_point=50000, use_v1=False, skip_empty_scene=False) diff --git a/sunrgbd/sunrgbd_detection_dataset.py b/sunrgbd/sunrgbd_detection_dataset.py new file mode 100644 index 0000000..951d4ff --- /dev/null +++ b/sunrgbd/sunrgbd_detection_dataset.py @@ -0,0 +1,218 @@ +# coding: utf-8 +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" Dataset for 3D object detection on SUN RGB-D (with support of vote supervision). + +A sunrgbd oriented bounding box is parameterized by (cx,cy,cz), (l,w,h) -- (dx,dy,dz) in upright depth coord +(Z is up, Y is forward, X is right ward), heading angle (from +X rotating to -Y) and semantic class + +Point clouds are in **upright_depth coordinate (X right, Y forward, Z upward)** +Return heading class, heading residual, size class and size residual for 3D bounding boxes. +Oriented bounding box is parameterized by (cx,cy,cz), (l,w,h), heading_angle and semantic class label. +(cx,cy,cz) is in upright depth coordinate +(l,h,w) are *half length* of the object sizes +The heading angle is a rotation rad from +X rotating towards -Y. (+X is 0, -Y is pi/2) + +Author: Charles R. Qi +Date: 2019 + +""" +import os +import pickle +import sys + +import numpy as np +from torch.utils.data import Dataset + +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, 'utils')) +import pc_util +import sunrgbd_utils +from model_util_sunrgbd import SunrgbdDatasetConfig + +DC = SunrgbdDatasetConfig() # dataset specific config +MAX_NUM_OBJ = 64 # maximum number of objects allowed per scene +MEAN_COLOR_RGB = np.array([0.5, 0.5, 0.5]) # sunrgbd color is in 0~1 + + +class SunrgbdDetectionVotesDataset(Dataset): + def __init__(self, split_set='train', num_points=20000, + use_color=False, use_height=False, use_v1=False, + augment=False, data_root=None): + + assert (num_points <= 50000) + self.use_v1 = use_v1 + + self.num_points = num_points + self.augment = augment + self.use_color = use_color + self.use_height = use_height + + if data_root is None: + data_root = ROOT_DIR + + if use_v1: + self.data_path = os.path.join(data_root, f'sunrgbd/sunrgbd_pc_bbox_votes_50k_v1_{split_set}') + else: + self.data_path = os.path.join(data_root, f'sunrgbd/sunrgbd_pc_bbox_votes_50k_v2_{split_set}') + + pickle_filename = os.path.join(self.data_path, 'all_obbs_modified_nearest_has_empty.pkl') + with open(pickle_filename, 'rb') as f: + self.bboxes_list = pickle.load(f) + print(f"{pickle_filename} loaded successfully !!!") + + pickle_filename = os.path.join(self.data_path, 'all_pc_modified_nearest_has_empty.pkl') + with open(pickle_filename, 'rb') as f: + self.point_cloud_list = pickle.load(f) + print(f"{pickle_filename} loaded successfully !!!") + + pickle_filename = os.path.join(self.data_path, 'all_point_labels_nearest_has_empty.pkl') + with open(pickle_filename, 'rb') as f: + self.point_labels_list = pickle.load(f) + print(f"{pickle_filename} loaded successfully !!!") + + def __len__(self): + return len(self.point_cloud_list) + + def __getitem__(self, idx): + """ + Returns a dict with following keys: + point_clouds: (N,3+C) + center_label: (MAX_NUM_OBJ,3) for GT box center XYZ + sem_cls_label: (MAX_NUM_OBJ,) semantic class index + heading_class_label: (MAX_NUM_OBJ,) with int values in 0,...,NUM_HEADING_BIN-1 + heading_residual_label: (MAX_NUM_OBJ,) + size_classe_label: (MAX_NUM_OBJ,) with int values in 0,...,NUM_SIZE_CLUSTER + size_residual_label: (MAX_NUM_OBJ,3) + box_label_mask: (MAX_NUM_OBJ) as 0/1 with 1 indicating a unique box + point_obj_mask: (N,) with 0/1 with 1 indicating the point is in one of the object's OBB. + point_instance_label: (N,) with int values in -1,...,num_box, indicating which object the point belongs to, -1 means a backgound point. + scan_idx: int scan index in scan_names list + max_gt_bboxes: unused + """ + point_cloud = self.point_cloud_list[idx] # Nx6 + bboxes = self.bboxes_list[idx] # K,8 + point_obj_mask = self.point_labels_list[idx][:, 0] + point_instance_label = self.point_labels_list[idx][:, -1] + + if not self.use_color: + point_cloud = point_cloud[:, 0:3] + else: + point_cloud = point_cloud[:, 0:6] + point_cloud[:, 3:] = (point_cloud[:, 3:] - MEAN_COLOR_RGB) + + if self.use_height: + floor_height = np.percentile(point_cloud[:, 2], 0.99) + height = point_cloud[:, 2] - floor_height + point_cloud = np.concatenate([point_cloud, np.expand_dims(height, 1)], 1) # (N,4) or (N,7) + + # ------------------------------- DATA AUGMENTATION ------------------------------ + if self.augment: + if np.random.random() > 0.5: + # Flipping along the YZ plane + point_cloud[:, 0] = -1 * point_cloud[:, 0] + bboxes[:, 0] = -1 * bboxes[:, 0] + bboxes[:, 6] = np.pi - bboxes[:, 6] + + # Rotation along up-axis/Z-axis + rot_angle = (np.random.random() * np.pi / 3) - np.pi / 6 # -30 ~ +30 degree + rot_mat = sunrgbd_utils.rotz(rot_angle) + + point_cloud[:, 0:3] = np.dot(point_cloud[:, 0:3], np.transpose(rot_mat)) + bboxes[:, 0:3] = np.dot(bboxes[:, 0:3], np.transpose(rot_mat)) + bboxes[:, 6] -= rot_angle + + # Augment RGB color + if self.use_color: + rgb_color = point_cloud[:, 3:6] + MEAN_COLOR_RGB + rgb_color *= (1 + 0.4 * np.random.random(3) - 0.2) # brightness change for each channel + rgb_color += (0.1 * np.random.random(3) - 0.05) # color shift for each channel + rgb_color += np.expand_dims((0.05 * np.random.random(point_cloud.shape[0]) - 0.025), + -1) # jittering on each pixel + rgb_color = np.clip(rgb_color, 0, 1) + # randomly drop out 30% of the points' colors + rgb_color *= np.expand_dims(np.random.random(point_cloud.shape[0]) > 0.3, -1) + point_cloud[:, 3:6] = rgb_color - MEAN_COLOR_RGB + + # Augment point cloud scale: 0.85x-1.15x + scale_ratio = np.random.random() * 0.3 + 0.85 + scale_ratio = np.expand_dims(np.tile(scale_ratio, 3), 0) + point_cloud[:, 0:3] *= scale_ratio + bboxes[:, 0:3] *= scale_ratio + bboxes[:, 3:6] *= scale_ratio + if self.use_height: + point_cloud[:, -1] *= scale_ratio[0, 0] + + # ------------------------------- LABELS ------------------------------ + box3d_centers = np.zeros((MAX_NUM_OBJ, 3)) + box3d_sizes = np.zeros((MAX_NUM_OBJ, 3)) + angle_classes = np.zeros((MAX_NUM_OBJ,)) + angle_residuals = np.zeros((MAX_NUM_OBJ,)) + size_classes = np.zeros((MAX_NUM_OBJ,)) + size_residuals = np.zeros((MAX_NUM_OBJ, 3)) + label_mask = np.zeros((MAX_NUM_OBJ)) + label_mask[0:bboxes.shape[0]] = 1 + max_bboxes = np.zeros((MAX_NUM_OBJ, 8)) + max_bboxes[0:bboxes.shape[0], :] = bboxes + + for i in range(bboxes.shape[0]): + bbox = bboxes[i] + semantic_class = bbox[7] + box3d_center = bbox[0:3] + angle_class, angle_residual = DC.angle2class(bbox[6]) + # NOTE: The mean size stored in size2class is of full length of box edges, + # while in sunrgbd_data.py data dumping we dumped *half* length l,w,h.. so have to time it by 2 here + box3d_size = bbox[3:6] * 2 + size_class, size_residual = DC.size2class(box3d_size, DC.class2type[semantic_class]) + box3d_centers[i, :] = box3d_center + angle_classes[i] = angle_class + angle_residuals[i] = angle_residual + size_classes[i] = size_class + size_residuals[i] = size_residual + box3d_sizes[i, :] = box3d_size + + target_bboxes_mask = label_mask + target_bboxes = np.zeros((MAX_NUM_OBJ, 6)) + target_bboxes[:, 0:3] += 1000.0 + size_gts = np.zeros((MAX_NUM_OBJ, 3)) + for i in range(bboxes.shape[0]): + bbox = bboxes[i] + corners_3d = sunrgbd_utils.my_compute_box_3d(bbox[0:3], bbox[3:6], bbox[6]) + # compute axis aligned box + xmin = np.min(corners_3d[:, 0]) + ymin = np.min(corners_3d[:, 1]) + zmin = np.min(corners_3d[:, 2]) + xmax = np.max(corners_3d[:, 0]) + ymax = np.max(corners_3d[:, 1]) + zmax = np.max(corners_3d[:, 2]) + target_bbox = np.array( + [(xmin + xmax) / 2, (ymin + ymax) / 2, (zmin + zmax) / 2, xmax - xmin, ymax - ymin, zmax - zmin]) + target_bboxes[i, :] = target_bbox + size_gts[i, :] = target_bbox[3:6] + + point_cloud, choices = pc_util.random_sampling(point_cloud, self.num_points, return_choices=True) + point_obj_mask = point_obj_mask[choices] + point_instance_label = point_instance_label[choices] + + ret_dict = {} + ret_dict['point_clouds'] = point_cloud.astype(np.float32) + ret_dict['center_label'] = target_bboxes.astype(np.float32)[:, 0:3] + ret_dict['heading_class_label'] = angle_classes.astype(np.int64) + ret_dict['heading_residual_label'] = angle_residuals.astype(np.float32) + ret_dict['size_class_label'] = size_classes.astype(np.int64) + ret_dict['size_residual_label'] = size_residuals.astype(np.float32) + ret_dict['size_gts'] = size_gts.astype(np.float32) + target_bboxes_semcls = np.zeros((MAX_NUM_OBJ)) + target_bboxes_semcls[0:bboxes.shape[0]] = bboxes[:, -1] # from 0 to 9 + ret_dict['sem_cls_label'] = target_bboxes_semcls.astype(np.int64) + ret_dict['box_label_mask'] = target_bboxes_mask.astype(np.float32) + ret_dict['point_obj_mask'] = point_obj_mask.astype(np.int64) + ret_dict['point_instance_label'] = point_instance_label.astype(np.int64) + ret_dict['scan_idx'] = np.array(idx).astype(np.int64) + ret_dict['max_gt_bboxes'] = max_bboxes + return ret_dict diff --git a/sunrgbd/sunrgbd_trainval/.gitignore b/sunrgbd/sunrgbd_trainval/.gitignore new file mode 100644 index 0000000..d6b7ef3 --- /dev/null +++ b/sunrgbd/sunrgbd_trainval/.gitignore @@ -0,0 +1,2 @@ +* +!.gitignore diff --git a/sunrgbd/sunrgbd_utils.py b/sunrgbd/sunrgbd_utils.py new file mode 100644 index 0000000..456a1d7 --- /dev/null +++ b/sunrgbd/sunrgbd_utils.py @@ -0,0 +1,341 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +''' Provides Python helper function to read My SUNRGBD dataset. + +Author: Charles R. Qi +Date: October, 2017 + +Updated by Charles R. Qi +Date: December, 2018 +Note: removed basis loading. +''' +import numpy as np +import cv2 +import os +import scipy.io as sio # to load .mat files for depth points + +type2class = {'bed': 0, 'table': 1, 'sofa': 2, 'chair': 3, 'toilet': 4, 'desk': 5, 'dresser': 6, 'night_stand': 7, + 'bookshelf': 8, 'bathtub': 9} +class2type = {type2class[t]: t for t in type2class} + + +def flip_axis_to_camera(pc): + ''' Flip X-right,Y-forward,Z-up to X-right,Y-down,Z-forward + Input and output are both (N,3) array + ''' + pc2 = np.copy(pc) + pc2[:, [0, 1, 2]] = pc2[:, [0, 2, 1]] # cam X,Y,Z = depth X,-Z,Y + pc2[:, 1] *= -1 + return pc2 + + +def flip_axis_to_depth(pc): + pc2 = np.copy(pc) + pc2[:, [0, 1, 2]] = pc2[:, [0, 2, 1]] # depth X,Y,Z = cam X,Z,-Y + pc2[:, 2] *= -1 + return pc2 + + +class SUNObject3d(object): + def __init__(self, line): + data = line.split(' ') + data[1:] = [float(x) for x in data[1:]] + self.classname = data[0] + self.xmin = data[1] + self.ymin = data[2] + self.xmax = data[1] + data[3] + self.ymax = data[2] + data[4] + self.box2d = np.array([self.xmin, self.ymin, self.xmax, self.ymax]) + self.centroid = np.array([data[5], data[6], data[7]]) + self.unused_dimension = np.array([data[8], data[9], data[10]]) + self.w = data[8] + self.l = data[9] + self.h = data[10] + self.orientation = np.zeros((3,)) + self.orientation[0] = data[11] + self.orientation[1] = data[12] + self.heading_angle = -1 * np.arctan2(self.orientation[1], self.orientation[0]) + + +class SUNRGBD_Calibration(object): + ''' Calibration matrices and utils + We define five coordinate system in SUN RGBD dataset + + camera coodinate: + Z is forward, Y is downward, X is rightward + + depth coordinate: + Just change axis order and flip up-down axis from camera coord + + upright depth coordinate: tilted depth coordinate by Rtilt such that Z is gravity direction, + Z is up-axis, Y is forward, X is right-ward + + upright camera coordinate: + Just change axis order and flip up-down axis from upright depth coordinate + + image coordinate: + ----> x-axis (u) + | + v + y-axis (v) + + depth points are stored in upright depth coordinate. + labels for 3d box (basis, centroid, size) are in upright depth coordinate. + 2d boxes are in image coordinate + + We generate frustum point cloud and 3d box in upright camera coordinate + ''' + + def __init__(self, calib_filepath): + lines = [line.rstrip() for line in open(calib_filepath)] + Rtilt = np.array([float(x) for x in lines[0].split(' ')]) + self.Rtilt = np.reshape(Rtilt, (3, 3), order='F') + K = np.array([float(x) for x in lines[1].split(' ')]) + self.K = np.reshape(K, (3, 3), order='F') + self.f_u = self.K[0, 0] + self.f_v = self.K[1, 1] + self.c_u = self.K[0, 2] + self.c_v = self.K[1, 2] + + def project_upright_depth_to_camera(self, pc): + ''' project point cloud from depth coord to camera coordinate + Input: (N,3) Output: (N,3) + ''' + # Project upright depth to depth coordinate + pc2 = np.dot(np.transpose(self.Rtilt), np.transpose(pc[:, 0:3])) # (3,n) + return flip_axis_to_camera(np.transpose(pc2)) + + def project_upright_depth_to_image(self, pc): + ''' Input: (N,3) Output: (N,2) UV and (N,) depth ''' + pc2 = self.project_upright_depth_to_camera(pc) + uv = np.dot(pc2, np.transpose(self.K)) # (n,3) + uv[:, 0] /= uv[:, 2] + uv[:, 1] /= uv[:, 2] + return uv[:, 0:2], pc2[:, 2] + + def project_upright_depth_to_upright_camera(self, pc): + return flip_axis_to_camera(pc) + + def project_upright_camera_to_upright_depth(self, pc): + return flip_axis_to_depth(pc) + + def project_image_to_camera(self, uv_depth): + n = uv_depth.shape[0] + x = ((uv_depth[:, 0] - self.c_u) * uv_depth[:, 2]) / self.f_u + y = ((uv_depth[:, 1] - self.c_v) * uv_depth[:, 2]) / self.f_v + pts_3d_camera = np.zeros((n, 3)) + pts_3d_camera[:, 0] = x + pts_3d_camera[:, 1] = y + pts_3d_camera[:, 2] = uv_depth[:, 2] + return pts_3d_camera + + def project_image_to_upright_camerea(self, uv_depth): + pts_3d_camera = self.project_image_to_camera(uv_depth) + pts_3d_depth = flip_axis_to_depth(pts_3d_camera) + pts_3d_upright_depth = np.transpose(np.dot(self.Rtilt, np.transpose(pts_3d_depth))) + return self.project_upright_depth_to_upright_camera(pts_3d_upright_depth) + + +def rotx(t): + """Rotation about the x-axis.""" + c = np.cos(t) + s = np.sin(t) + return np.array([[1, 0, 0], + [0, c, -s], + [0, s, c]]) + + +def roty(t): + """Rotation about the y-axis.""" + c = np.cos(t) + s = np.sin(t) + return np.array([[c, 0, s], + [0, 1, 0], + [-s, 0, c]]) + + +def rotz(t): + """Rotation about the z-axis.""" + c = np.cos(t) + s = np.sin(t) + return np.array([[c, -s, 0], + [s, c, 0], + [0, 0, 1]]) + + +def transform_from_rot_trans(R, t): + """Transforation matrix from rotation matrix and translation vector.""" + R = R.reshape(3, 3) + t = t.reshape(3, 1) + return np.vstack((np.hstack([R, t]), [0, 0, 0, 1])) + + +def inverse_rigid_trans(Tr): + """Inverse a rigid body transform matrix (3x4 as [R|t]) + [R'|-R't; 0|1] + """ + inv_Tr = np.zeros_like(Tr) # 3x4 + inv_Tr[0:3, 0:3] = np.transpose(Tr[0:3, 0:3]) + inv_Tr[0:3, 3] = np.dot(-np.transpose(Tr[0:3, 0:3]), Tr[0:3, 3]) + return inv_Tr + + +def read_sunrgbd_label(label_filename): + lines = [line.rstrip() for line in open(label_filename)] + objects = [SUNObject3d(line) for line in lines] + return objects + + +def load_image(img_filename): + return cv2.imread(img_filename) + + +def load_depth_points(depth_filename): + depth = np.loadtxt(depth_filename) + return depth + + +def load_depth_points_mat(depth_filename): + depth = sio.loadmat(depth_filename)['instance'] + return depth + + +def random_shift_box2d(box2d, shift_ratio=0.1): + ''' Randomly shift box center, randomly scale width and height + ''' + r = shift_ratio + xmin, ymin, xmax, ymax = box2d + h = ymax - ymin + w = xmax - xmin + cx = (xmin + xmax) / 2.0 + cy = (ymin + ymax) / 2.0 + cx2 = cx + w * r * (np.random.random() * 2 - 1) + cy2 = cy + h * r * (np.random.random() * 2 - 1) + h2 = h * (1 + np.random.random() * 2 * r - r) # 0.9 to 1.1 + w2 = w * (1 + np.random.random() * 2 * r - r) # 0.9 to 1.1 + return np.array([cx2 - w2 / 2.0, cy2 - h2 / 2.0, cx2 + w2 / 2.0, cy2 + h2 / 2.0]) + + +def in_hull(p, hull): + from scipy.spatial import Delaunay + if not isinstance(hull, Delaunay): + hull = Delaunay(hull) + return hull.find_simplex(p) >= 0 + + +def extract_pc_in_box3d(pc, box3d): + ''' pc: (N,3), box3d: (8,3) ''' + box3d_roi_inds = in_hull(pc[:, 0:3], box3d) + return pc[box3d_roi_inds, :], box3d_roi_inds + + +def my_compute_box_3d(center, size, heading_angle): + R = rotz(-1 * heading_angle) + l, w, h = size + x_corners = [-l, l, l, -l, -l, l, l, -l] + y_corners = [w, w, -w, -w, w, w, -w, -w] + z_corners = [h, h, h, h, -h, -h, -h, -h] + corners_3d = np.dot(R, np.vstack([x_corners, y_corners, z_corners])) + corners_3d[0, :] += center[0] + corners_3d[1, :] += center[1] + corners_3d[2, :] += center[2] + return np.transpose(corners_3d) + + +def compute_box_3d(obj, calib): + ''' Takes an object and a projection matrix (P) and projects the 3d + bounding box into the image plane. + Returns: + corners_2d: (8,2) array in image coord. + corners_3d: (8,3) array in in upright depth coord. + ''' + center = obj.centroid + + # compute rotational matrix around yaw axis + R = rotz(-1 * obj.heading_angle) + # b,a,c = dimension + # print R, a,b,c + + # 3d bounding box dimensions + l = obj.l # along heading arrow + w = obj.w # perpendicular to heading arrow + h = obj.h + + # rotate and translate 3d bounding box + x_corners = [-l, l, l, -l, -l, l, l, -l] + y_corners = [w, w, -w, -w, w, w, -w, -w] + z_corners = [h, h, h, h, -h, -h, -h, -h] + corners_3d = np.dot(R, np.vstack([x_corners, y_corners, z_corners])) + corners_3d[0, :] += center[0] + corners_3d[1, :] += center[1] + corners_3d[2, :] += center[2] + + # project the 3d bounding box into the image plane + corners_2d, _ = calib.project_upright_depth_to_image(np.transpose(corners_3d)) + # print 'corners_2d: ', corners_2d + return corners_2d, np.transpose(corners_3d) + + +def compute_orientation_3d(obj, calib): + ''' Takes an object and a projection matrix (P) and projects the 3d + object orientation vector into the image plane. + Returns: + orientation_2d: (2,2) array in image coord. + orientation_3d: (2,3) array in depth coord. + ''' + + # orientation in object coordinate system + ori = obj.orientation + orientation_3d = np.array([[0, ori[0]], [0, ori[1]], [0, 0]]) + center = obj.centroid + orientation_3d[0, :] = orientation_3d[0, :] + center[0] + orientation_3d[1, :] = orientation_3d[1, :] + center[1] + orientation_3d[2, :] = orientation_3d[2, :] + center[2] + + # project orientation into the image plane + orientation_2d, _ = calib.project_upright_depth_to_image(np.transpose(orientation_3d)) + return orientation_2d, np.transpose(orientation_3d) + + +def draw_projected_box3d(image, qs, color=(255, 255, 255), thickness=2): + ''' Draw 3d bounding box in image + qs: (8,2) array of vertices for the 3d box in following order: + 1 -------- 0 + /| /| + 2 -------- 3 . + | | | | + . 5 -------- 4 + |/ |/ + 6 -------- 7 + ''' + qs = qs.astype(np.int32) + for k in range(0, 4): + # http://docs.enthought.com/mayavi/mayavi/auto/mlab_helper_functions.html + i, j = k, (k + 1) % 4 + cv2.line(image, (qs[i, 0], qs[i, 1]), (qs[j, 0], qs[j, 1]), color, thickness, + cv2.CV_AA) # use LINE_AA for opencv3 + + i, j = k + 4, (k + 1) % 4 + 4 + cv2.line(image, (qs[i, 0], qs[i, 1]), (qs[j, 0], qs[j, 1]), color, thickness, cv2.CV_AA) + + i, j = k, k + 4 + cv2.line(image, (qs[i, 0], qs[i, 1]), (qs[j, 0], qs[j, 1]), color, thickness, cv2.CV_AA) + return image + + +import pickle +import gzip + + +def save_zipped_pickle(obj, filename, protocol=-1): + with gzip.open(filename, 'wb') as f: + pickle.dump(obj, f, protocol) + + +def load_zipped_pickle(filename): + with gzip.open(filename, 'rb') as f: + loaded_object = pickle.load(f) + return loaded_object diff --git a/train_dist.py b/train_dist.py new file mode 100644 index 0000000..fea4f36 --- /dev/null +++ b/train_dist.py @@ -0,0 +1,493 @@ +import os +import sys +import time +import numpy as np +import json +import argparse + +import torch +import torch.optim as optim +from torch.utils.data import DataLoader +import torch.distributed as dist +from torch.nn.parallel import DistributedDataParallel + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +ROOT_DIR = BASE_DIR +sys.path.append(os.path.join(ROOT_DIR, 'pointnet2')) + +from utils import get_scheduler, setup_logger +from models import GroupFreeDetector, get_loss +from models import APCalculator, parse_predictions, parse_groundtruths + + +def parse_option(): + parser = argparse.ArgumentParser() + # Model + parser.add_argument('--width', default=1, type=int, help='backbone width') + parser.add_argument('--num_target', type=int, default=256, help='Proposal number [default: 256]') + parser.add_argument('--sampling', default='kps', type=str, help='Query points sampling method (kps, fps)') + + # Transformer + parser.add_argument('--nhead', default=8, type=int, help='multi-head number') + parser.add_argument('--num_decoder_layers', default=6, type=int, help='number of decoder layers') + parser.add_argument('--dim_feedforward', default=2048, type=int, help='dim_feedforward') + parser.add_argument('--transformer_dropout', default=0.1, type=float, help='transformer_dropout') + parser.add_argument('--transformer_activation', default='relu', type=str, help='transformer_activation') + parser.add_argument('--self_position_embedding', default='loc_learned', type=str, + help='position_embedding in self attention (none, xyz_learned, loc_learned)') + parser.add_argument('--cross_position_embedding', default='xyz_learned', type=str, + help='position embedding in cross attention (none, xyz_learned)') + + # Loss + parser.add_argument('--query_points_generator_loss_coef', default=0.8, type=float) + parser.add_argument('--obj_loss_coef', default=0.1, type=float, help='Loss weight for objectness loss') + parser.add_argument('--box_loss_coef', default=1, type=float, help='Loss weight for box loss') + parser.add_argument('--sem_cls_loss_coef', default=0.1, type=float, help='Loss weight for classification loss') + parser.add_argument('--center_loss_type', default='smoothl1', type=str, help='(smoothl1, l1)') + parser.add_argument('--center_delta', default=1.0, type=float, help='delta for smoothl1 loss in center loss') + parser.add_argument('--size_loss_type', default='smoothl1', type=str, help='(smoothl1, l1)') + parser.add_argument('--size_delta', default=1.0, type=float, help='delta for smoothl1 loss in size loss') + parser.add_argument('--heading_loss_type', default='smoothl1', type=str, help='(smoothl1, l1)') + parser.add_argument('--heading_delta', default=1.0, type=float, help='delta for smoothl1 loss in heading loss') + parser.add_argument('--query_points_obj_topk', default=4, type=int, help='query_points_obj_topk') + + # Data + parser.add_argument('--batch_size', type=int, default=8, help='Batch Size during training [default: 8]') + parser.add_argument('--dataset', default='scannet', help='Dataset name. sunrgbd or scannet. [default: scannet]') + parser.add_argument('--num_point', type=int, default=50000, help='Point Number [default: 50000]') + parser.add_argument('--data_root', default='data', help='data root path') + parser.add_argument('--use_height', action='store_true', help='Use height signal in input.') + parser.add_argument('--use_color', action='store_true', help='Use RGB color in input.') + parser.add_argument('--use_sunrgbd_v2', action='store_true', help='Use V2 box labels for SUN RGB-D dataset') + parser.add_argument('--num_workers', type=int, default=4, help='num of workers to use') + + # Training + parser.add_argument('--start_epoch', type=int, default=1, help='Epoch to run [default: 1]') + parser.add_argument('--max_epoch', type=int, default=400, help='Epoch to run [default: 180]') + parser.add_argument('--optimizer', type=str, default='adamW', help='optimizer') + parser.add_argument('--momentum', type=float, default=0.9, help='momentum for SGD') + parser.add_argument('--weight_decay', type=float, default=0.0005, + help='Optimization L2 weight decay [default: 0.0005]') + parser.add_argument('--learning_rate', type=float, default=0.004, + help='Initial learning rate for all except decoder [default: 0.004]') + parser.add_argument('--decoder_learning_rate', type=float, default=0.0004, + help='Initial learning rate for decoder [default: 0.0004]') + parser.add_argument('--lr-scheduler', type=str, default='step', + choices=["step", "cosine"], help="learning rate scheduler") + parser.add_argument('--warmup-epoch', type=int, default=-1, help='warmup epoch') + parser.add_argument('--warmup-multiplier', type=int, default=100, help='warmup multiplier') + parser.add_argument('--lr_decay_epochs', type=int, default=[280, 340], nargs='+', + help='for step scheduler. where to decay lr, can be a list') + parser.add_argument('--lr_decay_rate', type=float, default=0.1, + help='for step scheduler. decay rate for learning rate') + parser.add_argument('--clip_norm', default=0.1, type=float, + help='gradient clipping max norm') + parser.add_argument('--bn_momentum', type=float, default=0.1, help='Default bn momeuntum') + parser.add_argument('--syncbn', action='store_true', help='whether to use sync bn') + + # io + parser.add_argument('--checkpoint_path', default=None, help='Model checkpoint path [default: None]') + parser.add_argument('--log_dir', default='log', help='Dump dir to save model checkpoint [default: log]') + parser.add_argument('--print_freq', type=int, default=10, help='print frequency') + parser.add_argument('--save_freq', type=int, default=100, help='save frequency') + parser.add_argument('--val_freq', type=int, default=50, help='val frequency') + + # others + parser.add_argument("--local_rank", type=int, help='local rank for DistributedDataParallel') + parser.add_argument('--ap_iou_thresholds', type=float, default=[0.25, 0.5], nargs='+', + help='A list of AP IoU thresholds [default: 0.25,0.5]') + parser.add_argument("--rng_seed", type=int, default=0, help='manual seed') + + args, unparsed = parser.parse_known_args() + + return args + + +def load_checkpoint(args, model, optimizer, scheduler): + logger.info("=> loading checkpoint '{}'".format(args.checkpoint_path)) + + checkpoint = torch.load(args.checkpoint_path, map_location='cpu') + args.start_epoch = checkpoint['epoch'] + 1 + model.load_state_dict(checkpoint['model']) + optimizer.load_state_dict(checkpoint['optimizer']) + scheduler.load_state_dict(checkpoint['scheduler']) + + logger.info("=> loaded successfully '{}' (epoch {})".format(args.checkpoint_path, checkpoint['epoch'])) + + del checkpoint + torch.cuda.empty_cache() + + +def save_checkpoint(args, epoch, model, optimizer, scheduler, save_cur=False): + logger.info('==> Saving...') + state = { + 'config': args, + 'save_path': '', + 'model': model.state_dict(), + 'optimizer': optimizer.state_dict(), + 'scheduler': scheduler.state_dict(), + 'epoch': epoch, + } + + if save_cur: + state['save_path'] = os.path.join(args.log_dir, f'ckpt_epoch_{epoch}.pth') + torch.save(state, os.path.join(args.log_dir, f'ckpt_epoch_{epoch}.pth')) + logger.info("Saved in {}".format(os.path.join(args.log_dir, f'ckpt_epoch_{epoch}.pth'))) + elif epoch % args.save_freq == 0: + state['save_path'] = os.path.join(args.log_dir, f'ckpt_epoch_{epoch}.pth') + torch.save(state, os.path.join(args.log_dir, f'ckpt_epoch_{epoch}.pth')) + logger.info("Saved in {}".format(os.path.join(args.log_dir, f'ckpt_epoch_{epoch}.pth'))) + else: + # state['save_path'] = 'current.pth' + # torch.save(state, os.path.join(args.log_dir, 'current.pth')) + print("not saving checkpoint") + pass + + +def get_loader(args): + # Init datasets and dataloaders + def my_worker_init_fn(worker_id): + np.random.seed(np.random.get_state()[1][0] + worker_id) + + # Create Dataset and Dataloader + if args.dataset == 'sunrgbd': + from sunrgbd.sunrgbd_detection_dataset import SunrgbdDetectionVotesDataset + from sunrgbd.model_util_sunrgbd import SunrgbdDatasetConfig + + DATASET_CONFIG = SunrgbdDatasetConfig() + TRAIN_DATASET = SunrgbdDetectionVotesDataset('train', num_points=args.num_point, + augment=True, + use_color=True if args.use_color else False, + use_height=True if args.use_height else False, + use_v1=(not args.use_sunrgbd_v2), + data_root=args.data_root) + TEST_DATASET = SunrgbdDetectionVotesDataset('val', num_points=args.num_point, + augment=False, + use_color=True if args.use_color else False, + use_height=True if args.use_height else False, + use_v1=(not args.use_sunrgbd_v2), + data_root=args.data_root) + elif args.dataset == 'scannet': + sys.path.append(os.path.join(ROOT_DIR, 'scannet')) + from scannet.scannet_detection_dataset import ScannetDetectionDataset + from scannet.model_util_scannet import ScannetDatasetConfig + + DATASET_CONFIG = ScannetDatasetConfig() + TRAIN_DATASET = ScannetDetectionDataset('train', num_points=args.num_point, + augment=True, + use_color=True if args.use_color else False, + use_height=True if args.use_height else False, + data_root=args.data_root) + TEST_DATASET = ScannetDetectionDataset('val', num_points=args.num_point, + augment=False, + use_color=True if args.use_color else False, + use_height=True if args.use_height else False, + data_root=args.data_root) + else: + raise NotImplementedError(f'Unknown dataset {args.dataset}. Exiting...') + + print(f"train_len: {len(TRAIN_DATASET)}, test_len: {len(TEST_DATASET)}") + + train_sampler = torch.utils.data.distributed.DistributedSampler(TRAIN_DATASET) + train_loader = torch.utils.data.DataLoader(TRAIN_DATASET, + batch_size=args.batch_size, + shuffle=False, + num_workers=args.num_workers, + worker_init_fn=my_worker_init_fn, + pin_memory=True, + sampler=train_sampler, + drop_last=True) + + test_sampler = torch.utils.data.distributed.DistributedSampler(TEST_DATASET, shuffle=False) + test_loader = torch.utils.data.DataLoader(TEST_DATASET, + batch_size=args.batch_size, + shuffle=False, + num_workers=args.num_workers, + worker_init_fn=my_worker_init_fn, + pin_memory=True, + sampler=test_sampler, + drop_last=False) + print(f"train_loader_len: {len(train_loader)}, test_loader_len: {len(test_loader)}") + + return train_loader, test_loader, DATASET_CONFIG + + +def get_model(args, DATASET_CONFIG): + if args.use_height: + num_input_channel = int(args.use_color) * 3 + 1 + else: + num_input_channel = int(args.use_color) * 3 + model = GroupFreeDetector(num_class=DATASET_CONFIG.num_class, + num_heading_bin=DATASET_CONFIG.num_heading_bin, + num_size_cluster=DATASET_CONFIG.num_size_cluster, + mean_size_arr=DATASET_CONFIG.mean_size_arr, + input_feature_dim=num_input_channel, + width=args.width, + bn_momentum=args.bn_momentum, + sync_bn=True if args.syncbn else False, + num_proposal=args.num_target, + sampling=args.sampling, + dropout=args.transformer_dropout, + activation=args.transformer_activation, + nhead=args.nhead, + num_decoder_layers=args.num_decoder_layers, + dim_feedforward=args.dim_feedforward, + self_position_embedding=args.self_position_embedding, + cross_position_embedding=args.cross_position_embedding) + + criterion = get_loss + return model, criterion + + +def main(args): + train_loader, test_loader, DATASET_CONFIG = get_loader(args) + n_data = len(train_loader.dataset) + logger.info(f"length of training dataset: {n_data}") + n_data = len(test_loader.dataset) + logger.info(f"length of testing dataset: {n_data}") + + model, criterion = get_model(args, DATASET_CONFIG) + if dist.get_rank() == 0: + logger.info(str(model)) + # optimizer + if args.optimizer == 'adamW': + param_dicts = [ + {"params": [p for n, p in model.named_parameters() if "decoder" not in n and p.requires_grad]}, + { + "params": [p for n, p in model.named_parameters() if "decoder" in n and p.requires_grad], + "lr": args.decoder_learning_rate, + }, + ] + optimizer = optim.AdamW(param_dicts, + lr=args.learning_rate, + weight_decay=args.weight_decay) + else: + raise NotImplementedError + + scheduler = get_scheduler(optimizer, len(train_loader), args) + model = model.cuda() + model = DistributedDataParallel(model, device_ids=[args.local_rank], broadcast_buffers=False) + + if args.checkpoint_path: + assert os.path.isfile(args.checkpoint_path) + load_checkpoint(args, model, optimizer, scheduler) + + # Used for AP calculation + CONFIG_DICT = {'remove_empty_box': False, 'use_3d_nms': True, + 'nms_iou': 0.25, 'use_old_type_nms': False, 'cls_nms': True, + 'per_class_proposal': True, 'conf_thresh': 0.0, + 'dataset_config': DATASET_CONFIG} + + for epoch in range(args.start_epoch, args.max_epoch + 1): + train_loader.sampler.set_epoch(epoch) + + tic = time.time() + + train_one_epoch(epoch, train_loader, DATASET_CONFIG, model, criterion, optimizer, scheduler, args) + + logger.info('epoch {}, total time {:.2f}, ' + 'lr_base {:.5f}, lr_decoder {:.5f}'.format(epoch, (time.time() - tic), + optimizer.param_groups[0]['lr'], + optimizer.param_groups[1]['lr'])) + + if epoch % args.val_freq == 0: + evaluate_one_epoch(test_loader, DATASET_CONFIG, CONFIG_DICT, args.ap_iou_thresholds, model, + criterion, args) + + if dist.get_rank() == 0: + # save model + save_checkpoint(args, epoch, model, optimizer, scheduler) + evaluate_one_epoch(test_loader, DATASET_CONFIG, CONFIG_DICT, args.ap_iou_thresholds, model, criterion, args) + save_checkpoint(args, 'last', model, optimizer, scheduler, save_cur=True) + logger.info("Saved in {}".format(os.path.join(args.log_dir, f'ckpt_epoch_last.pth'))) + return os.path.join(args.log_dir, f'ckpt_epoch_last.pth') + + +def train_one_epoch(epoch, train_loader, DATASET_CONFIG, model, criterion, optimizer, scheduler, config): + stat_dict = {} # collect statistics + model.train() # set model to training mode + for batch_idx, batch_data_label in enumerate(train_loader): + for key in batch_data_label: + batch_data_label[key] = batch_data_label[key].cuda(non_blocking=True) + inputs = {'point_clouds': batch_data_label['point_clouds']} + + # Forward pass + end_points = model(inputs) + + # Compute loss and gradients, update parameters. + for key in batch_data_label: + assert (key not in end_points) + end_points[key] = batch_data_label[key] + loss, end_points = criterion(end_points, DATASET_CONFIG, + num_decoder_layers=config.num_decoder_layers, + query_points_generator_loss_coef=config.query_points_generator_loss_coef, + obj_loss_coef=config.obj_loss_coef, + box_loss_coef=config.box_loss_coef, + sem_cls_loss_coef=config.sem_cls_loss_coef, + query_points_obj_topk=config.query_points_obj_topk, + center_loss_type=config.center_loss_type, + center_delta=config.center_delta, + size_loss_type=config.size_loss_type, + size_delta=config.size_delta, + heading_loss_type=config.heading_loss_type, + heading_delta=config.heading_delta, + ) + + optimizer.zero_grad() + loss.backward() + if config.clip_norm > 0: + grad_total_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), config.clip_norm) + optimizer.step() + scheduler.step() + + # Accumulate statistics and print out + stat_dict['grad_norm'] = grad_total_norm + for key in end_points: + if 'loss' in key or 'acc' in key or 'ratio' in key: + if key not in stat_dict: stat_dict[key] = 0 + if isinstance(end_points[key], float): + stat_dict[key] += end_points[key] + else: + stat_dict[key] += end_points[key].item() + + if (batch_idx + 1) % config.print_freq == 0: + logger.info(f'Train: [{epoch}][{batch_idx + 1}/{len(train_loader)}] ' + ''.join( + [f'{key} {stat_dict[key] / config.print_freq:.4f} \t' + for key in sorted(stat_dict.keys()) if 'loss' not in key])) + logger.info(f"grad_norm: {stat_dict['grad_norm']}") + logger.info(''.join([f'{key} {stat_dict[key] / config.print_freq:.4f} \t' + for key in sorted(stat_dict.keys()) if + 'loss' in key and 'proposal_' not in key and 'last_' not in key and 'head_' not in key])) + logger.info(''.join([f'{key} {stat_dict[key] / config.print_freq:.4f} \t' + for key in sorted(stat_dict.keys()) if 'last_' in key])) + logger.info(''.join([f'{key} {stat_dict[key] / config.print_freq:.4f} \t' + for key in sorted(stat_dict.keys()) if 'proposal_' in key])) + for ihead in range(config.num_decoder_layers - 2, -1, -1): + logger.info(''.join([f'{key} {stat_dict[key] / config.print_freq:.4f} \t' + for key in sorted(stat_dict.keys()) if f'{ihead}head_' in key])) + + for key in sorted(stat_dict.keys()): + stat_dict[key] = 0 + + +def evaluate_one_epoch(test_loader, DATASET_CONFIG, CONFIG_DICT, AP_IOU_THRESHOLDS, model, criterion, config): + stat_dict = {} + + if config.num_decoder_layers > 0: + prefixes = ['last_', 'proposal_'] + [f'{i}head_' for i in range(config.num_decoder_layers - 1)] + else: + prefixes = ['proposal_'] # only proposal + ap_calculator_list = [APCalculator(iou_thresh, DATASET_CONFIG.class2type) \ + for iou_thresh in AP_IOU_THRESHOLDS] + mAPs = [[iou_thresh, {k: 0 for k in prefixes}] for iou_thresh in AP_IOU_THRESHOLDS] + + model.eval() # set model to eval mode (for bn and dp) + batch_pred_map_cls_dict = {k: [] for k in prefixes} + batch_gt_map_cls_dict = {k: [] for k in prefixes} + + for batch_idx, batch_data_label in enumerate(test_loader): + for key in batch_data_label: + batch_data_label[key] = batch_data_label[key].cuda(non_blocking=True) + + # Forward pass + inputs = {'point_clouds': batch_data_label['point_clouds']} + with torch.no_grad(): + end_points = model(inputs) + + # Compute loss + for key in batch_data_label: + assert (key not in end_points) + end_points[key] = batch_data_label[key] + loss, end_points = criterion(end_points, DATASET_CONFIG, + num_decoder_layers=config.num_decoder_layers, + query_points_generator_loss_coef=config.query_points_generator_loss_coef, + obj_loss_coef=config.obj_loss_coef, + box_loss_coef=config.box_loss_coef, + sem_cls_loss_coef=config.sem_cls_loss_coef, + query_points_obj_topk=config.query_points_obj_topk, + center_loss_type=config.center_loss_type, + center_delta=config.center_delta, + size_loss_type=config.size_loss_type, + size_delta=config.size_delta, + heading_loss_type=config.heading_loss_type, + heading_delta=config.heading_delta, + ) + + # Accumulate statistics and print out + for key in end_points: + if 'loss' in key or 'acc' in key or 'ratio' in key: + if key not in stat_dict: stat_dict[key] = 0 + if isinstance(end_points[key], float): + stat_dict[key] += end_points[key] + else: + stat_dict[key] += end_points[key].item() + + for prefix in prefixes: + batch_pred_map_cls = parse_predictions(end_points, CONFIG_DICT, prefix) + batch_gt_map_cls = parse_groundtruths(end_points, CONFIG_DICT) + batch_pred_map_cls_dict[prefix].append(batch_pred_map_cls) + batch_gt_map_cls_dict[prefix].append(batch_gt_map_cls) + + if (batch_idx + 1) % config.print_freq == 0: + logger.info(f'Eval: [{batch_idx + 1}/{len(test_loader)}] ' + ''.join( + [f'{key} {stat_dict[key] / (float(batch_idx + 1)):.4f} \t' + for key in sorted(stat_dict.keys()) if 'loss' not in key])) + logger.info(''.join([f'{key} {stat_dict[key] / (float(batch_idx + 1)):.4f} \t' + for key in sorted(stat_dict.keys()) if + 'loss' in key and 'proposal_' not in key and 'last_' not in key and 'head_' not in key])) + logger.info(''.join([f'{key} {stat_dict[key] / (float(batch_idx + 1)):.4f} \t' + for key in sorted(stat_dict.keys()) if 'last_' in key])) + logger.info(''.join([f'{key} {stat_dict[key] / (float(batch_idx + 1)):.4f} \t' + for key in sorted(stat_dict.keys()) if 'proposal_' in key])) + for ihead in range(config.num_decoder_layers - 2, -1, -1): + logger.info(''.join([f'{key} {stat_dict[key] / (float(batch_idx + 1)):.4f} \t' + for key in sorted(stat_dict.keys()) if f'{ihead}head_' in key])) + + mAP = 0.0 + for prefix in prefixes: + for (batch_pred_map_cls, batch_gt_map_cls) in zip(batch_pred_map_cls_dict[prefix], + batch_gt_map_cls_dict[prefix]): + for ap_calculator in ap_calculator_list: + ap_calculator.step(batch_pred_map_cls, batch_gt_map_cls) + # Evaluate average precision + for i, ap_calculator in enumerate(ap_calculator_list): + metrics_dict = ap_calculator.compute_metrics() + logger.info(f'=====================>{prefix} IOU THRESH: {AP_IOU_THRESHOLDS[i]}<=====================') + for key in metrics_dict: + logger.info(f'{key} {metrics_dict[key]}') + if prefix == 'last_' and ap_calculator.ap_iou_thresh > 0.3: + mAP = metrics_dict['mAP'] + mAPs[i][1][prefix] = metrics_dict['mAP'] + ap_calculator.reset() + + for mAP in mAPs: + logger.info(f'IoU[{mAP[0]}]:\t' + ''.join([f'{key}: {mAP[1][key]:.4f} \t' for key in sorted(mAP[1].keys())])) + + return mAP, mAPs + + +if __name__ == '__main__': + opt = parse_option() + torch.cuda.set_device(opt.local_rank) + torch.distributed.init_process_group(backend='nccl', init_method='env://') + torch.backends.cudnn.enabled = True + torch.backends.cudnn.benchmark = True + torch.backends.cudnn.deterministic = True + + LOG_DIR = os.path.join(opt.log_dir, 'group_free', + f'{opt.dataset}_{int(time.time())}', f'{np.random.randint(100000000)}') + while os.path.exists(LOG_DIR): + LOG_DIR = os.path.join(opt.log_dir, 'group_free', + f'{opt.dataset}_{int(time.time())}', f'{np.random.randint(100000000)}') + opt.log_dir = LOG_DIR + os.makedirs(opt.log_dir, exist_ok=True) + + logger = setup_logger(output=opt.log_dir, distributed_rank=dist.get_rank(), name="group-free") + if dist.get_rank() == 0: + path = os.path.join(opt.log_dir, "config.json") + with open(path, 'w') as f: + json.dump(vars(opt), f, indent=2) + logger.info("Full config saved to {}".format(path)) + logger.info(str(vars(opt))) + + ckpt_path = main(opt) diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000..c136fad --- /dev/null +++ b/utils/__init__.py @@ -0,0 +1,2 @@ +from .lr_scheduler import get_scheduler +from .logger import setup_logger \ No newline at end of file diff --git a/utils/box_util.py b/utils/box_util.py new file mode 100644 index 0000000..f21cd03 --- /dev/null +++ b/utils/box_util.py @@ -0,0 +1,320 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" Helper functions for calculating 2D and 3D bounding box IoU. + +Collected and written by Charles R. Qi +Last modified: Jul 2019 +""" +from __future__ import print_function + +import numpy as np +from scipy.spatial import ConvexHull + + +def polygon_clip(subjectPolygon, clipPolygon): + """ Clip a polygon with another polygon. + + Ref: https://rosettacode.org/wiki/Sutherland-Hodgman_polygon_clipping#Python + + Args: + subjectPolygon: a list of (x,y) 2d points, any polygon. + clipPolygon: a list of (x,y) 2d points, has to be *convex* + Note: + **points have to be counter-clockwise ordered** + + Return: + a list of (x,y) vertex point for the intersection polygon. + """ + + def inside(p): + return (cp2[0] - cp1[0]) * (p[1] - cp1[1]) > (cp2[1] - cp1[1]) * (p[0] - cp1[0]) + + def computeIntersection(): + dc = [cp1[0] - cp2[0], cp1[1] - cp2[1]] + dp = [s[0] - e[0], s[1] - e[1]] + n1 = cp1[0] * cp2[1] - cp1[1] * cp2[0] + n2 = s[0] * e[1] - s[1] * e[0] + n3 = 1.0 / (dc[0] * dp[1] - dc[1] * dp[0]) + return [(n1 * dp[0] - n2 * dc[0]) * n3, (n1 * dp[1] - n2 * dc[1]) * n3] + + outputList = subjectPolygon + cp1 = clipPolygon[-1] + + for clipVertex in clipPolygon: + cp2 = clipVertex + inputList = outputList + outputList = [] + s = inputList[-1] + + for subjectVertex in inputList: + e = subjectVertex + if inside(e): + if not inside(s): + outputList.append(computeIntersection()) + outputList.append(e) + elif inside(s): + outputList.append(computeIntersection()) + s = e + cp1 = cp2 + if len(outputList) == 0: + return None + return (outputList) + + +def poly_area(x, y): + """ Ref: http://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates """ + return 0.5 * np.abs(np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1))) + + +def convex_hull_intersection(p1, p2): + """ Compute area of two convex hull's intersection area. + p1,p2 are a list of (x,y) tuples of hull vertices. + return a list of (x,y) for the intersection and its volume + """ + inter_p = polygon_clip(p1, p2) + if inter_p is not None: + hull_inter = ConvexHull(inter_p) + return inter_p, hull_inter.volume + else: + return None, 0.0 + + +def box3d_vol(corners): + ''' corners: (8,3) no assumption on axis direction ''' + a = np.sqrt(np.sum((corners[0, :] - corners[1, :]) ** 2)) + b = np.sqrt(np.sum((corners[1, :] - corners[2, :]) ** 2)) + c = np.sqrt(np.sum((corners[0, :] - corners[4, :]) ** 2)) + return a * b * c + + +def is_clockwise(p): + x = p[:, 0] + y = p[:, 1] + return np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1)) > 0 + + +def box3d_iou(corners1, corners2): + ''' Compute 3D bounding box IoU. + + Input: + corners1: numpy array (8,3), assume up direction is negative Y + corners2: numpy array (8,3), assume up direction is negative Y + Output: + iou: 3D bounding box IoU + iou_2d: bird's eye view 2D bounding box IoU + + todo (rqi): add more description on corner points' orders. + ''' + # corner points are in counter clockwise order + rect1 = [(corners1[i, 0], corners1[i, 2]) for i in range(3, -1, -1)] + rect2 = [(corners2[i, 0], corners2[i, 2]) for i in range(3, -1, -1)] + area1 = poly_area(np.array(rect1)[:, 0], np.array(rect1)[:, 1]) + area2 = poly_area(np.array(rect2)[:, 0], np.array(rect2)[:, 1]) + inter, inter_area = convex_hull_intersection(rect1, rect2) + iou_2d = inter_area / (area1 + area2 - inter_area) + ymax = min(corners1[0, 1], corners2[0, 1]) + ymin = max(corners1[4, 1], corners2[4, 1]) + inter_vol = inter_area * max(0.0, ymax - ymin) + vol1 = box3d_vol(corners1) + vol2 = box3d_vol(corners2) + iou = inter_vol / (vol1 + vol2 - inter_vol) + return iou, iou_2d + + +def get_iou(bb1, bb2): + """ + Calculate the Intersection over Union (IoU) of two 2D bounding boxes. + + Parameters + ---------- + bb1 : dict + Keys: {'x1', 'x2', 'y1', 'y2'} + The (x1, y1) position is at the top left corner, + the (x2, y2) position is at the bottom right corner + bb2 : dict + Keys: {'x1', 'x2', 'y1', 'y2'} + The (x, y) position is at the top left corner, + the (x2, y2) position is at the bottom right corner + + Returns + ------- + float + in [0, 1] + """ + assert bb1['x1'] < bb1['x2'] + assert bb1['y1'] < bb1['y2'] + assert bb2['x1'] < bb2['x2'] + assert bb2['y1'] < bb2['y2'] + + # determine the coordinates of the intersection rectangle + x_left = max(bb1['x1'], bb2['x1']) + y_top = max(bb1['y1'], bb2['y1']) + x_right = min(bb1['x2'], bb2['x2']) + y_bottom = min(bb1['y2'], bb2['y2']) + + if x_right < x_left or y_bottom < y_top: + return 0.0 + + # The intersection of two axis-aligned bounding boxes is always an + # axis-aligned bounding box + intersection_area = (x_right - x_left) * (y_bottom - y_top) + + # compute the area of both AABBs + bb1_area = (bb1['x2'] - bb1['x1']) * (bb1['y2'] - bb1['y1']) + bb2_area = (bb2['x2'] - bb2['x1']) * (bb2['y2'] - bb2['y1']) + + # compute the intersection over union by taking the intersection + # area and dividing it by the sum of prediction + ground-truth + # areas - the interesection area + iou = intersection_area / float(bb1_area + bb2_area - intersection_area) + assert iou >= 0.0 + assert iou <= 1.0 + return iou + + +def box2d_iou(box1, box2): + ''' Compute 2D bounding box IoU. + + Input: + box1: tuple of (xmin,ymin,xmax,ymax) + box2: tuple of (xmin,ymin,xmax,ymax) + Output: + iou: 2D IoU scalar + ''' + return get_iou({'x1': box1[0], 'y1': box1[1], 'x2': box1[2], 'y2': box1[3]}, \ + {'x1': box2[0], 'y1': box2[1], 'x2': box2[2], 'y2': box2[3]}) + + +# ----------------------------------------------------------- +# Convert from box parameters to +# ----------------------------------------------------------- +def roty(t): + """Rotation about the y-axis.""" + c = np.cos(t) + s = np.sin(t) + return np.array([[c, 0, s], + [0, 1, 0], + [-s, 0, c]]) + + +def roty_batch(t): + """Rotation about the y-axis. + t: (x1,x2,...xn) + return: (x1,x2,...,xn,3,3) + """ + input_shape = t.shape + output = np.zeros(tuple(list(input_shape) + [3, 3])) + c = np.cos(t) + s = np.sin(t) + output[..., 0, 0] = c + output[..., 0, 2] = s + output[..., 1, 1] = 1 + output[..., 2, 0] = -s + output[..., 2, 2] = c + return output + + +def get_3d_box(box_size, heading_angle, center): + ''' box_size is array(l,w,h), heading_angle is radius clockwise from pos x axis, center is xyz of box center + output (8,3) array for 3D box cornders + Similar to utils/compute_orientation_3d + ''' + R = roty(heading_angle) + l, w, h = box_size + x_corners = [l / 2, l / 2, -l / 2, -l / 2, l / 2, l / 2, -l / 2, -l / 2]; + y_corners = [h / 2, h / 2, h / 2, h / 2, -h / 2, -h / 2, -h / 2, -h / 2]; + z_corners = [w / 2, -w / 2, -w / 2, w / 2, w / 2, -w / 2, -w / 2, w / 2]; + corners_3d = np.dot(R, np.vstack([x_corners, y_corners, z_corners])) + corners_3d[0, :] = corners_3d[0, :] + center[0]; + corners_3d[1, :] = corners_3d[1, :] + center[1]; + corners_3d[2, :] = corners_3d[2, :] + center[2]; + corners_3d = np.transpose(corners_3d) + return corners_3d + + +def get_3d_box_batch(box_size, heading_angle, center): + ''' box_size: [x1,x2,...,xn,3] + heading_angle: [x1,x2,...,xn] + center: [x1,x2,...,xn,3] + Return: + [x1,x3,...,xn,8,3] + ''' + input_shape = heading_angle.shape + R = roty_batch(heading_angle) + l = np.expand_dims(box_size[..., 0], -1) # [x1,...,xn,1] + w = np.expand_dims(box_size[..., 1], -1) + h = np.expand_dims(box_size[..., 2], -1) + corners_3d = np.zeros(tuple(list(input_shape) + [8, 3])) + corners_3d[..., :, 0] = np.concatenate((l / 2, l / 2, -l / 2, -l / 2, l / 2, l / 2, -l / 2, -l / 2), -1) + corners_3d[..., :, 1] = np.concatenate((h / 2, h / 2, h / 2, h / 2, -h / 2, -h / 2, -h / 2, -h / 2), -1) + corners_3d[..., :, 2] = np.concatenate((w / 2, -w / 2, -w / 2, w / 2, w / 2, -w / 2, -w / 2, w / 2), -1) + tlist = [i for i in range(len(input_shape))] + tlist += [len(input_shape) + 1, len(input_shape)] + corners_3d = np.matmul(corners_3d, np.transpose(R, tuple(tlist))) + corners_3d += np.expand_dims(center, -2) + return corners_3d + + +if __name__ == '__main__': + + # Function for polygon ploting + import matplotlib + from matplotlib.patches import Polygon + from matplotlib.collections import PatchCollection + import matplotlib.pyplot as plt + + + def plot_polys(plist, scale=500.0): + fig, ax = plt.subplots() + patches = [] + for p in plist: + poly = Polygon(np.array(p) / scale, True) + patches.append(poly) + + + pc = PatchCollection(patches, cmap=matplotlib.cm.jet, alpha=0.5) + colors = 100 * np.random.rand(len(patches)) + pc.set_array(np.array(colors)) + ax.add_collection(pc) + plt.show() + + # Demo on ConvexHull + points = np.random.rand(30, 2) # 30 random points in 2-D + hull = ConvexHull(points) + # **In 2D "volume" is is area, "area" is perimeter + print(('Hull area: ', hull.volume)) + for simplex in hull.simplices: + print(simplex) + + # Demo on convex hull overlaps + sub_poly = [(0, 0), (300, 0), (300, 300), (0, 300)] + clip_poly = [(150, 150), (300, 300), (150, 450), (0, 300)] + inter_poly = polygon_clip(sub_poly, clip_poly) + print(poly_area(np.array(inter_poly)[:, 0], np.array(inter_poly)[:, 1])) + + # Test convex hull interaction function + rect1 = [(50, 0), (50, 300), (300, 300), (300, 0)] + rect2 = [(150, 150), (300, 300), (150, 450), (0, 300)] + plot_polys([rect1, rect2]) + inter, area = convex_hull_intersection(rect1, rect2) + print((inter, area)) + if inter is not None: + print(poly_area(np.array(inter)[:, 0], np.array(inter)[:, 1])) + + print('------------------') + rect1 = [(0.30026005199835404, 8.9408694211408424), \ + (-1.1571105364358421, 9.4686676477075533), \ + (0.1777082043006144, 13.154404877812102), \ + (1.6350787927348105, 12.626606651245391)] + rect1 = [rect1[0], rect1[3], rect1[2], rect1[1]] + rect2 = [(0.23908745901608636, 8.8551095691132886), \ + (-1.2771419487733995, 9.4269062966181956), \ + (0.13138836963152717, 13.161896351296868), \ + (1.647617777421013, 12.590099623791961)] + rect2 = [rect2[0], rect2[3], rect2[2], rect2[1]] + plot_polys([rect1, rect2]) + inter, area = convex_hull_intersection(rect1, rect2) + print((inter, area)) diff --git a/utils/eval_det.py b/utils/eval_det.py new file mode 100644 index 0000000..6e38069 --- /dev/null +++ b/utils/eval_det.py @@ -0,0 +1,274 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" Generic Code for Object Detection Evaluation + + Input: + For each class: + For each image: + Predictions: box, score + Groundtruths: box + + Output: + For each class: + precision-recal and average precision + + Author: Charles R. Qi + + Ref: https://raw.githubusercontent.com/rbgirshick/py-faster-rcnn/master/lib/datasets/voc_eval.py +""" +import numpy as np + + +def voc_ap(rec, prec, use_07_metric=False): + """ ap = voc_ap(rec, prec, [use_07_metric]) + Compute VOC AP given precision and recall. + If use_07_metric is true, uses the + VOC 07 11 point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0. + for t in np.arange(0., 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + + +import os +import sys + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +from metric_util import calc_iou # axis-aligned 3D box IoU + + +def get_iou(bb1, bb2): + """ Compute IoU of two bounding boxes. + ** Define your bod IoU function HERE ** + """ + # pass + iou3d = calc_iou(bb1, bb2) + return iou3d + + +from box_util import box3d_iou + + +def get_iou_obb(bb1, bb2): + iou3d, iou2d = box3d_iou(bb1, bb2) + return iou3d + + +def get_iou_main(get_iou_func, args): + return get_iou_func(*args) + + +def eval_det_cls(pred, gt, ovthresh=0.25, use_07_metric=False, get_iou_func=get_iou): + """ Generic functions to compute precision/recall for object detection + for a single class. + Input: + pred: map of {img_id: [(bbox, score)]} where bbox is numpy array + gt: map of {img_id: [bbox]} + ovthresh: scalar, iou threshold + use_07_metric: bool, if True use VOC07 11 point method + Output: + rec: numpy array of length nd + prec: numpy array of length nd + ap: scalar, average precision + """ + + # construct gt objects + class_recs = {} # {img_id: {'bbox': bbox list, 'det': matched list}} + npos = 0 + for img_id in gt.keys(): + bbox = np.array(gt[img_id]) + det = [False] * len(bbox) + npos += len(bbox) + class_recs[img_id] = {'bbox': bbox, 'det': det} + # pad empty list to all other imgids + for img_id in pred.keys(): + if img_id not in gt: + class_recs[img_id] = {'bbox': np.array([]), 'det': []} + + # construct dets + image_ids = [] + confidence = [] + BB = [] + for img_id in pred.keys(): + for box, score in pred[img_id]: + image_ids.append(img_id) + confidence.append(score) + BB.append(box) + confidence = np.array(confidence) + BB = np.array(BB) # (nd,4 or 8,3 or 6) + + # sort by confidence + sorted_ind = np.argsort(-confidence) + sorted_scores = np.sort(-confidence) + BB = BB[sorted_ind, ...] + image_ids = [image_ids[x] for x in sorted_ind] + + # go down dets and mark TPs and FPs + nd = len(image_ids) + tp = np.zeros(nd) + fp = np.zeros(nd) + for d in range(nd): + # if d%100==0: print(d) + R = class_recs[image_ids[d]] + bb = BB[d, ...].astype(float) + ovmax = -np.inf + BBGT = R['bbox'].astype(float) + + if BBGT.size > 0: + # compute overlaps + for j in range(BBGT.shape[0]): + iou = get_iou_main(get_iou_func, (bb, BBGT[j, ...])) + if iou > ovmax: + ovmax = iou + jmax = j + + # print d, ovmax + if ovmax > ovthresh: + if not R['det'][jmax]: + tp[d] = 1. + R['det'][jmax] = 1 + else: + fp[d] = 1. + else: + fp[d] = 1. + + # compute precision recall + fp = np.cumsum(fp) + tp = np.cumsum(tp) + rec = tp / float(npos) + # print('NPOS: ', npos) + # avoid divide by zero in case the first detection matches a difficult + # ground truth + prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) + ap = voc_ap(rec, prec, use_07_metric) + + return rec, prec, ap + + +def eval_det_cls_wrapper(arguments): + pred, gt, ovthresh, use_07_metric, get_iou_func = arguments + rec, prec, ap = eval_det_cls(pred, gt, ovthresh, use_07_metric, get_iou_func) + return (rec, prec, ap) + + +def eval_det(pred_all, gt_all, ovthresh=0.25, use_07_metric=False, get_iou_func=get_iou): + """ Generic functions to compute precision/recall for object detection + for multiple classes. + Input: + pred_all: map of {img_id: [(classname, bbox, score)]} + gt_all: map of {img_id: [(classname, bbox)]} + ovthresh: scalar, iou threshold + use_07_metric: bool, if true use VOC07 11 point method + Output: + rec: {classname: rec} + prec: {classname: prec_all} + ap: {classname: scalar} + """ + pred = {} # map {classname: pred} + gt = {} # map {classname: gt} + for img_id in pred_all.keys(): + for classname, bbox, score in pred_all[img_id]: + if classname not in pred: pred[classname] = {} + if img_id not in pred[classname]: + pred[classname][img_id] = [] + if classname not in gt: gt[classname] = {} + if img_id not in gt[classname]: + gt[classname][img_id] = [] + pred[classname][img_id].append((bbox, score)) + for img_id in gt_all.keys(): + for classname, bbox in gt_all[img_id]: + if classname not in gt: gt[classname] = {} + if img_id not in gt[classname]: + gt[classname][img_id] = [] + gt[classname][img_id].append(bbox) + + rec = {} + prec = {} + ap = {} + for classname in gt.keys(): + # print('Computing AP for class: ', classname) + rec[classname], prec[classname], ap[classname] = eval_det_cls(pred[classname], gt[classname], ovthresh, + use_07_metric, get_iou_func) + # print(classname, ap[classname]) + + return rec, prec, ap + + +from multiprocessing import Pool + + +def eval_det_multiprocessing(pred_all, gt_all, ovthresh=0.25, use_07_metric=False, get_iou_func=get_iou): + """ Generic functions to compute precision/recall for object detection + for multiple classes. + Input: + pred_all: map of {img_id: [(classname, bbox, score)]} + gt_all: map of {img_id: [(classname, bbox)]} + ovthresh: scalar, iou threshold + use_07_metric: bool, if true use VOC07 11 point method + Output: + rec: {classname: rec} + prec: {classname: prec_all} + ap: {classname: scalar} + """ + pred = {} # map {classname: pred} + gt = {} # map {classname: gt} + for img_id in pred_all.keys(): + for classname, bbox, score in pred_all[img_id]: + if classname not in pred: pred[classname] = {} + if img_id not in pred[classname]: + pred[classname][img_id] = [] + if classname not in gt: gt[classname] = {} + if img_id not in gt[classname]: + gt[classname][img_id] = [] + pred[classname][img_id].append((bbox, score)) + for img_id in gt_all.keys(): + for classname, bbox in gt_all[img_id]: + if classname not in gt: gt[classname] = {} + if img_id not in gt[classname]: + gt[classname][img_id] = [] + gt[classname][img_id].append(bbox) + + rec = {} + prec = {} + ap = {} + p = Pool(processes=10) + ret_values = p.map(eval_det_cls_wrapper, + [(pred[classname], gt[classname], ovthresh, use_07_metric, get_iou_func) for classname in + gt.keys() if classname in pred]) + p.close() + for i, classname in enumerate(gt.keys()): + if classname in pred: + rec[classname], prec[classname], ap[classname] = ret_values[i] + else: + rec[classname] = 0 + prec[classname] = 0 + ap[classname] = 0 + # print(classname, ap[classname]) + + return rec, prec, ap diff --git a/utils/logger.py b/utils/logger.py new file mode 100644 index 0000000..a86f498 --- /dev/null +++ b/utils/logger.py @@ -0,0 +1,94 @@ +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved +import functools +import logging +import os +import sys +from termcolor import colored + + +class _ColorfulFormatter(logging.Formatter): + def __init__(self, *args, **kwargs): + self._root_name = kwargs.pop("root_name") + "." + self._abbrev_name = kwargs.pop("abbrev_name", "") + if len(self._abbrev_name): + self._abbrev_name = self._abbrev_name + "." + super(_ColorfulFormatter, self).__init__(*args, **kwargs) + + def formatMessage(self, record): + record.name = record.name.replace(self._root_name, self._abbrev_name) + log = super(_ColorfulFormatter, self).formatMessage(record) + if record.levelno == logging.WARNING: + prefix = colored("WARNING", "red", attrs=["blink"]) + elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL: + prefix = colored("ERROR", "red", attrs=["blink", "underline"]) + else: + return log + return prefix + " " + log + + +# so that calling setup_logger multiple times won't add many handlers +@functools.lru_cache() +def setup_logger( + output=None, distributed_rank=0, *, color=True, name="log", abbrev_name=None +): + """ + Initialize the detectron2 logger and set its verbosity level to "INFO". + + Args: + output (str): a file name or a directory to save log. If None, will not save log file. + If ends with ".txt" or ".log", assumed to be a file name. + Otherwise, logs will be saved to `output/log.txt`. + name (str): the root module name of this logger + + Returns: + logging.Logger: a logger + """ + logger = logging.getLogger(name) + logger.setLevel(logging.DEBUG) + logger.propagate = False + + if abbrev_name is None: + abbrev_name = name + + plain_formatter = logging.Formatter( + "[%(asctime)s] %(name)s %(levelname)s: %(message)s", datefmt="%m/%d %H:%M:%S" + ) + # stdout logging: master only + if distributed_rank == 0: + ch = logging.StreamHandler(stream=sys.stdout) + ch.setLevel(logging.DEBUG) + if color: + formatter = _ColorfulFormatter( + colored("[%(asctime)s %(name)s]: ", "green") + "%(message)s", + datefmt="%m/%d %H:%M:%S", + root_name=name, + abbrev_name=str(abbrev_name), + ) + else: + formatter = plain_formatter + ch.setFormatter(formatter) + logger.addHandler(ch) + + # file logging: all workers + if output is not None: + if output.endswith(".txt") or output.endswith(".log"): + filename = output + else: + filename = os.path.join(output, "log.txt") + if distributed_rank > 0: + filename = filename + f".rank{distributed_rank}" + os.makedirs(os.path.dirname(filename), exist_ok=True) + + fh = logging.StreamHandler(_cached_log_stream(filename)) + fh.setLevel(logging.DEBUG) + fh.setFormatter(plain_formatter) + logger.addHandler(fh) + + return logger + + +# cache the opened file object, so that different calls to `setup_logger` +# with the same file name can safely write to the same file. +@functools.lru_cache(maxsize=None) +def _cached_log_stream(filename): + return open(filename, "a") diff --git a/utils/lr_scheduler.py b/utils/lr_scheduler.py new file mode 100644 index 0000000..b12f5d8 --- /dev/null +++ b/utils/lr_scheduler.py @@ -0,0 +1,87 @@ +# noinspection PyProtectedMember +from torch.optim.lr_scheduler import _LRScheduler, MultiStepLR, CosineAnnealingLR + + +# noinspection PyAttributeOutsideInit +class GradualWarmupScheduler(_LRScheduler): + """ Gradually warm-up(increasing) learning rate in optimizer. + Proposed in 'Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour'. + Args: + optimizer (Optimizer): Wrapped optimizer. + multiplier: init learning rate = base lr / multiplier + warmup_epoch: target learning rate is reached at warmup_epoch, gradually + after_scheduler: after target_epoch, use this scheduler(eg. ReduceLROnPlateau) + """ + + def __init__(self, optimizer, multiplier, warmup_epoch, after_scheduler, last_epoch=-1): + self.multiplier = multiplier + if self.multiplier <= 1.: + raise ValueError('multiplier should be greater than 1.') + self.warmup_epoch = warmup_epoch + self.after_scheduler = after_scheduler + self.finished = False + super().__init__(optimizer, last_epoch=last_epoch) + + def get_lr(self): + if self.last_epoch > self.warmup_epoch: + return self.after_scheduler.get_lr() + else: + return [base_lr / self.multiplier * ((self.multiplier - 1.) * self.last_epoch / self.warmup_epoch + 1.) + for base_lr in self.base_lrs] + + def step(self, epoch=None): + if epoch is None: + epoch = self.last_epoch + 1 + self.last_epoch = epoch + if epoch > self.warmup_epoch: + self.after_scheduler.step(epoch - self.warmup_epoch) + else: + super(GradualWarmupScheduler, self).step(epoch) + + def state_dict(self): + """Returns the state of the scheduler as a :class:`dict`. + + It contains an entry for every variable in self.__dict__ which + is not the optimizer. + """ + + state = {key: value for key, value in self.__dict__.items() if key != 'optimizer' and key != 'after_scheduler'} + state['after_scheduler'] = self.after_scheduler.state_dict() + return state + + def load_state_dict(self, state_dict): + """Loads the schedulers state. + + Arguments: + state_dict (dict): scheduler state. Should be an object returned + from a call to :meth:`state_dict`. + """ + + after_scheduler_state = state_dict.pop('after_scheduler') + self.__dict__.update(state_dict) + self.after_scheduler.load_state_dict(after_scheduler_state) + + +def get_scheduler(optimizer, n_iter_per_epoch, args): + if "cosine" in args.lr_scheduler: + scheduler = CosineAnnealingLR( + optimizer=optimizer, + eta_min=0.000001, + T_max=(args.max_epoch - args.warmup_epoch) * n_iter_per_epoch) + elif "step" in args.lr_scheduler: + if isinstance(args.lr_decay_epochs, int): + args.lr_decay_epochs = [args.lr_decay_epochs] + scheduler = MultiStepLR( + optimizer=optimizer, + gamma=args.lr_decay_rate, + milestones=[(m - args.warmup_epoch) * n_iter_per_epoch for m in args.lr_decay_epochs]) + else: + raise NotImplementedError(f"scheduler {args.lr_scheduler} not supported") + + if args.warmup_epoch > 0: + scheduler = GradualWarmupScheduler( + optimizer, + multiplier=args.warmup_multiplier, + after_scheduler=scheduler, + warmup_epoch=args.warmup_epoch * n_iter_per_epoch) + return scheduler diff --git a/utils/metric_util.py b/utils/metric_util.py new file mode 100644 index 0000000..da54d6d --- /dev/null +++ b/utils/metric_util.py @@ -0,0 +1,170 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" Utility functions for metric evaluation. + +Author: Or Litany and Charles R. Qi +""" + +import os +import sys +import torch + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(BASE_DIR) + +import numpy as np + +# Mesh IO +import trimesh + + +# ---------------------------------------- +# Precision and Recall +# ---------------------------------------- + +def multi_scene_precision_recall(labels, pred, iou_thresh, conf_thresh, label_mask, pred_mask=None): + ''' + Args: + labels: (B, N, 6) + pred: (B, M, 6) + iou_thresh: scalar + conf_thresh: scalar + label_mask: (B, N,) with values in 0 or 1 to indicate which GT boxes to consider. + pred_mask: (B, M,) with values in 0 or 1 to indicate which PRED boxes to consider. + Returns: + TP,FP,FN,Precision,Recall + ''' + # Make sure the masks are not Torch tensor, otherwise the mask==1 returns uint8 array instead + # of True/False array as in numpy + assert (not torch.is_tensor(label_mask)) + assert (not torch.is_tensor(pred_mask)) + TP, FP, FN = 0, 0, 0 + if label_mask is None: label_mask = np.ones((labels.shape[0], labels.shape[1])) + if pred_mask is None: pred_mask = np.ones((pred.shape[0], pred.shape[1])) + for batch_idx in range(labels.shape[0]): + TP_i, FP_i, FN_i = single_scene_precision_recall(labels[batch_idx, label_mask[batch_idx, :] == 1, :], + pred[batch_idx, pred_mask[batch_idx, :] == 1, :], + iou_thresh, conf_thresh) + TP += TP_i + FP += FP_i + FN += FN_i + + return TP, FP, FN, precision_recall(TP, FP, FN) + + +def single_scene_precision_recall(labels, pred, iou_thresh, conf_thresh): + """Compute P and R for predicted bounding boxes. Ignores classes! + Args: + labels: (N x bbox) ground-truth bounding boxes (6 dims) + pred: (M x (bbox + conf)) predicted bboxes with confidence and maybe classification + Returns: + TP, FP, FN + """ + + # for each pred box with high conf (C), compute IoU with all gt boxes. + # TP = number of times IoU > th ; FP = C - TP + # FN - number of scene objects without good match + + gt_bboxes = labels[:, :6] + + num_scene_bboxes = gt_bboxes.shape[0] + conf = pred[:, 6] + + conf_pred_bbox = pred[np.where(conf > conf_thresh)[0], :6] + num_conf_pred_bboxes = conf_pred_bbox.shape[0] + + # init an array to keep iou between generated and scene bboxes + iou_arr = np.zeros([num_conf_pred_bboxes, num_scene_bboxes]) + for g_idx in range(num_conf_pred_bboxes): + for s_idx in range(num_scene_bboxes): + iou_arr[g_idx, s_idx] = calc_iou(conf_pred_bbox[g_idx, :], gt_bboxes[s_idx, :]) + + good_match_arr = (iou_arr >= iou_thresh) + + TP = good_match_arr.any(axis=1).sum() + FP = num_conf_pred_bboxes - TP + FN = num_scene_bboxes - good_match_arr.any(axis=0).sum() + + return TP, FP, FN + + +def precision_recall(TP, FP, FN): + Prec = 1.0 * TP / (TP + FP) if TP + FP > 0 else 0 + Rec = 1.0 * TP / (TP + FN) + return Prec, Rec + + +def calc_iou(box_a, box_b): + """Computes IoU of two axis aligned bboxes. + Args: + box_a, box_b: 6D of center and lengths + Returns: + iou + """ + + max_a = box_a[0:3] + box_a[3:6] / 2 + max_b = box_b[0:3] + box_b[3:6] / 2 + min_max = np.array([max_a, max_b]).min(0) + + min_a = box_a[0:3] - box_a[3:6] / 2 + min_b = box_b[0:3] - box_b[3:6] / 2 + max_min = np.array([min_a, min_b]).max(0) + if not ((min_max > max_min).all()): + return 0.0 + + intersection = (min_max - max_min).prod() + vol_a = box_a[3:6].prod() + vol_b = box_b[3:6].prod() + union = vol_a + vol_b - intersection + return 1.0 * intersection / union + + +if __name__ == '__main__': + print('running some tests') + + ############ + ## Test IoU + ############ + box_a = np.array([0, 0, 0, 1, 1, 1]) + box_b = np.array([0, 0, 0, 2, 2, 2]) + expected_iou = 1.0 / 8 + pred_iou = calc_iou(box_a, box_b) + assert expected_iou == pred_iou, 'function returned wrong IoU' + + box_a = np.array([0, 0, 0, 1, 1, 1]) + box_b = np.array([10, 10, 10, 2, 2, 2]) + expected_iou = 0.0 + pred_iou = calc_iou(box_a, box_b) + assert expected_iou == pred_iou, 'function returned wrong IoU' + + print('IoU test -- PASSED') + + ######################### + ## Test Precition Recall + ######################### + gt_boxes = np.array([[0, 0, 0, 1, 1, 1], [3, 0, 1, 1, 10, 1]]) + detected_boxes = np.array([[0, 0, 0, 1, 1, 1, 1.0], [3, 0, 1, 1, 10, 1, 0.9]]) + TP, FP, FN = single_scene_precision_recall(gt_boxes, detected_boxes, 0.5, 0.5) + assert TP == 2 and FP == 0 and FN == 0 + assert precision_recall(TP, FP, FN) == (1, 1) + + detected_boxes = np.array([[0, 0, 0, 1, 1, 1, 1.0]]) + TP, FP, FN = single_scene_precision_recall(gt_boxes, detected_boxes, 0.5, 0.5) + assert TP == 1 and FP == 0 and FN == 1 + assert precision_recall(TP, FP, FN) == (1, 0.5) + + detected_boxes = np.array([[0, 0, 0, 1, 1, 1, 1.0], [-1, -1, 0, 0.1, 0.1, 1, 1.0]]) + TP, FP, FN = single_scene_precision_recall(gt_boxes, detected_boxes, 0.5, 0.5) + assert TP == 1 and FP == 1 and FN == 1 + assert precision_recall(TP, FP, FN) == (0.5, 0.5) + + # wrong box has low confidence + detected_boxes = np.array([[0, 0, 0, 1, 1, 1, 1.0], [-1, -1, 0, 0.1, 0.1, 1, 0.1]]) + TP, FP, FN = single_scene_precision_recall(gt_boxes, detected_boxes, 0.5, 0.5) + assert TP == 1 and FP == 0 and FN == 1 + assert precision_recall(TP, FP, FN) == (1, 0.5) + + print('Precition Recall test -- PASSED') diff --git a/utils/nms.py b/utils/nms.py new file mode 100644 index 0000000..ba4d744 --- /dev/null +++ b/utils/nms.py @@ -0,0 +1,183 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import numpy as np +from pc_util import bbox_corner_dist_measure + +# boxes are axis aigned 2D boxes of shape (n,5) in FLOAT numbers with (x1,y1,x2,y2,score) +''' Ref: https://www.pyimagesearch.com/2015/02/16/faster-non-maximum-suppression-python/ +Ref: https://github.com/vickyboy47/nms-python/blob/master/nms.py +''' + + +def nms_2d(boxes, overlap_threshold): + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + y2 = boxes[:, 3] + score = boxes[:, 4] + area = (x2 - x1) * (y2 - y1) + + I = np.argsort(score) + pick = [] + while (I.size != 0): + last = I.size + i = I[-1] + pick.append(i) + suppress = [last - 1] + for pos in range(last - 1): + j = I[pos] + xx1 = max(x1[i], x1[j]) + yy1 = max(y1[i], y1[j]) + xx2 = min(x2[i], x2[j]) + yy2 = min(y2[i], y2[j]) + w = xx2 - xx1 + h = yy2 - yy1 + if (w > 0 and h > 0): + o = w * h / area[j] + print('Overlap is', o) + if (o > overlap_threshold): + suppress.append(pos) + I = np.delete(I, suppress) + return pick + + +def nms_2d_faster(boxes, overlap_threshold, old_type=False): + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + y2 = boxes[:, 3] + score = boxes[:, 4] + area = (x2 - x1) * (y2 - y1) + + I = np.argsort(score) + pick = [] + while (I.size != 0): + last = I.size + i = I[-1] + pick.append(i) + + xx1 = np.maximum(x1[i], x1[I[:last - 1]]) + yy1 = np.maximum(y1[i], y1[I[:last - 1]]) + xx2 = np.minimum(x2[i], x2[I[:last - 1]]) + yy2 = np.minimum(y2[i], y2[I[:last - 1]]) + + w = np.maximum(0, xx2 - xx1) + h = np.maximum(0, yy2 - yy1) + + if old_type: + o = (w * h) / area[I[:last - 1]] + else: + inter = w * h + o = inter / (area[i] + area[I[:last - 1]] - inter) + + I = np.delete(I, np.concatenate(([last - 1], np.where(o > overlap_threshold)[0]))) + + return pick + + +def nms_3d_faster(boxes, overlap_threshold, old_type=False): + x1 = boxes[:, 0] + y1 = boxes[:, 1] + z1 = boxes[:, 2] + x2 = boxes[:, 3] + y2 = boxes[:, 4] + z2 = boxes[:, 5] + score = boxes[:, 6] + area = (x2 - x1) * (y2 - y1) * (z2 - z1) + + I = np.argsort(score) + pick = [] + while (I.size != 0): + last = I.size + i = I[-1] + pick.append(i) + + xx1 = np.maximum(x1[i], x1[I[:last - 1]]) + yy1 = np.maximum(y1[i], y1[I[:last - 1]]) + zz1 = np.maximum(z1[i], z1[I[:last - 1]]) + xx2 = np.minimum(x2[i], x2[I[:last - 1]]) + yy2 = np.minimum(y2[i], y2[I[:last - 1]]) + zz2 = np.minimum(z2[i], z2[I[:last - 1]]) + + l = np.maximum(0, xx2 - xx1) + w = np.maximum(0, yy2 - yy1) + h = np.maximum(0, zz2 - zz1) + + if old_type: + o = (l * w * h) / area[I[:last - 1]] + else: + inter = l * w * h + o = inter / (area[i] + area[I[:last - 1]] - inter) + + I = np.delete(I, np.concatenate(([last - 1], np.where(o > overlap_threshold)[0]))) + + return pick + + +def nms_3d_faster_samecls(boxes, overlap_threshold, old_type=False): + x1 = boxes[:, 0] + y1 = boxes[:, 1] + z1 = boxes[:, 2] + x2 = boxes[:, 3] + y2 = boxes[:, 4] + z2 = boxes[:, 5] + score = boxes[:, 6] + cls = boxes[:, 7] + area = (x2 - x1) * (y2 - y1) * (z2 - z1) + + I = np.argsort(score) + pick = [] + while (I.size != 0): + last = I.size + i = I[-1] + pick.append(i) + + xx1 = np.maximum(x1[i], x1[I[:last - 1]]) + yy1 = np.maximum(y1[i], y1[I[:last - 1]]) + zz1 = np.maximum(z1[i], z1[I[:last - 1]]) + xx2 = np.minimum(x2[i], x2[I[:last - 1]]) + yy2 = np.minimum(y2[i], y2[I[:last - 1]]) + zz2 = np.minimum(z2[i], z2[I[:last - 1]]) + cls1 = cls[i] + cls2 = cls[I[:last - 1]] + + l = np.maximum(0, xx2 - xx1) + w = np.maximum(0, yy2 - yy1) + h = np.maximum(0, zz2 - zz1) + + if old_type: + o = (l * w * h) / area[I[:last - 1]] + else: + inter = l * w * h + o = inter / (area[i] + area[I[:last - 1]] - inter) + o = o * (cls1 == cls2) + + I = np.delete(I, np.concatenate(([last - 1], np.where(o > overlap_threshold)[0]))) + + return pick + + +def nms_crnr_dist(boxes, conf, overlap_threshold): + I = np.argsort(conf) + pick = [] + while (I.size != 0): + last = I.size + i = I[-1] + pick.append(i) + + scores = [] + for ind in I[:-1]: + scores.append(bbox_corner_dist_measure(boxes[i, :], boxes[ind, :])) + + I = np.delete(I, np.concatenate(([last - 1], np.where(np.array(scores) > overlap_threshold)[0]))) + + return pick + + +if __name__ == '__main__': + a = np.random.random((100, 5)) + print(nms_2d(a, 0.9)) + print(nms_2d_faster(a, 0.9)) diff --git a/utils/nn_distance.py b/utils/nn_distance.py new file mode 100644 index 0000000..f9a302c --- /dev/null +++ b/utils/nn_distance.py @@ -0,0 +1,96 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" Chamfer distance in Pytorch. +Author: Charles R. Qi +""" + +import torch +import torch.nn as nn +import numpy as np + + +def huber_loss(error, delta=1.0): + """ + Args: + error: Torch tensor (d1,d2,...,dk) + Returns: + loss: Torch tensor (d1,d2,...,dk) + + x = error = pred - gt or dist(pred,gt) + 0.5 * |x|^2 if |x|<=d + 0.5 * d^2 + d * (|x|-d) if |x|>d + Ref: https://github.com/charlesq34/frustum-pointnets/blob/master/models/model_util.py + """ + abs_error = torch.abs(error) + # quadratic = torch.min(abs_error, torch.FloatTensor([delta])) + quadratic = torch.clamp(abs_error, max=delta) + linear = (abs_error - quadratic) + loss = 0.5 * quadratic ** 2 + delta * linear + return loss + + +def nn_distance(pc1, pc2, l1smooth=False, delta=1.0, l1=False): + """ + Input: + pc1: (B,N,C) torch tensor + pc2: (B,M,C) torch tensor + l1smooth: bool, whether to use l1smooth loss + delta: scalar, the delta used in l1smooth loss + Output: + dist1: (B,N) torch float32 tensor + idx1: (B,N) torch int64 tensor + dist2: (B,M) torch float32 tensor + idx2: (B,M) torch int64 tensor + """ + N = pc1.shape[1] + M = pc2.shape[1] + pc1_expand_tile = pc1.unsqueeze(2).repeat(1, 1, M, 1) + pc2_expand_tile = pc2.unsqueeze(1).repeat(1, N, 1, 1) + pc_diff = pc1_expand_tile - pc2_expand_tile + + if l1smooth: + pc_dist = torch.sum(huber_loss(pc_diff, delta), dim=-1) # (B,N,M) + elif l1: + pc_dist = torch.sum(torch.abs(pc_diff), dim=-1) # (B,N,M) + else: + pc_dist = torch.sum(pc_diff ** 2, dim=-1) # (B,N,M) + dist1, idx1 = torch.min(pc_dist, dim=2) # (B,N) + dist2, idx2 = torch.min(pc_dist, dim=1) # (B,M) + return dist1, idx1, dist2, idx2 + + +def demo_nn_distance(): + np.random.seed(0) + pc1arr = np.random.random((1, 5, 3)) + pc2arr = np.random.random((1, 6, 3)) + pc1 = torch.from_numpy(pc1arr.astype(np.float32)) + pc2 = torch.from_numpy(pc2arr.astype(np.float32)) + dist1, idx1, dist2, idx2 = nn_distance(pc1, pc2) + print(dist1) + print(idx1) + dist = np.zeros((5, 6)) + for i in range(5): + for j in range(6): + dist[i, j] = np.sum((pc1arr[0, i, :] - pc2arr[0, j, :]) ** 2) + print(dist) + print('-' * 30) + print('L1smooth dists:') + dist1, idx1, dist2, idx2 = nn_distance(pc1, pc2, True) + print(dist1) + print(idx1) + dist = np.zeros((5, 6)) + for i in range(5): + for j in range(6): + error = np.abs(pc1arr[0, i, :] - pc2arr[0, j, :]) + quad = np.minimum(error, 1.0) + linear = error - quad + loss = 0.5 * quad ** 2 + 1.0 * linear + dist[i, j] = np.sum(loss) + print(dist) + + +if __name__ == '__main__': + demo_nn_distance() diff --git a/utils/pc_util.py b/utils/pc_util.py new file mode 100644 index 0000000..480c386 --- /dev/null +++ b/utils/pc_util.py @@ -0,0 +1,560 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +""" Utility functions for processing point clouds. + +Author: Charles R. Qi and Or Litany +""" + +import os +import sys +import random + +BASE_DIR = os.path.dirname(os.path.abspath(__file__)) +ROOT_DIR = os.path.dirname(BASE_DIR) +sys.path.append(ROOT_DIR) +sys.path.append(os.path.join(ROOT_DIR, 'pointnet2')) +import pointnet2_utils + +# Point cloud IO +import numpy as np +import torch + +try: + from plyfile import PlyData, PlyElement +except: + print("Please install the module 'plyfile' for PLY i/o, e.g.") + print("pip install plyfile") + sys.exit(-1) + +# Mesh IO +import trimesh + +import matplotlib.pyplot as pyplot + + +# ---------------------------------------- +# Point Cloud Sampling +# ---------------------------------------- + +def random_sampling(pc, num_sample, replace=None, return_choices=False): + """ Input is NxC, output is num_samplexC + """ + if replace is None: replace = (pc.shape[0] < num_sample) + choices = np.random.choice(pc.shape[0], num_sample, replace=replace) + if return_choices: + return pc[choices], choices + else: + return pc[choices] + + +# ---------------------------------------- +# 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 + + +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, :]) + + 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: + pc = random_sampling(pc, num_sample, False) + elif pc.shape[0] < num_sample: + pc = np.lib.pad(pc, ((0, num_sample - pc.shape[0]), (0, 0)), 'edge') + # Normalize + pc_center = (np.array([i, j, k]) + 0.5) * voxel - radius + pc = (pc - pc_center) / voxel # shift and scale + vol[i, j, k, :, :] = pc + return vol + + +def point_cloud_to_image_batch(point_clouds, imgsize, radius=1.0, num_sample=128): + """ Input is BxNx3 a batch of point cloud + Output is BxIxIxnum_samplex3 + Added on Feb 19 + """ + img_list = [] + for b in range(point_clouds.shape[0]): + img = point_cloud_to_image(point_clouds[b, :, :], imgsize, radius, num_sample) + img_list.append(np.expand_dims(img, 0)) + return np.concatenate(img_list, 0) + + +def point_cloud_to_image(points, imgsize, radius=1.0, num_sample=128): + """ input is Nx3 points + output is imgsize*imgsize*num_sample*3 + assumes points are in range [-radius, radius] + samples num_sample points in each pixel, if there are less than + num_sample points, replicate the points + Added on Feb 19 + """ + img = np.zeros((imgsize, imgsize, num_sample, 3)) + pixel = 2 * radius / float(imgsize) + locations = (points[:, 0:2] + radius) / pixel # Nx2 + 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, :]) + for i in range(imgsize): + for j in range(imgsize): + if (i, j) not in loc2pc: + img[i, j, :, :] = np.zeros((num_sample, 3)) + else: + pc = loc2pc[(i, j)] + pc = np.vstack(pc) + if pc.shape[0] > num_sample: + pc = random_sampling(pc, num_sample, False) + elif pc.shape[0] < num_sample: + pc = np.lib.pad(pc, ((0, num_sample - pc.shape[0]), (0, 0)), 'edge') + pc_center = (np.array([i, j]) + 0.5) * pixel - radius + pc[:, 0:2] = (pc[:, 0:2] - pc_center) / pixel + img[i, j, :, :] = pc + return img + + +# ---------------------------------------- +# 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) + + +def write_ply_color(points, labels, filename, num_classes=None, colormap=pyplot.cm.jet): + """ Color (N,3) points with labels (N) within range 0 ~ num_classes-1 as OBJ file """ + 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)) + + vertex = [] + # colors = [pyplot.cm.jet(i/float(num_classes)) for i in range(num_classes)] + colors = [colormap(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] + vertex.append((points[i, 0], points[i, 1], points[i, 2], c[0], c[1], c[2])) + vertex = np.array(vertex, + dtype=[('x', 'f4'), ('y', 'f4'), ('z', 'f4'), ('red', 'u1'), ('green', 'u1'), ('blue', 'u1')]) + + el = PlyElement.describe(vertex, 'vertex', comments=['vertices']) + PlyData([el], text=True).write(filename) + + +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() + + +# ---------------------------------------- +# Simple Point cloud and Volume Renderers +# ---------------------------------------- + +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) + + +# ---------------------------------------- +# Simple Point manipulations +# ---------------------------------------- +def rotate_point_cloud(points, rotation_matrix=None): + """ Input: (n,3), Output: (n,3) """ + # Rotate in-place around Z axis. + if rotation_matrix is None: + rotation_angle = np.random.uniform() * 2 * np.pi + sinval, cosval = np.sin(rotation_angle), np.cos(rotation_angle) + rotation_matrix = np.array([[cosval, sinval, 0], + [-sinval, cosval, 0], + [0, 0, 1]]) + ctr = points.mean(axis=0) + rotated_data = np.dot(points - ctr, rotation_matrix) + ctr + return rotated_data, rotation_matrix + + +def rotate_pc_along_y(pc, rot_angle): + ''' Input ps is NxC points with first 3 channels as XYZ + z is facing forward, x is left ward, y is downward + ''' + cosval = np.cos(rot_angle) + sinval = np.sin(rot_angle) + rotmat = np.array([[cosval, -sinval], [sinval, cosval]]) + pc[:, [0, 2]] = np.dot(pc[:, [0, 2]], np.transpose(rotmat)) + return pc + + +def roty(t): + """Rotation about the y-axis.""" + c = np.cos(t) + s = np.sin(t) + return np.array([[c, 0, s], + [0, 1, 0], + [-s, 0, c]]) + + +def roty_batch(t): + """Rotation about the y-axis. + t: (x1,x2,...xn) + return: (x1,x2,...,xn,3,3) + """ + input_shape = t.shape + output = np.zeros(tuple(list(input_shape) + [3, 3])) + c = np.cos(t) + s = np.sin(t) + output[..., 0, 0] = c + output[..., 0, 2] = s + output[..., 1, 1] = 1 + output[..., 2, 0] = -s + output[..., 2, 2] = c + return output + + +def rotz(t): + """Rotation about the z-axis.""" + c = np.cos(t) + s = np.sin(t) + return np.array([[c, -s, 0], + [s, c, 0], + [0, 0, 1]]) + + +# ---------------------------------------- +# BBox +# ---------------------------------------- +def bbox_corner_dist_measure(crnr1, crnr2): + """ compute distance between box corners to replace iou + Args: + crnr1, crnr2: Nx3 points of box corners in camera axis (y points down) + output is a scalar between 0 and 1 + """ + + dist = sys.maxsize + for y in range(4): + rows = ([(x + y) % 4 for x in range(4)] + [4 + (x + y) % 4 for x in range(4)]) + d_ = np.linalg.norm(crnr2[rows, :] - crnr1, axis=1).sum() / 8.0 + if d_ < dist: + dist = d_ + + u = sum([np.linalg.norm(x[0, :] - x[6, :]) for x in [crnr1, crnr2]]) / 2.0 + + measure = max(1.0 - dist / u, 0) + print(measure) + + return measure + + +def point_cloud_to_bbox(points): + """ Extract the axis aligned box from a pcl or batch of pcls + Args: + points: Nx3 points or BxNx3 + output is 6 dim: xyz pos of center and 3 lengths + """ + which_dim = len(points.shape) - 2 # first dim if a single cloud and second if batch + mn, mx = points.min(which_dim), points.max(which_dim) + lengths = mx - mn + cntr = 0.5 * (mn + mx) + return np.concatenate([cntr, lengths], axis=which_dim) + + +def write_bbox(scene_bbox, out_filename): + """Export scene bbox to meshes + Args: + scene_bbox: (N x 6 numpy array): xyz pos of center and 3 lengths + out_filename: (string) filename + + Note: + To visualize the boxes in MeshLab. + 1. Select the objects (the boxes) + 2. Filters -> Polygon and Quad Mesh -> Turn into Quad-Dominant Mesh + 3. Select Wireframe view. + """ + + def convert_box_to_trimesh_fmt(box): + ctr = box[:3] + lengths = box[3:] + trns = np.eye(4) + trns[0:3, 3] = ctr + trns[3, 3] = 1.0 + box_trimesh_fmt = trimesh.creation.box(lengths, trns) + return box_trimesh_fmt + + scene = trimesh.scene.Scene() + for box in scene_bbox: + scene.add_geometry(convert_box_to_trimesh_fmt(box)) + + mesh_list = trimesh.util.concatenate(scene.dump()) + # save to ply file + mesh_list.export(out_filename) + + return + + +def write_oriented_bbox(scene_bbox, out_filename): + """Export oriented (around Z axis) scene bbox to meshes + Args: + scene_bbox: (N x 7 numpy array): xyz pos of center and 3 lengths (dx,dy,dz) + and heading angle around Z axis. + Y forward, X right, Z upward. heading angle of positive X is 0, + heading angle of positive Y is 90 degrees. + out_filename: (string) filename + """ + + def heading2rotmat(heading_angle): + pass + rotmat = np.zeros((3, 3)) + rotmat[2, 2] = 1 + cosval = np.cos(heading_angle) + sinval = np.sin(heading_angle) + rotmat[0:2, 0:2] = np.array([[cosval, -sinval], [sinval, cosval]]) + return rotmat + + def convert_oriented_box_to_trimesh_fmt(box): + ctr = box[:3] + lengths = box[3:6] + trns = np.eye(4) + trns[0:3, 3] = ctr + trns[3, 3] = 1.0 + trns[0:3, 0:3] = heading2rotmat(box[6]) + box_trimesh_fmt = trimesh.creation.box(lengths, trns) + return box_trimesh_fmt + + scene = trimesh.scene.Scene() + for box in scene_bbox: + scene.add_geometry(convert_oriented_box_to_trimesh_fmt(box)) + + mesh_list = trimesh.util.concatenate(scene.dump()) + # save to ply file + mesh_list.export(out_filename) + + return + + +def write_oriented_bbox_camera_coord(scene_bbox, out_filename): + """Export oriented (around Y axis) scene bbox to meshes + Args: + scene_bbox: (N x 7 numpy array): xyz pos of center and 3 lengths (dx,dy,dz) + and heading angle around Y axis. + Z forward, X rightward, Y downward. heading angle of positive X is 0, + heading angle of negative Z is 90 degrees. + out_filename: (string) filename + """ + + def heading2rotmat(heading_angle): + pass + rotmat = np.zeros((3, 3)) + rotmat[1, 1] = 1 + cosval = np.cos(heading_angle) + sinval = np.sin(heading_angle) + rotmat[0, :] = np.array([cosval, 0, sinval]) + rotmat[2, :] = np.array([-sinval, 0, cosval]) + return rotmat + + def convert_oriented_box_to_trimesh_fmt(box): + ctr = box[:3] + lengths = box[3:6] + trns = np.eye(4) + trns[0:3, 3] = ctr + trns[3, 3] = 1.0 + trns[0:3, 0:3] = heading2rotmat(box[6]) + box_trimesh_fmt = trimesh.creation.box(lengths, trns) + return box_trimesh_fmt + + scene = trimesh.scene.Scene() + for box in scene_bbox: + scene.add_geometry(convert_oriented_box_to_trimesh_fmt(box)) + + mesh_list = trimesh.util.concatenate(scene.dump()) + # save to ply file + mesh_list.export(out_filename) + + return + + +def write_lines_as_cylinders(pcl, filename, rad=0.005, res=64): + """Create lines represented as cylinders connecting pairs of 3D points + Args: + pcl: (N x 2 x 3 numpy array): N pairs of xyz pos + filename: (string) filename for the output mesh (ply) file + rad: radius for the cylinder + res: number of sections used to create the cylinder + """ + scene = trimesh.scene.Scene() + for src, tgt in pcl: + # compute line + vec = tgt - src + M = trimesh.geometry.align_vectors([0, 0, 1], vec, False) + vec = tgt - src # compute again since align_vectors modifies vec in-place! + M[:3, 3] = 0.5 * src + 0.5 * tgt + height = np.sqrt(np.dot(vec, vec)) + scene.add_geometry(trimesh.creation.cylinder(radius=rad, height=height, sections=res, transform=M)) + mesh_list = trimesh.util.concatenate(scene.dump()) + mesh_list.export('%s.ply' % (filename)) + + +# ---------------------------------------- +# Testing +# ---------------------------------------- +if __name__ == '__main__': + print('running some tests') + + ############ + ## Test "write_lines_as_cylinders" + ############ + pcl = np.random.rand(32, 2, 3) + write_lines_as_cylinders(pcl, 'point_connectors') + input() + + scene_bbox = np.zeros((1, 7)) + scene_bbox[0, 3:6] = np.array([1, 2, 3]) # dx,dy,dz + scene_bbox[0, 6] = np.pi / 4 # 45 degrees + write_oriented_bbox(scene_bbox, 'single_obb_45degree.ply') + ############ + ## Test point_cloud_to_bbox + ############ + pcl = np.random.rand(32, 16, 3) + pcl_bbox = point_cloud_to_bbox(pcl) + assert pcl_bbox.shape == (32, 6) + + pcl = np.random.rand(16, 3) + pcl_bbox = point_cloud_to_bbox(pcl) + assert pcl_bbox.shape == (6,) + + ############ + ## Test corner distance + ############ + crnr1 = np.array([[2.59038660e+00, 8.96107932e-01, 4.73305349e+00], + [4.12281644e-01, 8.96107932e-01, 4.48046631e+00], + [2.97129656e-01, 8.96107932e-01, 5.47344275e+00], + [2.47523462e+00, 8.96107932e-01, 5.72602993e+00], + [2.59038660e+00, 4.41155793e-03, 4.73305349e+00], + [4.12281644e-01, 4.41155793e-03, 4.48046631e+00], + [2.97129656e-01, 4.41155793e-03, 5.47344275e+00], + [2.47523462e+00, 4.41155793e-03, 5.72602993e+00]]) + crnr2 = crnr1 + + print(bbox_corner_dist_measure(crnr1, crnr2)) + + print('tests PASSED')

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