diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..2c1d7b64c47bc3b72646f0f9c904f8e14fc6d997 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,20 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +examples/drone_video.mp4 filter=lfs diff=lfs merge=lfs -text +examples/IMG_9730.mov filter=lfs diff=lfs merge=lfs -text +examples/IMG_9731.mov filter=lfs diff=lfs merge=lfs -text +examples/IMG_9732.mov filter=lfs diff=lfs merge=lfs -text +examples/test16.mov filter=lfs diff=lfs merge=lfs -text +examples/test17.mov filter=lfs diff=lfs merge=lfs -text +examples/test18.mov filter=lfs diff=lfs merge=lfs -text +examples/test19.mov filter=lfs diff=lfs merge=lfs -text +third-party/DPVO/build/lib.win-amd64-3.9/lietorch_backends.cp39-win_amd64.pyd filter=lfs diff=lfs merge=lfs -text +third-party/DPVO/build/temp.win-amd64-3.9/Release/dpvo/altcorr/correlation.obj filter=lfs diff=lfs merge=lfs -text +third-party/DPVO/build/temp.win-amd64-3.9/Release/dpvo/altcorr/correlation_kernel.obj filter=lfs diff=lfs merge=lfs -text +third-party/DPVO/build/temp.win-amd64-3.9/Release/dpvo/fastba/ba.obj filter=lfs diff=lfs merge=lfs -text +third-party/DPVO/build/temp.win-amd64-3.9/Release/dpvo/fastba/ba_cuda.obj filter=lfs diff=lfs merge=lfs -text +third-party/DPVO/build/temp.win-amd64-3.9/Release/dpvo/lietorch/src/lietorch.obj filter=lfs diff=lfs merge=lfs -text +third-party/DPVO/build/temp.win-amd64-3.9/Release/dpvo/lietorch/src/lietorch_cpu.obj filter=lfs diff=lfs merge=lfs -text +third-party/DPVO/build/temp.win-amd64-3.9/Release/dpvo/lietorch/src/lietorch_gpu.obj filter=lfs diff=lfs merge=lfs -text +third-party/DPVO/dist/dpvo-0.0.0-py3.9-win-amd64.egg filter=lfs diff=lfs merge=lfs -text diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..cfd2c0ac8c42ba5f555553be291595b170717b60 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Soyong Shin + +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 index 4a1d051a1c83a04f6e20264ae633dbf6eb1ec8d5..378da23c710053edf04083c9d2679032b126a2db 100644 --- a/README.md +++ b/README.md @@ -1,11 +1,120 @@ ---- -title: Motionbert Meta Sapiens -emoji: 🌍 -colorFrom: green -colorTo: indigo -sdk: docker -pinned: false -short_description: Sapiens ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +# WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion + +PyTorch [![report](https://img.shields.io/badge/arxiv-report-red)](https://arxiv.org/abs/2312.07531) Project [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1ysUtGSwidTQIdBQRhq0hj63KbseFujkn?usp=sharing) + [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/wham-reconstructing-world-grounded-humans/3d-human-pose-estimation-on-3dpw)](https://paperswithcode.com/sota/3d-human-pose-estimation-on-3dpw?p=wham-reconstructing-world-grounded-humans) [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/wham-reconstructing-world-grounded-humans/3d-human-pose-estimation-on-emdb)](https://paperswithcode.com/sota/3d-human-pose-estimation-on-emdb?p=wham-reconstructing-world-grounded-humans) + + +https://github.com/yohanshin/WHAM/assets/46889727/da4602b4-0597-4e64-8da4-ab06931b23ee + + +## Introduction +This repository is the official [Pytorch](https://pytorch.org/) implementation of [WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion](https://arxiv.org/abs/2312.07531). For more information, please visit our [project page](https://wham.is.tue.mpg.de/). + + +## Installation +Please see [Installation](docs/INSTALL.md) for details. + + +## Quick Demo + +### [ Google Colab for WHAM demo is now available](https://colab.research.google.com/drive/1ysUtGSwidTQIdBQRhq0hj63KbseFujkn?usp=sharing) + +### Registration + +To download SMPL body models (Neutral, Female, and Male), you need to register for [SMPL](https://smpl.is.tue.mpg.de/) and [SMPLify](https://smplify.is.tue.mpg.de/). The username and password for both homepages will be used while fetching the demo data. + +Next, run the following script to fetch demo data. This script will download all the required dependencies including trained models and demo videos. + +```bash +bash fetch_demo_data.sh +``` + +You can try with one examplar video: +``` +python demo.py --video examples/IMG_9732.mov --visualize +``` + +We assume camera focal length following [CLIFF](https://github.com/haofanwang/CLIFF). You can specify known camera intrinsics [fx fy cx cy] for SLAM as the demo example below: +``` +python demo.py --video examples/drone_video.mp4 --calib examples/drone_calib.txt --visualize +``` + +You can skip SLAM if you only want to get camera-coordinate motion. You can run as: +``` +python demo.py --video examples/IMG_9732.mov --visualize --estimate_local_only +``` + +You can further refine the results of WHAM using Temporal SMPLify as a post processing. This will allow better 2D alignment as well as 3D accuracy. All you need to do is add `--run_smplify` flag when running demo. + +## Docker + +Please refer to [Docker](docs/DOCKER.md) for details. + +## Python API + +Please refer to [API](docs/API.md) for details. + +## Dataset +Please see [Dataset](docs/DATASET.md) for details. + +## Evaluation +```bash +# Evaluate on 3DPW dataset +python -m lib.eval.evaluate_3dpw --cfg configs/yamls/demo.yaml TRAIN.CHECKPOINT checkpoints/wham_vit_w_3dpw.pth.tar + +# Evaluate on RICH dataset +python -m lib.eval.evaluate_rich --cfg configs/yamls/demo.yaml TRAIN.CHECKPOINT checkpoints/wham_vit_w_3dpw.pth.tar + +# Evaluate on EMDB dataset (also computes W-MPJPE and WA-MPJPE) +python -m lib.eval.evaluate_emdb --cfg configs/yamls/demo.yaml --eval-split 1 TRAIN.CHECKPOINT checkpoints/wham_vit_w_3dpw.pth.tar # EMDB 1 + +python -m lib.eval.evaluate_emdb --cfg configs/yamls/demo.yaml --eval-split 2 TRAIN.CHECKPOINT checkpoints/wham_vit_w_3dpw.pth.tar # EMDB 2 +``` + +## Training +WHAM training involves into two different stages; (1) 2D to SMPL lifting through AMASS dataset and (2) finetuning with feature integration using the video datasets. Please see [Dataset](docs/DATASET.md) for preprocessing the training datasets. + +### Stage 1. +```bash +python train.py --cfg configs/yamls/stage1.yaml +``` + +### Stage 2. +Training stage 2 requires pretrained results from the stage 1. You can use your pretrained results, or download the weight from [Google Drive](https://drive.google.com/file/d/1Erjkho7O0bnZFawarntICRUCroaKabRE/view?usp=sharing) save as `checkpoints/wham_stage1.tar.pth`. +```bash +python train.py --cfg configs/yamls/stage2.yaml TRAIN.CHECKPOINT +``` + +### Train with BEDLAM +TBD + +## Acknowledgement +We would like to sincerely appreciate Hongwei Yi and Silvia Zuffi for the discussion and proofreading. Part of this work was done when Soyong Shin was an intern at the Max Planck Institute for Intelligence System. + +The base implementation is largely borrowed from [VIBE](https://github.com/mkocabas/VIBE) and [TCMR](https://github.com/hongsukchoi/TCMR_RELEASE). We use [ViTPose](https://github.com/ViTAE-Transformer/ViTPose) for 2D keypoints detection and [DPVO](https://github.com/princeton-vl/DPVO), [DROID-SLAM](https://github.com/princeton-vl/DROID-SLAM) for extracting camera motion. Please visit their official websites for more details. + +## TODO + +- [ ] Data preprocessing + +- [x] Training implementation + +- [x] Colab demo release + +- [x] Demo for custom videos + +## Citation +``` +@InProceedings{shin2023wham, +title={WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion}, +author={Shin, Soyong and Kim, Juyong and Halilaj, Eni and Black, Michael J.}, +booktitle={Computer Vision and Pattern Recognition (CVPR)}, +year={2024} +} +``` + +## License +Please see [License](./LICENSE) for details. + +## Contact +Please contact soyongs@andrew.cmu.edu for any questions related to this work. diff --git a/checkpoints/dpvo.pth b/checkpoints/dpvo.pth new file mode 100644 index 0000000000000000000000000000000000000000..25b16864668c8625f38021d17cc534258ff6297f --- /dev/null +++ b/checkpoints/dpvo.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:30d02dc2b88a321cf99aad8e4ea1152a44d791b5b65bf95ad036922819c0ff12 +size 14167743 diff --git 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a/configs/__pycache__/config.cpython-39.pyc b/configs/__pycache__/config.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d54c1c5e7237aaa07a716b7513de4a26bfdbac2c Binary files /dev/null and b/configs/__pycache__/config.cpython-39.pyc differ diff --git a/configs/__pycache__/constants.cpython-39.pyc b/configs/__pycache__/constants.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5159979482d30d508ad35f1370115d7154714364 Binary files /dev/null and b/configs/__pycache__/constants.cpython-39.pyc differ diff --git a/configs/config.py b/configs/config.py new file mode 100644 index 0000000000000000000000000000000000000000..2d58747ff089106fb22345a461345e833d711f6a --- /dev/null +++ b/configs/config.py @@ -0,0 +1,111 @@ +import argparse +from yacs.config import CfgNode as CN + +# Configuration variable +cfg = CN() + +cfg.TITLE = 'default' +cfg.OUTPUT_DIR = 'results' +cfg.EXP_NAME = 'default' +cfg.DEVICE = 'cuda' +cfg.DEBUG = False +cfg.EVAL = False +cfg.RESUME = False +cfg.LOGDIR = '' +cfg.NUM_WORKERS = 5 +cfg.SEED_VALUE = -1 +cfg.SUMMARY_ITER = 50 +cfg.MODEL_CONFIG = '' +cfg.FLIP_EVAL = False + +cfg.TRAIN = CN() +cfg.TRAIN.STAGE = 'stage1' +cfg.TRAIN.DATASET_EVAL = '3dpw' +cfg.TRAIN.CHECKPOINT = '' +cfg.TRAIN.BATCH_SIZE = 64 +cfg.TRAIN.START_EPOCH = 0 +cfg.TRAIN.END_EPOCH = 999 +cfg.TRAIN.OPTIM = 'Adam' +cfg.TRAIN.LR = 3e-4 +cfg.TRAIN.LR_FINETUNE = 5e-5 +cfg.TRAIN.LR_PATIENCE = 5 +cfg.TRAIN.LR_DECAY_RATIO = 0.1 +cfg.TRAIN.WD = 0.0 +cfg.TRAIN.MOMENTUM = 0.9 +cfg.TRAIN.MILESTONES = [50, 70] + +cfg.DATASET = CN() +cfg.DATASET.SEQLEN = 81 +cfg.DATASET.RATIO = [1.0, 0, 0, 0, 0] + +cfg.MODEL = CN() +cfg.MODEL.BACKBONE = 'vit' + +cfg.LOSS = CN() +cfg.LOSS.SHAPE_LOSS_WEIGHT = 0.001 +cfg.LOSS.JOINT2D_LOSS_WEIGHT = 5. +cfg.LOSS.JOINT3D_LOSS_WEIGHT = 5. +cfg.LOSS.VERTS3D_LOSS_WEIGHT = 1. +cfg.LOSS.POSE_LOSS_WEIGHT = 1. +cfg.LOSS.CASCADED_LOSS_WEIGHT = 0.0 +cfg.LOSS.CONTACT_LOSS_WEIGHT = 0.04 +cfg.LOSS.ROOT_VEL_LOSS_WEIGHT = 0.001 +cfg.LOSS.ROOT_POSE_LOSS_WEIGHT = 0.4 +cfg.LOSS.SLIDING_LOSS_WEIGHT = 0.5 +cfg.LOSS.CAMERA_LOSS_WEIGHT = 0.04 +cfg.LOSS.LOSS_WEIGHT = 60. +cfg.LOSS.CAMERA_LOSS_SKIP_EPOCH = 5 + + +def get_cfg_defaults(): + """Get a yacs CfgNode object with default values for my_project.""" + # Return a clone so that the defaults will not be altered + # This is for the "local variable" use pattern + return cfg.clone() + + +def get_cfg(args, test): + """ + Define configuration. + """ + import os + + cfg = get_cfg_defaults() + if os.path.exists(args.cfg): + cfg.merge_from_file(args.cfg) + + cfg.merge_from_list(args.opts) + if test: + cfg.merge_from_list(['EVAL', True]) + + return cfg.clone() + + +def bool_arg(value): + if value.lower() in ('yes', 'true', 't', 'y', '1'): + return True + elif value.lower() in ('no', 'false', 'f', 'n', '0'): + return False + + +def parse_args(test=False): + parser = argparse.ArgumentParser() + parser.add_argument('-c', '--cfg', type=str, default='./configs/debug.yaml', help='cfg file path') + parser.add_argument( + "--eval-set", type=str, default='3dpw', help="Evaluation dataset") + parser.add_argument( + "--eval-split", type=str, default='test', help="Evaluation data split") + parser.add_argument('--render', default=False, type=bool_arg, + help='Render SMPL meshes after the evaluation') + parser.add_argument('--save-results', default=False, type=bool_arg, + help='Save SMPL parameters after the evaluation') + parser.add_argument( + "opts", default=None, nargs=argparse.REMAINDER, + help="Modify config options using the command-line") + + args = parser.parse_args() + print(args, end='\n\n') + cfg_file = args.cfg + cfg = get_cfg(args, test) + + return cfg, cfg_file, args \ No newline at end of file diff --git a/configs/constants.py b/configs/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..ded9d2241a5ecfa192c09af4f27cfd969d1e11fa --- /dev/null +++ b/configs/constants.py @@ -0,0 +1,59 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import torch + +IMG_FEAT_DIM = { + 'resnet': 2048, + 'vit': 1024 +} + +N_JOINTS = 17 +root = 'dataset' +class PATHS: + # Raw data folders + PARSED_DATA = f'{root}/parsed_data' + AMASS_PTH = f'{root}/AMASS' + THREEDPW_PTH = f'{root}/3DPW' + HUMAN36M_PTH = f'{root}/Human36M' + RICH_PTH = f'{root}/RICH' + EMDB_PTH = f'{root}/EMDB' + + # Processed labels + AMASS_LABEL = f'{root}/parsed_data/amass.pth' + THREEDPW_LABEL = f'{root}/parsed_data/3dpw_dset_backbone.pth' + MPII3D_LABEL = f'{root}/parsed_data/mpii3d_dset_backbone.pth' + HUMAN36M_LABEL = f'{root}/parsed_data/human36m_dset_backbone.pth' + INSTA_LABEL = f'{root}/parsed_data/insta_dset_backbone.pth' + BEDLAM_LABEL = f'{root}/parsed_data/bedlam_train_backbone.pth' + +class KEYPOINTS: + NUM_JOINTS = N_JOINTS + H36M_TO_J17 = [6, 5, 4, 1, 2, 3, 16, 15, 14, 11, 12, 13, 8, 10, 0, 7, 9] + H36M_TO_J14 = H36M_TO_J17[:14] + J17_TO_H36M = [14, 3, 4, 5, 2, 1, 0, 15, 12, 16, 13, 9, 10, 11, 8, 7, 6] + COCO_AUG_DICT = f'{root}/body_models/coco_aug_dict.pth' + TREE = [[5, 6], 0, 0, 1, 2, -1, -1, 5, 6, 7, 8, -1, -1, 11, 12, 13, 14, 15, 15, 15, 16, 16, 16] + + # STD scale for video noise + S_BIAS = 1e-1 + S_JITTERING = 5e-2 + S_PEAK = 3e-1 + S_PEAK_MASK = 5e-3 + S_MASK = 0.03 + + +class BMODEL: + MAIN_JOINTS = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21] # reduced_joints + + FLDR = f'{root}/body_models/smpl/' + SMPLX2SMPL = f'{root}/body_models/smplx2smpl.pkl' + FACES = f'{root}/body_models/smpl_faces.npy' + MEAN_PARAMS = f'{root}/body_models/smpl_mean_params.npz' + JOINTS_REGRESSOR_WHAM = f'{root}/body_models/J_regressor_wham.npy' + JOINTS_REGRESSOR_H36M = f'{root}/body_models/J_regressor_h36m.npy' + JOINTS_REGRESSOR_EXTRA = f'{root}/body_models/J_regressor_extra.npy' + JOINTS_REGRESSOR_FEET = f'{root}/body_models/J_regressor_feet.npy' + PARENTS = torch.tensor([ + -1, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 9, 12, 13, 14, 16, 17, 18, 19, 20, 21]) \ No newline at end of file diff --git a/configs/yamls/demo.yaml b/configs/yamls/demo.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ed67a32e22ee65ee9c0c67cf4e98bd3a8f577af6 --- /dev/null +++ b/configs/yamls/demo.yaml @@ -0,0 +1,14 @@ +LOGDIR: '' +DEVICE: 'cuda' +EXP_NAME: 'demo' +OUTPUT_DIR: 'experiments/' +NUM_WORKERS: 0 +MODEL_CONFIG: 'configs/yamls/model_base.yaml' +FLIP_EVAL: True + +TRAIN: + STAGE: 'stage2' + CHECKPOINT: 'checkpoints/wham_vit_bedlam_w_3dpw.pth.tar' + +MODEL: + BACKBONE: 'vit' \ No newline at end of file diff --git a/configs/yamls/model_base.yaml b/configs/yamls/model_base.yaml new file mode 100644 index 0000000000000000000000000000000000000000..89a1bd8c7392a973cc269601db24960e9924d42e --- /dev/null +++ b/configs/yamls/model_base.yaml @@ -0,0 +1,7 @@ +architecture: 'RNN' +in_dim: 49 +n_iters: 1 +pose_dr: 0.15 +d_embed: 512 +n_layers: 3 +layer: 'LSTM' \ No newline at end of file diff --git a/configs/yamls/stage1.yaml b/configs/yamls/stage1.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a25fd4bd219cc0842a7f81dcb226048c0793a12c --- /dev/null +++ b/configs/yamls/stage1.yaml @@ -0,0 +1,28 @@ +LOGDIR: '' +DEVICE: 'cuda' +EXP_NAME: 'train_stage1' +OUTPUT_DIR: 'experiments/' +NUM_WORKERS: 8 +MODEL_CONFIG: 'configs/yamls/model_base.yaml' +FLIP_EVAL: True +SEED_VALUE: 42 + +TRAIN: + LR: 5e-4 + BATCH_SIZE: 64 + END_EPOCH: 100 + STAGE: 'stage1' + CHECKPOINT: '' + MILESTONES: [60, 80] + +LOSS: + SHAPE_LOSS_WEIGHT: 0.004 + JOINT3D_LOSS_WEIGHT: 0.4 + JOINT2D_LOSS_WEIGHT: 0.1 + POSE_LOSS_WEIGHT: 8.0 + CASCADED_LOSS_WEIGHT: 0.0 + SLIDING_LOSS_WEIGHT: 0.5 + CAMERA_LOSS_WEIGHT: 0.04 + ROOT_VEL_LOSS_WEIGHT: 0.001 + LOSS_WEIGHT: 50.0 + CAMERA_LOSS_SKIP_EPOCH: 5 \ No newline at end of file diff --git a/configs/yamls/stage2.yaml b/configs/yamls/stage2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..57e69e0a3bc740daa073e829e01e9053e6dae885 --- /dev/null +++ b/configs/yamls/stage2.yaml @@ -0,0 +1,37 @@ +LOGDIR: '' +DEVICE: 'cuda' +EXP_NAME: 'train_stage2' +OUTPUT_DIR: 'experiments' +NUM_WORKERS: 8 +MODEL_CONFIG: 'configs/yamls/model_base.yaml' +FLIP_EVAL: True +SEED_VALUE: 42 + +TRAIN: + LR: 1e-4 + LR_FINETUNE: 1e-5 + STAGE: 'stage2' + CHECKPOINT: 'checkpoints/wham_stage1.pth.tar' + BATCH_SIZE: 64 + END_EPOCH: 40 + MILESTONES: [20, 30] + LR_DECAY_RATIO: 0.2 + +MODEL: + BACKBONE: 'vit' + +LOSS: + SHAPE_LOSS_WEIGHT: 0.0 + JOINT2D_LOSS_WEIGHT: 3.0 + JOINT3D_LOSS_WEIGHT: 6.0 + POSE_LOSS_WEIGHT: 1.0 + CASCADED_LOSS_WEIGHT: 0.05 + SLIDING_LOSS_WEIGHT: 0.5 + CAMERA_LOSS_WEIGHT: 0.01 + ROOT_VEL_LOSS_WEIGHT: 0.001 + LOSS_WEIGHT: 60.0 + CAMERA_LOSS_SKIP_EPOCH: 0 + +DATASET: + SEQLEN: 81 + RATIO: [0.2, 0.2, 0.2, 0.2, 0.2] \ No newline at end of file diff --git a/configs/yamls/stage2_b.yaml b/configs/yamls/stage2_b.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9dfa7524d95f943edcbaaf72918c565535773e35 --- /dev/null +++ b/configs/yamls/stage2_b.yaml @@ -0,0 +1,38 @@ +LOGDIR: '' +DEVICE: 'cuda' +EXP_NAME: 'train_stage2_b' +OUTPUT_DIR: 'experiments' +NUM_WORKERS: 8 +MODEL_CONFIG: 'configs/yamls/model_base.yaml' +FLIP_EVAL: True +SEED_VALUE: 42 + +TRAIN: + LR: 1e-4 + LR_FINETUNE: 1e-5 + STAGE: 'stage2' + CHECKPOINT: 'checkpoints/wham_stage1.pth.tar' + BATCH_SIZE: 64 + END_EPOCH: 80 + MILESTONES: [40, 50, 70] + LR_DECAY_RATIO: 0.2 + +MODEL: + BACKBONE: 'vit' + +LOSS: + SHAPE_LOSS_WEIGHT: 0.0 + JOINT2D_LOSS_WEIGHT: 5.0 + JOINT3D_LOSS_WEIGHT: 5.0 + VERTS3D_LOSS_WEIGHT: 1.0 + POSE_LOSS_WEIGHT: 3.0 + CASCADED_LOSS_WEIGHT: 0.05 + SLIDING_LOSS_WEIGHT: 0.5 + CAMERA_LOSS_WEIGHT: 0.01 + ROOT_VEL_LOSS_WEIGHT: 0.001 + LOSS_WEIGHT: 60.0 + CAMERA_LOSS_SKIP_EPOCH: 0 + +DATASET: + SEQLEN: 81 + RATIO: [0.2, 0.2, 0.2, 0.2, 0.0, 0.2] \ No newline at end of file diff --git a/dataset/body_models/J_regressor_coco.npy 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a/dataset/body_models/smpl/__MACOSX/smpl/smpl_webuser/hello_world/._render_smpl.py b/dataset/body_models/smpl/__MACOSX/smpl/smpl_webuser/hello_world/._render_smpl.py new file mode 100644 index 0000000000000000000000000000000000000000..6b8b3bece60fad5e282a73384e6e06fa8d04737d Binary files /dev/null and b/dataset/body_models/smpl/__MACOSX/smpl/smpl_webuser/hello_world/._render_smpl.py differ diff --git a/dataset/body_models/smpl_faces.npy b/dataset/body_models/smpl_faces.npy new file mode 100644 index 0000000000000000000000000000000000000000..4b0c3c149ef8a1899182c056ed2cb24746ae7199 --- /dev/null +++ b/dataset/body_models/smpl_faces.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:51fc11ebadb0487d74bef220c4eea43f014609249f0121413c1fc629d859fecb +size 165392 diff --git a/dataset/body_models/smpl_mean_params.npz b/dataset/body_models/smpl_mean_params.npz new file mode 100644 index 0000000000000000000000000000000000000000..c6f60a76976b877cbc08345b2977c6ddd83ced87 --- /dev/null +++ b/dataset/body_models/smpl_mean_params.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6fd6dd687800da946d0a0492383f973b92ec20f166a0b829775882868c35fcdd +size 1310 diff --git a/dataset/body_models/smplx2smpl.pkl b/dataset/body_models/smplx2smpl.pkl new file mode 100644 index 0000000000000000000000000000000000000000..0f25e10571181989524020c803280607b7ee9a85 --- /dev/null +++ b/dataset/body_models/smplx2smpl.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c1d912d121ad98132e4492d8e7a0f1a8cf4412811e14a7ef8cb337bb48eef99e +size 578019251 diff --git a/demo.py b/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..178255379cec258316cfe7e642d9688219c39faf --- /dev/null +++ b/demo.py @@ -0,0 +1,234 @@ +import os +import argparse +import os.path as osp +from glob import glob +from collections import defaultdict + +import cv2 +import torch +import joblib +import numpy as np +from loguru import logger +from progress.bar import Bar + +from configs.config import get_cfg_defaults +from lib.data.datasets import CustomDataset +from lib.utils.imutils import avg_preds +from lib.utils.transforms import matrix_to_axis_angle +from lib.models import build_network, build_body_model +from lib.models.preproc.detector import DetectionModel +from lib.models.preproc.extractor import FeatureExtractor +from lib.models.smplify import TemporalSMPLify + +try: + from lib.models.preproc.slam import SLAMModel + _run_global = True +except: + logger.info('DPVO is not properly installed. Only estimate in local coordinates !') + _run_global = False + +def run(cfg, + video, + output_pth, + network, + calib=None, + run_global=True, + save_pkl=False, + visualize=False): + + cap = cv2.VideoCapture(video) + assert cap.isOpened(), f'Faild to load video file {video}' + fps = cap.get(cv2.CAP_PROP_FPS) + length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + width, height = cap.get(cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT) + + # Whether or not estimating motion in global coordinates + run_global = run_global and _run_global + + # Preprocess + with torch.no_grad(): + if not (osp.exists(osp.join(output_pth, 'tracking_results.pth')) and + osp.exists(osp.join(output_pth, 'slam_results.pth'))): + + detector = DetectionModel(cfg.DEVICE.lower()) + extractor = FeatureExtractor(cfg.DEVICE.lower(), cfg.FLIP_EVAL) + + if run_global: slam = SLAMModel(video, output_pth, width, height, calib) + else: slam = None + + bar = Bar('Preprocess: 2D detection and SLAM', fill='#', max=length) + while (cap.isOpened()): + flag, img = cap.read() + if not flag: break + + # 2D detection and tracking + detector.track(img, fps, length) + + # SLAM + if slam is not None: + slam.track() + + bar.next() + + tracking_results = detector.process(fps) + + if slam is not None: + slam_results = slam.process() + else: + slam_results = np.zeros((length, 7)) + slam_results[:, 3] = 1.0 # Unit quaternion + + # Extract image features + # TODO: Merge this into the previous while loop with an online bbox smoothing. + tracking_results = extractor.run(video, tracking_results) + logger.info('Complete Data preprocessing!') + + # Save the processed data + joblib.dump(tracking_results, osp.join(output_pth, 'tracking_results.pth')) + joblib.dump(slam_results, osp.join(output_pth, 'slam_results.pth')) + logger.info(f'Save processed data at {output_pth}') + + # If the processed data already exists, load the processed data + else: + tracking_results = joblib.load(osp.join(output_pth, 'tracking_results.pth')) + slam_results = joblib.load(osp.join(output_pth, 'slam_results.pth')) + logger.info(f'Already processed data exists at {output_pth} ! Load the data .') + + # Build dataset + dataset = CustomDataset(cfg, tracking_results, slam_results, width, height, fps) + + # run WHAM + results = defaultdict(dict) + + n_subjs = len(dataset) + for subj in range(n_subjs): + + with torch.no_grad(): + if cfg.FLIP_EVAL: + # Forward pass with flipped input + flipped_batch = dataset.load_data(subj, True) + _id, x, inits, features, mask, init_root, cam_angvel, frame_id, kwargs = flipped_batch + flipped_pred = network(x, inits, features, mask=mask, init_root=init_root, cam_angvel=cam_angvel, return_y_up=True, **kwargs) + + # Forward pass with normal input + batch = dataset.load_data(subj) + _id, x, inits, features, mask, init_root, cam_angvel, frame_id, kwargs = batch + pred = network(x, inits, features, mask=mask, init_root=init_root, cam_angvel=cam_angvel, return_y_up=True, **kwargs) + + # Merge two predictions + flipped_pose, flipped_shape = flipped_pred['pose'].squeeze(0), flipped_pred['betas'].squeeze(0) + pose, shape = pred['pose'].squeeze(0), pred['betas'].squeeze(0) + flipped_pose, pose = flipped_pose.reshape(-1, 24, 6), pose.reshape(-1, 24, 6) + avg_pose, avg_shape = avg_preds(pose, shape, flipped_pose, flipped_shape) + avg_pose = avg_pose.reshape(-1, 144) + avg_contact = (flipped_pred['contact'][..., [2, 3, 0, 1]] + pred['contact']) / 2 + + # Refine trajectory with merged prediction + network.pred_pose = avg_pose.view_as(network.pred_pose) + network.pred_shape = avg_shape.view_as(network.pred_shape) + network.pred_contact = avg_contact.view_as(network.pred_contact) + output = network.forward_smpl(**kwargs) + pred = network.refine_trajectory(output, cam_angvel, return_y_up=True) + + else: + # data + batch = dataset.load_data(subj) + _id, x, inits, features, mask, init_root, cam_angvel, frame_id, kwargs = batch + + # inference + pred = network(x, inits, features, mask=mask, init_root=init_root, cam_angvel=cam_angvel, return_y_up=True, **kwargs) + + # if False: + if args.run_smplify: + smplify = TemporalSMPLify(smpl, img_w=width, img_h=height, device=cfg.DEVICE) + input_keypoints = dataset.tracking_results[_id]['keypoints'] + pred = smplify.fit(pred, input_keypoints, **kwargs) + + with torch.no_grad(): + network.pred_pose = pred['pose'] + network.pred_shape = pred['betas'] + network.pred_cam = pred['cam'] + output = network.forward_smpl(**kwargs) + pred = network.refine_trajectory(output, cam_angvel, return_y_up=True) + + # ========= Store results ========= # + pred_body_pose = matrix_to_axis_angle(pred['poses_body']).cpu().numpy().reshape(-1, 69) + pred_root = matrix_to_axis_angle(pred['poses_root_cam']).cpu().numpy().reshape(-1, 3) + pred_root_world = matrix_to_axis_angle(pred['poses_root_world']).cpu().numpy().reshape(-1, 3) + pred_pose = np.concatenate((pred_root, pred_body_pose), axis=-1) + pred_pose_world = np.concatenate((pred_root_world, pred_body_pose), axis=-1) + pred_trans = (pred['trans_cam'] - network.output.offset).cpu().numpy() + + results[_id]['pose'] = pred_pose + results[_id]['trans'] = pred_trans + results[_id]['pose_world'] = pred_pose_world + results[_id]['trans_world'] = pred['trans_world'].cpu().squeeze(0).numpy() + results[_id]['betas'] = pred['betas'].cpu().squeeze(0).numpy() + results[_id]['verts'] = (pred['verts_cam'] + pred['trans_cam'].unsqueeze(1)).cpu().numpy() + results[_id]['frame_ids'] = frame_id + + if save_pkl: + joblib.dump(results, osp.join(output_pth, "wham_output.pkl")) + + # Visualize + if visualize: + from lib.vis.run_vis import run_vis_on_demo + with torch.no_grad(): + run_vis_on_demo(cfg, video, results, output_pth, network.smpl, vis_global=run_global) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + + parser.add_argument('--video', type=str, + default='examples/demo_video.mp4', + help='input video path or youtube link') + + parser.add_argument('--output_pth', type=str, default='output/demo', + help='output folder to write results') + + parser.add_argument('--calib', type=str, default=None, + help='Camera calibration file path') + + parser.add_argument('--estimate_local_only', action='store_true', + help='Only estimate motion in camera coordinate if True') + + parser.add_argument('--visualize', action='store_true', + help='Visualize the output mesh if True') + + parser.add_argument('--save_pkl', action='store_true', + help='Save output as pkl file') + + parser.add_argument('--run_smplify', action='store_true', + help='Run Temporal SMPLify for post processing') + + args = parser.parse_args() + + cfg = get_cfg_defaults() + cfg.merge_from_file('configs/yamls/demo.yaml') + + logger.info(f'GPU name -> {torch.cuda.get_device_name()}') + logger.info(f'GPU feat -> {torch.cuda.get_device_properties("cuda")}') + + # ========= Load WHAM ========= # + smpl_batch_size = cfg.TRAIN.BATCH_SIZE * cfg.DATASET.SEQLEN + smpl = build_body_model(cfg.DEVICE, smpl_batch_size) + network = build_network(cfg, smpl) + network.eval() + + # Output folder + sequence = '.'.join(args.video.split('/')[-1].split('.')[:-1]) + output_pth = osp.join(args.output_pth, sequence) + os.makedirs(output_pth, exist_ok=True) + + run(cfg, + args.video, + output_pth, + network, + args.calib, + run_global=not args.estimate_local_only, + save_pkl=args.save_pkl, + visualize=args.visualize) + + print() + logger.info('Done !') \ No newline at end of file diff --git a/docs/API.md b/docs/API.md new file mode 100644 index 0000000000000000000000000000000000000000..511ac3a0374218d567a97a7e36562d2140d6d074 --- /dev/null +++ b/docs/API.md @@ -0,0 +1,18 @@ +## Python API + +To use python API of WHAM, please finish the basic installation first ([Installation](INSTALL.md) or [Docker](DOCKER.md)). + +If you use Docker environment, please run: + +```bash +cd /path/to/WHAM +docker run -it -v .:/code/ --rm yusun9/wham-vitpose-dpvo-cuda11.3-python3.9 python +``` + +Then you can run wham via python code like +```bash +from wham_api import WHAM_API +wham_model = WHAM_API() +input_video_path = 'examples/IMG_9732.mov' +results, tracking_results, slam_results = wham_model(input_video_path) +``` \ No newline at end of file diff --git a/docs/DATASET.md b/docs/DATASET.md new file mode 100644 index 0000000000000000000000000000000000000000..b47a4def1b56f8c39f56052b556edd6602ce8ab0 --- /dev/null +++ b/docs/DATASET.md @@ -0,0 +1,42 @@ +# Dataset + +## Training Data +We use [AMASS](https://amass.is.tue.mpg.de/), [InstaVariety](https://github.com/akanazawa/human_dynamics/blob/master/doc/insta_variety.md), [MPI-INF-3DHP](https://vcai.mpi-inf.mpg.de/3dhp-dataset/), [Human3.6M](http://vision.imar.ro/human3.6m/description.php), and [3DPW](https://virtualhumans.mpi-inf.mpg.de/3DPW/) datasets for training. Please register to their websites to download and process the data. You can download parsed ViT version of InstaVariety, MPI-INF-3DHP, Human3.6M, and 3DPW data from the [Google Drive](https://drive.google.com/drive/folders/13T2ghVvrw_fEk3X-8L0e6DVSYx_Og8o3?usp=sharing). You can save the data under `dataset/parsed_data` folder. + +### Process AMASS dataset +After downloading AMASS dataset, you can process it by running: +```bash +python -m lib.data_utils.amass_utils +``` +The processed data will be stored at `dataset/parsed_data/amass.pth`. + +### Process 3DPW, MPII3D, Human3.6M, and InstaVariety datasets +First, visit [TCMR](https://github.com/hongsukchoi/TCMR_RELEASE) and download preprocessed data at `dataset/parsed_data/TCMR_preproc/'. + +Next, prepare 2D keypoints detection using [ViTPose](https://github.com/ViTAE-Transformer/ViTPose) and store the results at `dataset/detection_results/\/\'. You may need to download all images to prepare the detection results. + +For Human36M, MPII3D, and InstaVariety datasets, you need to also download [NeuralAnnot](https://github.com/mks0601/NeuralAnnot_RELEASE) pseudo groundtruth SMPL label. As mentioned in our paper, we do not supervise WHAM on this label, but use it for neural initialization step. + +Finally, run following codes to preprocess all training data. +```bash +python -m lib.data_utils.threedpw_train_utils # 3DPW dataset +# [Coming] python -m lib.data_utils.human36m_train_utils # Human3.6M dataset +# [Coming] python -m lib.data_utils.mpii3d_train_utils # MPI-INF-3DHP dataset +# [Coming] python -m lib.data_utils.insta_train_utils # InstaVariety dataset +``` + +### Process BEDLAM dataset +Will be updated. + +## Evaluation Data +We use [3DPW](https://virtualhumans.mpi-inf.mpg.de/3DPW/), [RICH](https://rich.is.tue.mpg.de/), and [EMDB](https://eth-ait.github.io/emdb/) for the evaluation. We provide the parsed data for the evaluation. Please download the data from [Google Drive](https://drive.google.com/drive/folders/13T2ghVvrw_fEk3X-8L0e6DVSYx_Og8o3?usp=sharing) and place them at `dataset/parsed_data/`. + +To process the data at your end, please +1) Download parsed 3DPW data from [TCMR](https://github.com/hongsukchoi/TCMR_RELEASE) and store `dataset/parsed_data/TCMR_preproc/'. +2) Run [ViTPose](https://github.com/ViTAE-Transformer/ViTPose) on all test data and store the results at `dataset/detection_results/\'. +3) Run following codes. +```bash +python -m lib.data_utils.threedpw_eval_utils --split <"val" or "test"> # 3DPW dataset +python -m lib.data_utils.emdb_eval_utils --split <"1" or "2"> # EMDB dataset +python -m lib.data_utils.rich_eval_utils # RICH dataset +``` \ No newline at end of file diff --git a/docs/DOCKER.md b/docs/DOCKER.md new file mode 100644 index 0000000000000000000000000000000000000000..23f45a20ed665442674f3398ba18b363bb5f7508 --- /dev/null +++ b/docs/DOCKER.md @@ -0,0 +1,23 @@ +## Installation + +### Pre-requirments +1. Please make sure that you have properly installed the [Docker](https://www.docker.com/) and [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) before installation. + +2. Please prepare the essential data for inference: +To download SMPL body models (Neutral, Female, and Male), you need to register for [SMPL](https://smpl.is.tue.mpg.de/) and [SMPLify](https://smplify.is.tue.mpg.de/). The username and password for both homepages will be used while fetching the demo data. +Next, run the following script to fetch demo data. This script will download all the required dependencies including trained models and demo videos. +```bash +bash fetch_demo_data.sh +``` + +### Usage +1. Pulling the docker image from docker hub: +```bash +docker pull yusun9/wham-vitpose-dpvo-cuda11.3-python3.9:latest +``` + +2. Run the code with docker environment: +```bash +cd /path/to/WHAM +docker run -v .:/code/ --rm yusun9/wham-vitpose-dpvo-cuda11.3-python3.9 python demo.py --video examples/IMG_9732.mov +``` \ No newline at end of file diff --git a/docs/INSTALL.md b/docs/INSTALL.md new file mode 100644 index 0000000000000000000000000000000000000000..9f9038009109d1d375b88fd5533d22750a0c53a3 --- /dev/null +++ b/docs/INSTALL.md @@ -0,0 +1,38 @@ +# Installation + +WHAM has been implemented and tested on Ubuntu 20.04 and 22.04 with python = 3.9. We provide [anaconda](https://www.anaconda.com/) environment to run WHAM as below. + +```bash +# Clone the repo +git clone https://github.com/yohanshin/WHAM.git --recursive +cd WHAM/ + +# Create Conda environment +conda create -n wham python=3.9 +conda activate wham + +# Install PyTorch libraries +conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch + +# Install PyTorch3D (optional) for visualization +conda install -c fvcore -c iopath -c conda-forge fvcore iopath +pip install pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py39_cu113_pyt1110/download.html + +# Install WHAM dependencies +pip install -r requirements.txt + +# Install ViTPose +pip install -v -e third-party/ViTPose + +# Install DPVO +cd third-party/DPVO +wget https://gitlab.com/libeigen/eigen/-/archive/3.4.0/eigen-3.4.0.zip +unzip eigen-3.4.0.zip -d thirdparty && rm -rf eigen-3.4.0.zip +conda install pytorch-scatter=2.0.9 -c rusty1s +conda install cudatoolkit-dev=11.3.1 -c conda-forge + +# ONLY IF your GCC version is larger than 10 +conda install -c conda-forge gxx=9.5 + +pip install . +``` diff --git a/examples/IMG_9730.mov b/examples/IMG_9730.mov new file mode 100644 index 0000000000000000000000000000000000000000..96e46dcccd85d1561f436db14563ae10103364b8 --- /dev/null +++ b/examples/IMG_9730.mov @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3739b87ba0c64d047df3d8f5479c530377788fdab4c2283925477894a1d252f9 +size 21526220 diff --git a/examples/IMG_9731.mov b/examples/IMG_9731.mov new file mode 100644 index 0000000000000000000000000000000000000000..4d409b0a40c04ffeccf55ee215737ca7cb2c14a3 --- /dev/null +++ b/examples/IMG_9731.mov @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:116ad3f95743524283a234fd9e7a1152b28a04536ab5975f4e4e71c547d9e1a6 +size 22633328 diff --git a/examples/IMG_9732.mov b/examples/IMG_9732.mov new file mode 100644 index 0000000000000000000000000000000000000000..7ba45a3d48213e3c52c46569131485c93c44429d --- /dev/null +++ b/examples/IMG_9732.mov @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:168773c92e0112361dcd1da4154c915983490e58ff89102c1a65edb28d505813 +size 23960355 diff --git a/examples/drone_calib.txt b/examples/drone_calib.txt new file mode 100644 index 0000000000000000000000000000000000000000..00052336e94e9f785a3ce455700a4bd888d213ce --- /dev/null +++ b/examples/drone_calib.txt @@ -0,0 +1 @@ +1321.0 1321.0 960.0 540.0 \ No newline at end of file diff --git a/examples/drone_video.mp4 b/examples/drone_video.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..bdcf1548b1ccff376f1d7f358ddd3ff9184ed7af --- /dev/null +++ b/examples/drone_video.mp4 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0da55210a305c3c75caa732c46b7330bb3d4e39ebeb9bc3af1e2b100dd8990c1 +size 20601030 diff --git a/examples/test16.mov b/examples/test16.mov new file mode 100644 index 0000000000000000000000000000000000000000..b00eafc06cd037c17fd61b660030fefabda6a38a --- /dev/null +++ b/examples/test16.mov @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f068400bf962e732e5517af45397694f84fae0a6592085b9dd3781fdbacaa550 +size 1567779 diff --git a/examples/test17.mov b/examples/test17.mov new file mode 100644 index 0000000000000000000000000000000000000000..de4b0669d6867fb70d5acbfe24c0929eb96b2c79 --- /dev/null +++ b/examples/test17.mov @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce06d8885332fd0b770273010dbd4da20a0867a386dc55925f85198651651253 +size 2299497 diff --git a/examples/test18.mov b/examples/test18.mov new file mode 100644 index 0000000000000000000000000000000000000000..97d0466333a92cb4877c80e0d22eeedde01732ee --- /dev/null +++ b/examples/test18.mov @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:66fc6eb20e1c8525070c8004bed621e0acc2712accace1dbf1eb72fced62bb14 +size 2033756 diff --git a/examples/test19.mov b/examples/test19.mov new file mode 100644 index 0000000000000000000000000000000000000000..5414b6061cf39260b47ebd8bcfb124980be6fe3e --- /dev/null +++ b/examples/test19.mov @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:878219571dbf0e8ff56f4ba4bf325f90f46a730b57a35a2df91f4f509af616d8 +size 1940593 diff --git a/fetch_demo_data.sh b/fetch_demo_data.sh new file mode 100644 index 0000000000000000000000000000000000000000..1f5fcfd2a4558a3df705854404c734cbb5a82f66 --- /dev/null +++ b/fetch_demo_data.sh @@ -0,0 +1,50 @@ +#!/bin/bash +urle () { [[ "${1}" ]] || return 1; local LANG=C i x; for (( i = 0; i < ${#1}; i++ )); do x="${1:i:1}"; [[ "${x}" == [a-zA-Z0-9.~-] ]] && echo -n "${x}" || printf '%%%02X' "'${x}"; done; echo; } + +# SMPL Neutral model +echo -e "\nYou need to register at https://smplify.is.tue.mpg.de" +read -p "Username (SMPLify):" username +read -p "Password (SMPLify):" password +username=$(urle $username) +password=$(urle $password) + +mkdir -p dataset/body_models/smpl +wget --post-data "username=$username&password=$password" 'https://download.is.tue.mpg.de/download.php?domain=smplify&resume=1&sfile=mpips_smplify_public_v2.zip' -O './dataset/body_models/smplify.zip' --no-check-certificate --continue +unzip dataset/body_models/smplify.zip -d dataset/body_models/smplify +mv dataset/body_models/smplify/smplify_public/code/models/basicModel_neutral_lbs_10_207_0_v1.0.0.pkl dataset/body_models/smpl/SMPL_NEUTRAL.pkl +rm -rf dataset/body_models/smplify +rm -rf dataset/body_models/smplify.zip + +# SMPL Male and Female model +echo -e "\nYou need to register at https://smpl.is.tue.mpg.de" +read -p "Username (SMPL):" username +read -p "Password (SMPL):" password +username=$(urle $username) +password=$(urle $password) + +wget --post-data "username=$username&password=$password" 'https://download.is.tue.mpg.de/download.php?domain=smpl&sfile=SMPL_python_v.1.0.0.zip' -O './dataset/body_models/smpl.zip' --no-check-certificate --continue +unzip dataset/body_models/smpl.zip -d dataset/body_models/smpl +mv dataset/body_models/smpl/smpl/models/basicModel_f_lbs_10_207_0_v1.0.0.pkl dataset/body_models/smpl/SMPL_FEMALE.pkl +mv dataset/body_models/smpl/smpl/models/basicmodel_m_lbs_10_207_0_v1.0.0.pkl dataset/body_models/smpl/SMPL_MALE.pkl +rm -rf dataset/body_models/smpl/smpl +rm -rf dataset/body_models/smpl.zip + +# Auxiliary SMPL-related data +wget "https://drive.google.com/uc?id=1pbmzRbWGgae6noDIyQOnohzaVnX_csUZ&export=download&confirm=t" -O 'dataset/body_models.tar.gz' +tar -xvf dataset/body_models.tar.gz -C dataset/ +rm -rf dataset/body_models.tar.gz + +# Checkpoints +mkdir checkpoints +gdown "https://drive.google.com/uc?id=1i7kt9RlCCCNEW2aYaDWVr-G778JkLNcB&export=download&confirm=t" -O 'checkpoints/wham_vit_w_3dpw.pth.tar' +gdown "https://drive.google.com/uc?id=19qkI-a6xuwob9_RFNSPWf1yWErwVVlks&export=download&confirm=t" -O 'checkpoints/wham_vit_bedlam_w_3dpw.pth.tar' +gdown "https://drive.google.com/uc?id=1J6l8teyZrL0zFzHhzkC7efRhU0ZJ5G9Y&export=download&confirm=t" -O 'checkpoints/hmr2a.ckpt' +gdown "https://drive.google.com/uc?id=1kXTV4EYb-BI3H7J-bkR3Bc4gT9zfnHGT&export=download&confirm=t" -O 'checkpoints/dpvo.pth' +gdown "https://drive.google.com/uc?id=1zJ0KP23tXD42D47cw1Gs7zE2BA_V_ERo&export=download&confirm=t" -O 'checkpoints/yolov8x.pt' +gdown "https://drive.google.com/uc?id=1xyF7F3I7lWtdq82xmEPVQ5zl4HaasBso&export=download&confirm=t" -O 'checkpoints/vitpose-h-multi-coco.pth' + +# Demo videos +gdown "https://drive.google.com/uc?id=1KjfODCcOUm_xIMLLR54IcjJtf816Dkc7&export=download&confirm=t" -O 'examples.tar.gz' +tar -xvf examples.tar.gz +rm -rf examples.tar.gz + diff --git a/lib/core/loss.py b/lib/core/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..1fc182812479a90cfab611a140e57a787dab3ce3 --- /dev/null +++ b/lib/core/loss.py @@ -0,0 +1,438 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import torch +import torch.nn as nn +from torch.nn import functional as F + +from configs import constants as _C +from lib.utils import transforms +from lib.utils.kp_utils import root_centering + +class WHAMLoss(nn.Module): + def __init__( + self, + cfg=None, + device=None, + ): + super(WHAMLoss, self).__init__() + + self.cfg = cfg + self.n_joints = _C.KEYPOINTS.NUM_JOINTS + self.criterion = nn.MSELoss() + self.criterion_noreduce = nn.MSELoss(reduction='none') + + self.pose_loss_weight = cfg.LOSS.POSE_LOSS_WEIGHT + self.shape_loss_weight = cfg.LOSS.SHAPE_LOSS_WEIGHT + self.keypoint_2d_loss_weight = cfg.LOSS.JOINT2D_LOSS_WEIGHT + self.keypoint_3d_loss_weight = cfg.LOSS.JOINT3D_LOSS_WEIGHT + self.cascaded_loss_weight = cfg.LOSS.CASCADED_LOSS_WEIGHT + self.vertices_loss_weight = cfg.LOSS.VERTS3D_LOSS_WEIGHT + self.contact_loss_weight = cfg.LOSS.CONTACT_LOSS_WEIGHT + self.root_vel_loss_weight = cfg.LOSS.ROOT_VEL_LOSS_WEIGHT + self.root_pose_loss_weight = cfg.LOSS.ROOT_POSE_LOSS_WEIGHT + self.sliding_loss_weight = cfg.LOSS.SLIDING_LOSS_WEIGHT + self.camera_loss_weight = cfg.LOSS.CAMERA_LOSS_WEIGHT + self.loss_weight = cfg.LOSS.LOSS_WEIGHT + + kp_weights = [ + 0.5, 0.5, 0.5, 0.5, 0.5, # Face + 1.5, 1.5, 4, 4, 4, 4, # Arms + 1.5, 1.5, 4, 4, 4, 4, # Legs + 4, 4, 1.5, 1.5, 4, 4, # Legs + 4, 4, 1.5, 1.5, 4, 4, # Arms + 0.5, 0.5 # Head + ] + + theta_weights = [ + 0.1, 1.0, 1.0, 1.0, 1.0, # pelvis, lhip, rhip, spine1, lknee + 1.0, 1.0, 1.0, 1.0, 1.0, # rknn, spine2, lankle, rankle, spin3 + 0.1, 0.1, # Foot + 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, # neck, lisldr, risldr, head, losldr, rosldr, + 1.0, 1.0, 1.0, 1.0, # lelbow, relbow, lwrist, rwrist + 0.1, 0.1, # Hand + ] + self.theta_weights = torch.tensor([[theta_weights]]).float().to(device) + self.theta_weights /= self.theta_weights.mean() + self.kp_weights = torch.tensor([kp_weights]).float().to(device) + + self.epoch = -1 + self.step() + + def step(self): + self.epoch += 1 + self.skip_camera_loss = self.epoch < self.cfg.LOSS.CAMERA_LOSS_SKIP_EPOCH + + def forward(self, pred, gt): + + loss = 0.0 + b, f = gt['kp3d'].shape[:2] + + # <======= Predictions and Groundtruths + pred_betas = pred['betas'] + pred_pose = pred['pose'].reshape(b, f, -1, 6) + pred_kp3d_nn = pred['kp3d_nn'] + pred_kp3d_smpl = root_centering(pred['kp3d'].reshape(b, f, -1, 3)) + pred_full_kp2d = pred['full_kp2d'] + pred_weak_kp2d = pred['weak_kp2d'] + pred_contact = pred['contact'] + pred_vel_root = pred['vel_root'] + pred_pose_root = pred['poses_root_r6d'][:, 1:] + pred_vel_root_ref = pred['vel_root_refined'] + pred_pose_root_ref = pred['poses_root_r6d_refined'][:, 1:] + pred_cam_r = transforms.matrix_to_rotation_6d(pred['R']) + + gt_betas = gt['betas'] + gt_pose = gt['pose'] + gt_kp3d = root_centering(gt['kp3d']) + gt_full_kp2d = gt['full_kp2d'] + gt_weak_kp2d = gt['weak_kp2d'] + gt_contact = gt['contact'] + gt_vel_root = gt['vel_root'] + gt_pose_root = gt['pose_root'][:, 1:] + gt_cam_angvel = gt['cam_angvel'] + gt_cam_r = transforms.matrix_to_rotation_6d(gt['R'][:, 1:]) + bbox = gt['bbox'] + # =======> + + loss_keypoints_full = full_projected_keypoint_loss( + pred_full_kp2d, + gt_full_kp2d, + bbox, + self.kp_weights, + criterion=self.criterion_noreduce, + ) + + loss_keypoints_weak = weak_projected_keypoint_loss( + pred_weak_kp2d, + gt_weak_kp2d, + self.kp_weights, + criterion=self.criterion_noreduce + ) + + # Compute 3D keypoint loss + loss_keypoints_3d_nn = keypoint_3d_loss( + pred_kp3d_nn, + gt_kp3d[:, :, :self.n_joints], + self.kp_weights[:, :self.n_joints], + criterion=self.criterion_noreduce, + ) + + loss_keypoints_3d_smpl = keypoint_3d_loss( + pred_kp3d_smpl, + gt_kp3d, + self.kp_weights, + criterion=self.criterion_noreduce, + ) + + loss_cascaded = keypoint_3d_loss( + pred_kp3d_nn, + torch.cat((pred_kp3d_smpl[:, :, :self.n_joints], gt_kp3d[:, :, :self.n_joints, -1:]), dim=-1), + self.kp_weights[:, :self.n_joints] * 0.5, + criterion=self.criterion_noreduce, + ) + + loss_vertices = vertices_loss( + pred['verts_cam'], + gt['verts'], + gt['has_verts'], + criterion=self.criterion_noreduce, + ) + + # Compute loss on SMPL parameters + smpl_mask = gt['has_smpl'] + loss_regr_pose, loss_regr_betas = smpl_losses( + pred_pose, + pred_betas, + gt_pose, + gt_betas, + self.theta_weights, + smpl_mask, + criterion=self.criterion_noreduce + ) + + # Compute loss on foot contact + loss_contact = contact_loss( + pred_contact, + gt_contact, + self.criterion_noreduce + ) + + # Compute loss on root velocity and angular velocity + loss_vel_root, loss_pose_root = root_loss( + pred_vel_root, + pred_pose_root, + gt_vel_root, + gt_pose_root, + gt_contact, + self.criterion_noreduce + ) + + # Root loss after trajectory refinement + loss_vel_root_ref, loss_pose_root_ref = root_loss( + pred_vel_root_ref, + pred_pose_root_ref, + gt_vel_root, + gt_pose_root, + gt_contact, + self.criterion_noreduce + ) + + # Camera prediction loss + loss_camera = camera_loss( + pred_cam_r, + gt_cam_r, + gt_cam_angvel[:, 1:], + gt['has_traj'], + self.criterion_noreduce, + self.skip_camera_loss + ) + + # Foot sliding loss + loss_sliding = sliding_loss( + pred['feet'], + gt_contact, + ) + + # Foot sliding loss + loss_sliding_ref = sliding_loss( + pred['feet_refined'], + gt_contact, + ) + + loss_keypoints = loss_keypoints_full + loss_keypoints_weak + loss_keypoints *= self.keypoint_2d_loss_weight + loss_keypoints_3d_smpl *= self.keypoint_3d_loss_weight + loss_keypoints_3d_nn *= self.keypoint_3d_loss_weight + loss_cascaded *= self.cascaded_loss_weight + loss_vertices *= self.vertices_loss_weight + loss_contact *= self.contact_loss_weight + loss_root = loss_vel_root * self.root_vel_loss_weight + loss_pose_root * self.root_pose_loss_weight + loss_root_ref = loss_vel_root_ref * self.root_vel_loss_weight + loss_pose_root_ref * self.root_pose_loss_weight + + loss_regr_pose *= self.pose_loss_weight + loss_regr_betas *= self.shape_loss_weight + + loss_sliding *= self.sliding_loss_weight + loss_camera *= self.camera_loss_weight + loss_sliding_ref *= self.sliding_loss_weight + + loss_dict = { + 'pose': loss_regr_pose * self.loss_weight, + 'betas': loss_regr_betas * self.loss_weight, + '2d': loss_keypoints * self.loss_weight, + '3d': loss_keypoints_3d_smpl * self.loss_weight, + '3d_nn': loss_keypoints_3d_nn * self.loss_weight, + 'casc': loss_cascaded * self.loss_weight, + 'v3d': loss_vertices * self.loss_weight, + 'contact': loss_contact * self.loss_weight, + 'root': loss_root * self.loss_weight, + 'root_ref': loss_root_ref * self.loss_weight, + 'sliding': loss_sliding * self.loss_weight, + 'camera': loss_camera * self.loss_weight, + 'sliding_ref': loss_sliding_ref * self.loss_weight, + } + + loss = sum(loss for loss in loss_dict.values()) + + return loss, loss_dict + + +def root_loss( + pred_vel_root, + pred_pose_root, + gt_vel_root, + gt_pose_root, + stationary, + criterion +): + + mask_r = (gt_pose_root != 0.0).all(dim=-1).all(dim=-1) + mask_v = (gt_vel_root != 0.0).all(dim=-1).all(dim=-1) + mask_s = (stationary != -1).any(dim=1).any(dim=1) + mask_v = mask_v * mask_s + + if mask_r.any(): + loss_r = criterion(pred_pose_root, gt_pose_root)[mask_r].mean() + else: + loss_r = torch.FloatTensor(1).fill_(0.).to(gt_pose_root.device)[0] + + if mask_v.any(): + loss_v = 0 + T = gt_vel_root.shape[0] + ws_list = [1, 3, 9, 27] + for ws in ws_list: + tmp_v = 0 + for m in range(T//ws): + cumulative_v = torch.sum(pred_vel_root[:, m:(m+1)*ws] - gt_vel_root[:, m:(m+1)*ws], dim=1) + tmp_v += torch.norm(cumulative_v, dim=-1) + loss_v += tmp_v + loss_v = loss_v[mask_v].mean() + else: + loss_v = torch.FloatTensor(1).fill_(0.).to(gt_vel_root.device)[0] + + return loss_v, loss_r + + +def contact_loss( + pred_stationary, + gt_stationary, + criterion, +): + + mask = gt_stationary != -1 + if mask.any(): + loss = criterion(pred_stationary, gt_stationary)[mask].mean() + else: + loss = torch.FloatTensor(1).fill_(0.).to(gt_stationary.device)[0] + return loss + + + +def full_projected_keypoint_loss( + pred_keypoints_2d, + gt_keypoints_2d, + bbox, + weight, + criterion, +): + + scale = bbox[..., 2:] * 200. + conf = gt_keypoints_2d[..., -1] + + if (conf > 0).any(): + loss = torch.mean( + weight * (conf * torch.norm(pred_keypoints_2d - gt_keypoints_2d[..., :2], dim=-1) + ) / scale, dim=1).mean() * conf.mean() + else: + loss = torch.FloatTensor(1).fill_(0.).to(gt_keypoints_2d.device)[0] + return loss + + +def weak_projected_keypoint_loss( + pred_keypoints_2d, + gt_keypoints_2d, + weight, + criterion, +): + + conf = gt_keypoints_2d[..., -1] + if (conf > 0).any(): + loss = torch.mean( + weight * (conf * torch.norm(pred_keypoints_2d - gt_keypoints_2d[..., :2], dim=-1) + ), dim=1).mean() * conf.mean() * 5 + else: + loss = torch.FloatTensor(1).fill_(0.).to(gt_keypoints_2d.device)[0] + return loss + + +def keypoint_3d_loss( + pred_keypoints_3d, + gt_keypoints_3d, + weight, + criterion, +): + + conf = gt_keypoints_3d[..., -1] + if (conf > 0).any(): + if weight.shape[-2] > 17: + pred_keypoints_3d[..., -14:] = pred_keypoints_3d[..., -14:] - pred_keypoints_3d[..., -14:].mean(dim=-2, keepdims=True) + gt_keypoints_3d[..., -14:] = gt_keypoints_3d[..., -14:] - gt_keypoints_3d[..., -14:].mean(dim=-2, keepdims=True) + + loss = torch.mean( + weight * (conf * torch.norm(pred_keypoints_3d - gt_keypoints_3d[..., :3], dim=-1) + ), dim=1).mean() * conf.mean() + else: + loss = torch.FloatTensor(1).fill_(0.).to(gt_keypoints_3d.device)[0] + return loss + + +def vertices_loss( + pred_verts, + gt_verts, + mask, + criterion, +): + + if mask.sum() > 0: + # Align + pred_verts = pred_verts.view_as(gt_verts) + pred_verts = pred_verts - pred_verts.mean(-2, True) + gt_verts = gt_verts - gt_verts.mean(-2, True) + + # loss = criterion(pred_verts, gt_verts).mean() * mask.float().mean() + # loss = torch.mean( + # `(torch.norm(pred_verts - gt_verts, dim=-1)[mask]` + # ), dim=1).mean() * mask.float().mean() + loss = torch.mean( + (torch.norm(pred_verts - gt_verts, p=1, dim=-1)[mask] + ), dim=1).mean() * mask.float().mean() + else: + loss = torch.FloatTensor(1).fill_(0.).to(gt_verts.device)[0] + return loss + + +def smpl_losses( + pred_pose, + pred_betas, + gt_pose, + gt_betas, + weight, + mask, + criterion, +): + + if mask.any().item(): + loss_regr_pose = torch.mean( + weight * torch.square(pred_pose - gt_pose)[mask].mean(-1) + ) * mask.float().mean() + loss_regr_betas = F.mse_loss(pred_betas, gt_betas, reduction='none')[mask].mean() * mask.float().mean() + else: + loss_regr_pose = torch.FloatTensor(1).fill_(0.).to(gt_pose.device)[0] + loss_regr_betas = torch.FloatTensor(1).fill_(0.).to(gt_pose.device)[0] + + return loss_regr_pose, loss_regr_betas + + +def camera_loss( + pred_cam_r, + gt_cam_r, + cam_angvel, + mask, + criterion, + skip +): + # mask = (gt_cam_r != 0.0).all(dim=-1).all(dim=-1) + + if mask.any() and not skip: + # Camera pose loss in 6D representation + loss_r = criterion(pred_cam_r, gt_cam_r)[mask].mean() + + # Reconstruct camera angular velocity and compute reconstruction loss + pred_R = transforms.rotation_6d_to_matrix(pred_cam_r) + cam_angvel_from_R = transforms.matrix_to_rotation_6d(pred_R[:, :-1] @ pred_R[:, 1:].transpose(-1, -2)) + cam_angvel_from_R = (cam_angvel_from_R - torch.tensor([[[1, 0, 0, 0, 1, 0]]]).to(cam_angvel)) * 30 + loss_a = criterion(cam_angvel, cam_angvel_from_R)[mask].mean() + + loss = loss_r + loss_a + else: + loss = torch.FloatTensor(1).fill_(0.).to(gt_cam_r.device)[0] + + return loss + + +def sliding_loss( + foot_position, + contact_prob, +): + """ Compute foot skate loss when foot is assumed to be on contact with ground + + foot_position: 3D foot (heel and toe) position, torch.Tensor (B, F, 4, 3) + contact_prob: contact probability of foot (heel and toe), torch.Tensor (B, F, 4) + """ + + contact_mask = (contact_prob > 0.5).detach().float() + foot_velocity = foot_position[:, 1:] - foot_position[:, :-1] + loss = (torch.norm(foot_velocity, dim=-1) * contact_mask[:, 1:]).mean() + return loss diff --git a/lib/core/trainer.py b/lib/core/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..90d10d4a4bfec7e47373b2520dd78c4eef9ee2ae --- /dev/null +++ b/lib/core/trainer.py @@ -0,0 +1,341 @@ +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +import time +import torch +import shutil +import logging +import numpy as np +import os.path as osp +from progress.bar import Bar + +from configs import constants as _C +from lib.utils import transforms +from lib.utils.utils import AverageMeter, prepare_batch +from lib.eval.eval_utils import ( + compute_accel, + compute_error_accel, + batch_align_by_pelvis, + batch_compute_similarity_transform_torch, +) +from lib.models import build_body_model + +logger = logging.getLogger(__name__) + +class Trainer(): + def __init__(self, + data_loaders, + network, + optimizer, + criterion=None, + train_stage='syn', + start_epoch=0, + checkpoint=None, + end_epoch=999, + lr_scheduler=None, + device=None, + writer=None, + debug=False, + resume=False, + logdir='output', + performance_type='min', + summary_iter=1, + ): + + self.train_loader, self.valid_loader = data_loaders + + # Model and optimizer + self.network = network + self.optimizer = optimizer + + # Training parameters + self.train_stage = train_stage + self.start_epoch = start_epoch + self.end_epoch = end_epoch + self.criterion = criterion + self.lr_scheduler = lr_scheduler + self.device = device + self.writer = writer + self.debug = debug + self.resume = resume + self.logdir = logdir + self.summary_iter = summary_iter + + self.performance_type = performance_type + self.train_global_step = 0 + self.valid_global_step = 0 + self.epoch = 0 + self.best_performance = float('inf') if performance_type == 'min' else -float('inf') + self.summary_loss_keys = ['pose'] + + self.evaluation_accumulators = dict.fromkeys( + ['pred_j3d', 'target_j3d', 'pve'])# 'pred_verts', 'target_verts']) + + self.J_regressor_eval = torch.from_numpy( + np.load(_C.BMODEL.JOINTS_REGRESSOR_H36M) + )[_C.KEYPOINTS.H36M_TO_J14, :].unsqueeze(0).float().to(device) + + if self.writer is None: + from torch.utils.tensorboard import SummaryWriter + self.writer = SummaryWriter(log_dir=self.logdir) + + if self.device is None: + self.device = 'cuda' if torch.cuda.is_available() else 'cpu' + + if checkpoint is not None: + self.load_pretrained(checkpoint) + + def train(self, ): + # Single epoch training routine + + losses = AverageMeter() + kp_2d_loss = AverageMeter() + kp_3d_loss = AverageMeter() + + timer = { + 'data': 0, + 'forward': 0, + 'loss': 0, + 'backward': 0, + 'batch': 0, + } + self.network.train() + start = time.time() + summary_string = '' + + bar = Bar(f'Epoch {self.epoch + 1}/{self.end_epoch}', fill='#', max=len(self.train_loader)) + for i, batch in enumerate(self.train_loader): + + # <======= Feedforward + x, inits, features, kwargs, gt = prepare_batch(batch, self.device, self.train_stage=='stage2') + timer['data'] = time.time() - start + start = time.time() + pred = self.network(x, inits, features, **kwargs) + timer['forward'] = time.time() - start + start = time.time() + # =======> + + # <======= Backprop + loss, loss_dict = self.criterion(pred, gt) + timer['loss'] = time.time() - start + start = time.time() + + # Clip gradients + self.optimizer.zero_grad() + loss.backward() + torch.nn.utils.clip_grad_norm_(self.network.parameters(), 1.0) + self.optimizer.step() + # =======> + + # <======= Log training info + total_loss = loss + losses.update(total_loss.item(), x.size(0)) + kp_2d_loss.update(loss_dict['2d'].item(), x.size(0)) + kp_3d_loss.update(loss_dict['3d'].item(), x.size(0)) + + timer['backward'] = time.time() - start + timer['batch'] = timer['data'] + timer['forward'] + timer['loss'] + timer['backward'] + start = time.time() + + summary_string = f'({i + 1}/{len(self.train_loader)}) | Total: {bar.elapsed_td} ' \ + f'| loss: {losses.avg:.2f} | 2d: {kp_2d_loss.avg:.2f} ' \ + f'| 3d: {kp_3d_loss.avg:.2f} ' + + for k, v in loss_dict.items(): + if k in self.summary_loss_keys: + summary_string += f' | {k}: {v:.2f}' + if (i + 1) % self.summary_iter == 0: + self.writer.add_scalar('train_loss/'+k, v, global_step=self.train_global_step) + + if (i + 1) % self.summary_iter == 0: + self.writer.add_scalar('train_loss/loss', total_loss.item(), global_step=self.train_global_step) + + self.train_global_step += 1 + bar.suffix = summary_string + bar.next(1) + + if torch.isnan(total_loss): + exit('Nan value in loss, exiting!...') + # =======> + + logger.info(summary_string) + bar.finish() + + def validate(self, ): + self.network.eval() + + start = time.time() + summary_string = '' + bar = Bar('Validation', fill='#', max=len(self.valid_loader)) + + if self.evaluation_accumulators is not None: + for k,v in self.evaluation_accumulators.items(): + self.evaluation_accumulators[k] = [] + + with torch.no_grad(): + for i, batch in enumerate(self.valid_loader): + x, inits, features, kwargs, gt = prepare_batch(batch, self.device, self.train_stage=='stage2') + + # <======= Feedforward + pred = self.network(x, inits, features, **kwargs) + + # 3DPW dataset has groundtruth vertices + # NOTE: Following SPIN, we compute PVE against ground truth from Gendered SMPL mesh + smpl = build_body_model(self.device, batch_size=len(pred['verts_cam']), gender=batch['gender'][0]) + gt_output = smpl.get_output( + body_pose=transforms.rotation_6d_to_matrix(gt['pose'][0, :, 1:]), + global_orient=transforms.rotation_6d_to_matrix(gt['pose'][0, :, :1]), + betas=gt['betas'][0], + pose2rot=False + ) + + pred_j3d = torch.matmul(self.J_regressor_eval, pred['verts_cam']).cpu() + target_j3d = torch.matmul(self.J_regressor_eval, gt_output.vertices).cpu() + pred_verts = pred['verts_cam'].cpu() + target_verts = gt_output.vertices.cpu() + + pred_j3d, target_j3d, pred_verts, target_verts = batch_align_by_pelvis( + [pred_j3d, target_j3d, pred_verts, target_verts], [2, 3] + ) + + self.evaluation_accumulators['pred_j3d'].append(pred_j3d.numpy()) + self.evaluation_accumulators['target_j3d'].append(target_j3d.numpy()) + pve = np.sqrt(np.sum((target_verts.numpy() - pred_verts.numpy()) ** 2, axis=-1)).mean(-1) * 1e3 + self.evaluation_accumulators['pve'].append(pve[:, None]) + # =======> + + batch_time = time.time() - start + + summary_string = f'({i + 1}/{len(self.valid_loader)}) | batch: {batch_time * 10.0:.4}ms | ' \ + f'Total: {bar.elapsed_td} | ETA: {bar.eta_td:}' + + self.valid_global_step += 1 + bar.suffix = summary_string + bar.next() + + logger.info(summary_string) + + bar.finish() + + def evaluate(self, ): + for k, v in self.evaluation_accumulators.items(): + self.evaluation_accumulators[k] = np.vstack(v) + + pred_j3ds = self.evaluation_accumulators['pred_j3d'] + target_j3ds = self.evaluation_accumulators['target_j3d'] + + pred_j3ds = torch.from_numpy(pred_j3ds).float() + target_j3ds = torch.from_numpy(target_j3ds).float() + + print(f'Evaluating on {pred_j3ds.shape[0]} number of poses...') + errors = torch.sqrt(((pred_j3ds - target_j3ds) ** 2).sum(dim=-1)).mean(dim=-1).cpu().numpy() + S1_hat = batch_compute_similarity_transform_torch(pred_j3ds, target_j3ds) + errors_pa = torch.sqrt(((S1_hat - target_j3ds) ** 2).sum(dim=-1)).mean(dim=-1).cpu().numpy() + + m2mm = 1000 + accel = np.mean(compute_accel(pred_j3ds)) * m2mm + accel_err = np.mean(compute_error_accel(joints_pred=pred_j3ds, joints_gt=target_j3ds)) * m2mm + mpjpe = np.mean(errors) * m2mm + pa_mpjpe = np.mean(errors_pa) * m2mm + + eval_dict = { + 'mpjpe': mpjpe, + 'pa-mpjpe': pa_mpjpe, + 'accel': accel, + 'accel_err': accel_err + } + + if 'pred_verts' in self.evaluation_accumulators.keys(): + eval_dict.update({'pve': self.evaluation_accumulators['pve'].mean()}) + + log_str = f'Epoch {self.epoch}, ' + log_str += ' '.join([f'{k.upper()}: {v:.4f},'for k,v in eval_dict.items()]) + logger.info(log_str) + + for k,v in eval_dict.items(): + self.writer.add_scalar(f'error/{k}', v, global_step=self.epoch) + + # return (mpjpe + pa_mpjpe) / 2. + return pa_mpjpe + + def save_model(self, performance, epoch): + save_dict = { + 'epoch': epoch, + 'model': self.network.state_dict(), + 'performance': performance, + 'optimizer': self.optimizer.state_dict(), + } + + filename = osp.join(self.logdir, 'checkpoint.pth.tar') + torch.save(save_dict, filename) + + if self.performance_type == 'min': + is_best = performance < self.best_performance + else: + is_best = performance > self.best_performance + + if is_best: + logger.info('Best performance achived, saving it!') + self.best_performance = performance + shutil.copyfile(filename, osp.join(self.logdir, 'model_best.pth.tar')) + + with open(osp.join(self.logdir, 'best.txt'), 'w') as f: + f.write(str(float(performance))) + + def fit(self): + for epoch in range(self.start_epoch, self.end_epoch): + self.epoch = epoch + self.train() + self.validate() + performance = self.evaluate() + + self.criterion.step() + if self.lr_scheduler is not None: + self.lr_scheduler.step() + + # log the learning rate + for param_group in self.optimizer.param_groups[:2]: + print(f'Learning rate {param_group["lr"]}') + self.writer.add_scalar('lr', param_group['lr'], global_step=self.epoch) + + logger.info(f'Epoch {epoch+1} performance: {performance:.4f}') + + self.save_model(performance, epoch) + self.train_loader.dataset.prepare_video_batch() + + self.writer.close() + + def load_pretrained(self, model_path): + if osp.isfile(model_path): + checkpoint = torch.load(model_path) + + # network + ignore_keys = ['smpl.body_pose', 'smpl.betas', 'smpl.global_orient', 'smpl.J_regressor_extra', 'smpl.J_regressor_eval'] + ignore_keys2 = [k for k in checkpoint['model'].keys() if 'integrator' in k] + ignore_keys.extend(ignore_keys2) + model_state_dict = {k: v for k, v in checkpoint['model'].items() if k not in ignore_keys} + model_state_dict = {k: v for k, v in model_state_dict.items() if k in self.network.state_dict().keys()} + self.network.load_state_dict(model_state_dict, strict=False) + + if self.resume: + self.start_epoch = checkpoint['epoch'] + self.best_performance = checkpoint['performance'] + self.optimizer.load_state_dict(checkpoint['optimizer']) + + logger.info(f"=> loaded checkpoint '{model_path}' " + f"(epoch {self.start_epoch}, performance {self.best_performance})") + else: + logger.info(f"=> no checkpoint found at '{model_path}'") \ No newline at end of file diff --git a/lib/data/__init__.py b/lib/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lib/data/__pycache__/__init__.cpython-39.pyc b/lib/data/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9a50ecdd3317f879777921e8717d6f0c6a46e68d Binary files /dev/null and b/lib/data/__pycache__/__init__.cpython-39.pyc differ diff --git a/lib/data/__pycache__/_dataset.cpython-39.pyc b/lib/data/__pycache__/_dataset.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e6eec1e688837ce128f0d6eded25c07424d45025 Binary files /dev/null and b/lib/data/__pycache__/_dataset.cpython-39.pyc differ diff --git a/lib/data/_dataset.py b/lib/data/_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..17889ecacbf4c5bf601bb4c058d51e8b4f603745 --- /dev/null +++ b/lib/data/_dataset.py @@ -0,0 +1,77 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import torch +import numpy as np +from skimage.util.shape import view_as_windows + +from configs import constants as _C +from .utils.normalizer import Normalizer +from ..utils.imutils import transform + +class BaseDataset(torch.utils.data.Dataset): + def __init__(self, cfg, training=True): + super(BaseDataset, self).__init__() + self.epoch = 0 + self.training = training + self.n_joints = _C.KEYPOINTS.NUM_JOINTS + self.n_frames = cfg.DATASET.SEQLEN + 1 + self.keypoints_normalizer = Normalizer(cfg) + + def prepare_video_batch(self): + r = self.epoch % 4 + + self.video_indices = [] + vid_name = self.labels['vid'] + if isinstance(vid_name, torch.Tensor): vid_name = vid_name.numpy() + video_names_unique, group = np.unique( + vid_name, return_index=True) + perm = np.argsort(group) + group_perm = group[perm] + indices = np.split( + np.arange(0, self.labels['vid'].shape[0]), group_perm[1:] + ) + for idx in range(len(video_names_unique)): + indexes = indices[idx] + if indexes.shape[0] < self.n_frames: continue + chunks = view_as_windows( + indexes, (self.n_frames), step=self.n_frames // 4 + ) + start_finish = chunks[r::4, (0, -1)].tolist() + self.video_indices += start_finish + + self.epoch += 1 + + def __len__(self): + if self.training: + return len(self.video_indices) + else: + return len(self.labels['kp2d']) + + def __getitem__(self, index): + return self.get_single_sequence(index) + + def get_single_sequence(self, index): + NotImplementedError('get_single_sequence is not implemented') + + def get_naive_intrinsics(self, res): + # Assume 45 degree FOV + img_w, img_h = res + self.focal_length = (img_w * img_w + img_h * img_h) ** 0.5 + self.cam_intrinsics = torch.eye(3).repeat(1, 1, 1).float() + self.cam_intrinsics[:, 0, 0] = self.focal_length + self.cam_intrinsics[:, 1, 1] = self.focal_length + self.cam_intrinsics[:, 0, 2] = img_w/2. + self.cam_intrinsics[:, 1, 2] = img_h/2. + + def j2d_processing(self, kp, bbox): + center = bbox[..., :2] + scale = bbox[..., -1:] + nparts = kp.shape[0] + for i in range(nparts): + kp[i, 0:2] = transform(kp[i, 0:2] + 1, center, scale, + [224, 224]) + kp[:, :2] = 2. * kp[:, :2] / 224 - 1. + kp = kp.astype('float32') + return kp \ No newline at end of file diff --git a/lib/data/dataloader.py b/lib/data/dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..7ff536d354d01bc2f1d5cb0f3531d7daeacd7bc1 --- /dev/null +++ b/lib/data/dataloader.py @@ -0,0 +1,46 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import torch + +from .datasets import EvalDataset, DataFactory +from ..utils.data_utils import make_collate_fn + + +def setup_eval_dataloader(cfg, data, split='test', backbone=None): + if backbone is None: + backbone = cfg.MODEL.BACKBONE + + dataset = EvalDataset(cfg, data, split, backbone) + dloader = torch.utils.data.DataLoader( + dataset, + batch_size=1, + num_workers=0, + shuffle=False, + pin_memory=True, + collate_fn=make_collate_fn() + ) + return dloader + + +def setup_train_dataloader(cfg, ): + n_workers = 0 if cfg.DEBUG else cfg.NUM_WORKERS + + train_dataset = DataFactory(cfg, cfg.TRAIN.STAGE) + dloader = torch.utils.data.DataLoader( + train_dataset, + batch_size=cfg.TRAIN.BATCH_SIZE, + num_workers=n_workers, + shuffle=True, + pin_memory=True, + collate_fn=make_collate_fn() + ) + return dloader + + +def setup_dloaders(cfg, dset='3dpw', split='val'): + test_dloader = setup_eval_dataloader(cfg, dset, split, cfg.MODEL.BACKBONE) + train_dloader = setup_train_dataloader(cfg) + + return train_dloader, test_dloader \ No newline at end of file diff --git a/lib/data/datasets/__init__.py 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differ diff --git a/lib/data/datasets/__pycache__/videos.cpython-39.pyc b/lib/data/datasets/__pycache__/videos.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f9c7b3e16f22dd028aad45cf03b716e145506643 Binary files /dev/null and b/lib/data/datasets/__pycache__/videos.cpython-39.pyc differ diff --git a/lib/data/datasets/amass.py b/lib/data/datasets/amass.py new file mode 100644 index 0000000000000000000000000000000000000000..e05cef153787bc17d143272bb7ad8c077d03902b --- /dev/null +++ b/lib/data/datasets/amass.py @@ -0,0 +1,173 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import torch +import joblib +from lib.utils import transforms + +from configs import constants as _C + +from ..utils.augmentor import * +from .._dataset import BaseDataset +from ...models import build_body_model +from ...utils import data_utils as d_utils +from ...utils.kp_utils import root_centering + + + +def compute_contact_label(feet, thr=1e-2, alpha=5): + vel = torch.zeros_like(feet[..., 0]) + label = torch.zeros_like(feet[..., 0]) + + vel[1:-1] = (feet[2:] - feet[:-2]).norm(dim=-1) / 2.0 + vel[0] = vel[1].clone() + vel[-1] = vel[-2].clone() + + label = 1 / (1 + torch.exp(alpha * (thr ** -1) * (vel - thr))) + return label + + +class AMASSDataset(BaseDataset): + def __init__(self, cfg): + label_pth = _C.PATHS.AMASS_LABEL + super(AMASSDataset, self).__init__(cfg, training=True) + + self.supervise_pose = cfg.TRAIN.STAGE == 'stage1' + self.labels = joblib.load(label_pth) + self.SequenceAugmentor = SequenceAugmentor(cfg.DATASET.SEQLEN + 1) + + # Load augmentators + self.VideoAugmentor = VideoAugmentor(cfg) + self.SMPLAugmentor = SMPLAugmentor(cfg) + self.d_img_feature = _C.IMG_FEAT_DIM[cfg.MODEL.BACKBONE] + + self.n_frames = int(cfg.DATASET.SEQLEN * self.SequenceAugmentor.l_factor) + 1 + self.smpl = build_body_model('cpu', self.n_frames) + self.prepare_video_batch() + + # Naive assumption of image intrinsics + self.img_w, self.img_h = 1000, 1000 + self.get_naive_intrinsics((self.img_w, self.img_h)) + + self.CameraAugmentor = CameraAugmentor(cfg.DATASET.SEQLEN + 1, self.img_w, self.img_h, self.focal_length) + + + @property + def __name__(self, ): + return 'AMASS' + + def get_input(self, target): + gt_kp3d = target['kp3d'] + inpt_kp3d = self.VideoAugmentor(gt_kp3d[:, :self.n_joints, :-1].clone()) + kp2d = perspective_projection(inpt_kp3d, self.cam_intrinsics) + mask = self.VideoAugmentor.get_mask() + kp2d, bbox = self.keypoints_normalizer(kp2d, target['res'], self.cam_intrinsics, 224, 224) + + target['bbox'] = bbox[1:] + target['kp2d'] = kp2d + target['mask'] = mask[1:] + target['features'] = torch.zeros((self.SMPLAugmentor.n_frames, self.d_img_feature)).float() + return target + + def get_groundtruth(self, target): + # GT 1. Joints + gt_kp3d = target['kp3d'] + gt_kp2d = perspective_projection(gt_kp3d, self.cam_intrinsics) + target['kp3d'] = torch.cat((gt_kp3d, torch.ones_like(gt_kp3d[..., :1]) * float(self.supervise_pose)), dim=-1) + target['full_kp2d'] = torch.cat((gt_kp2d, torch.ones_like(gt_kp2d[..., :1]) * float(self.supervise_pose)), dim=-1)[1:] + target['weak_kp2d'] = torch.zeros_like(target['full_kp2d']) + target['init_kp3d'] = root_centering(gt_kp3d[:1, :self.n_joints].clone()).reshape(1, -1) + target['verts'] = torch.zeros((self.SMPLAugmentor.n_frames, 6890, 3)).float() + + # GT 2. Root pose + vel_world = (target['transl'][1:] - target['transl'][:-1]) + pose_root = target['pose_root'].clone() + vel_root = (pose_root[:-1].transpose(-1, -2) @ vel_world.unsqueeze(-1)).squeeze(-1) + target['vel_root'] = vel_root.clone() + target['pose_root'] = transforms.matrix_to_rotation_6d(pose_root) + target['init_root'] = target['pose_root'][:1].clone() + + # GT 3. Foot contact + contact = compute_contact_label(target['feet']) + if 'tread' in target['vid']: + target['contact'] = torch.ones_like(contact) * (-1) + else: + target['contact'] = contact + + return target + + def forward_smpl(self, target): + output = self.smpl.get_output( + body_pose=torch.cat((target['init_pose'][:, 1:], target['pose'][1:, 1:])), + global_orient=torch.cat((target['init_pose'][:, :1], target['pose'][1:, :1])), + betas=target['betas'], + pose2rot=False) + + target['transl'] = target['transl'] - output.offset + target['transl'] = target['transl'] - target['transl'][0] + target['kp3d'] = output.joints + target['feet'] = output.feet[1:] + target['transl'][1:].unsqueeze(-2) + + return target + + def augment_data(self, target): + # Augmentation 1. SMPL params augmentation + target = self.SMPLAugmentor(target) + + # Augmentation 2. Sequence speed augmentation + target = self.SequenceAugmentor(target) + + # Get world-coordinate SMPL + target = self.forward_smpl(target) + + # Augmentation 3. Virtual camera generation + target = self.CameraAugmentor(target) + + return target + + def load_amass(self, index, target): + start_index, end_index = self.video_indices[index] + + # Load AMASS labels + pose = torch.from_numpy(self.labels['pose'][start_index:end_index+1].copy()) + pose = transforms.axis_angle_to_matrix(pose.reshape(-1, 24, 3)) + transl = torch.from_numpy(self.labels['transl'][start_index:end_index+1].copy()) + betas = torch.from_numpy(self.labels['betas'][start_index:end_index+1].copy()) + + # Stack GT + target.update({'vid': self.labels['vid'][start_index], + 'pose': pose, + 'transl': transl, + 'betas': betas}) + + return target + + def get_single_sequence(self, index): + target = {'res': torch.tensor([self.img_w, self.img_h]).float(), + 'cam_intrinsics': self.cam_intrinsics.clone(), + 'has_full_screen': torch.tensor(True), + 'has_smpl': torch.tensor(self.supervise_pose), + 'has_traj': torch.tensor(True), + 'has_verts': torch.tensor(False),} + + target = self.load_amass(index, target) + target = self.augment_data(target) + target = self.get_groundtruth(target) + target = self.get_input(target) + + target = d_utils.prepare_keypoints_data(target) + target = d_utils.prepare_smpl_data(target) + + return target + + +def perspective_projection(points, cam_intrinsics, rotation=None, translation=None): + K = cam_intrinsics + if rotation is not None: + points = torch.matmul(rotation, points.transpose(1, 2)).transpose(1, 2) + if translation is not None: + points = points + translation.unsqueeze(1) + projected_points = points / points[:, :, -1].unsqueeze(-1) + projected_points = torch.einsum('bij,bkj->bki', K, projected_points.float()) + return projected_points[:, :, :-1] \ No newline at end of file diff --git a/lib/data/datasets/bedlam.py b/lib/data/datasets/bedlam.py new file mode 100644 index 0000000000000000000000000000000000000000..9aba9757388218c16b641aa2ad9fcaa00fc37900 --- /dev/null +++ b/lib/data/datasets/bedlam.py @@ -0,0 +1,165 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import torch +import joblib +from lib.utils import transforms + +from configs import constants as _C + +from .amass import compute_contact_label, perspective_projection +from ..utils.augmentor import * +from .._dataset import BaseDataset +from ...models import build_body_model +from ...utils import data_utils as d_utils +from ...utils.kp_utils import root_centering + +class BEDLAMDataset(BaseDataset): + def __init__(self, cfg): + label_pth = _C.PATHS.BEDLAM_LABEL.replace('backbone', cfg.MODEL.BACKBONE) + super(BEDLAMDataset, self).__init__(cfg, training=True) + + self.labels = joblib.load(label_pth) + + self.VideoAugmentor = VideoAugmentor(cfg) + self.SMPLAugmentor = SMPLAugmentor(cfg, False) + + self.smpl = build_body_model('cpu', self.n_frames) + self.prepare_video_batch() + + @property + def __name__(self, ): + return 'BEDLAM' + + def get_inputs(self, index, target, vis_thr=0.6): + start_index, end_index = self.video_indices[index] + + bbox = self.labels['bbox'][start_index:end_index+1].clone() + bbox[:, 2] = bbox[:, 2] / 200 + + gt_kp3d = target['kp3d'] + inpt_kp3d = self.VideoAugmentor(gt_kp3d[:, :self.n_joints, :-1].clone()) + # kp2d = perspective_projection(inpt_kp3d, target['K']) + kp2d = perspective_projection(inpt_kp3d, self.cam_intrinsics) + mask = self.VideoAugmentor.get_mask() + # kp2d, bbox = self.keypoints_normalizer(kp2d, target['res'], self.cam_intrinsics, 224, 224, bbox) + kp2d, bbox = self.keypoints_normalizer(kp2d, target['res'], self.cam_intrinsics, 224, 224) + + target['bbox'] = bbox[1:] + target['kp2d'] = kp2d + target['mask'] = mask[1:] + + # Image features + target['features'] = self.labels['features'][start_index+1:end_index+1].clone() + + return target + + def get_groundtruth(self, index, target): + start_index, end_index = self.video_indices[index] + + # GT 1. Joints + gt_kp3d = target['kp3d'] + # gt_kp2d = perspective_projection(gt_kp3d, target['K']) + gt_kp2d = perspective_projection(gt_kp3d, self.cam_intrinsics) + target['kp3d'] = torch.cat((gt_kp3d, torch.ones_like(gt_kp3d[..., :1])), dim=-1) + # target['full_kp2d'] = torch.cat((gt_kp2d, torch.zeros_like(gt_kp2d[..., :1])), dim=-1)[1:] + target['full_kp2d'] = torch.cat((gt_kp2d, torch.ones_like(gt_kp2d[..., :1])), dim=-1)[1:] + target['weak_kp2d'] = torch.zeros_like(target['full_kp2d']) + target['init_kp3d'] = root_centering(gt_kp3d[:1, :self.n_joints].clone()).reshape(1, -1) + + # GT 2. Root pose + w_transl = self.labels['w_trans'][start_index:end_index+1] + pose_root = transforms.axis_angle_to_matrix(self.labels['root'][start_index:end_index+1]) + vel_world = (w_transl[1:] - w_transl[:-1]) + vel_root = (pose_root[:-1].transpose(-1, -2) @ vel_world.unsqueeze(-1)).squeeze(-1) + target['vel_root'] = vel_root.clone() + target['pose_root'] = transforms.matrix_to_rotation_6d(pose_root) + target['init_root'] = target['pose_root'][:1].clone() + + return target + + def forward_smpl(self, target): + output = self.smpl.get_output( + body_pose=torch.cat((target['init_pose'][:, 1:], target['pose'][1:, 1:])), + global_orient=torch.cat((target['init_pose'][:, :1], target['pose'][1:, :1])), + betas=target['betas'], + transl=target['transl'], + pose2rot=False) + + target['kp3d'] = output.joints + output.offset.unsqueeze(1) + target['feet'] = output.feet[1:] + target['transl'][1:].unsqueeze(-2) + target['verts'] = output.vertices[1:, ].clone() + + return target + + def augment_data(self, target): + # Augmentation 1. SMPL params augmentation + target = self.SMPLAugmentor(target) + + # Get world-coordinate SMPL + target = self.forward_smpl(target) + + return target + + def load_camera(self, index, target): + start_index, end_index = self.video_indices[index] + + # Get camera info + extrinsics = self.labels['extrinsics'][start_index:end_index+1].clone() + R = extrinsics[:, :3, :3] + T = extrinsics[:, :3, -1] + K = self.labels['intrinsics'][start_index:end_index+1].clone() + width, height = K[0, 0, 2] * 2, K[0, 1, 2] * 2 + target['R'] = R + target['res'] = torch.tensor([width, height]).float() + + # Compute angular velocity + cam_angvel = transforms.matrix_to_rotation_6d(R[:-1] @ R[1:].transpose(-1, -2)) + cam_angvel = cam_angvel - torch.tensor([[1, 0, 0, 0, 1, 0]]).to(cam_angvel) # Normalize + target['cam_angvel'] = cam_angvel * 3e1 # BEDLAM is 30-fps + + target['K'] = K # Use GT camera intrinsics for projecting keypoints + self.get_naive_intrinsics(target['res']) + target['cam_intrinsics'] = self.cam_intrinsics + + return target + + def load_params(self, index, target): + start_index, end_index = self.video_indices[index] + + # Load AMASS labels + pose = self.labels['pose'][start_index:end_index+1].clone() + pose = transforms.axis_angle_to_matrix(pose.reshape(-1, 24, 3)) + transl = self.labels['c_trans'][start_index:end_index+1].clone() + betas = self.labels['betas'][start_index:end_index+1, :10].clone() + + # Stack GT + target.update({'vid': self.labels['vid'][start_index].clone(), + 'pose': pose, + 'transl': transl, + 'betas': betas}) + + return target + + + def get_single_sequence(self, index): + target = {'has_full_screen': torch.tensor(True), + 'has_smpl': torch.tensor(True), + 'has_traj': torch.tensor(False), + 'has_verts': torch.tensor(True), + + # Null contact label + 'contact': torch.ones((self.n_frames - 1, 4)) * (-1), + } + + target = self.load_params(index, target) + target = self.load_camera(index, target) + target = self.augment_data(target) + target = self.get_groundtruth(index, target) + target = self.get_inputs(index, target) + + target = d_utils.prepare_keypoints_data(target) + target = d_utils.prepare_smpl_data(target) + + return target \ No newline at end of file diff --git a/lib/data/datasets/dataset2d.py b/lib/data/datasets/dataset2d.py new file mode 100644 index 0000000000000000000000000000000000000000..d2b06f8b5d61c951922f55ca17e4982cc3a0ba07 --- /dev/null +++ b/lib/data/datasets/dataset2d.py @@ -0,0 +1,140 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import torch +import joblib + +from .._dataset import BaseDataset +from ..utils.augmentor import * +from ...utils import data_utils as d_utils +from ...utils import transforms +from ...models import build_body_model +from ...utils.kp_utils import convert_kps, root_centering + + +class Dataset2D(BaseDataset): + def __init__(self, cfg, fname, training): + super(Dataset2D, self).__init__(cfg, training) + + self.epoch = 0 + self.n_frames = cfg.DATASET.SEQLEN + 1 + self.labels = joblib.load(fname) + + if self.training: + self.prepare_video_batch() + + self.smpl = build_body_model('cpu', self.n_frames) + self.SMPLAugmentor = SMPLAugmentor(cfg, False) + + def __getitem__(self, index): + return self.get_single_sequence(index) + + def get_inputs(self, index, target, vis_thr=0.6): + start_index, end_index = self.video_indices[index] + + # 2D keypoints detection + kp2d = self.labels['kp2d'][start_index:end_index+1][..., :2].clone() + kp2d, bbox = self.keypoints_normalizer(kp2d, target['res'], target['cam_intrinsics'], 224, 224, target['bbox']) + target['bbox'] = bbox[1:] + target['kp2d'] = kp2d + + # Detection mask + target['mask'] = ~self.labels['joints2D'][start_index+1:end_index+1][..., -1].clone().bool() + + # Image features + target['features'] = self.labels['features'][start_index+1:end_index+1].clone() + + return target + + def get_labels(self, index, target): + start_index, end_index = self.video_indices[index] + + # SMPL parameters + # NOTE: We use NeuralAnnot labels for Human36m and MPII3D only for the 0th frame input. + # We do not supervise the network on SMPL parameters. + target['pose'] = transforms.axis_angle_to_matrix( + self.labels['pose'][start_index:end_index+1].clone().reshape(-1, 24, 3)) + target['betas'] = self.labels['betas'][start_index:end_index+1].clone() # No t + + # Apply SMPL augmentor (y-axis rotation and initial frame noise) + target = self.SMPLAugmentor(target) + + # 2D keypoints + kp2d = self.labels['kp2d'][start_index:end_index+1].clone().float()[..., :2] + gt_kp2d = torch.zeros((self.n_frames - 1, 31, 2)) + gt_kp2d[:, :17] = kp2d[1:].clone() + + # Set 0 confidence to the masked keypoints + mask = torch.zeros((self.n_frames - 1, 31)) + mask[:, :17] = self.labels['joints2D'][start_index+1:end_index+1][..., -1].clone() + mask = torch.logical_and(gt_kp2d.mean(-1) != 0, mask) + gt_kp2d = torch.cat((gt_kp2d, mask.float().unsqueeze(-1)), dim=-1) + + _gt_kp2d = gt_kp2d.clone() + for idx in range(len(_gt_kp2d)): + _gt_kp2d[idx][..., :2] = torch.from_numpy( + self.j2d_processing(gt_kp2d[idx][..., :2].numpy().copy(), + target['bbox'][idx].numpy().copy())) + + target['weak_kp2d'] = _gt_kp2d.clone() + target['full_kp2d'] = torch.zeros_like(gt_kp2d) + target['kp3d'] = torch.zeros((kp2d.shape[0], 31, 4)) + + # No SMPL vertices available + target['verts'] = torch.zeros((self.n_frames - 1, 6890, 3)).float() + return target + + def get_init_frame(self, target): + # Prepare initial frame + output = self.smpl.get_output( + body_pose=target['init_pose'][:, 1:], + global_orient=target['init_pose'][:, :1], + betas=target['betas'][:1], + pose2rot=False + ) + target['init_kp3d'] = root_centering(output.joints[:1, :self.n_joints]).reshape(1, -1) + + return target + + def get_single_sequence(self, index): + # Camera parameters + res = (224.0, 224.0) + bbox = torch.tensor([112.0, 112.0, 1.12]) + res = torch.tensor(res) + self.get_naive_intrinsics(res) + bbox = bbox.repeat(self.n_frames, 1) + + # Universal target + target = {'has_full_screen': torch.tensor(False), + 'has_smpl': torch.tensor(self.has_smpl), + 'has_traj': torch.tensor(self.has_traj), + 'has_verts': torch.tensor(False), + 'transl': torch.zeros((self.n_frames, 3)), + + # Camera parameters and bbox + 'res': res, + 'cam_intrinsics': self.cam_intrinsics, + 'bbox': bbox, + + # Null camera motion + 'R': torch.eye(3).repeat(self.n_frames, 1, 1), + 'cam_angvel': torch.zeros((self.n_frames - 1, 6)), + + # Null root orientation and velocity + 'pose_root': torch.zeros((self.n_frames, 6)), + 'vel_root': torch.zeros((self.n_frames - 1, 3)), + 'init_root': torch.zeros((1, 6)), + + # Null contact label + 'contact': torch.ones((self.n_frames - 1, 4)) * (-1) + } + + self.get_inputs(index, target) + self.get_labels(index, target) + self.get_init_frame(target) + + target = d_utils.prepare_keypoints_data(target) + target = d_utils.prepare_smpl_data(target) + + return target \ No newline at end of file diff --git a/lib/data/datasets/dataset3d.py b/lib/data/datasets/dataset3d.py new file mode 100644 index 0000000000000000000000000000000000000000..ed8e3c122d1df5f96a9c0b3695da9927460a2ee0 --- /dev/null +++ b/lib/data/datasets/dataset3d.py @@ -0,0 +1,172 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import torch +import joblib +import numpy as np + +from .._dataset import BaseDataset +from ..utils.augmentor import * +from ...utils import data_utils as d_utils +from ...utils import transforms +from ...models import build_body_model +from ...utils.kp_utils import convert_kps, root_centering + + +class Dataset3D(BaseDataset): + def __init__(self, cfg, fname, training): + super(Dataset3D, self).__init__(cfg, training) + + self.epoch = 0 + self.labels = joblib.load(fname) + self.n_frames = cfg.DATASET.SEQLEN + 1 + + if self.training: + self.prepare_video_batch() + + self.smpl = build_body_model('cpu', self.n_frames) + self.SMPLAugmentor = SMPLAugmentor(cfg, False) + self.VideoAugmentor = VideoAugmentor(cfg) + + def __getitem__(self, index): + return self.get_single_sequence(index) + + def get_inputs(self, index, target, vis_thr=0.6): + start_index, end_index = self.video_indices[index] + + # 2D keypoints detection + kp2d = self.labels['kp2d'][start_index:end_index+1][..., :2].clone() + bbox = self.labels['bbox'][start_index:end_index+1][..., [0, 1, -1]].clone() + bbox[:, 2] = bbox[:, 2] / 200 + kp2d, bbox = self.keypoints_normalizer(kp2d, target['res'], self.cam_intrinsics, 224, 224, bbox) + + target['bbox'] = bbox[1:] + target['kp2d'] = kp2d + target['mask'] = self.labels['kp2d'][start_index+1:end_index+1][..., -1] < vis_thr + + # Image features + target['features'] = self.labels['features'][start_index+1:end_index+1].clone() + + return target + + def get_labels(self, index, target): + start_index, end_index = self.video_indices[index] + + # SMPL parameters + # NOTE: We use NeuralAnnot labels for Human36m and MPII3D only for the 0th frame input. + # We do not supervise the network on SMPL parameters. + target['pose'] = transforms.axis_angle_to_matrix( + self.labels['pose'][start_index:end_index+1].clone().reshape(-1, 24, 3)) + target['betas'] = self.labels['betas'][start_index:end_index+1].clone() # No t + + # Apply SMPL augmentor (y-axis rotation and initial frame noise) + target = self.SMPLAugmentor(target) + + # 3D and 2D keypoints + if self.__name__ == 'ThreeDPW': # 3DPW has SMPL labels + gt_kp3d = self.labels['joints3D'][start_index:end_index+1].clone() + gt_kp2d = self.labels['joints2D'][start_index+1:end_index+1, ..., :2].clone() + gt_kp3d = root_centering(gt_kp3d.clone()) + + else: # Human36m and MPII do not have SMPL labels + gt_kp3d = torch.zeros((self.n_frames, self.n_joints + 14, 3)) + gt_kp3d[:, self.n_joints:] = convert_kps(self.labels['joints3D'][start_index:end_index+1], 'spin', 'common') + gt_kp2d = torch.zeros((self.n_frames - 1, self.n_joints + 14, 2)) + gt_kp2d[:, self.n_joints:] = convert_kps(self.labels['joints2D'][start_index+1:end_index+1, ..., :2], 'spin', 'common') + + conf = self.mask.repeat(self.n_frames, 1).unsqueeze(-1) + gt_kp2d = torch.cat((gt_kp2d, conf[1:]), dim=-1) + gt_kp3d = torch.cat((gt_kp3d, conf), dim=-1) + target['kp3d'] = gt_kp3d + target['full_kp2d'] = gt_kp2d + target['weak_kp2d'] = torch.zeros_like(gt_kp2d) + + if self.__name__ != 'ThreeDPW': # 3DPW does not contain world-coordinate motion + # Foot ground contact labels for Human36M and MPII3D + target['contact'] = self.labels['stationaries'][start_index+1:end_index+1].clone() + else: + # No foot ground contact label available for 3DPW + target['contact'] = torch.ones((self.n_frames - 1, 4)) * (-1) + + if self.has_verts: + # SMPL vertices available for 3DPW + with torch.no_grad(): + start_index, end_index = self.video_indices[index] + gender = self.labels['gender'][start_index].item() + output = self.smpl_gender[gender]( + body_pose=target['pose'][1:, 1:], + global_orient=target['pose'][1:, :1], + betas=target['betas'][1:], + pose2rot=False, + ) + target['verts'] = output.vertices.clone() + else: + # No SMPL vertices available + target['verts'] = torch.zeros((self.n_frames - 1, 6890, 3)).float() + + return target + + def get_init_frame(self, target): + # Prepare initial frame + output = self.smpl.get_output( + body_pose=target['init_pose'][:, 1:], + global_orient=target['init_pose'][:, :1], + betas=target['betas'][:1], + pose2rot=False + ) + target['init_kp3d'] = root_centering(output.joints[:1, :self.n_joints]).reshape(1, -1) + + return target + + def get_camera_info(self, index, target): + start_index, end_index = self.video_indices[index] + + # Intrinsics + target['res'] = self.labels['res'][start_index:end_index+1][0].clone() + self.get_naive_intrinsics(target['res']) + target['cam_intrinsics'] = self.cam_intrinsics.clone() + + # Extrinsics pose + R = self.labels['cam_poses'][start_index:end_index+1, :3, :3].clone().float() + yaw = transforms.axis_angle_to_matrix(torch.tensor([[0, 2 * np.pi * np.random.uniform(), 0]])).float() + if self.__name__ == 'Human36M': + # Map Z-up to Y-down coordinate + zup2ydown = transforms.axis_angle_to_matrix(torch.tensor([[-np.pi/2, 0, 0]])).float() + zup2ydown = torch.matmul(yaw, zup2ydown) + R = torch.matmul(R, zup2ydown) + elif self.__name__ == 'MPII3D': + # Map Y-up to Y-down coordinate + yup2ydown = transforms.axis_angle_to_matrix(torch.tensor([[np.pi, 0, 0]])).float() + yup2ydown = torch.matmul(yaw, yup2ydown) + R = torch.matmul(R, yup2ydown) + + return target + + def get_single_sequence(self, index): + # Universal target + target = {'has_full_screen': torch.tensor(True), + 'has_smpl': torch.tensor(self.has_smpl), + 'has_traj': torch.tensor(self.has_traj), + 'has_verts': torch.tensor(self.has_verts), + 'transl': torch.zeros((self.n_frames, 3)), + + # Null camera motion + 'R': torch.eye(3).repeat(self.n_frames, 1, 1), + 'cam_angvel': torch.zeros((self.n_frames - 1, 6)), + + # Null root orientation and velocity + 'pose_root': torch.zeros((self.n_frames, 6)), + 'vel_root': torch.zeros((self.n_frames - 1, 3)), + 'init_root': torch.zeros((1, 6)), + } + + self.get_camera_info(index, target) + self.get_inputs(index, target) + self.get_labels(index, target) + self.get_init_frame(target) + + target = d_utils.prepare_keypoints_data(target) + target = d_utils.prepare_smpl_data(target) + + return target \ No newline at end of file diff --git a/lib/data/datasets/dataset_custom.py b/lib/data/datasets/dataset_custom.py new file mode 100644 index 0000000000000000000000000000000000000000..dd27140050725aa715a42f411dc270c12c13723d --- /dev/null +++ b/lib/data/datasets/dataset_custom.py @@ -0,0 +1,115 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import torch + +from ..utils.normalizer import Normalizer +from ...models import build_body_model +from ...utils import transforms +from ...utils.kp_utils import root_centering +from ...utils.imutils import compute_cam_intrinsics + +KEYPOINTS_THR = 0.3 + +def convert_dpvo_to_cam_angvel(traj, fps): + """Function to convert DPVO trajectory output to camera angular velocity""" + + # 0 ~ 3: translation, 3 ~ 7: Quaternion + quat = traj[:, 3:] + + # Convert (x,y,z,q) to (q,x,y,z) + quat = quat[:, [3, 0, 1, 2]] + + # Quat is camera to world transformation. Convert it to world to camera + world2cam = transforms.quaternion_to_matrix(torch.from_numpy(quat)).float() + R = world2cam.mT + + # Compute the rotational changes over time. + cam_angvel = transforms.matrix_to_axis_angle(R[:-1] @ R[1:].transpose(-1, -2)) + + # Convert matrix to 6D representation + cam_angvel = transforms.matrix_to_rotation_6d(transforms.axis_angle_to_matrix(cam_angvel)) + + # Normalize 6D angular velocity + cam_angvel = cam_angvel - torch.tensor([[1, 0, 0, 0, 1, 0]]).to(cam_angvel) # Normalize + cam_angvel = cam_angvel * fps + cam_angvel = torch.cat((cam_angvel, cam_angvel[:1]), dim=0) + return cam_angvel + + +class CustomDataset(torch.utils.data.Dataset): + def __init__(self, cfg, tracking_results, slam_results, width, height, fps): + + self.tracking_results = tracking_results + self.slam_results = slam_results + self.width = width + self.height = height + self.fps = fps + self.res = torch.tensor([width, height]).float() + self.intrinsics = compute_cam_intrinsics(self.res) + + self.device = cfg.DEVICE.lower() + + self.smpl = build_body_model('cpu') + self.keypoints_normalizer = Normalizer(cfg) + + self._to = lambda x: x.unsqueeze(0).to(self.device) + + def __len__(self): + return len(self.tracking_results.keys()) + + def load_data(self, index, flip=False): + if flip: + self.prefix = 'flipped_' + else: + self.prefix = '' + + return self.__getitem__(index) + + def __getitem__(self, _index): + if _index >= len(self): return + + index = sorted(list(self.tracking_results.keys()))[_index] + + # Process 2D keypoints + kp2d = torch.from_numpy(self.tracking_results[index][self.prefix + 'keypoints']).float() + mask = kp2d[..., -1] < KEYPOINTS_THR + bbox = torch.from_numpy(self.tracking_results[index][self.prefix + 'bbox']).float() + + norm_kp2d, _ = self.keypoints_normalizer( + kp2d[..., :-1].clone(), self.res, self.intrinsics, 224, 224, bbox + ) + + # Process image features + features = self.tracking_results[index][self.prefix + 'features'] + + # Process initial pose + init_output = self.smpl.get_output( + global_orient=self.tracking_results[index][self.prefix + 'init_global_orient'], + body_pose=self.tracking_results[index][self.prefix + 'init_body_pose'], + betas=self.tracking_results[index][self.prefix + 'init_betas'], + pose2rot=False, + return_full_pose=True + ) + init_kp3d = root_centering(init_output.joints[:, :17], 'coco') + init_kp = torch.cat((init_kp3d.reshape(1, -1), norm_kp2d[0].clone().reshape(1, -1)), dim=-1) + init_smpl = transforms.matrix_to_rotation_6d(init_output.full_pose) + init_root = transforms.matrix_to_rotation_6d(init_output.global_orient) + + # Process SLAM results + cam_angvel = convert_dpvo_to_cam_angvel(self.slam_results, self.fps) + + return ( + index, # subject id + self._to(norm_kp2d), # 2d keypoints + (self._to(init_kp), self._to(init_smpl)), # initial pose + self._to(features), # image features + self._to(mask), # keypoints mask + init_root.to(self.device), # initial root orientation + self._to(cam_angvel), # camera angular velocity + self.tracking_results[index]['frame_id'], # frame indices + {'cam_intrinsics': self._to(self.intrinsics), # other keyword arguments + 'bbox': self._to(bbox), + 'res': self._to(self.res)}, + ) \ No newline at end of file diff --git a/lib/data/datasets/dataset_eval.py b/lib/data/datasets/dataset_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..85d7569027b09b08b91c6cc9c35404a800709aee --- /dev/null +++ b/lib/data/datasets/dataset_eval.py @@ -0,0 +1,113 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os +import torch +import joblib + +from configs import constants as _C +from .._dataset import BaseDataset +from ...utils import transforms +from ...utils import data_utils as d_utils +from ...utils.kp_utils import root_centering + +FPS = 30 +class EvalDataset(BaseDataset): + def __init__(self, cfg, data, split, backbone): + super(EvalDataset, self).__init__(cfg, False) + + self.prefix = '' + self.data = data + parsed_data_path = os.path.join(_C.PATHS.PARSED_DATA, f'{data}_{split}_{backbone}.pth') + self.labels = joblib.load(parsed_data_path) + + def load_data(self, index, flip=False): + if flip: + self.prefix = 'flipped_' + else: + self.prefix = '' + + target = self.__getitem__(index) + for key, val in target.items(): + if isinstance(val, torch.Tensor): + target[key] = val.unsqueeze(0) + return target + + def __getitem__(self, index): + target = {} + target = self.get_data(index) + target = d_utils.prepare_keypoints_data(target) + target = d_utils.prepare_smpl_data(target) + + return target + + def __len__(self): + return len(self.labels['kp2d']) + + def prepare_labels(self, index, target): + # Ground truth SMPL parameters + target['pose'] = transforms.axis_angle_to_matrix(self.labels['pose'][index].reshape(-1, 24, 3)) + target['betas'] = self.labels['betas'][index] + target['gender'] = self.labels['gender'][index] + + # Sequence information + target['res'] = self.labels['res'][index][0] + target['vid'] = self.labels['vid'][index] + target['frame_id'] = self.labels['frame_id'][index][1:] + + # Camera information + self.get_naive_intrinsics(target['res']) + target['cam_intrinsics'] = self.cam_intrinsics + R = self.labels['cam_poses'][index][:, :3, :3].clone() + if 'emdb' in self.data.lower(): + # Use groundtruth camera angular velocity. + # Can be updated with SLAM results if you have it. + cam_angvel = transforms.matrix_to_rotation_6d(R[:-1] @ R[1:].transpose(-1, -2)) + cam_angvel = (cam_angvel - torch.tensor([[1, 0, 0, 0, 1, 0]]).to(cam_angvel)) * FPS + target['R'] = R + else: + cam_angvel = torch.zeros((len(target['pose']) - 1, 6)) + target['cam_angvel'] = cam_angvel + return target + + def prepare_inputs(self, index, target): + for key in ['features', 'bbox']: + data = self.labels[self.prefix + key][index][1:] + target[key] = data + + bbox = self.labels[self.prefix + 'bbox'][index][..., [0, 1, -1]].clone().float() + bbox[:, 2] = bbox[:, 2] / 200 + + # Normalize keypoints + kp2d, bbox = self.keypoints_normalizer( + self.labels[self.prefix + 'kp2d'][index][..., :2].clone().float(), + target['res'], target['cam_intrinsics'], 224, 224, bbox) + target['kp2d'] = kp2d + target['bbox'] = bbox[1:] + + # Masking out low confident keypoints + mask = self.labels[self.prefix + 'kp2d'][index][..., -1] < 0.3 + target['input_kp2d'] = self.labels['kp2d'][index][1:] + target['input_kp2d'][mask[1:]] *= 0 + target['mask'] = mask[1:] + + return target + + def prepare_initialization(self, index, target): + # Initial frame per-frame estimation + target['init_kp3d'] = root_centering(self.labels[self.prefix + 'init_kp3d'][index][:1, :self.n_joints]).reshape(1, -1) + target['init_pose'] = transforms.axis_angle_to_matrix(self.labels[self.prefix + 'init_pose'][index][:1]).cpu() + pose_root = target['pose'][:, 0].clone() + target['init_root'] = transforms.matrix_to_rotation_6d(pose_root) + + return target + + def get_data(self, index): + target = {} + + target = self.prepare_labels(index, target) + target = self.prepare_inputs(index, target) + target = self.prepare_initialization(index, target) + + return target \ No newline at end of file diff --git a/lib/data/datasets/mixed_dataset.py b/lib/data/datasets/mixed_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..33168238d2745014b92c8d9809d54c8e422291f4 --- /dev/null +++ b/lib/data/datasets/mixed_dataset.py @@ -0,0 +1,61 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import torch +import numpy as np + +from .amass import AMASSDataset +from .videos import Human36M, ThreeDPW, MPII3D, InstaVariety +from .bedlam import BEDLAMDataset +from lib.utils.data_utils import make_collate_fn + + +class DataFactory(torch.utils.data.Dataset): + def __init__(self, cfg, train_stage='syn'): + super(DataFactory, self).__init__() + + if train_stage == 'stage1': + self.datasets = [AMASSDataset(cfg)] + self.dataset_names = ['AMASS'] + elif train_stage == 'stage2': + self.datasets = [ + AMASSDataset(cfg), ThreeDPW(cfg), + Human36M(cfg), MPII3D(cfg), InstaVariety(cfg) + ] + self.dataset_names = ['AMASS', '3DPW', 'Human36M', 'MPII3D', 'Insta'] + + if len(cfg.DATASET.RATIO) == 6: # Use BEDLAM + self.datasets.append(BEDLAMDataset(cfg)) + self.dataset_names.append('BEDLAM') + + self._set_partition(cfg.DATASET.RATIO) + self.lengths = [len(ds) for ds in self.datasets] + + @property + def __name__(self, ): + return 'MixedData' + + def prepare_video_batch(self): + [ds.prepare_video_batch() for ds in self.datasets] + self.lengths = [len(ds) for ds in self.datasets] + + def _set_partition(self, partition): + self.partition = partition + self.ratio = partition + self.partition = np.array(self.partition).cumsum() + self.partition /= self.partition[-1] + + def __len__(self): + return int(np.array([l for l, r in zip(self.lengths, self.ratio) if r > 0]).mean()) + + def __getitem__(self, index): + # Get the dataset to sample from + p = np.random.rand() + for i in range(len(self.datasets)): + if p <= self.partition[i]: + if len(self.datasets) == 1: + return self.datasets[i][index % self.lengths[i]] + else: + d_index = np.random.randint(0, self.lengths[i]) + return self.datasets[i][d_index] \ No newline at end of file diff --git a/lib/data/datasets/videos.py b/lib/data/datasets/videos.py new file mode 100644 index 0000000000000000000000000000000000000000..2001e86c64774849ef5f47f36248b7cef8f54afa --- /dev/null +++ b/lib/data/datasets/videos.py @@ -0,0 +1,105 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os +import torch + +from configs import constants as _C +from .dataset3d import Dataset3D +from .dataset2d import Dataset2D +from ...utils.kp_utils import convert_kps +from smplx import SMPL + + +class Human36M(Dataset3D): + def __init__(self, cfg, dset='train'): + parsed_data_path = os.path.join(_C.PATHS.PARSED_DATA, f'human36m_{dset}_backbone.pth') + parsed_data_path = parsed_data_path.replace('backbone', cfg.MODEL.BACKBONE.lower()) + super(Human36M, self).__init__(cfg, parsed_data_path, dset=='train') + + self.has_3d = True + self.has_traj = True + self.has_smpl = False + self.has_verts = False + + # Among 31 joints format, 14 common joints are avaialable + self.mask = torch.zeros(_C.KEYPOINTS.NUM_JOINTS + 14) + self.mask[-14:] = 1 + + @property + def __name__(self, ): + return 'Human36M' + + def compute_3d_keypoints(self, index): + return convert_kps(self.labels['joints3D'][index], 'spin', 'h36m' + )[:, _C.KEYPOINTS.H36M_TO_J14].float() + +class MPII3D(Dataset3D): + def __init__(self, cfg, dset='train'): + parsed_data_path = os.path.join(_C.PATHS.PARSED_DATA, f'mpii3d_{dset}_backbone.pth') + parsed_data_path = parsed_data_path.replace('backbone', cfg.MODEL.BACKBONE.lower()) + super(MPII3D, self).__init__(cfg, parsed_data_path, dset=='train') + + self.has_3d = True + self.has_traj = True + self.has_smpl = False + self.has_verts = False + + # Among 31 joints format, 14 common joints are avaialable + self.mask = torch.zeros(_C.KEYPOINTS.NUM_JOINTS + 14) + self.mask[-14:] = 1 + + @property + def __name__(self, ): + return 'MPII3D' + + def compute_3d_keypoints(self, index): + return convert_kps(self.labels['joints3D'][index], 'spin', 'h36m' + )[:, _C.KEYPOINTS.H36M_TO_J17].float() + +class ThreeDPW(Dataset3D): + def __init__(self, cfg, dset='train'): + parsed_data_path = os.path.join(_C.PATHS.PARSED_DATA, f'3dpw_{dset}_backbone.pth') + parsed_data_path = parsed_data_path.replace('backbone', cfg.MODEL.BACKBONE.lower()) + super(ThreeDPW, self).__init__(cfg, parsed_data_path, dset=='train') + + self.has_3d = True + self.has_traj = False + self.has_smpl = True + self.has_verts = True # In testing + + # Among 31 joints format, 14 common joints are avaialable + self.mask = torch.zeros(_C.KEYPOINTS.NUM_JOINTS + 14) + self.mask[:-14] = 1 + + self.smpl_gender = { + 0: SMPL(_C.BMODEL.FLDR, gender='male', num_betas=10), + 1: SMPL(_C.BMODEL.FLDR, gender='female', num_betas=10) + } + + @property + def __name__(self, ): + return 'ThreeDPW' + + def compute_3d_keypoints(self, index): + return self.labels['joints3D'][index] + + +class InstaVariety(Dataset2D): + def __init__(self, cfg, dset='train'): + parsed_data_path = os.path.join(_C.PATHS.PARSED_DATA, f'insta_{dset}_backbone.pth') + parsed_data_path = parsed_data_path.replace('backbone', cfg.MODEL.BACKBONE.lower()) + super(InstaVariety, self).__init__(cfg, parsed_data_path, dset=='train') + + self.has_3d = False + self.has_traj = False + self.has_smpl = False + + # Among 31 joints format, 17 coco joints are avaialable + self.mask = torch.zeros(_C.KEYPOINTS.NUM_JOINTS + 14) + self.mask[:17] = 1 + + @property + def __name__(self, ): + return 'InstaVariety' \ No newline at end of file diff --git a/lib/data/utils/__pycache__/augmentor.cpython-39.pyc b/lib/data/utils/__pycache__/augmentor.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c0330486fd2e6ed222ac687d420bb112eea0349b Binary files /dev/null and b/lib/data/utils/__pycache__/augmentor.cpython-39.pyc differ diff --git a/lib/data/utils/__pycache__/normalizer.cpython-39.pyc b/lib/data/utils/__pycache__/normalizer.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c46765d436822c52ba518881a0c17eca7da9b165 Binary files /dev/null and b/lib/data/utils/__pycache__/normalizer.cpython-39.pyc differ diff --git a/lib/data/utils/augmentor.py b/lib/data/utils/augmentor.py new file mode 100644 index 0000000000000000000000000000000000000000..baa338013ba668192f854b39faf8cf2399803996 --- /dev/null +++ b/lib/data/utils/augmentor.py @@ -0,0 +1,292 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +from configs import constants as _C + +import torch +import numpy as np +from torch.nn import functional as F + +from ...utils import transforms + +__all__ = ['VideoAugmentor', 'SMPLAugmentor', 'SequenceAugmentor', 'CameraAugmentor'] + + +num_joints = _C.KEYPOINTS.NUM_JOINTS +class VideoAugmentor(): + def __init__(self, cfg, train=True): + self.train = train + self.l = cfg.DATASET.SEQLEN + 1 + self.aug_dict = torch.load(_C.KEYPOINTS.COCO_AUG_DICT) + + def get_jitter(self, ): + """Guassian jitter modeling.""" + jittering_noise = torch.normal( + mean=torch.zeros((self.l, num_joints, 3)), + std=self.aug_dict['jittering'].reshape(1, num_joints, 1).expand(self.l, -1, 3) + ) * _C.KEYPOINTS.S_JITTERING + return jittering_noise + + def get_lfhp(self, ): + """Low-frequency high-peak noise modeling.""" + def get_peak_noise_mask(): + peak_noise_mask = torch.rand(self.l, num_joints).float() * self.aug_dict['pmask'].squeeze(0) + peak_noise_mask = peak_noise_mask < _C.KEYPOINTS.S_PEAK_MASK + return peak_noise_mask + + peak_noise_mask = get_peak_noise_mask() + peak_noise = peak_noise_mask.float().unsqueeze(-1).repeat(1, 1, 3) + peak_noise = peak_noise * torch.randn(3) * self.aug_dict['peak'].reshape(1, -1, 1) * _C.KEYPOINTS.S_PEAK + return peak_noise + + def get_bias(self, ): + """Bias noise modeling.""" + bias_noise = torch.normal( + mean=torch.zeros((num_joints, 3)), std=self.aug_dict['bias'].reshape(num_joints, 1) + ).unsqueeze(0) * _C.KEYPOINTS.S_BIAS + return bias_noise + + def get_mask(self, scale=None): + """Mask modeling.""" + + if scale is None: + scale = _C.KEYPOINTS.S_MASK + # Per-frame and joint + mask = torch.rand(self.l, num_joints) < scale + visible = (~mask).clone() + for child in range(num_joints): + parent = _C.KEYPOINTS.TREE[child] + if parent == -1: continue + if isinstance(parent, list): + visible[:, child] *= (visible[:, parent[0]] * visible[:, parent[1]]) + else: + visible[:, child] *= visible[:, parent] + mask = (~visible).clone() + + return mask + + def __call__(self, keypoints): + keypoints += self.get_bias() + self.get_jitter() + self.get_lfhp() + return keypoints + + +class SMPLAugmentor(): + noise_scale = 1e-2 + + def __init__(self, cfg, augment=True): + self.n_frames = cfg.DATASET.SEQLEN + self.augment = augment + + def __call__(self, target): + if not self.augment: + # Only add initial frame augmentation + if not 'init_pose' in target: + target['init_pose'] = target['pose'][:1] @ self.get_initial_pose_augmentation() + return target + + n_frames = target['pose'].shape[0] + + # Global rotation + rmat = self.get_global_augmentation() + target['pose'][:, 0] = rmat @ target['pose'][:, 0] + target['transl'] = (rmat.squeeze() @ target['transl'].T).T + + # Shape + shape_noise = self.get_shape_augmentation(n_frames) + target['betas'] = target['betas'] + shape_noise + + # Initial frames mis-prediction + target['init_pose'] = target['pose'][:1] @ self.get_initial_pose_augmentation() + + return target + + def get_global_augmentation(self, ): + """Global coordinate augmentation. Random rotation around y-axis""" + + angle_y = torch.rand(1) * 2 * np.pi * float(self.augment) + aa = torch.tensor([0.0, angle_y, 0.0]).float().unsqueeze(0) + rmat = transforms.axis_angle_to_matrix(aa) + + return rmat + + def get_shape_augmentation(self, n_frames): + """Shape noise modeling.""" + + shape_noise = torch.normal( + mean=torch.zeros((1, 10)), + std=torch.ones((1, 10)) * 0.1 * float(self.augment)).expand(n_frames, 10) + + return shape_noise + + def get_initial_pose_augmentation(self, ): + """Initial frame pose noise modeling. Random rotation around all joints.""" + + euler = torch.normal( + mean=torch.zeros((24, 3)), + std=torch.ones((24, 3)) + ) * self.noise_scale #* float(self.augment) + rmat = transforms.axis_angle_to_matrix(euler) + + return rmat.unsqueeze(0) + + +class SequenceAugmentor: + """Augment the play speed of the motion sequence""" + l_factor = 1.5 + def __init__(self, l_default): + self.l_default = l_default + + def __call__(self, target): + l = torch.randint(low=int(self.l_default / self.l_factor), high=int(self.l_default * self.l_factor), size=(1, )) + + pose = transforms.matrix_to_rotation_6d(target['pose']) + resampled_pose = F.interpolate( + pose[:l].permute(1, 2, 0), self.l_default, mode='linear', align_corners=True + ).permute(2, 0, 1) + resampled_pose = transforms.rotation_6d_to_matrix(resampled_pose) + + transl = target['transl'].unsqueeze(1) + resampled_transl = F.interpolate( + transl[:l].permute(1, 2, 0), self.l_default, mode='linear', align_corners=True + ).squeeze(0).T + + target['pose'] = resampled_pose + target['transl'] = resampled_transl + target['betas'] = target['betas'][:self.l_default] + + return target + + +class CameraAugmentor: + rx_factor = np.pi/8 + ry_factor = np.pi/4 + rz_factor = np.pi/8 + + pitch_std = np.pi/8 + pitch_mean = np.pi/36 + roll_std = np.pi/24 + t_factor = 1 + + tz_scale = 10 + tz_min = 2 + + motion_prob = 0.75 + interp_noise = 0.2 + + def __init__(self, l, w, h, f): + self.l = l + self.w = w + self.h = h + self.f = f + self.fov_tol = 1.2 * (0.5 ** 0.5) + + def __call__(self, target): + + R, T = self.create_camera(target) + + if np.random.rand() < self.motion_prob: + R = self.create_rotation_move(R) + T = self.create_translation_move(T) + + return self.apply(target, R, T) + + def create_camera(self, target): + """Create the initial frame camera pose""" + yaw = np.random.rand() * 2 * np.pi + pitch = np.random.normal(scale=self.pitch_std) + self.pitch_mean + roll = np.random.normal(scale=self.roll_std) + + yaw_rm = transforms.axis_angle_to_matrix(torch.tensor([[0, yaw, 0]]).float()) + pitch_rm = transforms.axis_angle_to_matrix(torch.tensor([[pitch, 0, 0]]).float()) + roll_rm = transforms.axis_angle_to_matrix(torch.tensor([[0, 0, roll]]).float()) + R = (roll_rm @ pitch_rm @ yaw_rm) + + # Place people in the scene + tz = np.random.rand() * self.tz_scale + self.tz_min + max_d = self.w * tz / self.f / 2 + tx = np.random.normal(scale=0.25) * max_d + ty = np.random.normal(scale=0.25) * max_d + dist = torch.tensor([tx, ty, tz]).float() + T = dist - torch.matmul(R, target['transl'][0]) + + return R.repeat(self.l, 1, 1), T.repeat(self.l, 1) + + def create_rotation_move(self, R): + """Create rotational move for the camera""" + + # Create final camera pose + rx = np.random.normal(scale=self.rx_factor) + ry = np.random.normal(scale=self.ry_factor) + rz = np.random.normal(scale=self.rz_factor) + Rf = R[0] @ transforms.axis_angle_to_matrix(torch.tensor([rx, ry, rz]).float()) + + # Inbetweening two poses + Rs = torch.stack((R[0], Rf)) + rs = transforms.matrix_to_rotation_6d(Rs).numpy() + rs_move = self.noisy_interpolation(rs) + R_move = transforms.rotation_6d_to_matrix(torch.from_numpy(rs_move).float()) + return R_move + + def create_translation_move(self, T): + """Create translational move for the camera""" + + # Create final camera position + tx = np.random.normal(scale=self.t_factor) + ty = np.random.normal(scale=self.t_factor) + tz = np.random.normal(scale=self.t_factor) + Ts = np.array([[0, 0, 0], [tx, ty, tz]]) + + T_move = self.noisy_interpolation(Ts) + T_move = torch.from_numpy(T_move).float() + return T_move + T + + def noisy_interpolation(self, data): + """Non-linear interpolation with noise""" + + dim = data.shape[-1] + output = np.zeros((self.l, dim)) + + linspace = np.stack([np.linspace(0, 1, self.l) for _ in range(dim)]) + noise = (linspace[0, 1] - linspace[0, 0]) * self.interp_noise + space_noise = np.stack([np.random.uniform(-noise, noise, self.l - 2) for _ in range(dim)]) + + linspace[:, 1:-1] = linspace[:, 1:-1] + space_noise + for i in range(dim): + output[:, i] = np.interp(linspace[i], np.array([0., 1.,]), data[:, i]) + return output + + def apply(self, target, R, T): + target['R'] = R + target['T'] = T + + # Recompute the translation + transl_cam = torch.matmul(R, target['transl'].unsqueeze(-1)).squeeze(-1) + transl_cam = transl_cam + T + if transl_cam[..., 2].min() < 0.5: # If the person is too close to the camera + transl_cam[..., 2] = transl_cam[..., 2] + (1.0 - transl_cam[..., 2].min()) + + # If the subject is away from the field of view, put the camera behind + fov = torch.div(transl_cam[..., :2], transl_cam[..., 2:]).abs() + if fov.max() > self.fov_tol: + t_max = transl_cam[fov.max(1)[0].max(0)[1].item()] + z_trg = t_max[:2].abs().max(0)[0] / self.fov_tol + pad = z_trg - t_max[2] + transl_cam[..., 2] = transl_cam[..., 2] + pad + + target['transl_cam'] = transl_cam + + # Transform world coordinate to camera coordinate + target['pose_root'] = target['pose'][:, 0].clone() + target['pose'][:, 0] = R @ target['pose'][:, 0] # pose + target['init_pose'][:, 0] = R[:1] @ target['init_pose'][:, 0] # init pose + + # Compute angular velocity + cam_angvel = transforms.matrix_to_rotation_6d(R[:-1] @ R[1:].transpose(-1, -2)) + cam_angvel = cam_angvel - torch.tensor([[1, 0, 0, 0, 1, 0]]).to(cam_angvel) # Normalize + target['cam_angvel'] = cam_angvel * 3e1 # assume 30-fps + + if 'kp3d' in target: + target['kp3d'] = torch.matmul(R, target['kp3d'].transpose(1, 2)).transpose(1, 2) + target['transl_cam'].unsqueeze(1) + + return target \ No newline at end of file diff --git a/lib/data/utils/normalizer.py b/lib/data/utils/normalizer.py new file mode 100644 index 0000000000000000000000000000000000000000..9ea8c049f424b4896c9c473802ff6908328e8b36 --- /dev/null +++ b/lib/data/utils/normalizer.py @@ -0,0 +1,105 @@ +import torch +import numpy as np + +from ...utils.imutils import transform_keypoints + +class Normalizer: + def __init__(self, cfg): + pass + + def __call__(self, kp_2d, res, cam_intrinsics, patch_width=224, patch_height=224, bbox=None, mask=None): + if bbox is None: + bbox = compute_bbox_from_keypoints(kp_2d, do_augment=True, mask=mask) + + out_kp_2d = self.bbox_normalization(kp_2d, bbox, res, patch_width, patch_height) + return out_kp_2d, bbox + + def bbox_normalization(self, kp_2d, bbox, res, patch_width, patch_height): + to_torch = False + if isinstance(kp_2d, torch.Tensor): + to_torch = True + kp_2d = kp_2d.numpy() + bbox = bbox.numpy() + + out_kp_2d = np.zeros_like(kp_2d) + for idx in range(len(out_kp_2d)): + out_kp_2d[idx] = transform_keypoints(kp_2d[idx], bbox[idx][:3], patch_width, patch_height)[0] + out_kp_2d[idx] = normalize_keypoints_to_patch(out_kp_2d[idx], patch_width) + + if to_torch: + out_kp_2d = torch.from_numpy(out_kp_2d) + bbox = torch.from_numpy(bbox) + + centers = normalize_keypoints_to_image(bbox[:, :2].unsqueeze(1), res).squeeze(1) + scale = bbox[:, 2:] * 200 / res.max() + location = torch.cat((centers, scale), dim=-1) + + out_kp_2d = out_kp_2d.reshape(out_kp_2d.shape[0], -1) + out_kp_2d = torch.cat((out_kp_2d, location), dim=-1) + return out_kp_2d + + +def normalize_keypoints_to_patch(kp_2d, crop_size=224, inv=False): + # Normalize keypoints between -1, 1 + if not inv: + ratio = 1.0 / crop_size + kp_2d = 2.0 * kp_2d * ratio - 1.0 + else: + ratio = 1.0 / crop_size + kp_2d = (kp_2d + 1.0)/(2*ratio) + + return kp_2d + + +def normalize_keypoints_to_image(x, res): + res = res.to(x.device) + scale = res.max(-1)[0].reshape(-1) + mean = torch.stack([res[..., 0] / scale, res[..., 1] / scale], dim=-1).to(x.device) + x = (2 * x / scale.reshape(*[1 for i in range(len(x.shape[1:]))]) - \ + mean.reshape(*[1 for i in range(len(x.shape[1:-1]))], -1)) + return x + + +def compute_bbox_from_keypoints(X, do_augment=False, mask=None): + def smooth_bbox(bb): + # Smooth bounding box detection + import scipy.signal as signal + smoothed = np.array([signal.medfilt(param, int(30 / 2)) for param in bb]) + return smoothed + + def do_augmentation(scale_factor=0.2, trans_factor=0.05): + _scaleFactor = np.random.uniform(1.0 - scale_factor, 1.2 + scale_factor) + _trans_x = np.random.uniform(-trans_factor, trans_factor) + _trans_y = np.random.uniform(-trans_factor, trans_factor) + + return _scaleFactor, _trans_x, _trans_y + + if do_augment: + scaleFactor, trans_x, trans_y = do_augmentation() + else: + scaleFactor, trans_x, trans_y = 1.2, 0.0, 0.0 + + if mask is None: + bbox = [X[:, :, 0].min(-1)[0], X[:, :, 1].min(-1)[0], + X[:, :, 0].max(-1)[0], X[:, :, 1].max(-1)[0]] + else: + bbox = [] + for x, _mask in zip(X, mask): + if _mask.sum() > 10: + _mask[:] = False + _bbox = [x[~_mask, 0].min(-1)[0], x[~_mask, 1].min(-1)[0], + x[~_mask, 0].max(-1)[0], x[~_mask, 1].max(-1)[0]] + bbox.append(_bbox) + bbox = torch.tensor(bbox).T + + cx, cy = [(bbox[2]+bbox[0])/2, (bbox[3]+bbox[1])/2] + bbox_w = bbox[2] - bbox[0] + bbox_h = bbox[3] - bbox[1] + bbox_size = torch.stack((bbox_w, bbox_h)).max(0)[0] + scale = bbox_size * scaleFactor + bbox = torch.stack((cx + trans_x * scale, cy + trans_y * scale, scale / 200)) + + if do_augment: + bbox = torch.from_numpy(smooth_bbox(bbox.numpy())) + + return bbox.T \ No newline at end of file diff --git a/lib/data_utils/amass_utils.py b/lib/data_utils/amass_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..94e4e5b6dad8772568928272ba97d972837a8dda --- /dev/null +++ b/lib/data_utils/amass_utils.py @@ -0,0 +1,107 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os +import os.path as osp +from collections import defaultdict + +import torch +import joblib +import numpy as np +from tqdm import tqdm +from smplx import SMPL + +from configs import constants as _C +from lib.utils.data_utils import map_dmpl_to_smpl, transform_global_coordinate + + +@torch.no_grad() +def process_amass(): + target_fps = 30 + + _, seqs, _ = next(os.walk(_C.PATHS.AMASS_PTH)) + + zup2ydown = torch.Tensor( + [[1, 0, 0], [0, 0, -1], [0, 1, 0]] + ).unsqueeze(0).float() + + smpl_dict = {'male': SMPL(model_path=_C.BMODEL.FLDR, gender='male'), + 'female': SMPL(model_path=_C.BMODEL.FLDR, gender='female'), + 'neutral': SMPL(model_path=_C.BMODEL.FLDR)} + processed_data = defaultdict(list) + + for seq in (seq_bar := tqdm(sorted(seqs), leave=True)): + seq_bar.set_description(f'Dataset: {seq}') + seq_fldr = osp.join(_C.PATHS.AMASS_PTH, seq) + _, subjs, _ = next(os.walk(seq_fldr)) + + for subj in (subj_bar := tqdm(sorted(subjs), leave=False)): + subj_bar.set_description(f'Subject: {subj}') + subj_fldr = osp.join(seq_fldr, subj) + acts = [x for x in os.listdir(subj_fldr) if x.endswith('.npz')] + + for act in (act_bar := tqdm(sorted(acts), leave=False)): + act_bar.set_description(f'Action: {act}') + + # Load data + fname = osp.join(subj_fldr, act) + if fname.endswith('shape.npz') or fname.endswith('stagei.npz'): + # Skip shape and stagei files + continue + data = dict(np.load(fname, allow_pickle=True)) + + # Resample data to target_fps + key = [k for k in data.keys() if 'mocap_frame' in k][0] + mocap_framerate = data[key] + retain_freq = int(mocap_framerate / target_fps + 0.5) + num_frames = len(data['poses'][::retain_freq]) + + # Skip if the sequence is too short + if num_frames < 25: continue + + # Get SMPL groundtruth from MoSh fitting + pose = map_dmpl_to_smpl(torch.from_numpy(data['poses'][::retain_freq]).float()) + transl = torch.from_numpy(data['trans'][::retain_freq]).float() + betas = torch.from_numpy( + np.repeat(data['betas'][:10][np.newaxis], pose.shape[0], axis=0)).float() + + # Convert Z-up coordinate to Y-down + pose, transl = transform_global_coordinate(pose, zup2ydown, transl) + pose = pose.reshape(-1, 72) + + # Create SMPL mesh + gender = str(data['gender']) + if not gender in ['male', 'female', 'neutral']: + if 'female' in gender: gender = 'female' + elif 'neutral' in gender: gender = 'neutral' + elif 'male' in gender: gender = 'male' + + output = smpl_dict[gender](body_pose=pose[:, 3:], + global_orient=pose[:, :3], + betas=betas, + transl=transl) + vertices = output.vertices + + # Assume motion starts with 0-height + init_height = vertices[0].max(0)[0][1] + transl[:, 1] = transl[:, 1] + init_height + vertices[:, :, 1] = vertices[:, :, 1] - init_height + + # Append data + processed_data['pose'].append(pose.numpy()) + processed_data['betas'].append(betas.numpy()) + processed_data['transl'].append(transl.numpy()) + processed_data['vid'].append(np.array([f'{seq}_{subj}_{act}'] * pose.shape[0])) + + for key, val in processed_data.items(): + processed_data[key] = np.concatenate(val) + + joblib.dump(processed_data, _C.PATHS.AMASS_LABEL) + print('\nDone!') + +if __name__ == '__main__': + out_path = '/'.join(_C.PATHS.AMASS_LABEL.split('/')[:-1]) + os.makedirs(out_path, exist_ok=True) + + process_amass() \ No newline at end of file diff --git a/lib/data_utils/emdb_eval_utils.py b/lib/data_utils/emdb_eval_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d519cb9d289c68534c84bef9dfa47183e415a67e --- /dev/null +++ b/lib/data_utils/emdb_eval_utils.py @@ -0,0 +1,189 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os +import os.path as osp +from glob import glob +from collections import defaultdict + +import cv2 +import torch +import pickle +import joblib +import argparse +import numpy as np +from loguru import logger +from progress.bar import Bar + +from configs import constants as _C +from lib.models.smpl import SMPL +from lib.models.preproc.extractor import FeatureExtractor +from lib.models.preproc.backbone.utils import process_image +from lib.utils import transforms +from lib.utils.imutils import ( + flip_kp, flip_bbox +) + +dataset = defaultdict(list) +detection_results_dir = 'dataset/detection_results/EMDB' + +def is_dset(emdb_pkl_file, dset): + target_dset = 'emdb' + dset + with open(emdb_pkl_file, "rb") as f: + data = pickle.load(f) + return data[target_dset] + +@torch.no_grad() +def preprocess(dset, batch_size): + + tt = lambda x: torch.from_numpy(x).float() + device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') + save_pth = osp.join(_C.PATHS.PARSED_DATA, f'emdb_{dset}_vit.pth') # Use ViT feature extractor + extractor = FeatureExtractor(device, flip_eval=True, max_batch_size=batch_size) + + all_emdb_pkl_files = sorted(glob(os.path.join(_C.PATHS.EMDB_PTH, "*/*/*_data.pkl"))) + emdb_sequence_roots = [] + both = [] + for emdb_pkl_file in all_emdb_pkl_files: + if is_dset(emdb_pkl_file, dset): + emdb_sequence_roots.append(os.path.dirname(emdb_pkl_file)) + + smpl = { + 'neutral': SMPL(model_path=_C.BMODEL.FLDR), + 'male': SMPL(model_path=_C.BMODEL.FLDR, gender='male'), + 'female': SMPL(model_path=_C.BMODEL.FLDR, gender='female'), + } + + for sequence in emdb_sequence_roots: + subj, seq = sequence.split('/')[-2:] + annot_pth = glob(osp.join(sequence, '*_data.pkl'))[0] + annot = pickle.load(open(annot_pth, 'rb')) + + # Get ground truth data + gender = annot['gender'] + masks = annot['good_frames_mask'] + poses_body = annot["smpl"]["poses_body"] + poses_root = annot["smpl"]["poses_root"] + betas = np.repeat(annot["smpl"]["betas"].reshape((1, -1)), repeats=annot["n_frames"], axis=0) + extrinsics = annot["camera"]["extrinsics"] + width, height = annot['camera']['width'], annot['camera']['height'] + xyxys = annot['bboxes']['bboxes'] + + # Map to camear coordinate + poses_root_cam = transforms.matrix_to_axis_angle(tt(extrinsics[:, :3, :3]) @ transforms.axis_angle_to_matrix(tt(poses_root))) + poses = np.concatenate([poses_root_cam, poses_body], axis=-1) + + pred_kp2d = np.load(osp.join(detection_results_dir, f'{subj}_{seq}.npy')) + + # ======== Extract features ======== # + imname_list = sorted(glob(osp.join(sequence, 'images/*'))) + bboxes, frame_ids, patch_list, features, flipped_features = [], [], [], [], [] + bar = Bar(f'Load images', fill='#', max=len(imname_list)) + for idx, (imname, xyxy, mask) in enumerate(zip(imname_list, xyxys, masks)): + if not mask: continue + + # ========= Load image ========= # + img_rgb = cv2.cvtColor(cv2.imread(imname), cv2.COLOR_BGR2RGB) + + # ========= Load bbox ========= # + x1, y1, x2, y2 = xyxy + bbox = np.array([(x1 + x2)/2., (y1 + y2)/2., max(x2 - x1, y2 - y1) / 1.1]) + + # ========= Process image ========= # + norm_img, crop_img = process_image(img_rgb, bbox[:2], bbox[2] / 200, 256, 256) + + patch_list.append(torch.from_numpy(norm_img).unsqueeze(0).float()) + bboxes.append(bbox) + frame_ids.append(idx) + bar.next() + + patch_list = torch.split(torch.cat(patch_list), batch_size) + bboxes = torch.from_numpy(np.stack(bboxes)).float() + for i, patch in enumerate(patch_list): + bbox = bboxes[i*batch_size:min((i+1)*batch_size, len(frame_ids))].float().cuda() + bbox_center = bbox[:, :2] + bbox_scale = bbox[:, 2] / 200 + + feature = extractor.model(patch.cuda(), encode=True) + features.append(feature.cpu()) + + flipped_feature = extractor.model(torch.flip(patch, (3, )).cuda(), encode=True) + flipped_features.append(flipped_feature.cpu()) + + if i == 0: + init_patch = patch[[0]].clone() + + features = torch.cat(features) + flipped_features = torch.cat(flipped_features) + res_h, res_w = img_rgb.shape[:2] + + # ======== Append data ======== # + dataset['gender'].append(gender) + dataset['bbox'].append(bboxes) + dataset['res'].append(torch.tensor([[width, height]]).repeat(len(frame_ids), 1).float()) + dataset['vid'].append(f'{subj}_{seq}') + dataset['pose'].append(tt(poses)[frame_ids]) + dataset['betas'].append(tt(betas)[frame_ids]) + dataset['kp2d'].append(tt(pred_kp2d)[frame_ids]) + dataset['frame_id'].append(torch.from_numpy(np.array(frame_ids))) + dataset['cam_poses'].append(tt(extrinsics)[frame_ids]) + dataset['features'].append(features) + dataset['flipped_features'].append(flipped_features) + + # Flipped data + dataset['flipped_bbox'].append( + torch.from_numpy(flip_bbox(dataset['bbox'][-1].clone().numpy(), res_w, res_h)).float() + ) + dataset['flipped_kp2d'].append( + torch.from_numpy(flip_kp(dataset['kp2d'][-1].clone().numpy(), res_w)).float() + ) + # ======== Append data ======== # + + # Pad 1 frame + for key, val in dataset.items(): + if isinstance(val[-1], torch.Tensor): + dataset[key][-1] = torch.cat((val[-1][:1].clone(), val[-1][:]), dim=0) + + # Initial predictions + bbox = bboxes[:1].clone().cuda() + bbox_center = bbox[:, :2].clone() + bbox_scale = bbox[:, 2].clone() / 200 + kwargs = {'img_w': torch.tensor(res_w).repeat(1).float().cuda(), + 'img_h': torch.tensor(res_h).repeat(1).float().cuda(), + 'bbox_center': bbox_center, 'bbox_scale': bbox_scale} + + pred_global_orient, pred_pose, pred_shape, _ = extractor.model(init_patch.cuda(), **kwargs) + pred_output = smpl['neutral'].get_output(global_orient=pred_global_orient.cpu(), + body_pose=pred_pose.cpu(), + betas=pred_shape.cpu(), + pose2rot=False) + init_kp3d = pred_output.joints + init_pose = transforms.matrix_to_axis_angle(torch.cat((pred_global_orient, pred_pose), dim=1)) + + dataset['init_kp3d'].append(init_kp3d) + dataset['init_pose'].append(init_pose.cpu()) + + # Flipped initial predictions + bbox_center[:, 0] = res_w - bbox_center[:, 0] + pred_global_orient, pred_pose, pred_shape, _ = extractor.model(torch.flip(init_patch, (3, )).cuda(), **kwargs) + pred_output = smpl['neutral'].get_output(global_orient=pred_global_orient.cpu(), + body_pose=pred_pose.cpu(), + betas=pred_shape.cpu(), + pose2rot=False) + init_kp3d = pred_output.joints + init_pose = transforms.matrix_to_axis_angle(torch.cat((pred_global_orient, pred_pose), dim=1)) + + dataset['flipped_init_kp3d'].append(init_kp3d) + dataset['flipped_init_pose'].append(init_pose.cpu()) + + joblib.dump(dataset, save_pth) + logger.info(f'==> Done !') + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-s', '--split', type=str, choices=['1', '2'], help='Data split') + parser.add_argument('-b', '--batch_size', type=int, default=128, help='Data split') + args = parser.parse_args() + + preprocess(args.split, args.batch_size) \ No newline at end of file diff --git a/lib/data_utils/rich_eval_utils.py b/lib/data_utils/rich_eval_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f4a174ad2a867159da007e59819a4030910728a7 --- /dev/null +++ b/lib/data_utils/rich_eval_utils.py @@ -0,0 +1,69 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os +import os.path as osp +from glob import glob +from collections import defaultdict + +import cv2 +import torch +import pickle +import joblib +import argparse +import numpy as np +from loguru import logger +from progress.bar import Bar + +from configs import constants as _C +from lib.models.smpl import SMPL +from lib.models.preproc.extractor import FeatureExtractor +from lib.models.preproc.backbone.utils import process_image +from lib.utils import transforms +from lib.utils.imutils import ( + flip_kp, flip_bbox +) + +dataset = defaultdict(list) +detection_results_dir = 'dataset/detection_results/RICH' + +def extract_cam_param_xml(xml_path='', dtype=torch.float32): + + import xml.etree.ElementTree as ET + tree = ET.parse(xml_path) + + extrinsics_mat = [float(s) for s in tree.find('./CameraMatrix/data').text.split()] + intrinsics_mat = [float(s) for s in tree.find('./Intrinsics/data').text.split()] + # distortion_vec = [float(s) for s in tree.find('./Distortion/data').text.split()] + + focal_length_x = intrinsics_mat[0] + focal_length_y = intrinsics_mat[4] + center = torch.tensor([[intrinsics_mat[2], intrinsics_mat[5]]], dtype=dtype) + + rotation = torch.tensor([[extrinsics_mat[0], extrinsics_mat[1], extrinsics_mat[2]], + [extrinsics_mat[4], extrinsics_mat[5], extrinsics_mat[6]], + [extrinsics_mat[8], extrinsics_mat[9], extrinsics_mat[10]]], dtype=dtype) + + translation = torch.tensor([[extrinsics_mat[3], extrinsics_mat[7], extrinsics_mat[11]]], dtype=dtype) + + # t = -Rc --> c = -R^Tt + cam_center = [ -extrinsics_mat[0]*extrinsics_mat[3] - extrinsics_mat[4]*extrinsics_mat[7] - extrinsics_mat[8]*extrinsics_mat[11], + -extrinsics_mat[1]*extrinsics_mat[3] - extrinsics_mat[5]*extrinsics_mat[7] - extrinsics_mat[9]*extrinsics_mat[11], + -extrinsics_mat[2]*extrinsics_mat[3] - extrinsics_mat[6]*extrinsics_mat[7] - extrinsics_mat[10]*extrinsics_mat[11]] + + cam_center = torch.tensor([cam_center], dtype=dtype) + + return focal_length_x, focal_length_y, center, rotation, translation, cam_center + +@torch.no_grad() +def preprocess(dset, batch_size): + import pdb; pdb.set_trace() + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-s', '--split', type=str, choices=['1', '2'], help='Data split') + parser.add_argument('-b', '--batch_size', type=int, default=128, help='Data split') + args = parser.parse_args() + + preprocess(args.split, args.batch_size) \ No newline at end of file diff --git a/lib/data_utils/threedpw_eval_utils.py b/lib/data_utils/threedpw_eval_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..ac981693c3a639fbbfd65af49d76b53c3e44c9e0 --- /dev/null +++ b/lib/data_utils/threedpw_eval_utils.py @@ -0,0 +1,185 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os.path as osp +from glob import glob +from collections import defaultdict + +import cv2 +import torch +import pickle +import joblib +import argparse +import numpy as np +from loguru import logger +from progress.bar import Bar + +from configs import constants as _C +from lib.models.smpl import SMPL +from lib.models.preproc.extractor import FeatureExtractor +from lib.models.preproc.backbone.utils import process_image +from lib.utils import transforms +from lib.utils.imutils import ( + flip_kp, flip_bbox +) + + +dataset = defaultdict(list) +detection_results_dir = 'dataset/detection_results/3DPW' +tcmr_annot_pth = 'dataset/parsed_data/TCMR_preproc/3dpw_dset_db.pt' + +@torch.no_grad() +def preprocess(dset, batch_size): + + if dset == 'val': _dset = 'validation' + else: _dset = dset + + device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') + save_pth = osp.join(_C.PATHS.PARSED_DATA, f'3pdw_{dset}_vit.pth') # Use ViT feature extractor + extractor = FeatureExtractor(device, flip_eval=True, max_batch_size=batch_size) + + tcmr_data = joblib.load(tcmr_annot_pth.replace('dset', dset)) + smpl_neutral = SMPL(model_path=_C.BMODEL.FLDR) + + annot_file_list, idxs = np.unique(tcmr_data['vid_name'], return_index=True) + idxs = idxs.tolist() + annot_file_list = [annot_file_list[idxs.index(idx)] for idx in sorted(idxs)] + annot_file_list = [osp.join(_C.PATHS.THREEDPW_PTH, 'sequenceFiles', _dset, annot_file[:-2] + '.pkl') for annot_file in annot_file_list] + annot_file_list = list(dict.fromkeys(annot_file_list)) + + for annot_file in annot_file_list: + seq = annot_file.split('/')[-1].split('.')[0] + + data = pickle.load(open(annot_file, 'rb'), encoding='latin1') + + num_people = len(data['poses']) + num_frames = len(data['img_frame_ids']) + assert (data['poses2d'][0].shape[0] == num_frames) + + K = torch.from_numpy(data['cam_intrinsics']).unsqueeze(0).float() + + for p_id in range(num_people): + + logger.info(f'==> {seq} {p_id}') + gender = {'m': 'male', 'f': 'female'}[data['genders'][p_id]] + + # ======== Add TCMR data ======== # + vid_name = f'{seq}_{p_id}' + tcmr_ids = [i for i, v in enumerate(tcmr_data['vid_name']) if vid_name in v] + frame_ids = tcmr_data['frame_id'][tcmr_ids] + + pose = torch.from_numpy(data['poses'][p_id]).float()[frame_ids] + shape = torch.from_numpy(data['betas'][p_id][:10]).float().repeat(pose.size(0), 1) + pose = torch.from_numpy(tcmr_data['pose'][tcmr_ids]).float() # Camera coordinate + cam_poses = torch.from_numpy(data['cam_poses'][frame_ids]).float() + + # ======== Get detection results ======== # + fname = f'{seq}_{p_id}.npy' + pred_kp2d = torch.from_numpy( + np.load(osp.join(detection_results_dir, fname)) + ).float()[frame_ids] + # ======== Get detection results ======== # + + img_paths = sorted(glob(osp.join(_C.PATHS.THREEDPW_PTH, 'imageFiles', seq, '*.jpg'))) + img_paths = [img_path for i, img_path in enumerate(img_paths) if i in frame_ids] + img = cv2.imread(img_paths[0]); res_h, res_w = img.shape[:2] + vid_idxs = fname.split('.')[0] + + # ======== Append data ======== # + dataset['gender'].append(gender) + dataset['vid'].append(vid_idxs) + dataset['pose'].append(pose) + dataset['betas'].append(shape) + dataset['cam_poses'].append(cam_poses) + dataset['frame_id'].append(torch.from_numpy(frame_ids)) + dataset['res'].append(torch.tensor([[res_w, res_h]]).repeat(len(frame_ids), 1).float()) + dataset['bbox'].append(torch.from_numpy(tcmr_data['bbox'][tcmr_ids].copy()).float()) + dataset['kp2d'].append(pred_kp2d) + + # Flipped data + dataset['flipped_bbox'].append( + torch.from_numpy(flip_bbox(dataset['bbox'][-1].clone().numpy(), res_w, res_h)).float() + ) + dataset['flipped_kp2d'].append( + torch.from_numpy(flip_kp(dataset['kp2d'][-1].clone().numpy(), res_w)).float() + ) + # ======== Append data ======== # + + # ======== Extract features ======== # + patch_list = [] + bboxes = dataset['bbox'][-1].clone().numpy() + bar = Bar(f'Load images', fill='#', max=len(img_paths)) + + for img_path, bbox in zip(img_paths, bboxes): + img_rgb = cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB) + norm_img, crop_img = process_image(img_rgb, bbox[:2], bbox[2] / 200, 256, 256) + patch_list.append(torch.from_numpy(norm_img).unsqueeze(0).float()) + bar.next() + + patch_list = torch.split(torch.cat(patch_list), batch_size) + features, flipped_features = [], [] + for i, patch in enumerate(patch_list): + feature = extractor.model(patch.cuda(), encode=True) + features.append(feature.cpu()) + + flipped_feature = extractor.model(torch.flip(patch, (3, )).cuda(), encode=True) + flipped_features.append(flipped_feature.cpu()) + + if i == 0: + init_patch = patch[[0]].clone() + + features = torch.cat(features) + flipped_features = torch.cat(flipped_features) + dataset['features'].append(features) + dataset['flipped_features'].append(flipped_features) + # ======== Extract features ======== # + + # Pad 1 frame + for key, val in dataset.items(): + if isinstance(val[-1], torch.Tensor): + dataset[key][-1] = torch.cat((val[-1][:1].clone(), val[-1][:]), dim=0) + + # Initial predictions + bbox = torch.from_numpy(bboxes[:1].copy()).float().cuda() + bbox_center = bbox[:, :2].clone() + bbox_scale = bbox[:, 2].clone() / 200 + kwargs = {'img_w': torch.tensor(res_w).repeat(1).float().cuda(), + 'img_h': torch.tensor(res_h).repeat(1).float().cuda(), + 'bbox_center': bbox_center, 'bbox_scale': bbox_scale} + + pred_global_orient, pred_pose, pred_shape, _ = extractor.model(init_patch.cuda(), **kwargs) + pred_output = smpl_neutral.get_output(global_orient=pred_global_orient.cpu(), + body_pose=pred_pose.cpu(), + betas=pred_shape.cpu(), + pose2rot=False) + init_kp3d = pred_output.joints + init_pose = transforms.matrix_to_axis_angle(torch.cat((pred_global_orient, pred_pose), dim=1)) + + dataset['init_kp3d'].append(init_kp3d) + dataset['init_pose'].append(init_pose.cpu()) + + # Flipped initial predictions + bbox_center[:, 0] = res_w - bbox_center[:, 0] + pred_global_orient, pred_pose, pred_shape, _ = extractor.model(torch.flip(init_patch, (3, )).cuda(), **kwargs) + pred_output = smpl_neutral.get_output(global_orient=pred_global_orient.cpu(), + body_pose=pred_pose.cpu(), + betas=pred_shape.cpu(), + pose2rot=False) + init_kp3d = pred_output.joints + init_pose = transforms.matrix_to_axis_angle(torch.cat((pred_global_orient, pred_pose), dim=1)) + + dataset['flipped_init_kp3d'].append(init_kp3d) + dataset['flipped_init_pose'].append(init_pose.cpu()) + + joblib.dump(dataset, save_pth) + logger.info(f'\n ==> Done !') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-s', '--split', type=str, choices=['val', 'test'], help='Data split') + parser.add_argument('-b', '--batch_size', type=int, default=128, help='Data split') + args = parser.parse_args() + + preprocess(args.split, args.batch_size) \ No newline at end of file diff --git a/lib/data_utils/threedpw_train_utils.py b/lib/data_utils/threedpw_train_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..637b0e22066f35d55ea57f7d4216e0b09e88db37 --- /dev/null +++ b/lib/data_utils/threedpw_train_utils.py @@ -0,0 +1,146 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os.path as osp +from glob import glob +from collections import defaultdict + +import cv2 +import torch +import pickle +import joblib +import argparse +import numpy as np +from loguru import logger +from progress.bar import Bar + +from configs import constants as _C +from lib.models.smpl import SMPL +from lib.models.preproc.extractor import FeatureExtractor +from lib.models.preproc.backbone.utils import process_image + +dataset = defaultdict(list) +detection_results_dir = 'dataset/detection_results/3DPW' +tcmr_annot_pth = 'dataset/parsed_data/TCMR_preproc/3dpw_train_db.pt' + + +@torch.no_grad() +def preprocess(batch_size): + device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') + save_pth = osp.join(_C.PATHS.PARSED_DATA, f'3pdw_train_vit.pth') # Use ViT feature extractor + extractor = FeatureExtractor(device, flip_eval=True, max_batch_size=batch_size) + + tcmr_data = joblib.load(tcmr_annot_pth) + + annot_file_list, idxs = np.unique(tcmr_data['vid_name'], return_index=True) + idxs = idxs.tolist() + annot_file_list = [annot_file_list[idxs.index(idx)] for idx in sorted(idxs)] + annot_file_list = [osp.join(_C.PATHS.THREEDPW_PTH, 'sequenceFiles', 'train', annot_file[:-2] + '.pkl') for annot_file in annot_file_list] + annot_file_list = list(dict.fromkeys(annot_file_list)) + + vid_idx = 0 + for annot_file in annot_file_list: + seq = annot_file.split('/')[-1].split('.')[0] + + data = pickle.load(open(annot_file, 'rb'), encoding='latin1') + + num_people = len(data['poses']) + num_frames = len(data['img_frame_ids']) + assert (data['poses2d'][0].shape[0] == num_frames) + + K = torch.from_numpy(data['cam_intrinsics']).unsqueeze(0).float() + + for p_id in range(num_people): + + logger.info(f'==> {seq} {p_id}') + gender = {'m': 'male', 'f': 'female'}[data['genders'][p_id]] + smpl_gender = SMPL(model_path=_C.BMODEL.FLDR, gender=gender) + + # ======== Add TCMR data ======== # + vid_name = f'{seq}_{p_id}' + tcmr_ids = [i for i, v in enumerate(tcmr_data['vid_name']) if vid_name in v] + frame_ids = tcmr_data['frame_id'][tcmr_ids] + + pose = torch.from_numpy(data['poses'][p_id]).float()[frame_ids] + shape = torch.from_numpy(data['betas'][p_id][:10]).float().repeat(pose.size(0), 1) + trans = torch.from_numpy(data['trans'][p_id]).float()[frame_ids] + cam_poses = torch.from_numpy(data['cam_poses'][frame_ids]).float() + + # ======== Align the mesh params ======== # + Rc = cam_poses[:, :3, :3] + Tc = cam_poses[:, :3, 3] + org_output = smpl_gender.get_output(betas=shape, body_pose=pose[:,3:], global_orient=pose[:,:3], transl=trans) + org_v0 = (org_output.vertices + org_output.offset.unsqueeze(1)).mean(1) + pose = torch.from_numpy(tcmr_data['pose'][tcmr_ids]).float() + + output = smpl_gender.get_output(betas=shape, body_pose=pose[:,3:], global_orient=pose[:,:3]) + v0 = (output.vertices + output.offset.unsqueeze(1)).mean(1) + trans = (Rc @ org_v0.reshape(-1, 3, 1)).reshape(-1, 3) + Tc - v0 + j3d = output.joints + (output.offset + trans).unsqueeze(1) + j2d = torch.div(j3d, j3d[..., 2:]) + kp2d = torch.matmul(K, j2d.transpose(-1, -2)).transpose(-1, -2)[..., :2] + # ======== Align the mesh params ======== # + + # ======== Get detection results ======== # + fname = f'{seq}_{p_id}.npy' + pred_kp2d = torch.from_numpy( + np.load(osp.join(detection_results_dir, fname)) + ).float()[frame_ids] + # ======== Get detection results ======== # + + img_paths = sorted(glob(osp.join(_C.PATHS.THREEDPW_PTH, 'imageFiles', seq, '*.jpg'))) + img_paths = [img_path for i, img_path in enumerate(img_paths) if i in frame_ids] + img = cv2.imread(img_paths[0]); res_h, res_w = img.shape[:2] + vid_idxs = torch.from_numpy(np.array([vid_idx] * len(img_paths)).astype(int)) + vid_idx += 1 + + # ======== Append data ======== # + dataset['bbox'].append(torch.from_numpy(tcmr_data['bbox'][tcmr_ids].copy()).float()) + dataset['res'].append(torch.tensor([[res_w, res_h]]).repeat(len(frame_ids), 1).float()) + dataset['vid'].append(vid_idxs) + dataset['pose'].append(pose) + dataset['betas'].append(shape) + dataset['transl'].append(trans) + dataset['kp2d'].append(pred_kp2d) + dataset['joints3D'].append(j3d) + dataset['joints2D'].append(kp2d) + dataset['frame_id'].append(torch.from_numpy(frame_ids)) + dataset['cam_poses'].append(cam_poses) + dataset['gender'].append(torch.tensor([['male','female'].index(gender)]).repeat(len(frame_ids))) + # ======== Append data ======== # + + # ======== Extract features ======== # + patch_list = [] + bboxes = dataset['bbox'][-1].clone().numpy() + bar = Bar(f'Load images', fill='#', max=len(img_paths)) + + for img_path, bbox in zip(img_paths, bboxes): + img_rgb = cv2.cvtColor(cv2.imread(img_path), cv2.COLOR_BGR2RGB) + norm_img, crop_img = process_image(img_rgb, bbox[:2], bbox[2] / 200, 256, 256) + patch_list.append(torch.from_numpy(norm_img).unsqueeze(0).float()) + bar.next() + + patch_list = torch.split(torch.cat(patch_list), batch_size) + features = [] + for i, patch in enumerate(patch_list): + pred = extractor.model(patch.cuda(), encode=True) + features.append(pred.cpu()) + + features = torch.cat(features) + dataset['features'].append(features) + # ======== Extract features ======== # + + for key in dataset.keys(): + dataset[key] = torch.cat(dataset[key]) + + joblib.dump(dataset, save_pth) + logger.info(f'\n ==> Done !') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('-b', '--batch_size', type=int, default=128, help='Data split') + args = parser.parse_args() + + preprocess(args.batch_size) \ No newline at end of file diff --git a/lib/eval/eval_utils.py b/lib/eval/eval_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..270491c09c942c3aa47dfc53224f050dea91353c --- /dev/null +++ b/lib/eval/eval_utils.py @@ -0,0 +1,482 @@ +# Some functions are borrowed from https://github.com/akanazawa/human_dynamics/blob/master/src/evaluation/eval_util.py +# Adhere to their licence to use these functions +from pathlib import Path + +import torch +import numpy as np +from matplotlib import pyplot as plt + + +def compute_accel(joints): + """ + Computes acceleration of 3D joints. + Args: + joints (Nx25x3). + Returns: + Accelerations (N-2). + """ + velocities = joints[1:] - joints[:-1] + acceleration = velocities[1:] - velocities[:-1] + acceleration_normed = np.linalg.norm(acceleration, axis=2) + return np.mean(acceleration_normed, axis=1) + + +def compute_error_accel(joints_gt, joints_pred, vis=None): + """ + Computes acceleration error: + 1/(n-2) \sum_{i=1}^{n-1} X_{i-1} - 2X_i + X_{i+1} + Note that for each frame that is not visible, three entries in the + acceleration error should be zero'd out. + Args: + joints_gt (Nx14x3). + joints_pred (Nx14x3). + vis (N). + Returns: + error_accel (N-2). + """ + # (N-2)x14x3 + accel_gt = joints_gt[:-2] - 2 * joints_gt[1:-1] + joints_gt[2:] + accel_pred = joints_pred[:-2] - 2 * joints_pred[1:-1] + joints_pred[2:] + + normed = np.linalg.norm(accel_pred - accel_gt, axis=2) + + if vis is None: + new_vis = np.ones(len(normed), dtype=bool) + else: + invis = np.logical_not(vis) + invis1 = np.roll(invis, -1) + invis2 = np.roll(invis, -2) + new_invis = np.logical_or(invis, np.logical_or(invis1, invis2))[:-2] + new_vis = np.logical_not(new_invis) + + return np.mean(normed[new_vis], axis=1) + + +def compute_error_verts(pred_verts, target_verts=None, target_theta=None): + """ + Computes MPJPE over 6890 surface vertices. + Args: + verts_gt (Nx6890x3). + verts_pred (Nx6890x3). + Returns: + error_verts (N). + """ + + if target_verts is None: + from lib.models.smpl import SMPL_MODEL_DIR + from lib.models.smpl import SMPL + device = 'cpu' + smpl = SMPL( + SMPL_MODEL_DIR, + batch_size=1, # target_theta.shape[0], + ).to(device) + + betas = torch.from_numpy(target_theta[:,75:]).to(device) + pose = torch.from_numpy(target_theta[:,3:75]).to(device) + + target_verts = [] + b_ = torch.split(betas, 5000) + p_ = torch.split(pose, 5000) + + for b,p in zip(b_,p_): + output = smpl(betas=b, body_pose=p[:, 3:], global_orient=p[:, :3], pose2rot=True) + target_verts.append(output.vertices.detach().cpu().numpy()) + + target_verts = np.concatenate(target_verts, axis=0) + + assert len(pred_verts) == len(target_verts) + error_per_vert = np.sqrt(np.sum((target_verts - pred_verts) ** 2, axis=2)) + return np.mean(error_per_vert, axis=1) + + +def compute_similarity_transform(S1, S2): + ''' + Computes a similarity transform (sR, t) that takes + a set of 3D points S1 (3 x N) closest to a set of 3D points S2, + where R is an 3x3 rotation matrix, t 3x1 translation, s scale. + i.e. solves the orthogonal Procrutes problem. + ''' + transposed = False + if S1.shape[0] != 3 and S1.shape[0] != 2: + S1 = S1.T + S2 = S2.T + transposed = True + assert(S2.shape[1] == S1.shape[1]) + + # 1. Remove mean. + mu1 = S1.mean(axis=1, keepdims=True) + mu2 = S2.mean(axis=1, keepdims=True) + X1 = S1 - mu1 + X2 = S2 - mu2 + + # 2. Compute variance of X1 used for scale. + var1 = np.sum(X1**2) + + # 3. The outer product of X1 and X2. + K = X1.dot(X2.T) + + # 4. Solution that Maximizes trace(R'K) is R=U*V', where U, V are + # singular vectors of K. + U, s, Vh = np.linalg.svd(K) + V = Vh.T + # Construct Z that fixes the orientation of R to get det(R)=1. + Z = np.eye(U.shape[0]) + Z[-1, -1] *= np.sign(np.linalg.det(U.dot(V.T))) + # Construct R. + R = V.dot(Z.dot(U.T)) + + # 5. Recover scale. + scale = np.trace(R.dot(K)) / var1 + + # 6. Recover translation. + t = mu2 - scale*(R.dot(mu1)) + + # 7. Error: + S1_hat = scale*R.dot(S1) + t + + if transposed: + S1_hat = S1_hat.T + + return S1_hat + + +def compute_similarity_transform_torch(S1, S2): + ''' + Computes a similarity transform (sR, t) that takes + a set of 3D points S1 (3 x N) closest to a set of 3D points S2, + where R is an 3x3 rotation matrix, t 3x1 translation, s scale. + i.e. solves the orthogonal Procrutes problem. + ''' + transposed = False + if S1.shape[0] != 3 and S1.shape[0] != 2: + S1 = S1.T + S2 = S2.T + transposed = True + assert (S2.shape[1] == S1.shape[1]) + + # 1. Remove mean. + mu1 = S1.mean(axis=1, keepdims=True) + mu2 = S2.mean(axis=1, keepdims=True) + X1 = S1 - mu1 + X2 = S2 - mu2 + + # print('X1', X1.shape) + + # 2. Compute variance of X1 used for scale. + var1 = torch.sum(X1 ** 2) + + # print('var', var1.shape) + + # 3. The outer product of X1 and X2. + K = X1.mm(X2.T) + + # 4. Solution that Maximizes trace(R'K) is R=U*V', where U, V are + # singular vectors of K. + U, s, V = torch.svd(K) + # V = Vh.T + # Construct Z that fixes the orientation of R to get det(R)=1. + Z = torch.eye(U.shape[0], device=S1.device) + Z[-1, -1] *= torch.sign(torch.det(U @ V.T)) + # Construct R. + R = V.mm(Z.mm(U.T)) + + # print('R', X1.shape) + + # 5. Recover scale. + scale = torch.trace(R.mm(K)) / var1 + # print(R.shape, mu1.shape) + # 6. Recover translation. + t = mu2 - scale * (R.mm(mu1)) + # print(t.shape) + + # 7. Error: + S1_hat = scale * R.mm(S1) + t + + if transposed: + S1_hat = S1_hat.T + + return S1_hat + + +def batch_compute_similarity_transform_torch(S1, S2): + ''' + Computes a similarity transform (sR, t) that takes + a set of 3D points S1 (3 x N) closest to a set of 3D points S2, + where R is an 3x3 rotation matrix, t 3x1 translation, s scale. + i.e. solves the orthogonal Procrutes problem. + ''' + transposed = False + if S1.shape[0] != 3 and S1.shape[0] != 2: + S1 = S1.permute(0,2,1) + S2 = S2.permute(0,2,1) + transposed = True + assert(S2.shape[1] == S1.shape[1]) + + # 1. Remove mean. + mu1 = S1.mean(axis=-1, keepdims=True) + mu2 = S2.mean(axis=-1, keepdims=True) + + X1 = S1 - mu1 + X2 = S2 - mu2 + + # 2. Compute variance of X1 used for scale. + var1 = torch.sum(X1**2, dim=1).sum(dim=1) + + # 3. The outer product of X1 and X2. + K = X1.bmm(X2.permute(0,2,1)) + + # 4. Solution that Maximizes trace(R'K) is R=U*V', where U, V are + # singular vectors of K. + U, s, V = torch.svd(K) + + # Construct Z that fixes the orientation of R to get det(R)=1. + Z = torch.eye(U.shape[1], device=S1.device).unsqueeze(0) + Z = Z.repeat(U.shape[0],1,1) + Z[:,-1, -1] *= torch.sign(torch.det(U.bmm(V.permute(0,2,1)))) + + # Construct R. + R = V.bmm(Z.bmm(U.permute(0,2,1))) + + # 5. Recover scale. + scale = torch.cat([torch.trace(x).unsqueeze(0) for x in R.bmm(K)]) / var1 + + # 6. Recover translation. + t = mu2 - (scale.unsqueeze(-1).unsqueeze(-1) * (R.bmm(mu1))) + + # 7. Error: + S1_hat = scale.unsqueeze(-1).unsqueeze(-1) * R.bmm(S1) + t + + if transposed: + S1_hat = S1_hat.permute(0,2,1) + + return S1_hat + + +def align_by_pelvis(joints): + """ + Assumes joints is 14 x 3 in LSP order. + Then hips are: [3, 2] + Takes mid point of these points, then subtracts it. + """ + + left_id = 2 + right_id = 3 + + pelvis = (joints[left_id, :] + joints[right_id, :]) / 2.0 + return joints - np.expand_dims(pelvis, axis=0) + + +def compute_errors(gt3ds, preds): + """ + Gets MPJPE after pelvis alignment + MPJPE after Procrustes. + Evaluates on the 14 common joints. + Inputs: + - gt3ds: N x 14 x 3 + - preds: N x 14 x 3 + """ + errors, errors_pa = [], [] + for i, (gt3d, pred) in enumerate(zip(gt3ds, preds)): + gt3d = gt3d.reshape(-1, 3) + # Root align. + gt3d = align_by_pelvis(gt3d) + pred3d = align_by_pelvis(pred) + + joint_error = np.sqrt(np.sum((gt3d - pred3d)**2, axis=1)) + errors.append(np.mean(joint_error)) + + # Get PA error. + pred3d_sym = compute_similarity_transform(pred3d, gt3d) + pa_error = np.sqrt(np.sum((gt3d - pred3d_sym)**2, axis=1)) + errors_pa.append(np.mean(pa_error)) + + return errors, errors_pa + + +def batch_align_by_pelvis(data_list, pelvis_idxs): + """ + Assumes data is given as [pred_j3d, target_j3d, pred_verts, target_verts]. + Each data is in shape of (frames, num_points, 3) + Pelvis is notated as one / two joints indices. + Align all data to the corresponding pelvis location. + """ + + pred_j3d, target_j3d, pred_verts, target_verts = data_list + + pred_pelvis = pred_j3d[:, pelvis_idxs].mean(dim=1, keepdims=True).clone() + target_pelvis = target_j3d[:, pelvis_idxs].mean(dim=1, keepdims=True).clone() + + # Align to the pelvis + pred_j3d = pred_j3d - pred_pelvis + target_j3d = target_j3d - target_pelvis + pred_verts = pred_verts - pred_pelvis + target_verts = target_verts - target_pelvis + + return (pred_j3d, target_j3d, pred_verts, target_verts) + +def compute_jpe(S1, S2): + return torch.sqrt(((S1 - S2) ** 2).sum(dim=-1)).mean(dim=-1).numpy() + + +# The functions below are borrowed from SLAHMR official implementation. +# Reference: https://github.com/vye16/slahmr/blob/main/slahmr/eval/tools.py +def global_align_joints(gt_joints, pred_joints): + """ + :param gt_joints (T, J, 3) + :param pred_joints (T, J, 3) + """ + s_glob, R_glob, t_glob = align_pcl( + gt_joints.reshape(-1, 3), pred_joints.reshape(-1, 3) + ) + pred_glob = ( + s_glob * torch.einsum("ij,tnj->tni", R_glob, pred_joints) + t_glob[None, None] + ) + return pred_glob + + +def first_align_joints(gt_joints, pred_joints): + """ + align the first two frames + :param gt_joints (T, J, 3) + :param pred_joints (T, J, 3) + """ + # (1, 1), (1, 3, 3), (1, 3) + s_first, R_first, t_first = align_pcl( + gt_joints[:2].reshape(1, -1, 3), pred_joints[:2].reshape(1, -1, 3) + ) + pred_first = ( + s_first * torch.einsum("tij,tnj->tni", R_first, pred_joints) + t_first[:, None] + ) + return pred_first + + +def local_align_joints(gt_joints, pred_joints): + """ + :param gt_joints (T, J, 3) + :param pred_joints (T, J, 3) + """ + s_loc, R_loc, t_loc = align_pcl(gt_joints, pred_joints) + pred_loc = ( + s_loc[:, None] * torch.einsum("tij,tnj->tni", R_loc, pred_joints) + + t_loc[:, None] + ) + return pred_loc + + +def align_pcl(Y, X, weight=None, fixed_scale=False): + """align similarity transform to align X with Y using umeyama method + X' = s * R * X + t is aligned with Y + :param Y (*, N, 3) first trajectory + :param X (*, N, 3) second trajectory + :param weight (*, N, 1) optional weight of valid correspondences + :returns s (*, 1), R (*, 3, 3), t (*, 3) + """ + *dims, N, _ = Y.shape + N = torch.ones(*dims, 1, 1) * N + + if weight is not None: + Y = Y * weight + X = X * weight + N = weight.sum(dim=-2, keepdim=True) # (*, 1, 1) + + # subtract mean + my = Y.sum(dim=-2) / N[..., 0] # (*, 3) + mx = X.sum(dim=-2) / N[..., 0] + y0 = Y - my[..., None, :] # (*, N, 3) + x0 = X - mx[..., None, :] + + if weight is not None: + y0 = y0 * weight + x0 = x0 * weight + + # correlation + C = torch.matmul(y0.transpose(-1, -2), x0) / N # (*, 3, 3) + U, D, Vh = torch.linalg.svd(C) # (*, 3, 3), (*, 3), (*, 3, 3) + + S = torch.eye(3).reshape(*(1,) * (len(dims)), 3, 3).repeat(*dims, 1, 1) + neg = torch.det(U) * torch.det(Vh.transpose(-1, -2)) < 0 + S[neg, 2, 2] = -1 + + R = torch.matmul(U, torch.matmul(S, Vh)) # (*, 3, 3) + + D = torch.diag_embed(D) # (*, 3, 3) + if fixed_scale: + s = torch.ones(*dims, 1, device=Y.device, dtype=torch.float32) + else: + var = torch.sum(torch.square(x0), dim=(-1, -2), keepdim=True) / N # (*, 1, 1) + s = ( + torch.diagonal(torch.matmul(D, S), dim1=-2, dim2=-1).sum( + dim=-1, keepdim=True + ) + / var[..., 0] + ) # (*, 1) + + t = my - s * torch.matmul(R, mx[..., None])[..., 0] # (*, 3) + + return s, R, t + + +def compute_foot_sliding(target_output, pred_output, masks, thr=1e-2): + """compute foot sliding error + The foot ground contact label is computed by the threshold of 1 cm/frame + Args: + target_output (SMPL ModelOutput). + pred_output (SMPL ModelOutput). + masks (N). + Returns: + error (N frames in contact). + """ + + # Foot vertices idxs + foot_idxs = [3216, 3387, 6617, 6787] + + # Compute contact label + foot_loc = target_output.vertices[masks][:, foot_idxs] + foot_disp = (foot_loc[1:] - foot_loc[:-1]).norm(2, dim=-1) + contact = foot_disp[:] < thr + + pred_feet_loc = pred_output.vertices[:, foot_idxs] + pred_disp = (pred_feet_loc[1:] - pred_feet_loc[:-1]).norm(2, dim=-1) + + error = pred_disp[contact] + + return error.cpu().numpy() + + +def compute_jitter(pred_output, fps=30): + """compute jitter of the motion + Args: + pred_output (SMPL ModelOutput). + fps (float). + Returns: + jitter (N-3). + """ + + pred3d = pred_output.joints[:, :24] + + pred_jitter = torch.norm( + (pred3d[3:] - 3 * pred3d[2:-1] + 3 * pred3d[1:-2] - pred3d[:-3]) * (fps**3), + dim=2, + ).mean(dim=-1) + + return pred_jitter.cpu().numpy() / 10.0 + + +def compute_rte(target_trans, pred_trans): + # Compute the global alignment + _, rot, trans = align_pcl(target_trans[None, :], pred_trans[None, :], fixed_scale=True) + pred_trans_hat = ( + torch.einsum("tij,tnj->tni", rot, pred_trans[None, :]) + trans[None, :] + )[0] + + # Compute the entire displacement of ground truth trajectory + disps, disp = [], 0 + for p1, p2 in zip(target_trans, target_trans[1:]): + delta = (p2 - p1).norm(2, dim=-1) + disp += delta + disps.append(disp) + + # Compute absolute root-translation-error (RTE) + rte = torch.norm(target_trans - pred_trans_hat, 2, dim=-1) + + # Normalize it to the displacement + return (rte / disp).numpy() \ No newline at end of file diff --git a/lib/eval/evaluate_3dpw.py b/lib/eval/evaluate_3dpw.py new file mode 100644 index 0000000000000000000000000000000000000000..36736288f5c02d128d17fffc1598606ada009631 --- /dev/null +++ b/lib/eval/evaluate_3dpw.py @@ -0,0 +1,181 @@ +import os +import time +import os.path as osp +from glob import glob +from collections import defaultdict + +import torch +import imageio +import numpy as np +from smplx import SMPL +from loguru import logger +from progress.bar import Bar + +from configs import constants as _C +from configs.config import parse_args +from lib.data.dataloader import setup_eval_dataloader +from lib.models import build_network, build_body_model +from lib.eval.eval_utils import ( + compute_error_accel, + batch_align_by_pelvis, + batch_compute_similarity_transform_torch, +) +from lib.utils import transforms +from lib.utils.utils import prepare_output_dir +from lib.utils.utils import prepare_batch +from lib.utils.imutils import avg_preds + +try: + from lib.vis.renderer import Renderer + _render = True +except: + print("PyTorch3D is not properly installed! Cannot render the SMPL mesh") + _render = False + + +m2mm = 1e3 +@torch.no_grad() +def main(cfg, args): + torch.backends.cuda.matmul.allow_tf32 = False + torch.backends.cudnn.allow_tf32 = False + + logger.info(f'GPU name -> {torch.cuda.get_device_name()}') + logger.info(f'GPU feat -> {torch.cuda.get_device_properties("cuda")}') + + # ========= Dataloaders ========= # + eval_loader = setup_eval_dataloader(cfg, '3dpw', 'test', cfg.MODEL.BACKBONE) + logger.info(f'Dataset loaded') + + # ========= Load WHAM ========= # + smpl_batch_size = cfg.TRAIN.BATCH_SIZE * cfg.DATASET.SEQLEN + smpl = build_body_model(cfg.DEVICE, smpl_batch_size) + network = build_network(cfg, smpl) + network.eval() + + # Build SMPL models with each gender + smpl = {k: SMPL(_C.BMODEL.FLDR, gender=k).to(cfg.DEVICE) for k in ['male', 'female', 'neutral']} + + # Load vertices -> joints regression matrix to evaluate + J_regressor_eval = torch.from_numpy( + np.load(_C.BMODEL.JOINTS_REGRESSOR_H36M) + )[_C.KEYPOINTS.H36M_TO_J14, :].unsqueeze(0).float().to(cfg.DEVICE) + pelvis_idxs = [2, 3] + + accumulator = defaultdict(list) + bar = Bar('Inference', fill='#', max=len(eval_loader)) + with torch.no_grad(): + for i in range(len(eval_loader)): + # Original batch + batch = eval_loader.dataset.load_data(i, False) + x, inits, features, kwargs, gt = prepare_batch(batch, cfg.DEVICE, cfg.TRAIN.STAGE=='stage2') + + if cfg.FLIP_EVAL: + flipped_batch = eval_loader.dataset.load_data(i, True) + f_x, f_inits, f_features, f_kwargs, _ = prepare_batch(flipped_batch, cfg.DEVICE, cfg.TRAIN.STAGE=='stage2') + + # Forward pass with flipped input + flipped_pred = network(f_x, f_inits, f_features, **f_kwargs) + + # Forward pass with normal input + pred = network(x, inits, features, **kwargs) + + if cfg.FLIP_EVAL: + # Merge two predictions + flipped_pose, flipped_shape = flipped_pred['pose'].squeeze(0), flipped_pred['betas'].squeeze(0) + pose, shape = pred['pose'].squeeze(0), pred['betas'].squeeze(0) + flipped_pose, pose = flipped_pose.reshape(-1, 24, 6), pose.reshape(-1, 24, 6) + avg_pose, avg_shape = avg_preds(pose, shape, flipped_pose, flipped_shape) + avg_pose = avg_pose.reshape(-1, 144) + + # Refine trajectory with merged prediction + network.pred_pose = avg_pose.view_as(network.pred_pose) + network.pred_shape = avg_shape.view_as(network.pred_shape) + pred = network.forward_smpl(**kwargs) + + # <======= Build predicted SMPL + pred_output = smpl['neutral'](body_pose=pred['poses_body'], + global_orient=pred['poses_root_cam'], + betas=pred['betas'].squeeze(0), + pose2rot=False) + pred_verts = pred_output.vertices.cpu() + pred_j3d = torch.matmul(J_regressor_eval, pred_output.vertices).cpu() + # =======> + + # <======= Build groundtruth SMPL + target_output = smpl[batch['gender']]( + body_pose=transforms.rotation_6d_to_matrix(gt['pose'][0, :, 1:]), + global_orient=transforms.rotation_6d_to_matrix(gt['pose'][0, :, :1]), + betas=gt['betas'][0], + pose2rot=False) + target_verts = target_output.vertices.cpu() + target_j3d = torch.matmul(J_regressor_eval, target_output.vertices).cpu() + # =======> + + # <======= Compute performance of the current sequence + pred_j3d, target_j3d, pred_verts, target_verts = batch_align_by_pelvis( + [pred_j3d, target_j3d, pred_verts, target_verts], pelvis_idxs + ) + S1_hat = batch_compute_similarity_transform_torch(pred_j3d, target_j3d) + pa_mpjpe = torch.sqrt(((S1_hat - target_j3d) ** 2).sum(dim=-1)).mean(dim=-1).numpy() * m2mm + mpjpe = torch.sqrt(((pred_j3d - target_j3d) ** 2).sum(dim=-1)).mean(dim=-1).numpy() * m2mm + pve = torch.sqrt(((pred_verts - target_verts) ** 2).sum(dim=-1)).mean(dim=-1).numpy() * m2mm + accel = compute_error_accel(joints_pred=pred_j3d, joints_gt=target_j3d)[1:-1] + accel = accel * (30 ** 2) # per frame^s to per s^2 + # =======> + + summary_string = f'{batch["vid"]} | PA-MPJPE: {pa_mpjpe.mean():.1f} MPJPE: {mpjpe.mean():.1f} PVE: {pve.mean():.1f}' + bar.suffix = summary_string + bar.next() + + # <======= Accumulate the results over entire sequences + accumulator['pa_mpjpe'].append(pa_mpjpe) + accumulator['mpjpe'].append(mpjpe) + accumulator['pve'].append(pve) + accumulator['accel'].append(accel) + # =======> + + # <======= (Optional) Render the prediction + if not (_render and args.render): + # Skip if PyTorch3D is not installed or rendering argument is not parsed. + continue + + # Save path + viz_pth = osp.join('output', 'visualization') + os.makedirs(viz_pth, exist_ok=True) + + # Build Renderer + width, height = batch['cam_intrinsics'][0][0, :2, -1].numpy() * 2 + focal_length = batch['cam_intrinsics'][0][0, 0, 0].item() + renderer = Renderer(width, height, focal_length, cfg.DEVICE, smpl['neutral'].faces) + + # Get images and writer + frame_list = batch['frame_id'][0].numpy() + imname_list = sorted(glob(osp.join(_C.PATHS.THREEDPW_PTH, 'imageFiles', batch['vid'][:-2], '*.jpg'))) + writer = imageio.get_writer(osp.join(viz_pth, batch['vid'] + '.mp4'), + mode='I', format='FFMPEG', fps=30, macro_block_size=1) + + # Skip the invalid frames + for i, frame in enumerate(frame_list): + image = imageio.imread(imname_list[frame]) + + # NOTE: pred['verts'] is different from pred_verts as we substracted offset from SMPL mesh. + # Check line 121 in lib/models/smpl.py + vertices = pred['verts_cam'][i] + pred['trans_cam'][[i]] + image = renderer.render_mesh(vertices, image) + writer.append_data(image) + writer.close() + # =======> + + for k, v in accumulator.items(): + accumulator[k] = np.concatenate(v).mean() + + print('') + log_str = 'Evaluation on 3DPW, ' + log_str += ' '.join([f'{k.upper()}: {v:.4f},'for k,v in accumulator.items()]) + logger.info(log_str) + +if __name__ == '__main__': + cfg, cfg_file, args = parse_args(test=True) + cfg = prepare_output_dir(cfg, cfg_file) + + main(cfg, args) \ No newline at end of file diff --git a/lib/eval/evaluate_emdb.py b/lib/eval/evaluate_emdb.py new file mode 100644 index 0000000000000000000000000000000000000000..09a0b7be239c6d13f03dd3e00a621aa9245e3da8 --- /dev/null +++ b/lib/eval/evaluate_emdb.py @@ -0,0 +1,228 @@ +import os +import time +import os.path as osp +from glob import glob +from collections import defaultdict + +import torch +import pickle +import numpy as np +from smplx import SMPL +from loguru import logger +from progress.bar import Bar + +from configs import constants as _C +from configs.config import parse_args +from lib.data.dataloader import setup_eval_dataloader +from lib.models import build_network, build_body_model +from lib.eval.eval_utils import ( + compute_jpe, + compute_rte, + compute_jitter, + compute_error_accel, + compute_foot_sliding, + batch_align_by_pelvis, + first_align_joints, + global_align_joints, + compute_rte, + compute_jitter, + compute_foot_sliding + batch_compute_similarity_transform_torch, +) +from lib.utils import transforms +from lib.utils.utils import prepare_output_dir +from lib.utils.utils import prepare_batch +from lib.utils.imutils import avg_preds + +""" +This is a tentative script to evaluate WHAM on EMDB dataset. +Current implementation requires EMDB dataset downloaded at ./datasets/EMDB/ +""" + +m2mm = 1e3 +@torch.no_grad() +def main(cfg, args): + torch.backends.cuda.matmul.allow_tf32 = False + torch.backends.cudnn.allow_tf32 = False + + logger.info(f'GPU name -> {torch.cuda.get_device_name()}') + logger.info(f'GPU feat -> {torch.cuda.get_device_properties("cuda")}') + + # ========= Dataloaders ========= # + eval_loader = setup_eval_dataloader(cfg, 'emdb', args.eval_split, cfg.MODEL.BACKBONE) + logger.info(f'Dataset loaded') + + # ========= Load WHAM ========= # + smpl_batch_size = cfg.TRAIN.BATCH_SIZE * cfg.DATASET.SEQLEN + smpl = build_body_model(cfg.DEVICE, smpl_batch_size) + network = build_network(cfg, smpl) + network.eval() + + # Build SMPL models with each gender + smpl = {k: SMPL(_C.BMODEL.FLDR, gender=k).to(cfg.DEVICE) for k in ['male', 'female', 'neutral']} + + # Load vertices -> joints regression matrix to evaluate + pelvis_idxs = [1, 2] + + # WHAM uses Y-down coordinate system, while EMDB dataset uses Y-up one. + yup2ydown = transforms.axis_angle_to_matrix(torch.tensor([[np.pi, 0, 0]])).float().to(cfg.DEVICE) + + # To torch tensor function + tt = lambda x: torch.from_numpy(x).float().to(cfg.DEVICE) + accumulator = defaultdict(list) + bar = Bar('Inference', fill='#', max=len(eval_loader)) + with torch.no_grad(): + for i in range(len(eval_loader)): + # Original batch + batch = eval_loader.dataset.load_data(i, False) + x, inits, features, kwargs, gt = prepare_batch(batch, cfg.DEVICE, cfg.TRAIN.STAGE == 'stage2') + + # Align with groundtruth data to the first frame + cam2yup = batch['R'][0][:1].to(cfg.DEVICE) + cam2ydown = cam2yup @ yup2ydown + cam2root = transforms.rotation_6d_to_matrix(inits[1][:, 0, 0]) + ydown2root = cam2ydown.mT @ cam2root + ydown2root = transforms.matrix_to_rotation_6d(ydown2root) + kwargs['init_root'][:, 0] = ydown2root + + if cfg.FLIP_EVAL: + flipped_batch = eval_loader.dataset.load_data(i, True) + f_x, f_inits, f_features, f_kwargs, _ = prepare_batch(flipped_batch, cfg.DEVICE, cfg.TRAIN.STAGE == 'stage2') + + # Forward pass with flipped input + flipped_pred = network(f_x, f_inits, f_features, **f_kwargs) + + # Forward pass with normal input + pred = network(x, inits, features, **kwargs) + + if cfg.FLIP_EVAL: + # Merge two predictions + flipped_pose, flipped_shape = flipped_pred['pose'].squeeze(0), flipped_pred['betas'].squeeze(0) + pose, shape = pred['pose'].squeeze(0), pred['betas'].squeeze(0) + flipped_pose, pose = flipped_pose.reshape(-1, 24, 6), pose.reshape(-1, 24, 6) + avg_pose, avg_shape = avg_preds(pose, shape, flipped_pose, flipped_shape) + avg_pose = avg_pose.reshape(-1, 144) + avg_contact = (flipped_pred['contact'][..., [2, 3, 0, 1]] + pred['contact']) / 2 + + # Refine trajectory with merged prediction + network.pred_pose = avg_pose.view_as(network.pred_pose) + network.pred_shape = avg_shape.view_as(network.pred_shape) + network.pred_contact = avg_contact.view_as(network.pred_contact) + output = network.forward_smpl(**kwargs) + pred = network.refine_trajectory(output, return_y_up=True, **kwargs) + + # <======= Prepare groundtruth data + subj, seq = batch['vid'][:2], batch['vid'][3:] + annot_pth = glob(osp.join(_C.PATHS.EMDB_PTH, subj, seq, '*_data.pkl'))[0] + annot = pickle.load(open(annot_pth, 'rb')) + + masks = annot['good_frames_mask'] + gender = annot['gender'] + poses_body = annot["smpl"]["poses_body"] + poses_root = annot["smpl"]["poses_root"] + betas = np.repeat(annot["smpl"]["betas"].reshape((1, -1)), repeats=annot["n_frames"], axis=0) + trans = annot["smpl"]["trans"] + extrinsics = annot["camera"]["extrinsics"] + + # # Map to camear coordinate + poses_root_cam = transforms.matrix_to_axis_angle(tt(extrinsics[:, :3, :3]) @ transforms.axis_angle_to_matrix(tt(poses_root))) + + # Groundtruth global motion + target_glob = smpl[gender](body_pose=tt(poses_body), global_orient=tt(poses_root), betas=tt(betas), transl=tt(trans)) + target_j3d_glob = target_glob.joints[:, :24][masks] + + # Groundtruth local motion + target_cam = smpl[gender](body_pose=tt(poses_body), global_orient=poses_root_cam, betas=tt(betas)) + target_verts_cam = target_cam.vertices[masks] + target_j3d_cam = target_cam.joints[:, :24][masks] + # =======> + + # Convert WHAM global orient to Y-up coordinate + poses_root = pred['poses_root_world'].squeeze(0) + pred_trans = pred['trans_world'].squeeze(0) + poses_root = yup2ydown.mT @ poses_root + pred_trans = (yup2ydown.mT @ pred_trans.unsqueeze(-1)).squeeze(-1) + + # <======= Build predicted motion + # Predicted global motion + pred_glob = smpl['neutral'](body_pose=pred['poses_body'], global_orient=poses_root.unsqueeze(1), betas=pred['betas'].squeeze(0), transl=pred_trans, pose2rot=False) + pred_j3d_glob = pred_glob.joints[:, :24] + + # Predicted local motion + pred_cam = smpl['neutral'](body_pose=pred['poses_body'], global_orient=pred['poses_root_cam'], betas=pred['betas'].squeeze(0), pose2rot=False) + pred_verts_cam = pred_cam.vertices + pred_j3d_cam = pred_cam.joints[:, :24] + # =======> + + # <======= Evaluation on the local motion + pred_j3d_cam, target_j3d_cam, pred_verts_cam, target_verts_cam = batch_align_by_pelvis( + [pred_j3d_cam, target_j3d_cam, pred_verts_cam, target_verts_cam], pelvis_idxs + ) + S1_hat = batch_compute_similarity_transform_torch(pred_j3d_cam, target_j3d_cam) + pa_mpjpe = torch.sqrt(((S1_hat - target_j3d_cam) ** 2).sum(dim=-1)).mean(dim=-1).cpu().numpy() * m2mm + mpjpe = torch.sqrt(((pred_j3d_cam - target_j3d_cam) ** 2).sum(dim=-1)).mean(dim=-1).cpu().numpy() * m2mm + pve = torch.sqrt(((pred_verts_cam - target_verts_cam) ** 2).sum(dim=-1)).mean(dim=-1).cpu().numpy() * m2mm + accel = compute_error_accel(joints_pred=pred_j3d_cam.cpu(), joints_gt=target_j3d_cam.cpu())[1:-1] + accel = accel * (30 ** 2) # per frame^s to per s^2 + + summary_string = f'{batch["vid"]} | PA-MPJPE: {pa_mpjpe.mean():.1f} MPJPE: {mpjpe.mean():.1f} PVE: {pve.mean():.1f}' + bar.suffix = summary_string + bar.next() + # =======> + + # <======= Evaluation on the global motion + chunk_length = 100 + w_mpjpe, wa_mpjpe = [], [] + for start in range(0, masks.sum(), chunk_length): + end = min(masks.sum(), start + chunk_length) + + target_j3d = target_j3d_glob[start:end].clone().cpu() + pred_j3d = pred_j3d_glob[start:end].clone().cpu() + + w_j3d = first_align_joints(target_j3d, pred_j3d) + wa_j3d = global_align_joints(target_j3d, pred_j3d) + + w_jpe = compute_jpe(target_j3d, w_j3d) + wa_jpe = compute_jpe(target_j3d, wa_j3d) + w_mpjpe.append(w_jpe) + wa_mpjpe.append(wa_jpe) + + w_mpjpe = np.concatenate(w_mpjpe) * m2mm + wa_mpjpe = np.concatenate(wa_mpjpe) * m2mm + + # Additional metrics + rte = compute_rte(torch.from_numpy(trans[masks]), pred_trans.cpu()) * 1e2 + jitter = compute_jitter(pred_glob, fps=30) + foot_sliding = compute_foot_sliding(target_glob, pred_glob, masks) * m2mm + # =======> + + # Additional metrics + rte = compute_rte(torch.from_numpy(trans[masks]), pred_trans.cpu()) * 1e2 + jitter = compute_jitter(pred_glob, fps=30) + foot_sliding = compute_foot_sliding(target_glob, pred_glob, masks) * m2mm + + # <======= Accumulate the results over entire sequences + accumulator['pa_mpjpe'].append(pa_mpjpe) + accumulator['mpjpe'].append(mpjpe) + accumulator['pve'].append(pve) + accumulator['accel'].append(accel) + accumulator['wa_mpjpe'].append(wa_mpjpe) + accumulator['w_mpjpe'].append(w_mpjpe) + accumulator['RTE'].append(rte) + accumulator['jitter'].append(jitter) + accumulator['FS'].append(foot_sliding) + # =======> + + for k, v in accumulator.items(): + accumulator[k] = np.concatenate(v).mean() + + print('') + log_str = f'Evaluation on EMDB {args.eval_split}, ' + log_str += ' '.join([f'{k.upper()}: {v:.4f},'for k,v in accumulator.items()]) + logger.info(log_str) + +if __name__ == '__main__': + cfg, cfg_file, args = parse_args(test=True) + cfg = prepare_output_dir(cfg, cfg_file) + + main(cfg, args) \ No newline at end of file diff --git a/lib/eval/evaluate_rich.py b/lib/eval/evaluate_rich.py new file mode 100644 index 0000000000000000000000000000000000000000..5504ca6862dd73b27a5e88420699862e507f9ad9 --- /dev/null +++ b/lib/eval/evaluate_rich.py @@ -0,0 +1,156 @@ +import os +import os.path as osp +from collections import defaultdict +from time import time + +import torch +import joblib +import numpy as np +from loguru import logger +from smplx import SMPL, SMPLX +from progress.bar import Bar + +from configs import constants as _C +from configs.config import parse_args +from lib.data.dataloader import setup_eval_dataloader +from lib.models import build_network, build_body_model +from lib.eval.eval_utils import ( + compute_error_accel, + batch_align_by_pelvis, + batch_compute_similarity_transform_torch, +) +from lib.utils import transforms +from lib.utils.utils import prepare_output_dir +from lib.utils.utils import prepare_batch +from lib.utils.imutils import avg_preds + +m2mm = 1e3 +smplx2smpl = torch.from_numpy(joblib.load(_C.BMODEL.SMPLX2SMPL)['matrix']).unsqueeze(0).float().cuda() +@torch.no_grad() +def main(cfg, args): + torch.backends.cuda.matmul.allow_tf32 = False + torch.backends.cudnn.allow_tf32 = False + + logger.info(f'GPU name -> {torch.cuda.get_device_name()}') + logger.info(f'GPU feat -> {torch.cuda.get_device_properties("cuda")}') + + # ========= Dataloaders ========= # + eval_loader = setup_eval_dataloader(cfg, 'rich', 'test', cfg.MODEL.BACKBONE) + logger.info(f'Dataset loaded') + + # ========= Load WHAM ========= # + smpl_batch_size = cfg.TRAIN.BATCH_SIZE * cfg.DATASET.SEQLEN + smpl = build_body_model(cfg.DEVICE, smpl_batch_size) + network = build_network(cfg, smpl) + network.eval() + + # Build neutral SMPL model for WHAM and gendered SMPLX models for the groundtruth data + smpl = SMPL(_C.BMODEL.FLDR, gender='neutral').to(cfg.DEVICE) + + # Load vertices -> joints regression matrix to evaluate + J_regressor_eval = smpl.J_regressor.clone().unsqueeze(0) + pelvis_idxs = [1, 2] + + accumulator = defaultdict(list) + bar = Bar('Inference', fill='#', max=len(eval_loader)) + with torch.no_grad(): + for i in range(len(eval_loader)): + time_dict = {} + _t = time() + + # Original batch + batch = eval_loader.dataset.load_data(i, False) + x, inits, features, kwargs, gt = prepare_batch(batch, cfg.DEVICE, cfg.TRAIN.STAGE=='stage2') + + # <======= Inference + if cfg.FLIP_EVAL: + flipped_batch = eval_loader.dataset.load_data(i, True) + f_x, f_inits, f_features, f_kwargs, _ = prepare_batch(flipped_batch, cfg.DEVICE, cfg.TRAIN.STAGE=='stage2') + + # Forward pass with flipped input + flipped_pred = network(f_x, f_inits, f_features, **f_kwargs) + time_dict['inference_flipped'] = time() - _t; _t = time() + + # Forward pass with normal input + pred = network(x, inits, features, **kwargs) + time_dict['inference'] = time() - _t; _t = time() + + if cfg.FLIP_EVAL: + # Merge two predictions + flipped_pose, flipped_shape = flipped_pred['pose'].squeeze(0), flipped_pred['betas'].squeeze(0) + pose, shape = pred['pose'].squeeze(0), pred['betas'].squeeze(0) + flipped_pose, pose = flipped_pose.reshape(-1, 24, 6), pose.reshape(-1, 24, 6) + avg_pose, avg_shape = avg_preds(pose, shape, flipped_pose, flipped_shape) + avg_pose = avg_pose.reshape(-1, 144) + + # Refine trajectory with merged prediction + network.pred_pose = avg_pose.view_as(network.pred_pose) + network.pred_shape = avg_shape.view_as(network.pred_shape) + pred = network.forward_smpl(**kwargs) + time_dict['averaging'] = time() - _t; _t = time() + # =======> + + # <======= Build predicted SMPL + pred_output = smpl(body_pose=pred['poses_body'], + global_orient=pred['poses_root_cam'], + betas=pred['betas'].squeeze(0), + pose2rot=False) + pred_verts = pred_output.vertices.cpu() + pred_j3d = torch.matmul(J_regressor_eval, pred_output.vertices).cpu() + time_dict['building prediction'] = time() - _t; _t = time() + # =======> + + # <======= Build groundtruth SMPL (from SMPLX) + smplx = SMPLX(_C.BMODEL.FLDR.replace('smpl', 'smplx'), + gender=batch['gender'], + batch_size=len(pred_verts) + ).to(cfg.DEVICE) + gt_pose = transforms.matrix_to_axis_angle(transforms.rotation_6d_to_matrix(gt['pose'][0])) + target_output = smplx( + body_pose=gt_pose[:, 1:-2].reshape(-1, 63), + global_orient=gt_pose[:, 0], + betas=gt['betas'][0]) + target_verts = torch.matmul(smplx2smpl, target_output.vertices.cuda()).cpu() + target_j3d = torch.matmul(J_regressor_eval, target_verts.to(cfg.DEVICE)).cpu() + time_dict['building target'] = time() - _t; _t = time() + # =======> + + # <======= Compute performance of the current sequence + pred_j3d, target_j3d, pred_verts, target_verts = batch_align_by_pelvis( + [pred_j3d, target_j3d, pred_verts, target_verts], pelvis_idxs + ) + S1_hat = batch_compute_similarity_transform_torch(pred_j3d, target_j3d) + pa_mpjpe = torch.sqrt(((S1_hat - target_j3d) ** 2).sum(dim=-1)).mean(dim=-1).numpy() * m2mm + mpjpe = torch.sqrt(((pred_j3d - target_j3d) ** 2).sum(dim=-1)).mean(dim=-1).numpy() * m2mm + pve = torch.sqrt(((pred_verts - target_verts) ** 2).sum(dim=-1)).mean(dim=-1).numpy() * m2mm + accel = compute_error_accel(joints_pred=pred_j3d, joints_gt=target_j3d)[1:-1] + accel = accel * (30 ** 2) # per frame^s to per s^2 + time_dict['evaluating'] = time() - _t; _t = time() + # =======> + + # summary_string = f'{batch["vid"]} | PA-MPJPE: {pa_mpjpe.mean():.1f} MPJPE: {mpjpe.mean():.1f} PVE: {pve.mean():.1f}' + summary_string = f'{batch["vid"]} | ' + ' '.join([f'{k}: {v:.1f} s' for k, v in time_dict.items()]) + bar.suffix = summary_string + bar.next() + + # <======= Accumulate the results over entire sequences + accumulator['pa_mpjpe'].append(pa_mpjpe) + accumulator['mpjpe'].append(mpjpe) + accumulator['pve'].append(pve) + accumulator['accel'].append(accel) + + # =======> + + for k, v in accumulator.items(): + accumulator[k] = np.concatenate(v).mean() + + print('') + log_str = 'Evaluation on RICH, ' + log_str += ' '.join([f'{k.upper()}: {v:.4f},'for k,v in accumulator.items()]) + logger.info(log_str) + +if __name__ == '__main__': + cfg, cfg_file, args = parse_args(test=True) + cfg = prepare_output_dir(cfg, cfg_file) + + main(cfg, args) \ No newline at end of file diff --git a/lib/models/__init__.py b/lib/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d203b287a2024e1d1d5c0ccf9846f59ca87efe55 --- /dev/null +++ b/lib/models/__init__.py @@ -0,0 +1,40 @@ +import os, sys +import yaml +import torch +from loguru import logger + +from configs import constants as _C +from .smpl import SMPL + + +def build_body_model(device, batch_size=1, gender='neutral', **kwargs): + sys.stdout = open(os.devnull, 'w') + body_model = SMPL( + model_path=_C.BMODEL.FLDR, + gender=gender, + batch_size=batch_size, + create_transl=False).to(device) + sys.stdout = sys.__stdout__ + return body_model + + +def build_network(cfg, smpl): + from .wham import Network + + with open(cfg.MODEL_CONFIG, 'r') as f: + model_config = yaml.safe_load(f) + model_config.update({'d_feat': _C.IMG_FEAT_DIM[cfg.MODEL.BACKBONE]}) + + network = Network(smpl, **model_config).to(cfg.DEVICE) + + # Load Checkpoint + if os.path.isfile(cfg.TRAIN.CHECKPOINT): + checkpoint = torch.load(cfg.TRAIN.CHECKPOINT) + ignore_keys = ['smpl.body_pose', 'smpl.betas', 'smpl.global_orient', 'smpl.J_regressor_extra', 'smpl.J_regressor_eval'] + model_state_dict = {k: v for k, v in checkpoint['model'].items() if k not in ignore_keys} + network.load_state_dict(model_state_dict, strict=False) + logger.info(f"=> loaded checkpoint '{cfg.TRAIN.CHECKPOINT}' ") + else: + logger.info(f"=> Warning! no checkpoint found at '{cfg.TRAIN.CHECKPOINT}'.") + + return network \ No newline at end of file diff --git a/lib/models/__pycache__/__init__.cpython-39.pyc b/lib/models/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 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+from .modules import MotionEncoder, MotionDecoder, TrajectoryDecoder, TrajectoryRefiner, Integrator +from .utils import rollout_global_motion, compute_camera_pose, reset_root_velocity, compute_camera_motion \ No newline at end of file diff --git a/lib/models/layers/__pycache__/__init__.cpython-39.pyc b/lib/models/layers/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cb6793839e899cd00867dd79278b82420a3a74f9 Binary files /dev/null and b/lib/models/layers/__pycache__/__init__.cpython-39.pyc differ diff --git a/lib/models/layers/__pycache__/modules.cpython-39.pyc b/lib/models/layers/__pycache__/modules.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4c9734807e8a2080b4ff90378dc5c98ab8604423 Binary files /dev/null and b/lib/models/layers/__pycache__/modules.cpython-39.pyc differ diff --git a/lib/models/layers/__pycache__/utils.cpython-39.pyc b/lib/models/layers/__pycache__/utils.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fc2b15810eafd2e56618bfb56a0b0958f520d44c Binary files /dev/null and b/lib/models/layers/__pycache__/utils.cpython-39.pyc differ diff --git a/lib/models/layers/modules.py b/lib/models/layers/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..b0f4aca22175f3676a204564a51fc9dd02cf434e --- /dev/null +++ b/lib/models/layers/modules.py @@ -0,0 +1,262 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import torch +import numpy as np +from torch import nn +from configs import constants as _C +from .utils import rollout_global_motion +from lib.utils.transforms import axis_angle_to_matrix + + +class Regressor(nn.Module): + def __init__(self, in_dim, hid_dim, out_dims, init_dim, layer='LSTM', n_layers=2, n_iters=1): + super().__init__() + self.n_outs = len(out_dims) + + self.rnn = getattr(nn, layer.upper())( + in_dim + init_dim, hid_dim, n_layers, + bidirectional=False, batch_first=True, dropout=0.3) + + for i, out_dim in enumerate(out_dims): + setattr(self, 'declayer%d'%i, nn.Linear(hid_dim, out_dim)) + nn.init.xavier_uniform_(getattr(self, 'declayer%d'%i).weight, gain=0.01) + + def forward(self, x, inits, h0): + xc = torch.cat([x, *inits], dim=-1) + xc, h0 = self.rnn(xc, h0) + + preds = [] + for j in range(self.n_outs): + out = getattr(self, 'declayer%d'%j)(xc) + preds.append(out) + + return preds, xc, h0 + + +class NeuralInitialization(nn.Module): + def __init__(self, in_dim, hid_dim, layer, n_layers): + super().__init__() + + out_dim = hid_dim + self.n_layers = n_layers + self.num_inits = int(layer.upper() == 'LSTM') + 1 + out_dim *= self.num_inits * n_layers + + self.linear1 = nn.Linear(in_dim, hid_dim) + self.linear2 = nn.Linear(hid_dim, hid_dim * self.n_layers) + self.linear3 = nn.Linear(hid_dim * self.n_layers, out_dim) + self.relu1 = nn.ReLU() + self.relu2 = nn.ReLU() + + def forward(self, x): + b = x.shape[0] + + out = self.linear3(self.relu2(self.linear2(self.relu1(self.linear1(x))))) + out = out.view(b, self.num_inits, self.n_layers, -1).permute(1, 2, 0, 3).contiguous() + + if self.num_inits == 2: + return tuple([_ for _ in out]) + return out[0] + + +class Integrator(nn.Module): + def __init__(self, in_channel, out_channel, hid_channel=1024): + super().__init__() + + self.layer1 = nn.Linear(in_channel, hid_channel) + self.relu1 = nn.ReLU() + self.dr1 = nn.Dropout(0.1) + + self.layer2 = nn.Linear(hid_channel, hid_channel) + self.relu2 = nn.ReLU() + self.dr2 = nn.Dropout(0.1) + + self.layer3 = nn.Linear(hid_channel, out_channel) + + + def forward(self, x, feat): + res = x + mask = (feat != 0).all(dim=-1).all(dim=-1) + + out = torch.cat((x, feat), dim=-1) + out = self.layer1(out) + out = self.relu1(out) + out = self.dr1(out) + + out = self.layer2(out) + out = self.relu2(out) + out = self.dr2(out) + + out = self.layer3(out) + out[mask] = out[mask] + res[mask] + + return out + + +class MotionEncoder(nn.Module): + def __init__(self, + in_dim, + d_embed, + pose_dr, + rnn_type, + n_layers, + n_joints): + super().__init__() + + self.n_joints = n_joints + + self.embed_layer = nn.Linear(in_dim, d_embed) + self.pos_drop = nn.Dropout(pose_dr) + + # Keypoints initializer + self.neural_init = NeuralInitialization(n_joints * 3 + in_dim, d_embed, rnn_type, n_layers) + + # 3d keypoints regressor + self.regressor = Regressor( + d_embed, d_embed, [n_joints * 3], n_joints * 3, rnn_type, n_layers) + + def forward(self, x, init): + """ Forward pass of motion encoder. + """ + + self.b, self.f = x.shape[:2] + x = self.embed_layer(x.reshape(self.b, self.f, -1)) + x = self.pos_drop(x) + + h0 = self.neural_init(init) + pred_list = [init[..., :self.n_joints * 3]] + motion_context_list = [] + + for i in range(self.f): + (pred_kp3d, ), motion_context, h0 = self.regressor(x[:, [i]], pred_list[-1:], h0) + motion_context_list.append(motion_context) + pred_list.append(pred_kp3d) + + pred_kp3d = torch.cat(pred_list[1:], dim=1).view(self.b, self.f, -1, 3) + motion_context = torch.cat(motion_context_list, dim=1) + + # Merge 3D keypoints with motion context + motion_context = torch.cat((motion_context, pred_kp3d.reshape(self.b, self.f, -1)), dim=-1) + return pred_kp3d, motion_context + + +class TrajectoryDecoder(nn.Module): + def __init__(self, + d_embed, + rnn_type, + n_layers): + super().__init__() + + # Trajectory regressor + self.regressor = Regressor( + d_embed, d_embed, [3, 6], 12, rnn_type, n_layers, ) + + def forward(self, x, root, cam_a, h0=None): + """ Forward pass of trajectory decoder. + """ + + b, f = x.shape[:2] + pred_root_list, pred_vel_list = [root[:, :1]], [] + + for i in range(f): + # Global coordinate estimation + (pred_rootv, pred_rootr), _, h0 = self.regressor( + x[:, [i]], [pred_root_list[-1], cam_a[:, [i]]], h0) + + pred_root_list.append(pred_rootr) + pred_vel_list.append(pred_rootv) + + pred_root = torch.cat(pred_root_list, dim=1).view(b, f + 1, -1) + pred_vel = torch.cat(pred_vel_list, dim=1).view(b, f, -1) + + return pred_root, pred_vel + + +class MotionDecoder(nn.Module): + def __init__(self, + d_embed, + rnn_type, + n_layers): + super().__init__() + + self.n_pose = 24 + + # SMPL pose initialization + self.neural_init = NeuralInitialization(len(_C.BMODEL.MAIN_JOINTS) * 6, d_embed, rnn_type, n_layers) + + # 3d keypoints regressor + self.regressor = Regressor( + d_embed, d_embed, [self.n_pose * 6, 10, 3, 4], self.n_pose * 6, rnn_type, n_layers) + + def forward(self, x, init): + """ Forward pass of motion decoder. + """ + b, f = x.shape[:2] + + h0 = self.neural_init(init[:, :, _C.BMODEL.MAIN_JOINTS].reshape(b, 1, -1)) + + # Recursive prediction of SMPL parameters + pred_pose_list = [init.reshape(b, 1, -1)] + pred_shape_list, pred_cam_list, pred_contact_list = [], [], [] + + for i in range(f): + # Camera coordinate estimation + (pred_pose, pred_shape, pred_cam, pred_contact), _, h0 = self.regressor(x[:, [i]], pred_pose_list[-1:], h0) + pred_pose_list.append(pred_pose) + pred_shape_list.append(pred_shape) + pred_cam_list.append(pred_cam) + pred_contact_list.append(pred_contact) + + pred_pose = torch.cat(pred_pose_list[1:], dim=1).view(b, f, -1) + pred_shape = torch.cat(pred_shape_list, dim=1).view(b, f, -1) + pred_cam = torch.cat(pred_cam_list, dim=1).view(b, f, -1) + pred_contact = torch.cat(pred_contact_list, dim=1).view(b, f, -1) + + return pred_pose, pred_shape, pred_cam, pred_contact + + +class TrajectoryRefiner(nn.Module): + def __init__(self, + d_embed, + d_hidden, + rnn_type, + n_layers): + super().__init__() + + d_input = d_embed + 12 + self.refiner = Regressor( + d_input, d_hidden, [6, 3], 9, rnn_type, n_layers) + + def forward(self, context, pred_vel, output, cam_angvel, return_y_up): + b, f = context.shape[:2] + + # Register values + pred_root = output['poses_root_r6d'].clone().detach() + feet = output['feet'].clone().detach() + contact = output['contact'].clone().detach() + + feet_vel = torch.cat((torch.zeros_like(feet[:, :1]), feet[:, 1:] - feet[:, :-1]), dim=1) * 30 # Normalize to 30 times + feet = (feet_vel * contact.unsqueeze(-1)).reshape(b, f, -1) # Velocity input + inpt_feat = torch.cat([context, feet], dim=-1) + + (delta_root, delta_vel), _, _ = self.refiner(inpt_feat, [pred_root[:, 1:], pred_vel], h0=None) + pred_root[:, 1:] = pred_root[:, 1:] + delta_root + pred_vel = pred_vel + delta_vel + + # root_world, trans_world = rollout_global_motion(pred_root, pred_vel) + + # if return_y_up: + # yup2ydown = axis_angle_to_matrix(torch.tensor([[np.pi, 0, 0]])).float().to(root_world.device) + # root_world = yup2ydown.mT @ root_world + # trans_world = (yup2ydown.mT @ trans_world.unsqueeze(-1)).squeeze(-1) + + output.update({ + 'poses_root_r6d_refined': pred_root, + 'vel_root_refined': pred_vel, + # 'poses_root_world': root_world, + # 'trans_world': trans_world, + }) + + return output \ No newline at end of file diff --git a/lib/models/layers/utils.py b/lib/models/layers/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..6a67ec77b8572f8ca26d4ded8f4731da4bb7b616 --- /dev/null +++ b/lib/models/layers/utils.py @@ -0,0 +1,52 @@ +import torch +from lib.utils import transforms + + + +def rollout_global_motion(root_r, root_v, init_trans=None): + b, f = root_v.shape[:2] + root = transforms.rotation_6d_to_matrix(root_r[:]) + vel_world = (root[:, :-1] @ root_v.unsqueeze(-1)).squeeze(-1) + trans = torch.cumsum(vel_world, dim=1) + + if init_trans is not None: trans = trans + init_trans + return root[:, 1:], trans + +def compute_camera_motion(output, root_c_d6d, root_w, trans, pred_cam): + root_c = transforms.rotation_6d_to_matrix(root_c_d6d) # Root orient in cam coord + cam_R = root_c @ root_w.mT + pelvis_cam = output.full_cam.view_as(pred_cam) + pelvis_world = (cam_R.mT @ pelvis_cam.unsqueeze(-1)).squeeze(-1) + cam_T_world = pelvis_world - trans + cam_T = (cam_R @ cam_T_world.unsqueeze(-1)).squeeze(-1) + + return cam_R, cam_T + +def compute_camera_pose(root_c_d6d, root_w): + root_c = transforms.rotation_6d_to_matrix(root_c_d6d) # Root orient in cam coord + cam_R = root_c @ root_w.mT + return cam_R + + +def reset_root_velocity(smpl, output, stationary, pred_ori, pred_vel, thr=0.7): + b, f = pred_vel.shape[:2] + + stationary_mask = (stationary.clone().detach() > thr).unsqueeze(-1).float() + poses_root = transforms.rotation_6d_to_matrix(pred_ori.clone().detach()) + vel_world = (poses_root[:, 1:] @ pred_vel.clone().detach().unsqueeze(-1)).squeeze(-1) + + output = smpl.get_output(body_pose=output.body_pose.clone().detach(), + global_orient=poses_root[:, 1:].reshape(-1, 1, 3, 3), + betas=output.betas.clone().detach(), + pose2rot=False) + feet = output.feet.reshape(b, f, 4, 3) + feet_vel = feet[:, 1:] - feet[:, :-1] + vel_world[:, 1:].unsqueeze(-2) + feet_vel = torch.cat((torch.zeros_like(feet_vel[:, :1]), feet_vel), dim=1) + + stationary_vel = feet_vel * stationary_mask + del_vel = stationary_vel.sum(dim=2) / ((stationary_vel != 0).sum(dim=2) + 1e-4) + vel_world_update = vel_world - del_vel + + vel_root = (poses_root[:, 1:].mT @ vel_world_update.unsqueeze(-1)).squeeze(-1) + + return vel_root \ No newline at end of file diff --git a/lib/models/preproc/__pycache__/detector.cpython-39.pyc b/lib/models/preproc/__pycache__/detector.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..79cc37dbe7ad97032f937cacc11496380dba2476 Binary files /dev/null and b/lib/models/preproc/__pycache__/detector.cpython-39.pyc differ diff --git a/lib/models/preproc/__pycache__/extractor.cpython-39.pyc b/lib/models/preproc/__pycache__/extractor.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2e2e36df8bd3833d3b5e804bab3ecd9a4aaaa4a2 Binary files /dev/null and b/lib/models/preproc/__pycache__/extractor.cpython-39.pyc differ diff --git a/lib/models/preproc/__pycache__/slam.cpython-39.pyc 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Args: + cfg (CfgNode): Config file as a yacs CfgNode + """ + super().__init__() + + # Create backbone feature extractor + self.backbone = vit() + + # Create SMPL head + self.smpl_head = SMPLTransformerDecoderHead() + + + def decode(self, x): + + batch_size = x.shape[0] + pred_smpl_params, pred_cam, _ = self.smpl_head(x) + + # Compute model vertices, joints and the projected joints + pred_smpl_params['global_orient'] = pred_smpl_params['global_orient'].reshape(batch_size, -1, 3, 3) + pred_smpl_params['body_pose'] = pred_smpl_params['body_pose'].reshape(batch_size, -1, 3, 3) + pred_smpl_params['betas'] = pred_smpl_params['betas'].reshape(batch_size, -1) + return pred_smpl_params['global_orient'], pred_smpl_params['body_pose'], pred_smpl_params['betas'], pred_cam + + def forward(self, x, encode=False, **kwargs): + """ + Run a forward step of the network + Args: + batch (Dict): Dictionary containing batch data + train (bool): Flag indicating whether it is training or validation mode + Returns: + Dict: Dictionary containing the regression output + """ + + # Use RGB image as input + batch_size = x.shape[0] + + # Compute conditioning features using the backbone + # if using ViT backbone, we need to use a different aspect ratio + conditioning_feats = self.backbone(x[:,:,:,32:-32]) + if encode: + conditioning_feats = einops.rearrange(conditioning_feats, 'b c h w -> b (h w) c') + token = torch.zeros(batch_size, 1, 1).to(x.device) + token_out = self.smpl_head.transformer(token, context=conditioning_feats) + return token_out.squeeze(1) + + pred_smpl_params, pred_cam, _ = self.smpl_head(conditioning_feats) + + # Compute model vertices, joints and the projected joints + pred_smpl_params['global_orient'] = pred_smpl_params['global_orient'].reshape(batch_size, -1, 3, 3) + pred_smpl_params['body_pose'] = pred_smpl_params['body_pose'].reshape(batch_size, -1, 3, 3) + pred_smpl_params['betas'] = pred_smpl_params['betas'].reshape(batch_size, -1) + return pred_smpl_params['global_orient'], pred_smpl_params['body_pose'], pred_smpl_params['betas'], pred_cam + + +def hmr2(checkpoint_pth): + model = HMR2() + if os.path.exists(checkpoint_pth): + model.load_state_dict(torch.load(checkpoint_pth, map_location='cpu')['state_dict'], strict=False) + print(f'Load backbone weight: {checkpoint_pth}') + return model \ No newline at end of file diff --git a/lib/models/preproc/backbone/pose_transformer.py b/lib/models/preproc/backbone/pose_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..ed09143c324169acf7231c020ed8b5ead0964423 --- /dev/null +++ b/lib/models/preproc/backbone/pose_transformer.py @@ -0,0 +1,357 @@ +from inspect import isfunction +from typing import Callable, Optional + +import torch +from einops import rearrange +from einops.layers.torch import Rearrange +from torch import nn + +from .t_cond_mlp import ( + AdaptiveLayerNorm1D, + FrequencyEmbedder, + normalization_layer, +) +# from .vit import Attention, FeedForward + + +def exists(val): + return val is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +class PreNorm(nn.Module): + def __init__(self, dim: int, fn: Callable, norm: str = "layer", norm_cond_dim: int = -1): + super().__init__() + self.norm = normalization_layer(norm, dim, norm_cond_dim) + self.fn = fn + + def forward(self, x: torch.Tensor, *args, **kwargs): + if isinstance(self.norm, AdaptiveLayerNorm1D): + return self.fn(self.norm(x, *args), **kwargs) + else: + return self.fn(self.norm(x), **kwargs) + + +class FeedForward(nn.Module): + def __init__(self, dim, hidden_dim, dropout=0.0): + super().__init__() + self.net = nn.Sequential( + nn.Linear(dim, hidden_dim), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(hidden_dim, dim), + nn.Dropout(dropout), + ) + + def forward(self, x): + return self.net(x) + + +class Attention(nn.Module): + def __init__(self, dim, heads=8, dim_head=64, dropout=0.0): + super().__init__() + inner_dim = dim_head * heads + project_out = not (heads == 1 and dim_head == dim) + + self.heads = heads + self.scale = dim_head**-0.5 + + self.attend = nn.Softmax(dim=-1) + self.dropout = nn.Dropout(dropout) + + self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False) + + self.to_out = ( + nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout)) + if project_out + else nn.Identity() + ) + + def forward(self, x): + qkv = self.to_qkv(x).chunk(3, dim=-1) + q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.heads), qkv) + + dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale + + attn = self.attend(dots) + attn = self.dropout(attn) + + out = torch.matmul(attn, v) + out = rearrange(out, "b h n d -> b n (h d)") + return self.to_out(out) + + +class CrossAttention(nn.Module): + def __init__(self, dim, context_dim=None, heads=8, dim_head=64, dropout=0.0): + super().__init__() + inner_dim = dim_head * heads + project_out = not (heads == 1 and dim_head == dim) + + self.heads = heads + self.scale = dim_head**-0.5 + + self.attend = nn.Softmax(dim=-1) + self.dropout = nn.Dropout(dropout) + + context_dim = default(context_dim, dim) + self.to_kv = nn.Linear(context_dim, inner_dim * 2, bias=False) + self.to_q = nn.Linear(dim, inner_dim, bias=False) + + self.to_out = ( + nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout)) + if project_out + else nn.Identity() + ) + + def forward(self, x, context=None): + context = default(context, x) + k, v = self.to_kv(context).chunk(2, dim=-1) + q = self.to_q(x) + q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.heads), [q, k, v]) + + dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale + + attn = self.attend(dots) + attn = self.dropout(attn) + + out = torch.matmul(attn, v) + out = rearrange(out, "b h n d -> b n (h d)") + return self.to_out(out) + + +class Transformer(nn.Module): + def __init__( + self, + dim: int, + depth: int, + heads: int, + dim_head: int, + mlp_dim: int, + dropout: float = 0.0, + norm: str = "layer", + norm_cond_dim: int = -1, + ): + super().__init__() + self.layers = nn.ModuleList([]) + for _ in range(depth): + sa = Attention(dim, heads=heads, dim_head=dim_head, dropout=dropout) + ff = FeedForward(dim, mlp_dim, dropout=dropout) + self.layers.append( + nn.ModuleList( + [ + PreNorm(dim, sa, norm=norm, norm_cond_dim=norm_cond_dim), + PreNorm(dim, ff, norm=norm, norm_cond_dim=norm_cond_dim), + ] + ) + ) + + def forward(self, x: torch.Tensor, *args): + for attn, ff in self.layers: + x = attn(x, *args) + x + x = ff(x, *args) + x + return x + + +class TransformerCrossAttn(nn.Module): + def __init__( + self, + dim: int, + depth: int, + heads: int, + dim_head: int, + mlp_dim: int, + dropout: float = 0.0, + norm: str = "layer", + norm_cond_dim: int = -1, + context_dim: Optional[int] = None, + ): + super().__init__() + self.layers = nn.ModuleList([]) + for _ in range(depth): + sa = Attention(dim, heads=heads, dim_head=dim_head, dropout=dropout) + ca = CrossAttention( + dim, context_dim=context_dim, heads=heads, dim_head=dim_head, dropout=dropout + ) + ff = FeedForward(dim, mlp_dim, dropout=dropout) + self.layers.append( + nn.ModuleList( + [ + PreNorm(dim, sa, norm=norm, norm_cond_dim=norm_cond_dim), + PreNorm(dim, ca, norm=norm, norm_cond_dim=norm_cond_dim), + PreNorm(dim, ff, norm=norm, norm_cond_dim=norm_cond_dim), + ] + ) + ) + + def forward(self, x: torch.Tensor, *args, context=None, context_list=None): + if context_list is None: + context_list = [context] * len(self.layers) + if len(context_list) != len(self.layers): + raise ValueError(f"len(context_list) != len(self.layers) ({len(context_list)} != {len(self.layers)})") + + for i, (self_attn, cross_attn, ff) in enumerate(self.layers): + x = self_attn(x, *args) + x + x = cross_attn(x, *args, context=context_list[i]) + x + x = ff(x, *args) + x + return x + + +class DropTokenDropout(nn.Module): + def __init__(self, p: float = 0.1): + super().__init__() + if p < 0 or p > 1: + raise ValueError( + "dropout probability has to be between 0 and 1, " "but got {}".format(p) + ) + self.p = p + + def forward(self, x: torch.Tensor): + # x: (batch_size, seq_len, dim) + if self.training and self.p > 0: + zero_mask = torch.full_like(x[0, :, 0], self.p).bernoulli().bool() + # TODO: permutation idx for each batch using torch.argsort + if zero_mask.any(): + x = x[:, ~zero_mask, :] + return x + + +class ZeroTokenDropout(nn.Module): + def __init__(self, p: float = 0.1): + super().__init__() + if p < 0 or p > 1: + raise ValueError( + "dropout probability has to be between 0 and 1, " "but got {}".format(p) + ) + self.p = p + + def forward(self, x: torch.Tensor): + # x: (batch_size, seq_len, dim) + if self.training and self.p > 0: + zero_mask = torch.full_like(x[:, :, 0], self.p).bernoulli().bool() + # Zero-out the masked tokens + x[zero_mask, :] = 0 + return x + + +class TransformerEncoder(nn.Module): + def __init__( + self, + num_tokens: int, + token_dim: int, + dim: int, + depth: int, + heads: int, + mlp_dim: int, + dim_head: int = 64, + dropout: float = 0.0, + emb_dropout: float = 0.0, + emb_dropout_type: str = "drop", + emb_dropout_loc: str = "token", + norm: str = "layer", + norm_cond_dim: int = -1, + token_pe_numfreq: int = -1, + ): + super().__init__() + if token_pe_numfreq > 0: + token_dim_new = token_dim * (2 * token_pe_numfreq + 1) + self.to_token_embedding = nn.Sequential( + Rearrange("b n d -> (b n) d", n=num_tokens, d=token_dim), + FrequencyEmbedder(token_pe_numfreq, token_pe_numfreq - 1), + Rearrange("(b n) d -> b n d", n=num_tokens, d=token_dim_new), + nn.Linear(token_dim_new, dim), + ) + else: + self.to_token_embedding = nn.Linear(token_dim, dim) + self.pos_embedding = nn.Parameter(torch.randn(1, num_tokens, dim)) + if emb_dropout_type == "drop": + self.dropout = DropTokenDropout(emb_dropout) + elif emb_dropout_type == "zero": + self.dropout = ZeroTokenDropout(emb_dropout) + else: + raise ValueError(f"Unknown emb_dropout_type: {emb_dropout_type}") + self.emb_dropout_loc = emb_dropout_loc + + self.transformer = Transformer( + dim, depth, heads, dim_head, mlp_dim, dropout, norm=norm, norm_cond_dim=norm_cond_dim + ) + + def forward(self, inp: torch.Tensor, *args, **kwargs): + x = inp + + if self.emb_dropout_loc == "input": + x = self.dropout(x) + x = self.to_token_embedding(x) + + if self.emb_dropout_loc == "token": + x = self.dropout(x) + b, n, _ = x.shape + x += self.pos_embedding[:, :n] + + if self.emb_dropout_loc == "token_afterpos": + x = self.dropout(x) + x = self.transformer(x, *args) + return x + + +class TransformerDecoder(nn.Module): + def __init__( + self, + num_tokens: int, + token_dim: int, + dim: int, + depth: int, + heads: int, + mlp_dim: int, + dim_head: int = 64, + dropout: float = 0.0, + emb_dropout: float = 0.0, + emb_dropout_type: str = 'drop', + norm: str = "layer", + norm_cond_dim: int = -1, + context_dim: Optional[int] = None, + skip_token_embedding: bool = False, + ): + super().__init__() + if not skip_token_embedding: + self.to_token_embedding = nn.Linear(token_dim, dim) + else: + self.to_token_embedding = nn.Identity() + if token_dim != dim: + raise ValueError( + f"token_dim ({token_dim}) != dim ({dim}) when skip_token_embedding is True" + ) + + self.pos_embedding = nn.Parameter(torch.randn(1, num_tokens, dim)) + if emb_dropout_type == "drop": + self.dropout = DropTokenDropout(emb_dropout) + elif emb_dropout_type == "zero": + self.dropout = ZeroTokenDropout(emb_dropout) + elif emb_dropout_type == "normal": + self.dropout = nn.Dropout(emb_dropout) + + self.transformer = TransformerCrossAttn( + dim, + depth, + heads, + dim_head, + mlp_dim, + dropout, + norm=norm, + norm_cond_dim=norm_cond_dim, + context_dim=context_dim, + ) + + def forward(self, inp: torch.Tensor, *args, context=None, context_list=None): + x = self.to_token_embedding(inp) + b, n, _ = x.shape + + x = self.dropout(x) + x += self.pos_embedding[:, :n] + + x = self.transformer(x, *args, context=context, context_list=context_list) + return x diff --git a/lib/models/preproc/backbone/smpl_head.py b/lib/models/preproc/backbone/smpl_head.py new file mode 100644 index 0000000000000000000000000000000000000000..14493b8398deff1afffd6a9584bb7e54f5146626 --- /dev/null +++ b/lib/models/preproc/backbone/smpl_head.py @@ -0,0 +1,128 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +import einops + +from configs import constants as _C +from lib.utils.transforms import axis_angle_to_matrix +from .pose_transformer import TransformerDecoder + +def rot6d_to_rotmat(x: torch.Tensor) -> torch.Tensor: + """ + Convert 6D rotation representation to 3x3 rotation matrix. + Based on Zhou et al., "On the Continuity of Rotation Representations in Neural Networks", CVPR 2019 + Args: + x (torch.Tensor): (B,6) Batch of 6-D rotation representations. + Returns: + torch.Tensor: Batch of corresponding rotation matrices with shape (B,3,3). + """ + x = x.reshape(-1,2,3).permute(0, 2, 1).contiguous() + a1 = x[:, :, 0] + a2 = x[:, :, 1] + b1 = F.normalize(a1) + b2 = F.normalize(a2 - torch.einsum('bi,bi->b', b1, a2).unsqueeze(-1) * b1) + b3 = torch.cross(b1, b2) + return torch.stack((b1, b2, b3), dim=-1) + +def build_smpl_head(cfg): + smpl_head_type = 'transformer_decoder' + if smpl_head_type == 'transformer_decoder': + return SMPLTransformerDecoderHead(cfg) + else: + raise ValueError('Unknown SMPL head type: {}'.format(smpl_head_type)) + +class SMPLTransformerDecoderHead(nn.Module): + """ Cross-attention based SMPL Transformer decoder + """ + + def __init__(self): + super().__init__() + self.joint_rep_type = '6d' + self.joint_rep_dim = {'6d': 6, 'aa': 3}[self.joint_rep_type] + npose = self.joint_rep_dim * 24 + self.npose = npose + self.input_is_mean_shape = False + transformer_args = dict( + num_tokens=1, + token_dim=(npose + 10 + 3) if self.input_is_mean_shape else 1, + dim=1024, + ) + transformer_args_from_cfg = dict( + depth=6, heads=8, mlp_dim=1024, dim_head=64, dropout=0.0, emb_dropout=0.0, norm='layer', context_dim=1280 + ) + transformer_args = (transformer_args | transformer_args_from_cfg) + self.transformer = TransformerDecoder( + **transformer_args + ) + dim=transformer_args['dim'] + self.decpose = nn.Linear(dim, npose) + self.decshape = nn.Linear(dim, 10) + self.deccam = nn.Linear(dim, 3) + + mean_params = np.load(_C.BMODEL.MEAN_PARAMS) + init_body_pose = torch.from_numpy(mean_params['pose'].astype(np.float32)).unsqueeze(0) + init_betas = torch.from_numpy(mean_params['shape'].astype('float32')).unsqueeze(0) + init_cam = torch.from_numpy(mean_params['cam'].astype(np.float32)).unsqueeze(0) + self.register_buffer('init_body_pose', init_body_pose) + self.register_buffer('init_betas', init_betas) + self.register_buffer('init_cam', init_cam) + + def forward(self, x, **kwargs): + + batch_size = x.shape[0] + # vit pretrained backbone is channel-first. Change to token-first + + init_body_pose = self.init_body_pose.expand(batch_size, -1) + init_betas = self.init_betas.expand(batch_size, -1) + init_cam = self.init_cam.expand(batch_size, -1) + + # TODO: Convert init_body_pose to aa rep if needed + if self.joint_rep_type == 'aa': + raise NotImplementedError + + pred_body_pose = init_body_pose + pred_betas = init_betas + pred_cam = init_cam + pred_body_pose_list = [] + pred_betas_list = [] + pred_cam_list = [] + + # Input token to transformer is zero token + if len(x.shape) > 2: + x = einops.rearrange(x, 'b c h w -> b (h w) c') + if self.input_is_mean_shape: + token = torch.cat([pred_body_pose, pred_betas, pred_cam], dim=1)[:,None,:] + else: + token = torch.zeros(batch_size, 1, 1).to(x.device) + + # Pass through transformer + token_out = self.transformer(token, context=x) + token_out = token_out.squeeze(1) # (B, C) + else: + token_out = x + + # Readout from token_out + pred_body_pose = self.decpose(token_out) + pred_body_pose + pred_betas = self.decshape(token_out) + pred_betas + pred_cam = self.deccam(token_out) + pred_cam + pred_body_pose_list.append(pred_body_pose) + pred_betas_list.append(pred_betas) + pred_cam_list.append(pred_cam) + + # Convert self.joint_rep_type -> rotmat + joint_conversion_fn = { + '6d': rot6d_to_rotmat, + 'aa': lambda x: axis_angle_to_matrix(x.view(-1, 3).contiguous()) + }[self.joint_rep_type] + + pred_smpl_params_list = {} + pred_smpl_params_list['body_pose'] = torch.cat([joint_conversion_fn(pbp).view(batch_size, -1, 3, 3)[:, 1:, :, :] for pbp in pred_body_pose_list], dim=0) + pred_smpl_params_list['betas'] = torch.cat(pred_betas_list, dim=0) + pred_smpl_params_list['cam'] = torch.cat(pred_cam_list, dim=0) + pred_body_pose = joint_conversion_fn(pred_body_pose).view(batch_size, 24, 3, 3) + + pred_smpl_params = {'global_orient': pred_body_pose[:, [0]], + 'body_pose': pred_body_pose[:, 1:], + 'betas': pred_betas} + return pred_smpl_params, pred_cam, pred_smpl_params_list \ No newline at end of file diff --git a/lib/models/preproc/backbone/t_cond_mlp.py b/lib/models/preproc/backbone/t_cond_mlp.py new file mode 100644 index 0000000000000000000000000000000000000000..954ae695b30f06dcb88ded4514da3f2c693149f6 --- /dev/null +++ b/lib/models/preproc/backbone/t_cond_mlp.py @@ -0,0 +1,198 @@ +import copy +from typing import List, Optional + +import torch + + +class AdaptiveLayerNorm1D(torch.nn.Module): + def __init__(self, data_dim: int, norm_cond_dim: int): + super().__init__() + if data_dim <= 0: + raise ValueError(f"data_dim must be positive, but got {data_dim}") + if norm_cond_dim <= 0: + raise ValueError(f"norm_cond_dim must be positive, but got {norm_cond_dim}") + self.norm = torch.nn.LayerNorm( + data_dim + ) # TODO: Check if elementwise_affine=True is correct + self.linear = torch.nn.Linear(norm_cond_dim, 2 * data_dim) + torch.nn.init.zeros_(self.linear.weight) + torch.nn.init.zeros_(self.linear.bias) + + def forward(self, x: torch.Tensor, t: torch.Tensor) -> torch.Tensor: + # x: (batch, ..., data_dim) + # t: (batch, norm_cond_dim) + # return: (batch, data_dim) + x = self.norm(x) + alpha, beta = self.linear(t).chunk(2, dim=-1) + + # Add singleton dimensions to alpha and beta + if x.dim() > 2: + alpha = alpha.view(alpha.shape[0], *([1] * (x.dim() - 2)), alpha.shape[1]) + beta = beta.view(beta.shape[0], *([1] * (x.dim() - 2)), beta.shape[1]) + + return x * (1 + alpha) + beta + + +class SequentialCond(torch.nn.Sequential): + def forward(self, input, *args, **kwargs): + for module in self: + if isinstance(module, (AdaptiveLayerNorm1D, SequentialCond, ResidualMLPBlock)): + # print(f'Passing on args to {module}', [a.shape for a in args]) + input = module(input, *args, **kwargs) + else: + # print(f'Skipping passing args to {module}', [a.shape for a in args]) + input = module(input) + return input + + +def normalization_layer(norm: Optional[str], dim: int, norm_cond_dim: int = -1): + if norm == "batch": + return torch.nn.BatchNorm1d(dim) + elif norm == "layer": + return torch.nn.LayerNorm(dim) + elif norm == "ada": + assert norm_cond_dim > 0, f"norm_cond_dim must be positive, got {norm_cond_dim}" + return AdaptiveLayerNorm1D(dim, norm_cond_dim) + elif norm is None: + return torch.nn.Identity() + else: + raise ValueError(f"Unknown norm: {norm}") + + +def linear_norm_activ_dropout( + input_dim: int, + output_dim: int, + activation: torch.nn.Module = torch.nn.ReLU(), + bias: bool = True, + norm: Optional[str] = "layer", # Options: ada/batch/layer + dropout: float = 0.0, + norm_cond_dim: int = -1, +) -> SequentialCond: + layers = [] + layers.append(torch.nn.Linear(input_dim, output_dim, bias=bias)) + if norm is not None: + layers.append(normalization_layer(norm, output_dim, norm_cond_dim)) + layers.append(copy.deepcopy(activation)) + if dropout > 0.0: + layers.append(torch.nn.Dropout(dropout)) + return SequentialCond(*layers) + + +def create_simple_mlp( + input_dim: int, + hidden_dims: List[int], + output_dim: int, + activation: torch.nn.Module = torch.nn.ReLU(), + bias: bool = True, + norm: Optional[str] = "layer", # Options: ada/batch/layer + dropout: float = 0.0, + norm_cond_dim: int = -1, +) -> SequentialCond: + layers = [] + prev_dim = input_dim + for hidden_dim in hidden_dims: + layers.extend( + linear_norm_activ_dropout( + prev_dim, hidden_dim, activation, bias, norm, dropout, norm_cond_dim + ) + ) + prev_dim = hidden_dim + layers.append(torch.nn.Linear(prev_dim, output_dim, bias=bias)) + return SequentialCond(*layers) + + +class ResidualMLPBlock(torch.nn.Module): + def __init__( + self, + input_dim: int, + hidden_dim: int, + num_hidden_layers: int, + output_dim: int, + activation: torch.nn.Module = torch.nn.ReLU(), + bias: bool = True, + norm: Optional[str] = "layer", # Options: ada/batch/layer + dropout: float = 0.0, + norm_cond_dim: int = -1, + ): + super().__init__() + if not (input_dim == output_dim == hidden_dim): + raise NotImplementedError( + f"input_dim {input_dim} != output_dim {output_dim} is not implemented" + ) + + layers = [] + prev_dim = input_dim + for i in range(num_hidden_layers): + layers.append( + linear_norm_activ_dropout( + prev_dim, hidden_dim, activation, bias, norm, dropout, norm_cond_dim + ) + ) + prev_dim = hidden_dim + self.model = SequentialCond(*layers) + self.skip = torch.nn.Identity() + + def forward(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor: + return x + self.model(x, *args, **kwargs) + + +class ResidualMLP(torch.nn.Module): + def __init__( + self, + input_dim: int, + hidden_dim: int, + num_hidden_layers: int, + output_dim: int, + activation: torch.nn.Module = torch.nn.ReLU(), + bias: bool = True, + norm: Optional[str] = "layer", # Options: ada/batch/layer + dropout: float = 0.0, + num_blocks: int = 1, + norm_cond_dim: int = -1, + ): + super().__init__() + self.input_dim = input_dim + self.model = SequentialCond( + linear_norm_activ_dropout( + input_dim, hidden_dim, activation, bias, norm, dropout, norm_cond_dim + ), + *[ + ResidualMLPBlock( + hidden_dim, + hidden_dim, + num_hidden_layers, + hidden_dim, + activation, + bias, + norm, + dropout, + norm_cond_dim, + ) + for _ in range(num_blocks) + ], + torch.nn.Linear(hidden_dim, output_dim, bias=bias), + ) + + def forward(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor: + return self.model(x, *args, **kwargs) + + +class FrequencyEmbedder(torch.nn.Module): + def __init__(self, num_frequencies, max_freq_log2): + super().__init__() + frequencies = 2 ** torch.linspace(0, max_freq_log2, steps=num_frequencies) + self.register_buffer("frequencies", frequencies) + + def forward(self, x): + # x should be of size (N,) or (N, D) + N = x.size(0) + if x.dim() == 1: # (N,) + x = x.unsqueeze(1) # (N, D) where D=1 + x_unsqueezed = x.unsqueeze(-1) # (N, D, 1) + scaled = self.frequencies.view(1, 1, -1) * x_unsqueezed # (N, D, num_frequencies) + s = torch.sin(scaled) + c = torch.cos(scaled) + embedded = torch.cat([s, c, x_unsqueezed], dim=-1).view( + N, -1 + ) # (N, D * 2 * num_frequencies + D) + return embedded diff --git a/lib/models/preproc/backbone/utils.py b/lib/models/preproc/backbone/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..44bbf64966953d246be2994f31656d90a4f7e8f9 --- /dev/null +++ b/lib/models/preproc/backbone/utils.py @@ -0,0 +1,115 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os +import os.path as osp +from collections import OrderedDict + +import cv2 +import numpy as np +from skimage.filters import gaussian + + +def get_transform(center, scale, res, rot=0): + """Generate transformation matrix.""" + # res: (height, width), (rows, cols) + crop_aspect_ratio = res[0] / float(res[1]) + h = 200 * scale + w = h / crop_aspect_ratio + t = np.zeros((3, 3)) + t[0, 0] = float(res[1]) / w + t[1, 1] = float(res[0]) / h + t[0, 2] = res[1] * (-float(center[0]) / w + .5) + t[1, 2] = res[0] * (-float(center[1]) / h + .5) + t[2, 2] = 1 + if not rot == 0: + rot = -rot # To match direction of rotation from cropping + rot_mat = np.zeros((3, 3)) + rot_rad = rot * np.pi / 180 + sn, cs = np.sin(rot_rad), np.cos(rot_rad) + rot_mat[0, :2] = [cs, -sn] + rot_mat[1, :2] = [sn, cs] + rot_mat[2, 2] = 1 + # Need to rotate around center + t_mat = np.eye(3) + t_mat[0, 2] = -res[1] / 2 + t_mat[1, 2] = -res[0] / 2 + t_inv = t_mat.copy() + t_inv[:2, 2] *= -1 + t = np.dot(t_inv, np.dot(rot_mat, np.dot(t_mat, t))) + return t + + +def transform(pt, center, scale, res, invert=0, rot=0): + """Transform pixel location to different reference.""" + t = get_transform(center, scale, res, rot=rot) + if invert: + t = np.linalg.inv(t) + new_pt = np.array([pt[0] - 1, pt[1] - 1, 1.]).T + new_pt = np.dot(t, new_pt) + return np.array([round(new_pt[0]), round(new_pt[1])], dtype=int) + 1 + + +def crop(img, center, scale, res): + """ + Crop image according to the supplied bounding box. + res: [rows, cols] + """ + # Upper left point + ul = np.array(transform([1, 1], center, scale, res, invert=1)) - 1 + # Bottom right point + br = np.array(transform([res[1] + 1, res[0] + 1], center, scale, res, invert=1)) - 1 + + new_shape = [br[1] - ul[1], br[0] - ul[0]] + if len(img.shape) > 2: + new_shape += [img.shape[2]] + new_img = np.zeros(new_shape, dtype=np.float32) + + # Range to fill new array + new_x = max(0, -ul[0]), min(br[0], len(img[0])) - ul[0] + new_y = max(0, -ul[1]), min(br[1], len(img)) - ul[1] + # Range to sample from original image + old_x = max(0, ul[0]), min(len(img[0]), br[0]) + old_y = max(0, ul[1]), min(len(img), br[1]) + try: + new_img[new_y[0]:new_y[1], new_x[0]:new_x[1]] = img[old_y[0]:old_y[1], old_x[0]:old_x[1]] + except Exception as e: + print(e) + + new_img = cv2.resize(new_img, (res[1], res[0])) # (cols, rows) + + return new_img, ul, br + + + +def process_image(orig_img_rgb, center, scale, crop_height=256, crop_width=192, blur=False, do_crop=True): + """ + Read image, do preprocessing and possibly crop it according to the bounding box. + If there are bounding box annotations, use them to crop the image. + If no bounding box is specified but openpose detections are available, use them to get the bounding box. + """ + + if blur: + # Blur image to avoid aliasing artifacts + downsampling_factor = ((scale * 200 * 1.0) / crop_height) + downsampling_factor = downsampling_factor / 2.0 + if downsampling_factor > 1.1: + orig_img_rgb = gaussian(orig_img_rgb, sigma=(downsampling_factor-1)/2, channel_axis=2, preserve_range=True) + + IMG_NORM_MEAN = [0.485, 0.456, 0.406] + IMG_NORM_STD = [0.229, 0.224, 0.225] + + if do_crop: + img, ul, br = crop(orig_img_rgb, center, scale, (crop_height, crop_width)) + else: + img = orig_img_rgb.copy() + crop_img = img.copy() + + img = img / 255. + mean = np.array(IMG_NORM_MEAN, dtype=np.float32) + std = np.array(IMG_NORM_STD, dtype=np.float32) + norm_img = (img - mean) / std + norm_img = np.transpose(norm_img, (2, 0, 1)) + + return norm_img, crop_img \ No newline at end of file diff --git a/lib/models/preproc/backbone/vit.py b/lib/models/preproc/backbone/vit.py new file mode 100644 index 0000000000000000000000000000000000000000..0d4cb7abeb3365aa7c9b13a0d204b0009abf6a2e --- /dev/null +++ b/lib/models/preproc/backbone/vit.py @@ -0,0 +1,348 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import math + +import torch +from functools import partial +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint + +from timm.models.layers import drop_path, to_2tuple, trunc_normal_ + +def vit(): + return ViT( + img_size=(256, 192), + patch_size=16, + embed_dim=1280, + depth=32, + num_heads=16, + ratio=1, + use_checkpoint=False, + mlp_ratio=4, + qkv_bias=True, + drop_path_rate=0.55, + ) + +def get_abs_pos(abs_pos, h, w, ori_h, ori_w, has_cls_token=True): + """ + Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token + dimension for the original embeddings. + Args: + abs_pos (Tensor): absolute positional embeddings with (1, num_position, C). + has_cls_token (bool): If true, has 1 embedding in abs_pos for cls token. + hw (Tuple): size of input image tokens. + + Returns: + Absolute positional embeddings after processing with shape (1, H, W, C) + """ + cls_token = None + B, L, C = abs_pos.shape + if has_cls_token: + cls_token = abs_pos[:, 0:1] + abs_pos = abs_pos[:, 1:] + + if ori_h != h or ori_w != w: + new_abs_pos = F.interpolate( + abs_pos.reshape(1, ori_h, ori_w, -1).permute(0, 3, 1, 2), + size=(h, w), + mode="bicubic", + align_corners=False, + ).permute(0, 2, 3, 1).reshape(B, -1, C) + + else: + new_abs_pos = abs_pos + + if cls_token is not None: + new_abs_pos = torch.cat([cls_token, new_abs_pos], dim=1) + return new_abs_pos + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + """ + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + def extra_repr(self): + return 'p={}'.format(self.drop_prob) + +class Mlp(nn.Module): + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.fc2(x) + x = self.drop(x) + return x + +class Attention(nn.Module): + def __init__( + self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., + proj_drop=0., attn_head_dim=None,): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.dim = dim + + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=qkv_bias) + + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv = self.qkv(x) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = (q @ k.transpose(-2, -1)) + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + x = self.proj(x) + x = self.proj_drop(x) + + return x + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, + drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, + norm_layer=nn.LayerNorm, attn_head_dim=None + ): + super().__init__() + + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, + attn_drop=attn_drop, proj_drop=drop, attn_head_dim=attn_head_dim + ) + + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + def forward(self, x): + x = x + self.drop_path(self.attn(self.norm1(x))) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, ratio=1): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) * (ratio ** 2) + self.patch_shape = (int(img_size[0] // patch_size[0] * ratio), int(img_size[1] // patch_size[1] * ratio)) + self.origin_patch_shape = (int(img_size[0] // patch_size[0]), int(img_size[1] // patch_size[1])) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=(patch_size[0] // ratio), padding=4 + 2 * (ratio//2-1)) + + def forward(self, x, **kwargs): + B, C, H, W = x.shape + x = self.proj(x) + Hp, Wp = x.shape[2], x.shape[3] + + x = x.flatten(2).transpose(1, 2) + return x, (Hp, Wp) + + +class HybridEmbed(nn.Module): + """ CNN Feature Map Embedding + Extract feature map from CNN, flatten, project to embedding dim. + """ + def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768): + super().__init__() + assert isinstance(backbone, nn.Module) + img_size = to_2tuple(img_size) + self.img_size = img_size + self.backbone = backbone + if feature_size is None: + with torch.no_grad(): + training = backbone.training + if training: + backbone.eval() + o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))[-1] + feature_size = o.shape[-2:] + feature_dim = o.shape[1] + backbone.train(training) + else: + feature_size = to_2tuple(feature_size) + feature_dim = self.backbone.feature_info.channels()[-1] + self.num_patches = feature_size[0] * feature_size[1] + self.proj = nn.Linear(feature_dim, embed_dim) + + def forward(self, x): + x = self.backbone(x)[-1] + x = x.flatten(2).transpose(1, 2) + x = self.proj(x) + return x + + +class ViT(nn.Module): + + def __init__(self, + img_size=224, patch_size=16, in_chans=3, num_classes=80, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., + drop_path_rate=0., hybrid_backbone=None, norm_layer=None, use_checkpoint=False, + frozen_stages=-1, ratio=1, last_norm=True, + patch_padding='pad', freeze_attn=False, freeze_ffn=False, + ): + # Protect mutable default arguments + super(ViT, self).__init__() + norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) + self.num_classes = num_classes + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + self.frozen_stages = frozen_stages + self.use_checkpoint = use_checkpoint + self.patch_padding = patch_padding + self.freeze_attn = freeze_attn + self.freeze_ffn = freeze_ffn + self.depth = depth + + if hybrid_backbone is not None: + self.patch_embed = HybridEmbed( + hybrid_backbone, img_size=img_size, in_chans=in_chans, embed_dim=embed_dim) + else: + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim, ratio=ratio) + num_patches = self.patch_embed.num_patches + + # since the pretraining model has class token + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, + ) + for i in range(depth)]) + + self.last_norm = norm_layer(embed_dim) if last_norm else nn.Identity() + + if self.pos_embed is not None: + trunc_normal_(self.pos_embed, std=.02) + + self._freeze_stages() + + def _freeze_stages(self): + """Freeze parameters.""" + if self.frozen_stages >= 0: + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + + for i in range(1, self.frozen_stages + 1): + m = self.blocks[i] + m.eval() + for param in m.parameters(): + param.requires_grad = False + + if self.freeze_attn: + for i in range(0, self.depth): + m = self.blocks[i] + m.attn.eval() + m.norm1.eval() + for param in m.attn.parameters(): + param.requires_grad = False + for param in m.norm1.parameters(): + param.requires_grad = False + + if self.freeze_ffn: + self.pos_embed.requires_grad = False + self.patch_embed.eval() + for param in self.patch_embed.parameters(): + param.requires_grad = False + for i in range(0, self.depth): + m = self.blocks[i] + m.mlp.eval() + m.norm2.eval() + for param in m.mlp.parameters(): + param.requires_grad = False + for param in m.norm2.parameters(): + param.requires_grad = False + + def init_weights(self): + """Initialize the weights in backbone. + Args: + pretrained (str, optional): Path to pre-trained weights. + Defaults to None. + """ + def _init_weights(m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + self.apply(_init_weights) + + def get_num_layers(self): + return len(self.blocks) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def forward_features(self, x): + B, C, H, W = x.shape + x, (Hp, Wp) = self.patch_embed(x) + + if self.pos_embed is not None: + # fit for multiple GPU training + # since the first element for pos embed (sin-cos manner) is zero, it will cause no difference + x = x + self.pos_embed[:, 1:] + self.pos_embed[:, :1] + + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x) + else: + x = blk(x) + + x = self.last_norm(x) + + xp = x.permute(0, 2, 1).reshape(B, -1, Hp, Wp).contiguous() + + return xp + + def forward(self, x): + x = self.forward_features(x) + return x + + def train(self, mode=True): + """Convert the model into training mode.""" + super().train(mode) + self._freeze_stages() \ No newline at end of file diff --git a/lib/models/preproc/detector.py b/lib/models/preproc/detector.py new file mode 100644 index 0000000000000000000000000000000000000000..d6fa0a8adac0f0f9cdda46b2a4ae38c203750e1f --- /dev/null +++ b/lib/models/preproc/detector.py @@ -0,0 +1,146 @@ +from __future__ import annotations + +import os +import os.path as osp +from collections import defaultdict + +import numpy as np +import torch +import torch.nn as nn +import scipy.signal as signal +from progress.bar import Bar + +from ultralytics import YOLO +from mmpose.apis import ( + inference_top_down_pose_model, + init_pose_model, + get_track_id, + vis_pose_result, +) + +ROOT_DIR = osp.abspath(f"{__file__}/../../../../") +VIT_DIR = osp.join(ROOT_DIR, "third-party/ViTPose") + +VIS_THRESH = 0.3 +BBOX_CONF = 0.5 +TRACKING_THR = 0.1 +MINIMUM_FRMAES = 30 +MINIMUM_JOINTS = 6 + +class DetectionModel(object): + def __init__(self, device): + + # ViTPose + pose_model_cfg = osp.join(VIT_DIR, 'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_huge_coco_256x192.py') + pose_model_ckpt = osp.join(ROOT_DIR, 'checkpoints', 'vitpose-h-multi-coco.pth') + self.pose_model = init_pose_model(pose_model_cfg, pose_model_ckpt, device=device.lower()) + + # YOLO + bbox_model_ckpt = osp.join(ROOT_DIR, 'checkpoints', 'yolov8x.pt') + self.bbox_model = YOLO(bbox_model_ckpt) + + self.device = device + self.initialize_tracking() + + def initialize_tracking(self, ): + self.next_id = 0 + self.frame_id = 0 + self.pose_results_last = [] + self.tracking_results = { + 'id': [], + 'frame_id': [], + 'bbox': [], + 'keypoints': [] + } + + def xyxy_to_cxcys(self, bbox, s_factor=1.05): + cx, cy = bbox[[0, 2]].mean(), bbox[[1, 3]].mean() + scale = max(bbox[2] - bbox[0], bbox[3] - bbox[1]) / 200 * s_factor + return np.array([[cx, cy, scale]]) + + def compute_bboxes_from_keypoints(self, s_factor=1.2): + X = self.tracking_results['keypoints'].copy() + mask = X[..., -1] > VIS_THRESH + + bbox = np.zeros((len(X), 3)) + for i, (kp, m) in enumerate(zip(X, mask)): + bb = [kp[m, 0].min(), kp[m, 1].min(), + kp[m, 0].max(), kp[m, 1].max()] + cx, cy = [(bb[2]+bb[0])/2, (bb[3]+bb[1])/2] + bb_w = bb[2] - bb[0] + bb_h = bb[3] - bb[1] + s = np.stack((bb_w, bb_h)).max() + bb = np.array((cx, cy, s)) + bbox[i] = bb + + bbox[:, 2] = bbox[:, 2] * s_factor / 200.0 + self.tracking_results['bbox'] = bbox + + def track(self, img, fps, length): + + # bbox detection + bboxes = self.bbox_model.predict( + img, device=self.device, classes=0, conf=BBOX_CONF, save=False, verbose=False + )[0].boxes.xyxy.detach().cpu().numpy() + bboxes = [{'bbox': bbox} for bbox in bboxes] + + # keypoints detection + pose_results, returned_outputs = inference_top_down_pose_model( + self.pose_model, + img, + person_results=bboxes, + format='xyxy', + return_heatmap=False, + outputs=None) + + # person identification + pose_results, self.next_id = get_track_id( + pose_results, + self.pose_results_last, + self.next_id, + use_oks=False, + tracking_thr=TRACKING_THR, + use_one_euro=True, + fps=fps) + + for pose_result in pose_results: + n_valid = (pose_result['keypoints'][:, -1] > VIS_THRESH).sum() + if n_valid < MINIMUM_JOINTS: continue + + _id = pose_result['track_id'] + xyxy = pose_result['bbox'] + bbox = self.xyxy_to_cxcys(xyxy) + + self.tracking_results['id'].append(_id) + self.tracking_results['frame_id'].append(self.frame_id) + self.tracking_results['bbox'].append(bbox) + self.tracking_results['keypoints'].append(pose_result['keypoints']) + + self.frame_id += 1 + self.pose_results_last = pose_results + + def process(self, fps): + for key in ['id', 'frame_id', 'keypoints']: + self.tracking_results[key] = np.array(self.tracking_results[key]) + self.compute_bboxes_from_keypoints() + + output = defaultdict(lambda: defaultdict(list)) + ids = np.unique(self.tracking_results['id']) + for _id in ids: + idxs = np.where(self.tracking_results['id'] == _id)[0] + for key, val in self.tracking_results.items(): + if key == 'id': continue + output[_id][key] = val[idxs] + + # Smooth bounding box detection + ids = list(output.keys()) + for _id in ids: + if len(output[_id]['bbox']) < MINIMUM_FRMAES: + del output[_id] + continue + + kernel = int(int(fps/2) / 2) * 2 + 1 + smoothed_bbox = np.array([signal.medfilt(param, kernel) for param in output[_id]['bbox'].T]).T + output[_id]['bbox'] = smoothed_bbox + + return output \ No newline at end of file diff --git a/lib/models/preproc/extractor.py b/lib/models/preproc/extractor.py new file mode 100644 index 0000000000000000000000000000000000000000..25a77973492d7e09160c6282c965dcfefd439c97 --- /dev/null +++ b/lib/models/preproc/extractor.py @@ -0,0 +1,112 @@ +from __future__ import annotations + +import os +import os.path as osp +from collections import defaultdict + +import cv2 +import torch +import numpy as np +import scipy.signal as signal +from progress.bar import Bar +from scipy.ndimage.filters import gaussian_filter1d + +from configs import constants as _C +from .backbone.hmr2 import hmr2 +from .backbone.utils import process_image +from ...utils.imutils import flip_kp, flip_bbox + +ROOT_DIR = osp.abspath(f"{__file__}/../../../../") + +class FeatureExtractor(object): + def __init__(self, device, flip_eval=False, max_batch_size=64): + + self.device = device + self.flip_eval = flip_eval + self.max_batch_size = max_batch_size + + ckpt = osp.join(ROOT_DIR, 'checkpoints', 'hmr2a.ckpt') + self.model = hmr2(ckpt).to(device).eval() + + def run(self, video, tracking_results, patch_h=256, patch_w=256): + + if osp.isfile(video): + cap = cv2.VideoCapture(video) + is_video = True + length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + width, height = cap.get(cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT) + else: # Image list + cap = video + is_video = False + length = len(video) + height, width = cv2.imread(video[0]).shape[:2] + + frame_id = 0 + bar = Bar('Feature extraction ...', fill='#', max=length) + while True: + if is_video: + flag, img = cap.read() + if not flag: + break + else: + if frame_id >= len(cap): + break + img = cv2.imread(cap[frame_id]) + + for _id, val in tracking_results.items(): + if not frame_id in val['frame_id']: continue + + frame_id2 = np.where(val['frame_id'] == frame_id)[0][0] + bbox = val['bbox'][frame_id2] + cx, cy, scale = bbox + + norm_img, crop_img = process_image(img[..., ::-1], [cx, cy], scale, patch_h, patch_w) + norm_img = torch.from_numpy(norm_img).unsqueeze(0).to(self.device) + feature = self.model(norm_img, encode=True) + tracking_results[_id]['features'].append(feature.cpu()) + + if frame_id2 == 0: # First frame of this subject + tracking_results = self.predict_init(norm_img, tracking_results, _id, flip_eval=False) + + if self.flip_eval: + flipped_bbox = flip_bbox(bbox, width, height) + tracking_results[_id]['flipped_bbox'].append(flipped_bbox) + + keypoints = val['keypoints'][frame_id2] + flipped_keypoints = flip_kp(keypoints, width) + tracking_results[_id]['flipped_keypoints'].append(flipped_keypoints) + + flipped_features = self.model(torch.flip(norm_img, (3, )), encode=True) + tracking_results[_id]['flipped_features'].append(flipped_features.cpu()) + + if frame_id2 == 0: + tracking_results = self.predict_init(torch.flip(norm_img, (3, )), tracking_results, _id, flip_eval=True) + + bar.next() + frame_id += 1 + + return self.process(tracking_results) + + def predict_init(self, norm_img, tracking_results, _id, flip_eval=False): + prefix = 'flipped_' if flip_eval else '' + + pred_global_orient, pred_body_pose, pred_betas, _ = self.model(norm_img, encode=False) + tracking_results[_id][prefix + 'init_global_orient'] = pred_global_orient.cpu() + tracking_results[_id][prefix + 'init_body_pose'] = pred_body_pose.cpu() + tracking_results[_id][prefix + 'init_betas'] = pred_betas.cpu() + return tracking_results + + def process(self, tracking_results): + output = defaultdict(dict) + + for _id, results in tracking_results.items(): + + for key, val in results.items(): + if isinstance(val, list): + if isinstance(val[0], torch.Tensor): + val = torch.cat(val) + elif isinstance(val[0], np.ndarray): + val = np.array(val) + output[_id][key] = val + + return output \ No newline at end of file diff --git a/lib/models/preproc/slam.py b/lib/models/preproc/slam.py new file mode 100644 index 0000000000000000000000000000000000000000..58f68198843eb4beef3f6e1f07783e00239a030a --- /dev/null +++ b/lib/models/preproc/slam.py @@ -0,0 +1,70 @@ +import cv2 +import numpy as np +import glob +import os.path as osp +import os +import time +import torch +from pathlib import Path +from multiprocessing import Process, Queue + +from dpvo.utils import Timer +from dpvo.dpvo import DPVO +from dpvo.config import cfg +from dpvo.stream import image_stream, video_stream + +ROOT_DIR = osp.abspath(f"{__file__}/../../../../") +DPVO_DIR = osp.join(ROOT_DIR, "third-party/DPVO") + + +class SLAMModel(object): + def __init__(self, video, output_pth, width, height, calib=None, stride=1, skip=0, buffer=2048): + + if calib == None or not osp.exists(calib): + calib = osp.join(output_pth, 'calib.txt') + if not osp.exists(calib): + self.estimate_intrinsics(width, height, calib) + + self.dpvo_cfg = osp.join(DPVO_DIR, 'config/default.yaml') + self.dpvo_ckpt = osp.join(ROOT_DIR, 'checkpoints', 'dpvo.pth') + + self.buffer = buffer + self.times = [] + self.slam = None + self.queue = Queue(maxsize=8) + self.reader = Process(target=video_stream, args=(self.queue, video, calib, stride, skip)) + self.reader.start() + + def estimate_intrinsics(self, width, height, calib): + focal_length = (height ** 2 + width ** 2) ** 0.5 + center_x = width / 2 + center_y = height / 2 + + with open(calib, 'w') as fopen: + line = f'{focal_length} {focal_length} {center_x} {center_y}' + fopen.write(line) + + def track(self, ): + (t, image, intrinsics) = self.queue.get() + + if t < 0: return + + image = torch.from_numpy(image).permute(2,0,1).cuda() + intrinsics = torch.from_numpy(intrinsics).cuda() + + if self.slam is None: + cfg.merge_from_file(self.dpvo_cfg) + cfg.BUFFER_SIZE = self.buffer + self.slam = DPVO(cfg, self.dpvo_ckpt, ht=image.shape[1], wd=image.shape[2], viz=False) + + with Timer("SLAM", enabled=False): + t = time.time() + self.slam(t, image, intrinsics) + self.times.append(time.time() - t) + + def process(self, ): + for _ in range(12): + self.slam.update() + + self.reader.join() + return self.slam.terminate()[0] \ No newline at end of file diff --git a/lib/models/smpl.py b/lib/models/smpl.py new file mode 100644 index 0000000000000000000000000000000000000000..663ae77b872e7623c032d73001f963cdc29abb0c --- /dev/null +++ b/lib/models/smpl.py @@ -0,0 +1,264 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import os, sys + +import torch +import numpy as np +from lib.utils import transforms + +from smplx import SMPL as _SMPL +from smplx.utils import SMPLOutput as ModelOutput +from smplx.lbs import vertices2joints + +from configs import constants as _C + +class SMPL(_SMPL): + """ Extension of the official SMPL implementation to support more joints """ + + def __init__(self, *args, **kwargs): + sys.stdout = open(os.devnull, 'w') + super(SMPL, self).__init__(*args, **kwargs) + sys.stdout = sys.__stdout__ + + J_regressor_wham = np.load(_C.BMODEL.JOINTS_REGRESSOR_WHAM) + J_regressor_eval = np.load(_C.BMODEL.JOINTS_REGRESSOR_H36M) + self.register_buffer('J_regressor_wham', torch.tensor( + J_regressor_wham, dtype=torch.float32)) + self.register_buffer('J_regressor_eval', torch.tensor( + J_regressor_eval, dtype=torch.float32)) + self.register_buffer('J_regressor_feet', torch.from_numpy( + np.load(_C.BMODEL.JOINTS_REGRESSOR_FEET) + ).float()) + + def get_local_pose_from_reduced_global_pose(self, reduced_pose): + full_pose = torch.eye( + 3, device=reduced_pose.device + )[(None, ) * 2].repeat(reduced_pose.shape[0], 24, 1, 1) + full_pose[:, _C.BMODEL.MAIN_JOINTS] = reduced_pose + return full_pose + + def forward(self, + pred_rot6d, + betas, + cam=None, + cam_intrinsics=None, + bbox=None, + res=None, + return_full_pose=False, + **kwargs): + + rotmat = transforms.rotation_6d_to_matrix(pred_rot6d.reshape(*pred_rot6d.shape[:2], -1, 6) + ).reshape(-1, 24, 3, 3) + + output = self.get_output(body_pose=rotmat[:, 1:], + global_orient=rotmat[:, :1], + betas=betas.view(-1, 10), + pose2rot=False, + return_full_pose=return_full_pose) + + if cam is not None: + joints3d = output.joints.reshape(*cam.shape[:2], -1, 3) + + # Weak perspective projection (for InstaVariety) + weak_cam = convert_weak_perspective_to_perspective(cam) + + weak_joints2d = weak_perspective_projection( + joints3d, + rotation=torch.eye(3, device=cam.device).unsqueeze(0).unsqueeze(0).expand(*cam.shape[:2], -1, -1), + translation=weak_cam, + focal_length=5000., + camera_center=torch.zeros(*cam.shape[:2], 2, device=cam.device) + ) + output.weak_joints2d = weak_joints2d + + # Full perspective projection + full_cam = convert_pare_to_full_img_cam( + cam, + bbox[:, :, 2] * 200., + bbox[:, :, :2], + res[:, 0].unsqueeze(-1), + res[:, 1].unsqueeze(-1), + focal_length=cam_intrinsics[:, :, 0, 0] + ) + + full_joints2d = full_perspective_projection( + joints3d, + translation=full_cam, + cam_intrinsics=cam_intrinsics, + ) + output.full_joints2d = full_joints2d + output.full_cam = full_cam.reshape(-1, 3) + + return output + + def forward_nd(self, + pred_rot6d, + root, + betas, + return_full_pose=False): + + rotmat = transforms.rotation_6d_to_matrix(pred_rot6d.reshape(*pred_rot6d.shape[:2], -1, 6) + ).reshape(-1, 24, 3, 3) + + output = self.get_output(body_pose=rotmat[:, 1:], + global_orient=root.reshape(-1, 1, 3, 3), + betas=betas.view(-1, 10), + pose2rot=False, + return_full_pose=return_full_pose) + + return output + + def get_output(self, *args, **kwargs): + kwargs['get_skin'] = True + smpl_output = super(SMPL, self).forward(*args, **kwargs) + joints = vertices2joints(self.J_regressor_wham, smpl_output.vertices) + feet = vertices2joints(self.J_regressor_feet, smpl_output.vertices) + + offset = joints[..., [11, 12], :].mean(-2) + if 'transl' in kwargs: + offset = offset - kwargs['transl'] + vertices = smpl_output.vertices - offset.unsqueeze(-2) + joints = joints - offset.unsqueeze(-2) + feet = feet - offset.unsqueeze(-2) + + output = ModelOutput(vertices=vertices, + global_orient=smpl_output.global_orient, + body_pose=smpl_output.body_pose, + joints=joints, + betas=smpl_output.betas, + full_pose=smpl_output.full_pose) + output.feet = feet + output.offset = offset + return output + + def get_offset(self, *args, **kwargs): + kwargs['get_skin'] = True + smpl_output = super(SMPL, self).forward(*args, **kwargs) + joints = vertices2joints(self.J_regressor_wham, smpl_output.vertices) + + offset = joints[..., [11, 12], :].mean(-2) + return offset + + def get_faces(self): + return np.array(self.faces) + + +def convert_weak_perspective_to_perspective( + weak_perspective_camera, + focal_length=5000., + img_res=224, +): + + perspective_camera = torch.stack( + [ + weak_perspective_camera[..., 1], + weak_perspective_camera[..., 2], + 2 * focal_length / (img_res * weak_perspective_camera[..., 0] + 1e-9) + ], + dim=-1 + ) + return perspective_camera + + +def weak_perspective_projection( + points, + rotation, + translation, + focal_length, + camera_center, + img_res=224, + normalize_joints2d=True, +): + """ + This function computes the perspective projection of a set of points. + Input: + points (b, f, N, 3): 3D points + rotation (b, f, 3, 3): Camera rotation + translation (b, f, 3): Camera translation + focal_length (b, f,) or scalar: Focal length + camera_center (b, f, 2): Camera center + """ + + K = torch.zeros([*points.shape[:2], 3, 3], device=points.device) + K[:,:,0,0] = focal_length + K[:,:,1,1] = focal_length + K[:,:,2,2] = 1. + K[:,:,:-1, -1] = camera_center + + # Transform points + points = torch.einsum('bfij,bfkj->bfki', rotation, points) + points = points + translation.unsqueeze(-2) + + # Apply perspective distortion + projected_points = points / points[...,-1].unsqueeze(-1) + + # Apply camera intrinsics + projected_points = torch.einsum('bfij,bfkj->bfki', K, projected_points) + + if normalize_joints2d: + projected_points = projected_points / (img_res / 2.) + + return projected_points[..., :-1] + + +def full_perspective_projection( + points, + cam_intrinsics, + rotation=None, + translation=None, +): + + K = cam_intrinsics + + if rotation is not None: + points = (rotation @ points.transpose(-1, -2)).transpose(-1, -2) + if translation is not None: + points = points + translation.unsqueeze(-2) + projected_points = points / points[..., -1].unsqueeze(-1) + projected_points = (K @ projected_points.transpose(-1, -2)).transpose(-1, -2) + return projected_points[..., :-1] + + +def convert_pare_to_full_img_cam( + pare_cam, + bbox_height, + bbox_center, + img_w, + img_h, + focal_length, + crop_res=224 +): + + s, tx, ty = pare_cam[..., 0], pare_cam[..., 1], pare_cam[..., 2] + res = crop_res + r = bbox_height / res + tz = 2 * focal_length / (r * res * s) + + cx = 2 * (bbox_center[..., 0] - (img_w / 2.)) / (s * bbox_height) + cy = 2 * (bbox_center[..., 1] - (img_h / 2.)) / (s * bbox_height) + + cam_t = torch.stack([tx + cx, ty + cy, tz], dim=-1) + return cam_t + + +def cam_crop2full(crop_cam, center, scale, full_img_shape, focal_length): + """ + convert the camera parameters from the crop camera to the full camera + :param crop_cam: shape=(N, 3) weak perspective camera in cropped img coordinates (s, tx, ty) + :param center: shape=(N, 2) bbox coordinates (c_x, c_y) + :param scale: shape=(N) square bbox resolution (b / 200) + :param full_img_shape: shape=(N, 2) original image height and width + :param focal_length: shape=(N,) + :return: + """ + img_h, img_w = full_img_shape[:, 0], full_img_shape[:, 1] + cx, cy, b = center[:, 0], center[:, 1], scale * 200 + w_2, h_2 = img_w / 2., img_h / 2. + bs = b * crop_cam[:, 0] + 1e-9 + tz = 2 * focal_length / bs + tx = (2 * (cx - w_2) / bs) + crop_cam[:, 1] + ty = (2 * (cy - h_2) / bs) + crop_cam[:, 2] + full_cam = torch.stack([tx, ty, tz], dim=-1) + return full_cam \ No newline at end of file diff --git a/lib/models/smplify/__init__.py b/lib/models/smplify/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..58929cd8baf5311a542399815dedd924342c079e --- /dev/null +++ b/lib/models/smplify/__init__.py @@ -0,0 +1 @@ +from .smplify import TemporalSMPLify \ No newline at end of file diff --git a/lib/models/smplify/__pycache__/__init__.cpython-39.pyc b/lib/models/smplify/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e0c0f394bb9c29d5cba7d7267e540a5d8c58591e Binary files /dev/null and b/lib/models/smplify/__pycache__/__init__.cpython-39.pyc differ diff --git a/lib/models/smplify/__pycache__/losses.cpython-39.pyc b/lib/models/smplify/__pycache__/losses.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..883e47f65196e5c4fe1960c725602410cd2adb5c Binary files /dev/null and b/lib/models/smplify/__pycache__/losses.cpython-39.pyc differ diff --git a/lib/models/smplify/__pycache__/smplify.cpython-39.pyc b/lib/models/smplify/__pycache__/smplify.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..34ad471d72e5e93d5a7bde1820434031b53a0522 Binary files /dev/null and b/lib/models/smplify/__pycache__/smplify.cpython-39.pyc differ diff --git a/lib/models/smplify/losses.py b/lib/models/smplify/losses.py new file mode 100644 index 0000000000000000000000000000000000000000..be42e2b1fbbc374109eeb5aab674cdf64999ceb5 --- /dev/null +++ b/lib/models/smplify/losses.py @@ -0,0 +1,87 @@ +import torch + +def gmof(x, sigma): + """ + Geman-McClure error function + """ + x_squared = x ** 2 + sigma_squared = sigma ** 2 + return (sigma_squared * x_squared) / (sigma_squared + x_squared) + + +def compute_jitter(x): + """ + Compute jitter for the input tensor + """ + return torch.linalg.norm(x[:, 2:] + x[:, :-2] - 2 * x[:, 1:-1], dim=-1) + + +class SMPLifyLoss(torch.nn.Module): + def __init__(self, + res, + cam_intrinsics, + init_pose, + device, + **kwargs + ): + + super().__init__() + + self.res = res + self.cam_intrinsics = cam_intrinsics + self.init_pose = torch.from_numpy(init_pose).float().to(device) + + def forward(self, output, params, input_keypoints, bbox, + reprojection_weight=100., regularize_weight=60.0, + consistency_weight=10.0, sprior_weight=0.04, + smooth_weight=20.0, sigma=100): + + pose, shape, cam = params + scale = bbox[..., 2:].unsqueeze(-1) * 200. + + # Loss 1. Data term + pred_keypoints = output.full_joints2d[..., :17, :] + joints_conf = input_keypoints[..., -1:] + reprojection_error = gmof(pred_keypoints - input_keypoints[..., :-1], sigma) + reprojection_error = ((reprojection_error * joints_conf) / scale).mean() + + # Loss 2. Regularization term + regularize_error = torch.linalg.norm(pose - self.init_pose, dim=-1).mean() + + # Loss 3. Shape prior and consistency error + consistency_error = shape.std(dim=1).mean() + sprior_error = torch.linalg.norm(shape, dim=-1).mean() + shape_error = sprior_weight * sprior_error + consistency_weight * consistency_error + + # Loss 4. Smooth loss + pose_diff = compute_jitter(pose).mean() + cam_diff = compute_jitter(cam).mean() + smooth_error = pose_diff + cam_diff + + # Sum up losses + loss = { + 'reprojection': reprojection_weight * reprojection_error, + 'regularize': regularize_weight * regularize_error, + 'shape': shape_error, + 'smooth': smooth_weight * smooth_error + } + + return loss + + def create_closure(self, + optimizer, + smpl, + params, + bbox, + input_keypoints): + + def closure(): + optimizer.zero_grad() + output = smpl(*params, cam_intrinsics=self.cam_intrinsics, bbox=bbox, res=self.res) + + loss_dict = self.forward(output, params, input_keypoints, bbox) + loss = sum(loss_dict.values()) + loss.backward() + return loss + + return closure \ No newline at end of file diff --git a/lib/models/smplify/smplify.py b/lib/models/smplify/smplify.py new file mode 100644 index 0000000000000000000000000000000000000000..4ffdfac904bce5187a5b5ad69f7d9f973c02eb49 --- /dev/null +++ b/lib/models/smplify/smplify.py @@ -0,0 +1,83 @@ +import os +import torch +from tqdm import tqdm + +from lib.models import build_body_model +from .losses import SMPLifyLoss + +class TemporalSMPLify(): + + def __init__(self, + smpl=None, + lr=1e-2, + num_iters=5, + num_steps=10, + img_w=None, + img_h=None, + device=None + ): + + self.smpl = smpl + self.lr = lr + self.num_iters = num_iters + self.num_steps = num_steps + self.img_w = img_w + self.img_h = img_h + self.device = device + + def fit(self, init_pred, keypoints, bbox, **kwargs): + + def to_params(param): + return torch.from_numpy(param).float().to(self.device).requires_grad_(True) + + pose = init_pred['pose'].detach().cpu().numpy() + betas = init_pred['betas'].detach().cpu().numpy() + cam = init_pred['cam'].detach().cpu().numpy() + keypoints = torch.from_numpy(keypoints).float().unsqueeze(0).to(self.device) + + BN = pose.shape[1] + lr = self.lr + + # Stage 1. Optimize translation + params = [to_params(pose), to_params(betas), to_params(cam)] + optim_params = [params[2]] + + optimizer = torch.optim.LBFGS( + optim_params, + lr=lr, + max_iter=self.num_iters, + line_search_fn='strong_wolfe') + + loss_fn = SMPLifyLoss(init_pose=pose, device=self.device, **kwargs) + + closure = loss_fn.create_closure(optimizer, + self.smpl, + params, + bbox, + keypoints) + + for j in (j_bar := tqdm(range(self.num_steps), leave=False)): + optimizer.zero_grad() + loss = optimizer.step(closure) + msg = f'Loss: {loss.item():.1f}' + j_bar.set_postfix_str(msg) + + + # Stage 2. Optimize all params + optimizer = torch.optim.LBFGS( + params, + lr=lr * BN, + max_iter=self.num_iters, + line_search_fn='strong_wolfe') + + for j in (j_bar := tqdm(range(self.num_steps), leave=False)): + optimizer.zero_grad() + loss = optimizer.step(closure) + msg = f'Loss: {loss.item():.1f}' + j_bar.set_postfix_str(msg) + + init_pred['pose'] = params[0].detach() + init_pred['betas'] = params[1].detach() + init_pred['cam'] = params[2].detach() + + return init_pred \ No newline at end of file diff --git a/lib/models/wham.py b/lib/models/wham.py new file mode 100644 index 0000000000000000000000000000000000000000..0a5dbb605c6080d3b6ae8e3ec3fb651e84f6b5e6 --- /dev/null +++ b/lib/models/wham.py @@ -0,0 +1,210 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import torch +from torch import nn +import numpy as np + +from configs import constants as _C +from lib.models.layers import (MotionEncoder, MotionDecoder, TrajectoryDecoder, TrajectoryRefiner, Integrator, + rollout_global_motion, reset_root_velocity, compute_camera_motion) +from lib.utils.transforms import axis_angle_to_matrix + + +class Network(nn.Module): + def __init__(self, + smpl, + pose_dr=0.1, + d_embed=512, + n_layers=3, + d_feat=2048, + rnn_type='LSTM', + **kwargs + ): + super().__init__() + + n_joints = _C.KEYPOINTS.NUM_JOINTS + self.smpl = smpl + in_dim = n_joints * 2 + 3 + d_context = d_embed + n_joints * 3 + + self.mask_embedding = nn.Parameter(torch.zeros(1, 1, n_joints, 2)) + + # Module 1. Motion Encoder + self.motion_encoder = MotionEncoder(in_dim=in_dim, + d_embed=d_embed, + pose_dr=pose_dr, + rnn_type=rnn_type, + n_layers=n_layers, + n_joints=n_joints) + + self.trajectory_decoder = TrajectoryDecoder(d_embed=d_context, + rnn_type=rnn_type, + n_layers=n_layers) + + # Module 3. Feature Integrator + self.integrator = Integrator(in_channel=d_feat + d_context, + out_channel=d_context) + + # Module 4. Motion Decoder + self.motion_decoder = MotionDecoder(d_embed=d_context, + rnn_type=rnn_type, + n_layers=n_layers) + + # Module 5. Trajectory Refiner + self.trajectory_refiner = TrajectoryRefiner(d_embed=d_context, + d_hidden=d_embed, + rnn_type=rnn_type, + n_layers=2) + + def compute_global_feet(self, root_world, trans): + # # Compute world-coordinate motion + cam_R, cam_T = compute_camera_motion(self.output, self.pred_pose[:, :, :6], root_world, trans, self.pred_cam) + feet_cam = self.output.feet.reshape(self.b, self.f, -1, 3) + self.output.full_cam.reshape(self.b, self.f, 1, 3) + feet_world = (cam_R.mT @ (feet_cam - cam_T.unsqueeze(-2)).mT).mT + + return feet_world, cam_R + + def forward_smpl(self, **kwargs): + self.output = self.smpl(self.pred_pose, + self.pred_shape, + cam=self.pred_cam, + return_full_pose=not self.training, + **kwargs, + ) + + from loguru import logger + logger.info(f"Output Joints: {self.output.joints}") + logger.info(f"Output Vertices: {self.output.vertices}") + + # Save joints and vertices as .npy arrays + + np.save('joints.npy', self.output.joints.cpu().numpy()) + np.save('vertices.npy', self.output.vertices.cpu().numpy()) + + # Feet location in global coordinate + root_world, trans = rollout_global_motion(self.pred_root, self.pred_vel) + feet_world, cam_R = self.compute_global_feet(root_world, trans) + + # Return output + output = {'feet': feet_world, + 'contact': self.pred_contact, + 'pose': self.pred_pose, + 'betas': self.pred_shape, + 'cam': self.pred_cam, + 'poses_root_cam': self.output.global_orient, + 'poses_root_r6d': self.pred_root, + 'vel_root': self.pred_vel, + 'pose_root': self.pred_root, + 'verts_cam': self.output.vertices} + + if self.training: + output.update({ + 'kp3d': self.output.joints, + 'kp3d_nn': self.pred_kp3d, + 'full_kp2d': self.output.full_joints2d, + 'weak_kp2d': self.output.weak_joints2d, + 'R': cam_R, + }) + else: + output.update({ + 'poses_root_r6d': self.pred_root, + 'trans_cam': self.output.full_cam, + 'poses_body': self.output.body_pose}) + + return output + + + def preprocess(self, x, mask): + self.b, self.f = x.shape[:2] + + # Treat masked keypoints + mask_embedding = mask.unsqueeze(-1) * self.mask_embedding + _mask = mask.unsqueeze(-1).repeat(1, 1, 1, 2).reshape(self.b, self.f, -1) + _mask = torch.cat((_mask, torch.zeros_like(_mask[..., :3])), dim=-1) + _mask_embedding = mask_embedding.reshape(self.b, self.f, -1) + _mask_embedding = torch.cat((_mask_embedding, torch.zeros_like(_mask_embedding[..., :3])), dim=-1) + x[_mask] = 0.0 + x = x + _mask_embedding + return x + + + def rollout(self, output, pred_root, pred_vel, return_y_up): + root_world, trans_world = rollout_global_motion(pred_root, pred_vel) + + if return_y_up: + yup2ydown = axis_angle_to_matrix(torch.tensor([[np.pi, 0, 0]])).float().to(root_world.device) + root_world = yup2ydown.mT @ root_world + trans_world = (yup2ydown.mT @ trans_world.unsqueeze(-1)).squeeze(-1) + + output.update({ + 'poses_root_world': root_world, + 'trans_world': trans_world, + }) + + return output + + + def refine_trajectory(self, output, cam_angvel, return_y_up, **kwargs): + + # --------- Refine trajectory --------- # + update_vel = reset_root_velocity(self.smpl, self.output, self.pred_contact, self.pred_root, self.pred_vel, thr=0.5) + output = self.trajectory_refiner(self.old_motion_context, update_vel, output, cam_angvel, return_y_up=return_y_up) + # --------- # + + # Do rollout + output = self.rollout(output, output['poses_root_r6d_refined'], output['vel_root_refined'], return_y_up) + + # --------- Compute refined feet --------- # + if self.training: + feet_world, cam_R = self.compute_global_feet(output['poses_root_world'], output['trans_world']) + output.update({'feet_refined': feet_world}) + + return output + + + def forward(self, x, inits, img_features=None, mask=None, init_root=None, cam_angvel=None, + cam_intrinsics=None, bbox=None, res=None, return_y_up=False, refine_traj=True, **kwargs): + + x = self.preprocess(x, mask) + init_kp, init_smpl = inits + + # --------- Inference --------- # + # Stage 1. Encode motion + pred_kp3d, motion_context = self.motion_encoder(x, init_kp) + self.old_motion_context = motion_context.detach().clone() + + # Stage 2. Decode global trajectory + pred_root, pred_vel = self.trajectory_decoder(motion_context, init_root, cam_angvel) + + # Stage 3. Integrate features + if img_features is not None and self.integrator is not None: + motion_context = self.integrator(motion_context, img_features) + + # Stage 4. Decode SMPL motion + pred_pose, pred_shape, pred_cam, pred_contact = self.motion_decoder(motion_context, init_smpl) + # --------- # + + # --------- Register predictions --------- # + self.pred_kp3d = pred_kp3d + self.pred_root = pred_root + self.pred_vel = pred_vel + self.pred_pose = pred_pose + self.pred_shape = pred_shape + self.pred_cam = pred_cam + self.pred_contact = pred_contact + # --------- # + + # --------- Build SMPL --------- # + output = self.forward_smpl(cam_intrinsics=cam_intrinsics, bbox=bbox, res=res) + # --------- # + + # --------- Refine trajectory --------- # + if refine_traj: + output = self.refine_trajectory(output, cam_angvel, return_y_up) + else: + output = self.rollout(output, self.pred_root, self.pred_vel, return_y_up) + # --------- # + + return output \ No newline at end of file diff --git a/lib/utils/__pycache__/data_utils.cpython-39.pyc b/lib/utils/__pycache__/data_utils.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5ed06882a0c504e0570e95131ea640028f364595 Binary files /dev/null and b/lib/utils/__pycache__/data_utils.cpython-39.pyc differ diff --git a/lib/utils/__pycache__/imutils.cpython-39.pyc b/lib/utils/__pycache__/imutils.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8af34e7acf1e79c9e7b19114f443bc52925624a3 Binary files /dev/null and b/lib/utils/__pycache__/imutils.cpython-39.pyc differ diff --git a/lib/utils/__pycache__/kp_utils.cpython-39.pyc b/lib/utils/__pycache__/kp_utils.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..9922411db11f03716c92d05f0d24d50b25024408 Binary files /dev/null and b/lib/utils/__pycache__/kp_utils.cpython-39.pyc differ diff --git a/lib/utils/__pycache__/transforms.cpython-39.pyc b/lib/utils/__pycache__/transforms.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7b48d3647a8ea3b9c860ee99c0bb4f6abf2ce0d1 Binary files /dev/null and b/lib/utils/__pycache__/transforms.cpython-39.pyc differ diff --git a/lib/utils/data_utils.py b/lib/utils/data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b53a19cb1f5486e268f94d9a1ab98a7b7b1eb1df --- /dev/null +++ b/lib/utils/data_utils.py @@ -0,0 +1,113 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import torch +import numpy as np + +from lib.utils import transforms + + +def make_collate_fn(): + def collate_fn(items): + items = list(filter(lambda x: x is not None , items)) + batch = dict() + try: batch['vid'] = [item['vid'] for item in items] + except: pass + try: batch['gender'] = [item['gender'] for item in items] + except: pass + for key in items[0].keys(): + try: batch[key] = torch.stack([item[key] for item in items]) + except: pass + return batch + + return collate_fn + + +def prepare_keypoints_data(target): + """Prepare keypoints data""" + + # Prepare 2D keypoints + target['init_kp2d'] = target['kp2d'][:1] + target['kp2d'] = target['kp2d'][1:] + if 'kp3d' in target: + target['kp3d'] = target['kp3d'][1:] + + return target + + +def prepare_smpl_data(target): + if 'pose' in target.keys(): + # Use only the main joints + pose = target['pose'][:] + # 6-D Rotation representation + pose6d = transforms.matrix_to_rotation_6d(pose) + target['pose'] = pose6d[1:] + + if 'betas' in target.keys(): + target['betas'] = target['betas'][1:] + + # Translation and shape parameters + if 'transl' in target.keys(): + target['cam'] = target['transl'][1:] + + # Initial pose and translation + target['init_pose'] = transforms.matrix_to_rotation_6d(target['init_pose']) + + return target + + +def append_target(target, label, key_list, idx1, idx2=None, pad=True): + for key in key_list: + if idx2 is None: data = label[key][idx1] + else: data = label[key][idx1:idx2+1] + if not pad: data = data[2:] + target[key] = data + + return target + + +def map_dmpl_to_smpl(pose): + """ Map AMASS DMPL pose representation to SMPL pose representation + + Args: + pose - tensor / array with shape of (n_frames, 156) + + Return: + pose - tensor / array with shape of (n_frames, 24, 3) + """ + + pose = pose.reshape(pose.shape[0], -1, 3) + pose[:, 23] = pose[:, 37] # right hand + if isinstance(pose, np.ndarray): pose = pose[:, :24].copy() + else: pose = pose[:, :24].clone() + return pose + + +def transform_global_coordinate(pose, T, transl=None): + """ Transform global coordinate of dataset with respect to the given matrix. + Various datasets have different global coordinate system, + thus we united all datasets to the cronical coordinate system. + + Args: + pose - SMPL pose; tensor / array + T - Transformation matrix + transl - SMPL translation + """ + + return_to_numpy = False + if isinstance(pose, np.ndarray): + return_to_numpy = True + pose = torch.from_numpy(pose).float() + if transl is not None: transl = torch.from_numpy(transl).float() + + pose = transforms.axis_angle_to_matrix(pose) + pose[:, 0] = T @ pose[:, 0] + pose = transforms.matrix_to_axis_angle(pose) + if transl is not None: + transl = (T @ transl.T).squeeze().T + + if return_to_numpy: + pose = pose.detach().numpy() + if transl is not None: transl = transl.detach().numpy() + return pose, transl \ No newline at end of file diff --git a/lib/utils/imutils.py b/lib/utils/imutils.py new file mode 100644 index 0000000000000000000000000000000000000000..fc8529ff58a6074d0ee46b0f55d15e601c206b2f --- /dev/null +++ b/lib/utils/imutils.py @@ -0,0 +1,363 @@ +import cv2 +import torch +import random +import numpy as np +from . import transforms + +def do_augmentation(scale_factor=0.2, trans_factor=0.1): + scale = random.uniform(1.2 - scale_factor, 1.2 + scale_factor) + trans_x = random.uniform(-trans_factor, trans_factor) + trans_y = random.uniform(-trans_factor, trans_factor) + + return scale, trans_x, trans_y + +def get_transform(center, scale, res, rot=0): + """Generate transformation matrix.""" + # res: (height, width), (rows, cols) + crop_aspect_ratio = res[0] / float(res[1]) + h = 200 * scale + w = h / crop_aspect_ratio + t = np.zeros((3, 3)) + t[0, 0] = float(res[1]) / w + t[1, 1] = float(res[0]) / h + t[0, 2] = res[1] * (-float(center[0]) / w + .5) + t[1, 2] = res[0] * (-float(center[1]) / h + .5) + t[2, 2] = 1 + if not rot == 0: + rot = -rot # To match direction of rotation from cropping + rot_mat = np.zeros((3, 3)) + rot_rad = rot * np.pi / 180 + sn, cs = np.sin(rot_rad), np.cos(rot_rad) + rot_mat[0, :2] = [cs, -sn] + rot_mat[1, :2] = [sn, cs] + rot_mat[2, 2] = 1 + # Need to rotate around center + t_mat = np.eye(3) + t_mat[0, 2] = -res[1] / 2 + t_mat[1, 2] = -res[0] / 2 + t_inv = t_mat.copy() + t_inv[:2, 2] *= -1 + t = np.dot(t_inv, np.dot(rot_mat, np.dot(t_mat, t))) + return t + + +def transform(pt, center, scale, res, invert=0, rot=0): + """Transform pixel location to different reference.""" + t = get_transform(center, scale, res, rot=rot) + if invert: + t = np.linalg.inv(t) + new_pt = np.array([pt[0] - 1, pt[1] - 1, 1.]).T + new_pt = np.dot(t, new_pt) + return np.array([round(new_pt[0]), round(new_pt[1])], dtype=int) + 1 + + +def crop_cliff(img, center, scale, res): + """ + Crop image according to the supplied bounding box. + res: [rows, cols] + """ + # Upper left point + ul = np.array(transform([1, 1], center, scale, res, invert=1)) - 1 + # Bottom right point + br = np.array(transform([res[1] + 1, res[0] + 1], center, scale, res, invert=1)) - 1 + + # Padding so that when rotated proper amount of context is included + pad = int(np.linalg.norm(br - ul) / 2 - float(br[1] - ul[1]) / 2) + + new_shape = [br[1] - ul[1], br[0] - ul[0]] + if len(img.shape) > 2: + new_shape += [img.shape[2]] + new_img = np.zeros(new_shape, dtype=np.float32) + + # Range to fill new array + new_x = max(0, -ul[0]), min(br[0], len(img[0])) - ul[0] + new_y = max(0, -ul[1]), min(br[1], len(img)) - ul[1] + # Range to sample from original image + old_x = max(0, ul[0]), min(len(img[0]), br[0]) + old_y = max(0, ul[1]), min(len(img), br[1]) + + try: + new_img[new_y[0]:new_y[1], new_x[0]:new_x[1]] = img[old_y[0]:old_y[1], old_x[0]:old_x[1]] + except Exception as e: + print(e) + + new_img = cv2.resize(new_img, (res[1], res[0])) # (cols, rows) + + return new_img, ul, br + + +def obtain_bbox(center, scale, res, org_res): + # Upper left point + ul = np.array(transform([1, 1], center, scale, res, invert=1)) - 1 + # Bottom right point + br = np.array(transform([res[1] + 1, res[0] + 1], center, scale, res, invert=1)) - 1 + + # Padding so that when rotated proper amount of context is included + pad = int(np.linalg.norm(br - ul) / 2 - float(br[1] - ul[1]) / 2) + + # Range to sample from original image + old_x = max(0, ul[0]), min(org_res[0], br[0]) + old_y = max(0, ul[1]), min(org_res[1], br[1]) + + return old_x, old_y + + +def cam_crop2full(crop_cam, bbox, full_img_shape, focal_length=None): + """ + convert the camera parameters from the crop camera to the full camera + :param crop_cam: shape=(N, 3) weak perspective camera in cropped img coordinates (s, tx, ty) + :param center: shape=(N, 2) bbox coordinates (c_x, c_y) + :param scale: shape=(N, 1) square bbox resolution (b / 200) + :param full_img_shape: shape=(N, 2) original image height and width + :param focal_length: shape=(N,) + :return: + """ + + cx = bbox[..., 0].clone(); cy = bbox[..., 1].clone(); b = bbox[..., 2].clone() * 200 + img_h, img_w = full_img_shape[:, 0], full_img_shape[:, 1] + w_2, h_2 = img_w / 2., img_h / 2. + bs = b * crop_cam[:, :, 0] + 1e-9 + + if focal_length is None: + focal_length = (img_w * img_w + img_h * img_h) ** 0.5 + + tz = 2 * focal_length.unsqueeze(-1) / bs + tx = (2 * (cx - w_2.unsqueeze(-1)) / bs) + crop_cam[:, :, 1] + ty = (2 * (cy - h_2.unsqueeze(-1)) / bs) + crop_cam[:, :, 2] + full_cam = torch.stack([tx, ty, tz], dim=-1) + return full_cam + + +def cam_pred2full(crop_cam, center, scale, full_img_shape, focal_length=2000.,): + """ + Reference CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation + + convert the camera parameters from the crop camera to the full camera + :param crop_cam: shape=(N, 3) weak perspective camera in cropped img coordinates (s, tx, ty) + :param center: shape=(N, 2) bbox coordinates (c_x, c_y) + :param scale: shape=(N, ) square bbox resolution (b / 200) + :param full_img_shape: shape=(N, 2) original image height and width + :param focal_length: shape=(N,) + :return: + """ + + # img_h, img_w = full_img_shape[:, 0], full_img_shape[:, 1] + img_w, img_h = full_img_shape[:, 0], full_img_shape[:, 1] + cx, cy, b = center[:, 0], center[:, 1], scale * 200 + w_2, h_2 = img_w / 2., img_h / 2. + bs = b * crop_cam[:, 0] + 1e-9 + tz = 2 * focal_length / bs + tx = (2 * (cx - w_2) / bs) + crop_cam[:, 1] + ty = (2 * (cy - h_2) / bs) + crop_cam[:, 2] + full_cam = torch.stack([tx, ty, tz], dim=-1) + return full_cam + + +def cam_full2pred(full_cam, center, scale, full_img_shape, focal_length=2000.): + # img_h, img_w = full_img_shape[:, 0], full_img_shape[:, 1] + img_w, img_h = full_img_shape[:, 0], full_img_shape[:, 1] + cx, cy, b = center[:, 0], center[:, 1], scale * 200 + w_2, h_2 = img_w / 2., img_h / 2. + + bs = (2 * focal_length / full_cam[:, 2]) + _s = bs / b + _tx = full_cam[:, 0] - (2 * (cx - w_2) / bs) + _ty = full_cam[:, 1] - (2 * (cy - h_2) / bs) + crop_cam = torch.stack([_s, _tx, _ty], dim=-1) + return crop_cam + + +def obtain_camera_intrinsics(image_shape, focal_length): + res_w = image_shape[..., 0].clone() + res_h = image_shape[..., 1].clone() + K = torch.eye(3).unsqueeze(0).expand(focal_length.shape[0], -1, -1).to(focal_length.device) + K[..., 0, 0] = focal_length.clone() + K[..., 1, 1] = focal_length.clone() + K[..., 0, 2] = res_w / 2 + K[..., 1, 2] = res_h / 2 + + return K.unsqueeze(1) + + +def trans_point2d(pt_2d, trans): + src_pt = np.array([pt_2d[0], pt_2d[1], 1.]).T + dst_pt = np.dot(trans, src_pt) + return dst_pt[0:2] + +def rotate_2d(pt_2d, rot_rad): + x = pt_2d[0] + y = pt_2d[1] + sn, cs = np.sin(rot_rad), np.cos(rot_rad) + xx = x * cs - y * sn + yy = x * sn + y * cs + return np.array([xx, yy], dtype=np.float32) + +def gen_trans_from_patch_cv(c_x, c_y, src_width, src_height, dst_width, dst_height, scale, rot, inv=False): + # augment size with scale + src_w = src_width * scale + src_h = src_height * scale + src_center = np.zeros(2) + src_center[0] = c_x + src_center[1] = c_y # np.array([c_x, c_y], dtype=np.float32) + # augment rotation + rot_rad = np.pi * rot / 180 + src_downdir = rotate_2d(np.array([0, src_h * 0.5], dtype=np.float32), rot_rad) + src_rightdir = rotate_2d(np.array([src_w * 0.5, 0], dtype=np.float32), rot_rad) + + dst_w = dst_width + dst_h = dst_height + dst_center = np.array([dst_w * 0.5, dst_h * 0.5], dtype=np.float32) + dst_downdir = np.array([0, dst_h * 0.5], dtype=np.float32) + dst_rightdir = np.array([dst_w * 0.5, 0], dtype=np.float32) + + src = np.zeros((3, 2), dtype=np.float32) + src[0, :] = src_center + src[1, :] = src_center + src_downdir + src[2, :] = src_center + src_rightdir + + dst = np.zeros((3, 2), dtype=np.float32) + dst[0, :] = dst_center + dst[1, :] = dst_center + dst_downdir + dst[2, :] = dst_center + dst_rightdir + + if inv: + trans = cv2.getAffineTransform(np.float32(dst), np.float32(src)) + else: + trans = cv2.getAffineTransform(np.float32(src), np.float32(dst)) + + return trans + +def transform_keypoints(kp_2d, bbox, patch_width, patch_height): + + center_x, center_y, scale = bbox[:3] + width = height = scale * 200 + # scale, rot = 1.2, 0 + scale, rot = 1.0, 0 + + # generate transformation + trans = gen_trans_from_patch_cv( + center_x, + center_y, + width, + height, + patch_width, + patch_height, + scale, + rot, + inv=False, + ) + + for n_jt in range(kp_2d.shape[0]): + kp_2d[n_jt] = trans_point2d(kp_2d[n_jt], trans) + + return kp_2d, trans + + +def transform(pt, center, scale, res, invert=0, rot=0): + """Transform pixel location to different reference.""" + t = get_transform(center, scale, res, rot=rot) + if invert: + t = np.linalg.inv(t) + new_pt = np.array([pt[0] - 1, pt[1] - 1, 1.]).T + new_pt = np.dot(t, new_pt) + return new_pt[:2].astype(int) + 1 + + +def compute_cam_intrinsics(res): + img_w, img_h = res + focal_length = (img_w * img_w + img_h * img_h) ** 0.5 + cam_intrinsics = torch.eye(3).repeat(1, 1, 1).float() + cam_intrinsics[:, 0, 0] = focal_length + cam_intrinsics[:, 1, 1] = focal_length + cam_intrinsics[:, 0, 2] = img_w/2. + cam_intrinsics[:, 1, 2] = img_h/2. + return cam_intrinsics + + +def flip_kp(kp, img_w=None): + """Flip keypoints.""" + + flipped_parts = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15] + kp = kp[..., flipped_parts, :] + + if img_w is not None: + # Assume 2D keypoints + kp[...,0] = img_w - kp[...,0] + return kp + + +def flip_bbox(bbox, img_w, img_h): + center = bbox[..., :2] + scale = bbox[..., -1:] + + WH = np.ones_like(center) + WH[..., 0] *= img_w + WH[..., 1] *= img_h + + center = center - WH/2 + center[...,0] = - center[...,0] + center = center + WH/2 + + flipped_bbox = np.concatenate((center, scale), axis=-1) + return flipped_bbox + + +def flip_pose(rotation, representation='rotation_6d'): + """Flip pose. + The flipping is based on SMPL parameters. + """ + + BN = rotation.shape[0] + + if representation == 'axis_angle': + pose = rotation.reshape(BN, -1).transpose(0, 1) + elif representation == 'matrix': + pose = transforms.matrix_to_axis_angle(rotation).reshape(BN, -1).transpose(0, 1) + elif representation == 'rotation_6d': + pose = transforms.matrix_to_axis_angle( + transforms.rotation_6d_to_matrix(rotation) + ).reshape(BN, -1).transpose(0, 1) + else: + raise ValueError(f"Unknown representation: {representation}") + + SMPL_JOINTS_FLIP_PERM = [0, 2, 1, 3, 5, 4, 6, 8, 7, 9, 11, 10, 12, 14, 13, 15, 17, 16, 19, 18, 21, 20, 23, 22] + SMPL_POSE_FLIP_PERM = [] + for i in SMPL_JOINTS_FLIP_PERM: + SMPL_POSE_FLIP_PERM.append(3*i) + SMPL_POSE_FLIP_PERM.append(3*i+1) + SMPL_POSE_FLIP_PERM.append(3*i+2) + + pose = pose[SMPL_POSE_FLIP_PERM] + + # we also negate the second and the third dimension of the axis-angle + pose[1::3] = -pose[1::3] + pose[2::3] = -pose[2::3] + pose = pose.transpose(0, 1).reshape(BN, -1, 3) + + if representation == 'aa': + return pose + elif representation == 'rotmat': + return transforms.axis_angle_to_matrix(pose) + else: + return transforms.matrix_to_rotation_6d( + transforms.axis_angle_to_matrix(pose) + ) + +def avg_preds(rotation, shape, flipped_rotation, flipped_shape, representation='rotation_6d'): + # Rotation + flipped_rotation = flip_pose(flipped_rotation, representation=representation) + + if representation != 'matrix': + flipped_rotation = eval(f'transforms.{representation}_to_matrix')(flipped_rotation) + rotation = eval(f'transforms.{representation}_to_matrix')(rotation) + + avg_rotation = torch.stack([rotation, flipped_rotation]) + avg_rotation = transforms.avg_rot(avg_rotation) + + if representation != 'matrix': + avg_rotation = eval(f'transforms.matrix_to_{representation}')(avg_rotation) + + # Shape + avg_shape = (shape + flipped_shape) / 2.0 + + return avg_rotation, avg_shape \ No newline at end of file diff --git a/lib/utils/kp_utils.py b/lib/utils/kp_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b23f4d012b65194363dcb98c554f722b52222251 --- /dev/null +++ b/lib/utils/kp_utils.py @@ -0,0 +1,761 @@ +from __future__ import absolute_import +from __future__ import print_function +from __future__ import division + +import torch +import numpy as np + +from configs import constants as _C + +def root_centering(X, joint_type='coco'): + """Center the root joint to the pelvis.""" + if joint_type != 'common' and X.shape[-2] == 14: return X + + conf = None + if X.shape[-1] == 4: + conf = X[..., -1:] + X = X[..., :-1] + + if X.shape[-2] == 31: + X[..., :17, :] = X[..., :17, :] - X[..., [12, 11], :].mean(-2, keepdims=True) + X[..., 17:, :] = X[..., 17:, :] - X[..., [19, 20], :].mean(-2, keepdims=True) + + elif joint_type == 'coco': + X = X - X[..., [12, 11], :].mean(-2, keepdims=True) + + elif joint_type == 'common': + X = X - X[..., [2, 3], :].mean(-2, keepdims=True) + + if conf is not None: + X = torch.cat((X, conf), dim=-1) + + return X + + +def convert_kps(joints2d, src, dst): + src_names = eval(f'get_{src}_joint_names')() + dst_names = eval(f'get_{dst}_joint_names')() + + if isinstance(joints2d, np.ndarray): + out_joints2d = np.zeros((*joints2d.shape[:-2], len(dst_names), joints2d.shape[-1])) + else: + out_joints2d = torch.zeros((*joints2d.shape[:-2], len(dst_names), joints2d.shape[-1]), device=joints2d.device) + + for idx, jn in enumerate(dst_names): + if jn in src_names: + out_joints2d[..., idx, :] = joints2d[..., src_names.index(jn), :] + + return out_joints2d + +def get_perm_idxs(src, dst): + src_names = eval(f'get_{src}_joint_names')() + dst_names = eval(f'get_{dst}_joint_names')() + idxs = [src_names.index(h) for h in dst_names if h in src_names] + return idxs + +def get_mpii3d_test_joint_names(): + return [ + 'headtop', # 'head_top', + 'neck', + 'rshoulder',# 'right_shoulder', + 'relbow',# 'right_elbow', + 'rwrist',# 'right_wrist', + 'lshoulder',# 'left_shoulder', + 'lelbow', # 'left_elbow', + 'lwrist', # 'left_wrist', + 'rhip', # 'right_hip', + 'rknee', # 'right_knee', + 'rankle',# 'right_ankle', + 'lhip',# 'left_hip', + 'lknee',# 'left_knee', + 'lankle',# 'left_ankle' + 'hip',# 'pelvis', + 'Spine (H36M)',# 'spine', + 'Head (H36M)',# 'head' + ] + +def get_mpii3d_joint_names(): + return [ + 'spine3', # 0, + 'spine4', # 1, + 'spine2', # 2, + 'Spine (H36M)', #'spine', # 3, + 'hip', # 'pelvis', # 4, + 'neck', # 5, + 'Head (H36M)', # 'head', # 6, + "headtop", # 'head_top', # 7, + 'left_clavicle', # 8, + "lshoulder", # 'left_shoulder', # 9, + "lelbow", # 'left_elbow',# 10, + "lwrist", # 'left_wrist',# 11, + 'left_hand',# 12, + 'right_clavicle',# 13, + 'rshoulder',# 'right_shoulder',# 14, + 'relbow',# 'right_elbow',# 15, + 'rwrist',# 'right_wrist',# 16, + 'right_hand',# 17, + 'lhip', # left_hip',# 18, + 'lknee', # 'left_knee',# 19, + 'lankle', #left ankle # 20 + 'left_foot', # 21 + 'left_toe', # 22 + "rhip", # 'right_hip',# 23 + "rknee", # 'right_knee',# 24 + "rankle", #'right_ankle', # 25 + 'right_foot',# 26 + 'right_toe' # 27 + ] + +def get_insta_joint_names(): + return [ + 'OP RHeel', + 'OP RKnee', + 'OP RHip', + 'OP LHip', + 'OP LKnee', + 'OP LHeel', + 'OP RWrist', + 'OP RElbow', + 'OP RShoulder', + 'OP LShoulder', + 'OP LElbow', + 'OP LWrist', + 'OP Neck', + 'headtop', + 'OP Nose', + 'OP LEye', + 'OP REye', + 'OP LEar', + 'OP REar', + 'OP LBigToe', + 'OP RBigToe', + 'OP LSmallToe', + 'OP RSmallToe', + 'OP LAnkle', + 'OP RAnkle', + ] + +def get_insta_skeleton(): + return np.array( + [ + [0 , 1], + [1 , 2], + [2 , 3], + [3 , 4], + [4 , 5], + [6 , 7], + [7 , 8], + [8 , 9], + [9 ,10], + [2 , 8], + [3 , 9], + [10,11], + [8 ,12], + [9 ,12], + [12,13], + [12,14], + [14,15], + [14,16], + [15,17], + [16,18], + [0 ,20], + [20,22], + [5 ,19], + [19,21], + [5 ,23], + [0 ,24], + ]) + +def get_staf_skeleton(): + return np.array( + [ + [0, 1], + [1, 2], + [2, 3], + [3, 4], + [1, 5], + [5, 6], + [6, 7], + [1, 8], + [8, 9], + [9, 10], + [10, 11], + [8, 12], + [12, 13], + [13, 14], + [0, 15], + [0, 16], + [15, 17], + [16, 18], + [2, 9], + [5, 12], + [1, 19], + [20, 19], + ] + ) + +def get_staf_joint_names(): + return [ + 'OP Nose', # 0, + 'OP Neck', # 1, + 'OP RShoulder', # 2, + 'OP RElbow', # 3, + 'OP RWrist', # 4, + 'OP LShoulder', # 5, + 'OP LElbow', # 6, + 'OP LWrist', # 7, + 'OP MidHip', # 8, + 'OP RHip', # 9, + 'OP RKnee', # 10, + 'OP RAnkle', # 11, + 'OP LHip', # 12, + 'OP LKnee', # 13, + 'OP LAnkle', # 14, + 'OP REye', # 15, + 'OP LEye', # 16, + 'OP REar', # 17, + 'OP LEar', # 18, + 'Neck (LSP)', # 19, + 'Top of Head (LSP)', # 20, + ] + +def get_spin_joint_names(): + return [ + 'OP Nose', # 0 + 'OP Neck', # 1 + 'OP RShoulder', # 2 + 'OP RElbow', # 3 + 'OP RWrist', # 4 + 'OP LShoulder', # 5 + 'OP LElbow', # 6 + 'OP LWrist', # 7 + 'OP MidHip', # 8 + 'OP RHip', # 9 + 'OP RKnee', # 10 + 'OP RAnkle', # 11 + 'OP LHip', # 12 + 'OP LKnee', # 13 + 'OP LAnkle', # 14 + 'OP REye', # 15 + 'OP LEye', # 16 + 'OP REar', # 17 + 'OP LEar', # 18 + 'OP LBigToe', # 19 + 'OP LSmallToe', # 20 + 'OP LHeel', # 21 + 'OP RBigToe', # 22 + 'OP RSmallToe', # 23 + 'OP RHeel', # 24 + 'rankle', # 25 + 'rknee', # 26 + 'rhip', # 27 + 'lhip', # 28 + 'lknee', # 29 + 'lankle', # 30 + 'rwrist', # 31 + 'relbow', # 32 + 'rshoulder', # 33 + 'lshoulder', # 34 + 'lelbow', # 35 + 'lwrist', # 36 + 'neck', # 37 + 'headtop', # 38 + 'hip', # 39 'Pelvis (MPII)', # 39 + 'thorax', # 40 'Thorax (MPII)', # 40 + 'Spine (H36M)', # 41 + 'Jaw (H36M)', # 42 + 'Head (H36M)', # 43 + 'nose', # 44 + 'leye', # 45 'Left Eye', # 45 + 'reye', # 46 'Right Eye', # 46 + 'lear', # 47 'Left Ear', # 47 + 'rear', # 48 'Right Ear', # 48 + ] + +def get_h36m_joint_names(): + return [ + 'hip', # 0 + 'lhip', # 1 + 'lknee', # 2 + 'lankle', # 3 + 'rhip', # 4 + 'rknee', # 5 + 'rankle', # 6 + 'Spine (H36M)', # 7 + 'neck', # 8 + 'Head (H36M)', # 9 + 'headtop', # 10 + 'lshoulder', # 11 + 'lelbow', # 12 + 'lwrist', # 13 + 'rshoulder', # 14 + 'relbow', # 15 + 'rwrist', # 16 + ] + +'Pelvis', 'R_Hip', 'R_Knee', 'R_Ankle', 'L_Hip', 'L_Knee', 'L_Ankle', 'Torso', 'Neck', 'Nose', 'Head_top', 'L_Shoulder', 'L_Elbow', 'L_Wrist', 'R_Shoulder', 'R_Elbow', 'R_Wrist' + +def get_spin_skeleton(): + return np.array( + [ + [0 , 1], + [1 , 2], + [2 , 3], + [3 , 4], + [1 , 5], + [5 , 6], + [6 , 7], + [1 , 8], + [8 , 9], + [9 ,10], + [10,11], + [8 ,12], + [12,13], + [13,14], + [0 ,15], + [0 ,16], + [15,17], + [16,18], + [21,19], + [19,20], + [14,21], + [11,24], + [24,22], + [22,23], + [0 ,38], + ] + ) + +def get_posetrack_joint_names(): + return [ + "nose", + "neck", + "headtop", + "lear", + "rear", + "lshoulder", + "rshoulder", + "lelbow", + "relbow", + "lwrist", + "rwrist", + "lhip", + "rhip", + "lknee", + "rknee", + "lankle", + "rankle" + ] + +def get_posetrack_original_kp_names(): + return [ + 'nose', + 'head_bottom', + 'head_top', + 'left_ear', + 'right_ear', + 'left_shoulder', + 'right_shoulder', + 'left_elbow', + 'right_elbow', + 'left_wrist', + 'right_wrist', + 'left_hip', + 'right_hip', + 'left_knee', + 'right_knee', + 'left_ankle', + 'right_ankle' + ] + +def get_pennaction_joint_names(): + return [ + "headtop", # 0 + "lshoulder", # 1 + "rshoulder", # 2 + "lelbow", # 3 + "relbow", # 4 + "lwrist", # 5 + "rwrist", # 6 + "lhip" , # 7 + "rhip" , # 8 + "lknee", # 9 + "rknee" , # 10 + "lankle", # 11 + "rankle" # 12 + ] + +def get_common_joint_names(): + return [ + "rankle", # 0 "lankle", # 0 + "rknee", # 1 "lknee", # 1 + "rhip", # 2 "lhip", # 2 + "lhip", # 3 "rhip", # 3 + "lknee", # 4 "rknee", # 4 + "lankle", # 5 "rankle", # 5 + "rwrist", # 6 "lwrist", # 6 + "relbow", # 7 "lelbow", # 7 + "rshoulder", # 8 "lshoulder", # 8 + "lshoulder", # 9 "rshoulder", # 9 + "lelbow", # 10 "relbow", # 10 + "lwrist", # 11 "rwrist", # 11 + "neck", # 12 "neck", # 12 + "headtop", # 13 "headtop", # 13 + ] + +def get_coco_common_joint_names(): + return [ + "nose", # 0 + "leye", # 1 + "reye", # 2 + "lear", # 3 + "rear", # 4 + "lshoulder", # 5 + "rshoulder", # 6 + "lelbow", # 7 + "relbow", # 8 + "lwrist", # 9 + "rwrist", # 10 + "lhip", # 11 + "rhip", # 12 + "lknee", # 13 + "rknee", # 14 + "lankle", # 15 + "rankle", # 16 + "neck", # 17 "neck", # 12 + "headtop", # 18 "headtop", # 13 + ] + +def get_common_skeleton(): + return np.array( + [ + [ 0, 1 ], + [ 1, 2 ], + [ 3, 4 ], + [ 4, 5 ], + [ 6, 7 ], + [ 7, 8 ], + [ 8, 2 ], + [ 8, 9 ], + [ 9, 3 ], + [ 2, 3 ], + [ 8, 12], + [ 9, 10], + [12, 9 ], + [10, 11], + [12, 13], + ] + ) + +def get_coco_joint_names(): + return [ + "nose", # 0 + "leye", # 1 + "reye", # 2 + "lear", # 3 + "rear", # 4 + "lshoulder", # 5 + "rshoulder", # 6 + "lelbow", # 7 + "relbow", # 8 + "lwrist", # 9 + "rwrist", # 10 + "lhip", # 11 + "rhip", # 12 + "lknee", # 13 + "rknee", # 14 + "lankle", # 15 + "rankle", # 16 + ] + +def get_coco_skeleton(): + # 0 - nose, + # 1 - leye, + # 2 - reye, + # 3 - lear, + # 4 - rear, + # 5 - lshoulder, + # 6 - rshoulder, + # 7 - lelbow, + # 8 - relbow, + # 9 - lwrist, + # 10 - rwrist, + # 11 - lhip, + # 12 - rhip, + # 13 - lknee, + # 14 - rknee, + # 15 - lankle, + # 16 - rankle, + return np.array( + [ + [15, 13], + [13, 11], + [16, 14], + [14, 12], + [11, 12], + [ 5, 11], + [ 6, 12], + [ 5, 6 ], + [ 5, 7 ], + [ 6, 8 ], + [ 7, 9 ], + [ 8, 10], + [ 1, 2 ], + [ 0, 1 ], + [ 0, 2 ], + [ 1, 3 ], + [ 2, 4 ], + [ 3, 5 ], + [ 4, 6 ] + ] + ) + +def get_mpii_joint_names(): + return [ + "rankle", # 0 + "rknee", # 1 + "rhip", # 2 + "lhip", # 3 + "lknee", # 4 + "lankle", # 5 + "hip", # 6 + "thorax", # 7 + "neck", # 8 + "headtop", # 9 + "rwrist", # 10 + "relbow", # 11 + "rshoulder", # 12 + "lshoulder", # 13 + "lelbow", # 14 + "lwrist", # 15 + ] + +def get_mpii_skeleton(): + # 0 - rankle, + # 1 - rknee, + # 2 - rhip, + # 3 - lhip, + # 4 - lknee, + # 5 - lankle, + # 6 - hip, + # 7 - thorax, + # 8 - neck, + # 9 - headtop, + # 10 - rwrist, + # 11 - relbow, + # 12 - rshoulder, + # 13 - lshoulder, + # 14 - lelbow, + # 15 - lwrist, + return np.array( + [ + [ 0, 1 ], + [ 1, 2 ], + [ 2, 6 ], + [ 6, 3 ], + [ 3, 4 ], + [ 4, 5 ], + [ 6, 7 ], + [ 7, 8 ], + [ 8, 9 ], + [ 7, 12], + [12, 11], + [11, 10], + [ 7, 13], + [13, 14], + [14, 15] + ] + ) + +def get_aich_joint_names(): + return [ + "rshoulder", # 0 + "relbow", # 1 + "rwrist", # 2 + "lshoulder", # 3 + "lelbow", # 4 + "lwrist", # 5 + "rhip", # 6 + "rknee", # 7 + "rankle", # 8 + "lhip", # 9 + "lknee", # 10 + "lankle", # 11 + "headtop", # 12 + "neck", # 13 + ] + +def get_aich_skeleton(): + # 0 - rshoulder, + # 1 - relbow, + # 2 - rwrist, + # 3 - lshoulder, + # 4 - lelbow, + # 5 - lwrist, + # 6 - rhip, + # 7 - rknee, + # 8 - rankle, + # 9 - lhip, + # 10 - lknee, + # 11 - lankle, + # 12 - headtop, + # 13 - neck, + return np.array( + [ + [ 0, 1 ], + [ 1, 2 ], + [ 3, 4 ], + [ 4, 5 ], + [ 6, 7 ], + [ 7, 8 ], + [ 9, 10], + [10, 11], + [12, 13], + [13, 0 ], + [13, 3 ], + [ 0, 6 ], + [ 3, 9 ] + ] + ) + +def get_3dpw_joint_names(): + return [ + "nose", # 0 + "thorax", # 1 + "rshoulder", # 2 + "relbow", # 3 + "rwrist", # 4 + "lshoulder", # 5 + "lelbow", # 6 + "lwrist", # 7 + "rhip", # 8 + "rknee", # 9 + "rankle", # 10 + "lhip", # 11 + "lknee", # 12 + "lankle", # 13 + ] + +def get_3dpw_skeleton(): + return np.array( + [ + [ 0, 1 ], + [ 1, 2 ], + [ 2, 3 ], + [ 3, 4 ], + [ 1, 5 ], + [ 5, 6 ], + [ 6, 7 ], + [ 2, 8 ], + [ 5, 11], + [ 8, 11], + [ 8, 9 ], + [ 9, 10], + [11, 12], + [12, 13] + ] + ) + +def get_smplcoco_joint_names(): + return [ + "rankle", # 0 + "rknee", # 1 + "rhip", # 2 + "lhip", # 3 + "lknee", # 4 + "lankle", # 5 + "rwrist", # 6 + "relbow", # 7 + "rshoulder", # 8 + "lshoulder", # 9 + "lelbow", # 10 + "lwrist", # 11 + "neck", # 12 + "headtop", # 13 + "nose", # 14 + "leye", # 15 + "reye", # 16 + "lear", # 17 + "rear", # 18 + ] + +def get_smplcoco_skeleton(): + return np.array( + [ + [ 0, 1 ], + [ 1, 2 ], + [ 3, 4 ], + [ 4, 5 ], + [ 6, 7 ], + [ 7, 8 ], + [ 8, 12], + [12, 9 ], + [ 9, 10], + [10, 11], + [12, 13], + [14, 15], + [15, 17], + [16, 18], + [14, 16], + [ 8, 2 ], + [ 9, 3 ], + [ 2, 3 ], + ] + ) + +def get_smpl_joint_names(): + return [ + 'hips', # 0 + 'leftUpLeg', # 1 + 'rightUpLeg', # 2 + 'spine', # 3 + 'leftLeg', # 4 + 'rightLeg', # 5 + 'spine1', # 6 + 'leftFoot', # 7 + 'rightFoot', # 8 + 'spine2', # 9 + 'leftToeBase', # 10 + 'rightToeBase', # 11 + 'neck', # 12 + 'leftShoulder', # 13 + 'rightShoulder', # 14 + 'head', # 15 + 'leftArm', # 16 + 'rightArm', # 17 + 'leftForeArm', # 18 + 'rightForeArm', # 19 + 'leftHand', # 20 + 'rightHand', # 21 + 'leftHandIndex1', # 22 + 'rightHandIndex1', # 23 + ] + +def get_smpl_skeleton(): + return np.array( + [ + [ 0, 1 ], + [ 0, 2 ], + [ 0, 3 ], + [ 1, 4 ], + [ 2, 5 ], + [ 3, 6 ], + [ 4, 7 ], + [ 5, 8 ], + [ 6, 9 ], + [ 7, 10], + [ 8, 11], + [ 9, 12], + [ 9, 13], + [ 9, 14], + [12, 15], + [13, 16], + [14, 17], + [16, 18], + [17, 19], + [18, 20], + [19, 21], + [20, 22], + [21, 23], + ] + ) \ No newline at end of file diff --git a/lib/utils/transforms.py b/lib/utils/transforms.py new file mode 100644 index 0000000000000000000000000000000000000000..cb207f302a51797b501e32d05c572ddc461c473e --- /dev/null +++ b/lib/utils/transforms.py @@ -0,0 +1,828 @@ +"""This transforms function is mainly borrowed from PyTorch3D""" + +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Optional, Union + +import torch +import torch.nn.functional as F + +Device = Union[str, torch.device] + +""" +The transformation matrices returned from the functions in this file assume +the points on which the transformation will be applied are column vectors. +i.e. the R matrix is structured as + + R = [ + [Rxx, Rxy, Rxz], + [Ryx, Ryy, Ryz], + [Rzx, Rzy, Rzz], + ] # (3, 3) + +This matrix can be applied to column vectors by post multiplication +by the points e.g. + + points = [[0], [1], [2]] # (3 x 1) xyz coordinates of a point + transformed_points = R * points + +To apply the same matrix to points which are row vectors, the R matrix +can be transposed and pre multiplied by the points: + +e.g. + points = [[0, 1, 2]] # (1 x 3) xyz coordinates of a point + transformed_points = points * R.transpose(1, 0) +""" + + +def quaternion_to_matrix(quaternions: torch.Tensor) -> torch.Tensor: + """ + Convert rotations given as quaternions to rotation matrices. + + Args: + quaternions: quaternions with real part first, + as tensor of shape (..., 4). + + Returns: + Rotation matrices as tensor of shape (..., 3, 3). + """ + r, i, j, k = torch.unbind(quaternions, -1) + # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`. + two_s = 2.0 / (quaternions * quaternions).sum(-1) + + o = torch.stack( + ( + 1 - two_s * (j * j + k * k), + two_s * (i * j - k * r), + two_s * (i * k + j * r), + two_s * (i * j + k * r), + 1 - two_s * (i * i + k * k), + two_s * (j * k - i * r), + two_s * (i * k - j * r), + two_s * (j * k + i * r), + 1 - two_s * (i * i + j * j), + ), + -1, + ) + return o.reshape(quaternions.shape[:-1] + (3, 3)) + + + +def _copysign(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: + """ + Return a tensor where each element has the absolute value taken from the, + corresponding element of a, with sign taken from the corresponding + element of b. This is like the standard copysign floating-point operation, + but is not careful about negative 0 and NaN. + + Args: + a: source tensor. + b: tensor whose signs will be used, of the same shape as a. + + Returns: + Tensor of the same shape as a with the signs of b. + """ + signs_differ = (a < 0) != (b < 0) + return torch.where(signs_differ, -a, a) + + +def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor: + """ + Returns torch.sqrt(torch.max(0, x)) + but with a zero subgradient where x is 0. + """ + ret = torch.zeros_like(x) + positive_mask = x > 0 + ret[positive_mask] = torch.sqrt(x[positive_mask]) + return ret + + +def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor: + """ + Convert rotations given as rotation matrices to quaternions. + + Args: + matrix: Rotation matrices as tensor of shape (..., 3, 3). + + Returns: + quaternions with real part first, as tensor of shape (..., 4). + """ + if matrix.size(-1) != 3 or matrix.size(-2) != 3: + raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.") + + batch_dim = matrix.shape[:-2] + m00, m01, m02, m10, m11, m12, m20, m21, m22 = torch.unbind( + matrix.reshape(batch_dim + (9,)), dim=-1 + ) + + q_abs = _sqrt_positive_part( + torch.stack( + [ + 1.0 + m00 + m11 + m22, + 1.0 + m00 - m11 - m22, + 1.0 - m00 + m11 - m22, + 1.0 - m00 - m11 + m22, + ], + dim=-1, + ) + ) + + # we produce the desired quaternion multiplied by each of r, i, j, k + quat_by_rijk = torch.stack( + [ + # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and + # `int`. + torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1), + # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and + # `int`. + torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1), + # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and + # `int`. + torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1), + # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and + # `int`. + torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1), + ], + dim=-2, + ) + + # We floor here at 0.1 but the exact level is not important; if q_abs is small, + # the candidate won't be picked. + flr = torch.tensor(0.1).to(dtype=q_abs.dtype, device=q_abs.device) + quat_candidates = quat_by_rijk / (2.0 * q_abs[..., None].max(flr)) + + # if not for numerical problems, quat_candidates[i] should be same (up to a sign), + # forall i; we pick the best-conditioned one (with the largest denominator) + + return quat_candidates[ + F.one_hot(q_abs.argmax(dim=-1), num_classes=4) > 0.5, : + ].reshape(batch_dim + (4,)) + + + +def _axis_angle_rotation(axis: str, angle: torch.Tensor) -> torch.Tensor: + """ + Return the rotation matrices for one of the rotations about an axis + of which Euler angles describe, for each value of the angle given. + + Args: + axis: Axis label "X" or "Y or "Z". + angle: any shape tensor of Euler angles in radians + + Returns: + Rotation matrices as tensor of shape (..., 3, 3). + """ + + cos = torch.cos(angle) + sin = torch.sin(angle) + one = torch.ones_like(angle) + zero = torch.zeros_like(angle) + + if axis == "X": + R_flat = (one, zero, zero, zero, cos, -sin, zero, sin, cos) + elif axis == "Y": + R_flat = (cos, zero, sin, zero, one, zero, -sin, zero, cos) + elif axis == "Z": + R_flat = (cos, -sin, zero, sin, cos, zero, zero, zero, one) + else: + raise ValueError("letter must be either X, Y or Z.") + + return torch.stack(R_flat, -1).reshape(angle.shape + (3, 3)) + + +def euler_angles_to_matrix(euler_angles: torch.Tensor, convention: str) -> torch.Tensor: + """ + Convert rotations given as Euler angles in radians to rotation matrices. + + Args: + euler_angles: Euler angles in radians as tensor of shape (..., 3). + convention: Convention string of three uppercase letters from + {"X", "Y", and "Z"}. + + Returns: + Rotation matrices as tensor of shape (..., 3, 3). + """ + if euler_angles.dim() == 0 or euler_angles.shape[-1] != 3: + raise ValueError("Invalid input euler angles.") + if len(convention) != 3: + raise ValueError("Convention must have 3 letters.") + if convention[1] in (convention[0], convention[2]): + raise ValueError(f"Invalid convention {convention}.") + for letter in convention: + if letter not in ("X", "Y", "Z"): + raise ValueError(f"Invalid letter {letter} in convention string.") + matrices = [ + _axis_angle_rotation(c, e) + for c, e in zip(convention, torch.unbind(euler_angles, -1)) + ] + # return functools.reduce(torch.matmul, matrices) + return torch.matmul(torch.matmul(matrices[0], matrices[1]), matrices[2]) + + + +def _angle_from_tan( + axis: str, other_axis: str, data, horizontal: bool, tait_bryan: bool +) -> torch.Tensor: + """ + Extract the first or third Euler angle from the two members of + the matrix which are positive constant times its sine and cosine. + + Args: + axis: Axis label "X" or "Y or "Z" for the angle we are finding. + other_axis: Axis label "X" or "Y or "Z" for the middle axis in the + convention. + data: Rotation matrices as tensor of shape (..., 3, 3). + horizontal: Whether we are looking for the angle for the third axis, + which means the relevant entries are in the same row of the + rotation matrix. If not, they are in the same column. + tait_bryan: Whether the first and third axes in the convention differ. + + Returns: + Euler Angles in radians for each matrix in data as a tensor + of shape (...). + """ + + i1, i2 = {"X": (2, 1), "Y": (0, 2), "Z": (1, 0)}[axis] + if horizontal: + i2, i1 = i1, i2 + even = (axis + other_axis) in ["XY", "YZ", "ZX"] + if horizontal == even: + return torch.atan2(data[..., i1], data[..., i2]) + if tait_bryan: + return torch.atan2(-data[..., i2], data[..., i1]) + return torch.atan2(data[..., i2], -data[..., i1]) + + +def _index_from_letter(letter: str) -> int: + if letter == "X": + return 0 + if letter == "Y": + return 1 + if letter == "Z": + return 2 + raise ValueError("letter must be either X, Y or Z.") + + +def matrix_to_euler_angles(matrix: torch.Tensor, convention: str) -> torch.Tensor: + """ + Convert rotations given as rotation matrices to Euler angles in radians. + + Args: + matrix: Rotation matrices as tensor of shape (..., 3, 3). + convention: Convention string of three uppercase letters. + + Returns: + Euler angles in radians as tensor of shape (..., 3). + """ + if len(convention) != 3: + raise ValueError("Convention must have 3 letters.") + if convention[1] in (convention[0], convention[2]): + raise ValueError(f"Invalid convention {convention}.") + for letter in convention: + if letter not in ("X", "Y", "Z"): + raise ValueError(f"Invalid letter {letter} in convention string.") + if matrix.size(-1) != 3 or matrix.size(-2) != 3: + raise ValueError(f"Invalid rotation matrix shape {matrix.shape}.") + i0 = _index_from_letter(convention[0]) + i2 = _index_from_letter(convention[2]) + tait_bryan = i0 != i2 + if tait_bryan: + central_angle = torch.asin( + matrix[..., i0, i2] * (-1.0 if i0 - i2 in [-1, 2] else 1.0) + ) + else: + central_angle = torch.acos(matrix[..., i0, i0]) + + o = ( + _angle_from_tan( + convention[0], convention[1], matrix[..., i2], False, tait_bryan + ), + central_angle, + _angle_from_tan( + convention[2], convention[1], matrix[..., i0, :], True, tait_bryan + ), + ) + return torch.stack(o, -1) + + + +def random_quaternions( + n: int, dtype: Optional[torch.dtype] = None, device: Optional[Device] = None +) -> torch.Tensor: + """ + Generate random quaternions representing rotations, + i.e. versors with nonnegative real part. + + Args: + n: Number of quaternions in a batch to return. + dtype: Type to return. + device: Desired device of returned tensor. Default: + uses the current device for the default tensor type. + + Returns: + Quaternions as tensor of shape (N, 4). + """ + if isinstance(device, str): + device = torch.device(device) + o = torch.randn((n, 4), dtype=dtype, device=device) + s = (o * o).sum(1) + o = o / _copysign(torch.sqrt(s), o[:, 0])[:, None] + return o + + + +def random_rotations( + n: int, dtype: Optional[torch.dtype] = None, device: Optional[Device] = None +) -> torch.Tensor: + """ + Generate random rotations as 3x3 rotation matrices. + + Args: + n: Number of rotation matrices in a batch to return. + dtype: Type to return. + device: Device of returned tensor. Default: if None, + uses the current device for the default tensor type. + + Returns: + Rotation matrices as tensor of shape (n, 3, 3). + """ + quaternions = random_quaternions(n, dtype=dtype, device=device) + return quaternion_to_matrix(quaternions) + + + +def random_rotation( + dtype: Optional[torch.dtype] = None, device: Optional[Device] = None +) -> torch.Tensor: + """ + Generate a single random 3x3 rotation matrix. + + Args: + dtype: Type to return + device: Device of returned tensor. Default: if None, + uses the current device for the default tensor type + + Returns: + Rotation matrix as tensor of shape (3, 3). + """ + return random_rotations(1, dtype, device)[0] + + + +def standardize_quaternion(quaternions: torch.Tensor) -> torch.Tensor: + """ + Convert a unit quaternion to a standard form: one in which the real + part is non negative. + + Args: + quaternions: Quaternions with real part first, + as tensor of shape (..., 4). + + Returns: + Standardized quaternions as tensor of shape (..., 4). + """ + return torch.where(quaternions[..., 0:1] < 0, -quaternions, quaternions) + + + +def quaternion_raw_multiply(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: + """ + Multiply two quaternions. + Usual torch rules for broadcasting apply. + + Args: + a: Quaternions as tensor of shape (..., 4), real part first. + b: Quaternions as tensor of shape (..., 4), real part first. + + Returns: + The product of a and b, a tensor of quaternions shape (..., 4). + """ + aw, ax, ay, az = torch.unbind(a, -1) + bw, bx, by, bz = torch.unbind(b, -1) + ow = aw * bw - ax * bx - ay * by - az * bz + ox = aw * bx + ax * bw + ay * bz - az * by + oy = aw * by - ax * bz + ay * bw + az * bx + oz = aw * bz + ax * by - ay * bx + az * bw + return torch.stack((ow, ox, oy, oz), -1) + + + +def quaternion_multiply(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: + """ + Multiply two quaternions representing rotations, returning the quaternion + representing their composition, i.e. the versor with nonnegative real part. + Usual torch rules for broadcasting apply. + + Args: + a: Quaternions as tensor of shape (..., 4), real part first. + b: Quaternions as tensor of shape (..., 4), real part first. + + Returns: + The product of a and b, a tensor of quaternions of shape (..., 4). + """ + ab = quaternion_raw_multiply(a, b) + return standardize_quaternion(ab) + + + +def quaternion_invert(quaternion: torch.Tensor) -> torch.Tensor: + """ + Given a quaternion representing rotation, get the quaternion representing + its inverse. + + Args: + quaternion: Quaternions as tensor of shape (..., 4), with real part + first, which must be versors (unit quaternions). + + Returns: + The inverse, a tensor of quaternions of shape (..., 4). + """ + + scaling = torch.tensor([1, -1, -1, -1], device=quaternion.device) + return quaternion * scaling + + + +def quaternion_apply(quaternion: torch.Tensor, point: torch.Tensor) -> torch.Tensor: + """ + Apply the rotation given by a quaternion to a 3D point. + Usual torch rules for broadcasting apply. + + Args: + quaternion: Tensor of quaternions, real part first, of shape (..., 4). + point: Tensor of 3D points of shape (..., 3). + + Returns: + Tensor of rotated points of shape (..., 3). + """ + if point.size(-1) != 3: + raise ValueError(f"Points are not in 3D, {point.shape}.") + real_parts = point.new_zeros(point.shape[:-1] + (1,)) + point_as_quaternion = torch.cat((real_parts, point), -1) + out = quaternion_raw_multiply( + quaternion_raw_multiply(quaternion, point_as_quaternion), + quaternion_invert(quaternion), + ) + return out[..., 1:] + + + +def axis_angle_to_matrix(axis_angle: torch.Tensor) -> torch.Tensor: + """ + Convert rotations given as axis/angle to rotation matrices. + + Args: + axis_angle: Rotations given as a vector in axis angle form, + as a tensor of shape (..., 3), where the magnitude is + the angle turned anticlockwise in radians around the + vector's direction. + + Returns: + Rotation matrices as tensor of shape (..., 3, 3). + """ + return quaternion_to_matrix(axis_angle_to_quaternion(axis_angle)) + + + +def matrix_to_axis_angle(matrix: torch.Tensor) -> torch.Tensor: + """ + Convert rotations given as rotation matrices to axis/angle. + + Args: + matrix: Rotation matrices as tensor of shape (..., 3, 3). + + Returns: + Rotations given as a vector in axis angle form, as a tensor + of shape (..., 3), where the magnitude is the angle + turned anticlockwise in radians around the vector's + direction. + """ + return quaternion_to_axis_angle(matrix_to_quaternion(matrix)) + + + +def axis_angle_to_quaternion(axis_angle: torch.Tensor) -> torch.Tensor: + """ + Convert rotations given as axis/angle to quaternions. + + Args: + axis_angle: Rotations given as a vector in axis angle form, + as a tensor of shape (..., 3), where the magnitude is + the angle turned anticlockwise in radians around the + vector's direction. + + Returns: + quaternions with real part first, as tensor of shape (..., 4). + """ + angles = torch.norm(axis_angle, p=2, dim=-1, keepdim=True) + half_angles = angles * 0.5 + eps = 1e-6 + small_angles = angles.abs() < eps + sin_half_angles_over_angles = torch.empty_like(angles) + sin_half_angles_over_angles[~small_angles] = ( + torch.sin(half_angles[~small_angles]) / angles[~small_angles] + ) + # for x small, sin(x/2) is about x/2 - (x/2)^3/6 + # so sin(x/2)/x is about 1/2 - (x*x)/48 + sin_half_angles_over_angles[small_angles] = ( + 0.5 - (angles[small_angles] * angles[small_angles]) / 48 + ) + quaternions = torch.cat( + [torch.cos(half_angles), axis_angle * sin_half_angles_over_angles], dim=-1 + ) + return quaternions + + + +def quaternion_to_axis_angle(quaternions: torch.Tensor) -> torch.Tensor: + """ + Convert rotations given as quaternions to axis/angle. + + Args: + quaternions: quaternions with real part first, + as tensor of shape (..., 4). + + Returns: + Rotations given as a vector in axis angle form, as a tensor + of shape (..., 3), where the magnitude is the angle + turned anticlockwise in radians around the vector's + direction. + """ + norms = torch.norm(quaternions[..., 1:], p=2, dim=-1, keepdim=True) + half_angles = torch.atan2(norms, quaternions[..., :1]) + angles = 2 * half_angles + eps = 1e-6 + small_angles = angles.abs() < eps + sin_half_angles_over_angles = torch.empty_like(angles) + sin_half_angles_over_angles[~small_angles] = ( + torch.sin(half_angles[~small_angles]) / angles[~small_angles] + ) + # for x small, sin(x/2) is about x/2 - (x/2)^3/6 + # so sin(x/2)/x is about 1/2 - (x*x)/48 + sin_half_angles_over_angles[small_angles] = ( + 0.5 - (angles[small_angles] * angles[small_angles]) / 48 + ) + return quaternions[..., 1:] / sin_half_angles_over_angles + + + +def rotation_6d_to_matrix(d6: torch.Tensor) -> torch.Tensor: + """ + Converts 6D rotation representation by Zhou et al. [1] to rotation matrix + using Gram--Schmidt orthogonalization per Section B of [1]. + Args: + d6: 6D rotation representation, of size (*, 6) + + Returns: + batch of rotation matrices of size (*, 3, 3) + + [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. + On the Continuity of Rotation Representations in Neural Networks. + IEEE Conference on Computer Vision and Pattern Recognition, 2019. + Retrieved from http://arxiv.org/abs/1812.07035 + """ + + a1, a2 = d6[..., :3], d6[..., 3:] + b1 = F.normalize(a1, dim=-1) + b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1 + b2 = F.normalize(b2, dim=-1) + b3 = torch.cross(b1, b2, dim=-1) + return torch.stack((b1, b2, b3), dim=-2) + + +def matrix_to_rotation_6d(matrix: torch.Tensor) -> torch.Tensor: + """ + Converts rotation matrices to 6D rotation representation by Zhou et al. [1] + by dropping the last row. Note that 6D representation is not unique. + Args: + matrix: batch of rotation matrices of size (*, 3, 3) + + Returns: + 6D rotation representation, of size (*, 6) + + [1] Zhou, Y., Barnes, C., Lu, J., Yang, J., & Li, H. + On the Continuity of Rotation Representations in Neural Networks. + IEEE Conference on Computer Vision and Pattern Recognition, 2019. + Retrieved from http://arxiv.org/abs/1812.07035 + """ + batch_dim = matrix.size()[:-2] + return matrix[..., :2, :].clone().reshape(batch_dim + (6,)) + + +def clean_rotation_6d(d6d: torch.Tensor) -> torch.Tensor: + """ + Clean rotation 6d by converting it to matrix and then reconvert to d6 + """ + matrix = rotation_6d_to_matrix(d6d) + d6d = matrix_to_rotation_6d(matrix) + return d6d + + +def rot6d_to_rotmat(x): + """Convert 6D rotation representation to 3x3 rotation matrix. + Based on Zhou et al., "On the Continuity of Rotation Representations in Neural Networks", CVPR 2019 + Input: + (B,6) Batch of 6-D rotation representations + Output: + (B,3,3) Batch of corresponding rotation matrices + """ + if x.shape[-1] == 6: + batch_dim = x.size()[:-1] + else: + x = x.reshape(*x.shape[:-1], -1, 6) + batch_dim = x.size()[:-1] + + x = x.reshape(*batch_dim, 3, 2) + a1, a2 = x[..., 0], x[..., 1] + + b1 = F.normalize(a1, dim=-1) + b2 = a2 - (b1 * a2).sum(-1, keepdim=True) * b1 + b2 = F.normalize(b2, dim=-1) + b3 = torch.cross(b1, b2, dim=-1) + + return torch.stack((b1, b2, b3), dim=-1) + + +def rotmat_to_rot6d(x): + """Inverse computation of rot6d_to_rotmat.""" + batch_dim = x.size()[:-2] + return x[..., :2].clone().reshape(batch_dim + (6,)) + + +def convert_rotation_matrix_to_homogeneous(rotation_matrix): + "Add empty translation vector to Rotation matrix""" + + transl = torch.zeros_like(rotation_matrix[...,:1]) + rotation_matrix_hom = torch.cat((rotation_matrix, transl), dim=-1) + + return rotation_matrix_hom + + +def rotation_matrix_to_angle_axis(rotation_matrix): + """Convert 3x4 rotation matrix to Rodrigues vector + + Args: + rotation_matrix (Tensor): rotation matrix. + + Returns: + Tensor: Rodrigues vector transformation. + + Shape: + - Input: :math:`(N, 3, 4)` + - Output: :math:`(N, 3)` + + Example: + >>> input = torch.rand(2, 3, 4) # Nx3x4 + >>> output = tgm.rotation_matrix_to_angle_axis(input) # Nx3 + """ + + if rotation_matrix.size(-1) == 3: + rotation_matrix = convert_rotation_matrix_to_homogeneous(rotation_matrix) + + quaternion = rotation_matrix_to_quaternion(rotation_matrix) + return quaternion_to_angle_axis(quaternion) + + +def rotation_matrix_to_quaternion(rotation_matrix, eps=1e-6): + """Convert 3x4 rotation matrix to 4d quaternion vector + + This algorithm is based on algorithm described in + https://github.com/KieranWynn/pyquaternion/blob/master/pyquaternion/quaternion.py#L201 + + Args: + rotation_matrix (Tensor): the rotation matrix to convert. + + Return: + Tensor: the rotation in quaternion + + Shape: + - Input: :math:`(N, 3, 4)` + - Output: :math:`(N, 4)` + + Example: + >>> input = torch.rand(4, 3, 4) # Nx3x4 + >>> output = tgm.rotation_matrix_to_quaternion(input) # Nx4 + """ + if not torch.is_tensor(rotation_matrix): + raise TypeError("Input type is not a torch.Tensor. Got {}".format( + type(rotation_matrix))) + + if len(rotation_matrix.shape) > 3: + raise ValueError( + "Input size must be a three dimensional tensor. Got {}".format( + rotation_matrix.shape)) + if not rotation_matrix.shape[-2:] == (3, 4): + raise ValueError( + "Input size must be a N x 3 x 4 tensor. Got {}".format( + rotation_matrix.shape)) + + rmat_t = torch.transpose(rotation_matrix, 1, 2) + + mask_d2 = rmat_t[:, 2, 2] < eps + + mask_d0_d1 = rmat_t[:, 0, 0] > rmat_t[:, 1, 1] + mask_d0_nd1 = rmat_t[:, 0, 0] < -rmat_t[:, 1, 1] + + t0 = 1 + rmat_t[:, 0, 0] - rmat_t[:, 1, 1] - rmat_t[:, 2, 2] + q0 = torch.stack([rmat_t[:, 1, 2] - rmat_t[:, 2, 1], + t0, rmat_t[:, 0, 1] + rmat_t[:, 1, 0], + rmat_t[:, 2, 0] + rmat_t[:, 0, 2]], -1) + t0_rep = t0.repeat(4, 1).t() + + t1 = 1 - rmat_t[:, 0, 0] + rmat_t[:, 1, 1] - rmat_t[:, 2, 2] + q1 = torch.stack([rmat_t[:, 2, 0] - rmat_t[:, 0, 2], + rmat_t[:, 0, 1] + rmat_t[:, 1, 0], + t1, rmat_t[:, 1, 2] + rmat_t[:, 2, 1]], -1) + t1_rep = t1.repeat(4, 1).t() + + t2 = 1 - rmat_t[:, 0, 0] - rmat_t[:, 1, 1] + rmat_t[:, 2, 2] + q2 = torch.stack([rmat_t[:, 0, 1] - rmat_t[:, 1, 0], + rmat_t[:, 2, 0] + rmat_t[:, 0, 2], + rmat_t[:, 1, 2] + rmat_t[:, 2, 1], t2], -1) + t2_rep = t2.repeat(4, 1).t() + + t3 = 1 + rmat_t[:, 0, 0] + rmat_t[:, 1, 1] + rmat_t[:, 2, 2] + q3 = torch.stack([t3, rmat_t[:, 1, 2] - rmat_t[:, 2, 1], + rmat_t[:, 2, 0] - rmat_t[:, 0, 2], + rmat_t[:, 0, 1] - rmat_t[:, 1, 0]], -1) + t3_rep = t3.repeat(4, 1).t() + + mask_c0 = mask_d2 * mask_d0_d1 + # mask_c1 = mask_d2 * (1 - mask_d0_d1) + mask_c1 = mask_d2 * ~mask_d0_d1 + # mask_c2 = (1 - mask_d2) * mask_d0_nd1 + mask_c2 = ~mask_d2 * mask_d0_nd1 + # mask_c3 = (1 - mask_d2) * (1 - mask_d0_nd1) + mask_c3 = ~mask_d2 * ~mask_d0_nd1 + mask_c0 = mask_c0.view(-1, 1).type_as(q0) + mask_c1 = mask_c1.view(-1, 1).type_as(q1) + mask_c2 = mask_c2.view(-1, 1).type_as(q2) + mask_c3 = mask_c3.view(-1, 1).type_as(q3) + + q = q0 * mask_c0 + q1 * mask_c1 + q2 * mask_c2 + q3 * mask_c3 + q /= torch.sqrt(t0_rep * mask_c0 + t1_rep * mask_c1 + # noqa + t2_rep * mask_c2 + t3_rep * mask_c3) # noqa + q *= 0.5 + return q + + +def quaternion_to_angle_axis(quaternion: torch.Tensor) -> torch.Tensor: + """Convert quaternion vector to angle axis of rotation. + + Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h + + Args: + quaternion (torch.Tensor): tensor with quaternions. + + Return: + torch.Tensor: tensor with angle axis of rotation. + + Shape: + - Input: :math:`(*, 4)` where `*` means, any number of dimensions + - Output: :math:`(*, 3)` + + Example: + >>> quaternion = torch.rand(2, 4) # Nx4 + >>> angle_axis = tgm.quaternion_to_angle_axis(quaternion) # Nx3 + """ + if not torch.is_tensor(quaternion): + raise TypeError("Input type is not a torch.Tensor. Got {}".format( + type(quaternion))) + + if not quaternion.shape[-1] == 4: + raise ValueError("Input must be a tensor of shape Nx4 or 4. Got {}" + .format(quaternion.shape)) + # unpack input and compute conversion + q1: torch.Tensor = quaternion[..., 1] + q2: torch.Tensor = quaternion[..., 2] + q3: torch.Tensor = quaternion[..., 3] + sin_squared_theta: torch.Tensor = q1 * q1 + q2 * q2 + q3 * q3 + + sin_theta: torch.Tensor = torch.sqrt(sin_squared_theta) + cos_theta: torch.Tensor = quaternion[..., 0] + two_theta: torch.Tensor = 2.0 * torch.where( + cos_theta < 0.0, + torch.atan2(-sin_theta, -cos_theta), + torch.atan2(sin_theta, cos_theta)) + + k_pos: torch.Tensor = two_theta / sin_theta + k_neg: torch.Tensor = 2.0 * torch.ones_like(sin_theta) + k: torch.Tensor = torch.where(sin_squared_theta > 0.0, k_pos, k_neg) + + angle_axis: torch.Tensor = torch.zeros_like(quaternion)[..., :3] + angle_axis[..., 0] += q1 * k + angle_axis[..., 1] += q2 * k + angle_axis[..., 2] += q3 * k + return angle_axis + + +def avg_rot(rot): + # input [B,...,3,3] --> output [...,3,3] + rot = rot.mean(dim=0) + U, _, V = torch.svd(rot) + rot = U @ V.transpose(-1, -2) + return rot \ No newline at end of file diff --git a/lib/utils/utils.py b/lib/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..36679d136065c2c14a91bfbf242825284e88892f --- /dev/null +++ b/lib/utils/utils.py @@ -0,0 +1,265 @@ +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2019 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +import os +import yaml +import torch +import shutil +import logging +import operator +from tqdm import tqdm +from os import path as osp +from functools import reduce +from typing import List, Union +from collections import OrderedDict +from torch.optim.lr_scheduler import _LRScheduler + +class CustomScheduler(_LRScheduler): + def __init__(self, optimizer, lr_lambda): + self.lr_lambda = lr_lambda + super(CustomScheduler, self).__init__(optimizer) + + def get_lr(self): + return [base_lr * self.lr_lambda(self.last_epoch) + for base_lr in self.base_lrs] + +def lr_decay_fn(epoch): + if epoch == 0: return 1.0 + if epoch % big_epoch == 0: + return big_decay + else: + return small_decay + +def save_obj(v, f, file_name='output.obj'): + obj_file = open(file_name, 'w') + for i in range(len(v)): + obj_file.write('v ' + str(v[i][0]) + ' ' + str(v[i][1]) + ' ' + str(v[i][2]) + '\n') + for i in range(len(f)): + obj_file.write('f ' + str(f[i][0]+1) + '/' + str(f[i][0]+1) + ' ' + str(f[i][1]+1) + '/' + str(f[i][1]+1) + ' ' + str(f[i][2]+1) + '/' + str(f[i][2]+1) + '\n') + obj_file.close() + + +def check_data_pararell(train_weight): + new_state_dict = OrderedDict() + for k, v in train_weight.items(): + name = k[7:] if k.startswith('module') else k # remove `module.` + new_state_dict[name] = v + return new_state_dict + + +def get_from_dict(dict, keys): + return reduce(operator.getitem, keys, dict) + + +def tqdm_enumerate(iter): + i = 0 + for y in tqdm(iter): + yield i, y + i += 1 + + +def iterdict(d): + for k,v in d.items(): + if isinstance(v, dict): + d[k] = dict(v) + iterdict(v) + return d + + +def accuracy(output, target): + _, pred = output.topk(1) + pred = pred.view(-1) + + correct = pred.eq(target).sum() + + return correct.item(), target.size(0) - correct.item() + + +def lr_decay(optimizer, step, lr, decay_step, gamma): + lr = lr * gamma ** (step/decay_step) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + return lr + + +def step_decay(optimizer, step, lr, decay_step, gamma): + lr = lr * gamma ** (step / decay_step) + for param_group in optimizer.param_groups: + param_group['lr'] = lr + return lr + + +def read_yaml(filename): + return yaml.load(open(filename, 'r')) + + +def write_yaml(filename, object): + with open(filename, 'w') as f: + yaml.dump(object, f) + + +def save_dict_to_yaml(obj, filename, mode='w'): + with open(filename, mode) as f: + yaml.dump(obj, f, default_flow_style=False) + + +def save_to_file(obj, filename, mode='w'): + with open(filename, mode) as f: + f.write(obj) + + +def concatenate_dicts(dict_list, dim=0): + rdict = dict.fromkeys(dict_list[0].keys()) + for k in rdict.keys(): + rdict[k] = torch.cat([d[k] for d in dict_list], dim=dim) + return rdict + + +def bool_to_string(x: Union[List[bool],bool]) -> Union[List[str],str]: + """ + boolean to string conversion + :param x: list or bool to be converted + :return: string converted thing + """ + if isinstance(x, bool): + return [str(x)] + for i, j in enumerate(x): + x[i]=str(j) + return x + + +def checkpoint2model(checkpoint, key='gen_state_dict'): + state_dict = checkpoint[key] + print(f'Performance of loaded model on 3DPW is {checkpoint["performance"]:.2f}mm') + # del state_dict['regressor.mean_theta'] + return state_dict + + +def get_optimizer(cfg, model, optim_type, momentum, stage): + if stage == 'stage2': + param_list = [{'params': model.integrator.parameters()}] + for name, param in model.named_parameters(): + # if 'integrator' not in name and 'motion_encoder' not in name and 'trajectory_decoder' not in name: + if 'integrator' not in name: + param_list.append({'params': param, 'lr': cfg.TRAIN.LR_FINETUNE}) + else: + param_list = [{'params': model.parameters()}] + + if optim_type in ['sgd', 'SGD']: + opt = torch.optim.SGD(lr=cfg.TRAIN.LR, params=param_list, momentum=momentum) + elif optim_type in ['Adam', 'adam', 'ADAM']: + opt = torch.optim.Adam(lr=cfg.TRAIN.LR, params=param_list, weight_decay=cfg.TRAIN.WD, betas=(0.9, 0.999)) + else: + raise ModuleNotFoundError + + return opt + + +def create_logger(logdir, phase='train'): + os.makedirs(logdir, exist_ok=True) + + log_file = osp.join(logdir, f'{phase}_log.txt') + + head = '%(asctime)-15s %(message)s' + logging.basicConfig(filename=log_file, + format=head) + logger = logging.getLogger() + logger.setLevel(logging.INFO) + console = logging.StreamHandler() + logging.getLogger('').addHandler(console) + + return logger + + +class AverageMeter(object): + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + +def prepare_output_dir(cfg, cfg_file): + + # ==== create logdir + logdir = osp.join(cfg.OUTPUT_DIR, cfg.EXP_NAME) + os.makedirs(logdir, exist_ok=True) + shutil.copy(src=cfg_file, dst=osp.join(cfg.OUTPUT_DIR, 'config.yaml')) + + cfg.LOGDIR = logdir + + # save config + save_dict_to_yaml(cfg, osp.join(cfg.LOGDIR, 'config.yaml')) + + return cfg + + +def prepare_groundtruth(batch, device): + groundtruths = dict() + gt_keys = ['pose', 'cam', 'betas', 'kp3d', 'bbox'] # Evaluation + gt_keys += ['pose_root', 'vel_root', 'weak_kp2d', 'verts', # Training + 'full_kp2d', 'contact', 'R', 'cam_angvel', + 'has_smpl', 'has_traj', 'has_full_screen', 'has_verts'] + for gt_key in gt_keys: + if gt_key in batch.keys(): + dtype = torch.float32 if batch[gt_key].dtype == torch.float64 else batch[gt_key].dtype + groundtruths[gt_key] = batch[gt_key].to(dtype=dtype, device=device) + + return groundtruths + +def prepare_auxiliary(batch, device): + aux = dict() + aux_keys = ['mask', 'bbox', 'res', 'cam_intrinsics', 'init_root', 'cam_angvel'] + for key in aux_keys: + if key in batch.keys(): + dtype = torch.float32 if batch[key].dtype == torch.float64 else batch[key].dtype + aux[key] = batch[key].to(dtype=dtype, device=device) + + return aux + +def prepare_input(batch, device, use_features): + # Input keypoints data + kp2d = batch['kp2d'].to(device).float() + + # Input features + if use_features and 'features' in batch.keys(): + features = batch['features'].to(device).float() + else: + features = None + + # Initial SMPL parameters + init_smpl = batch['init_pose'].to(device).float() + + # Initial keypoints + init_kp = torch.cat(( + batch['init_kp3d'], batch['init_kp2d'] + ), dim=-1).to(device).float() + + return kp2d, (init_kp, init_smpl), features + + +def prepare_batch(batch, device, use_features=True): + x, inits, features = prepare_input(batch, device, use_features) + aux = prepare_auxiliary(batch, device) + groundtruths = prepare_groundtruth(batch, device) + + return x, inits, features, aux, groundtruths \ No newline at end of file diff --git a/lib/vis/__pycache__/renderer.cpython-39.pyc b/lib/vis/__pycache__/renderer.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..efcbde1428aa6e99af3ad25474a154ea0358b64b Binary files /dev/null and b/lib/vis/__pycache__/renderer.cpython-39.pyc differ diff --git a/lib/vis/__pycache__/run_vis.cpython-39.pyc b/lib/vis/__pycache__/run_vis.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..28210c497b31cf312b0baf27d3608fcb3374fa8b Binary files /dev/null and b/lib/vis/__pycache__/run_vis.cpython-39.pyc differ diff --git a/lib/vis/__pycache__/tools.cpython-39.pyc b/lib/vis/__pycache__/tools.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..787508d20d954cf556be085974608fb11a103312 Binary files /dev/null and b/lib/vis/__pycache__/tools.cpython-39.pyc differ diff --git a/lib/vis/renderer.py b/lib/vis/renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..1f4dc6e4e78f99c96272c38e2035bd4af9c645a3 --- /dev/null +++ b/lib/vis/renderer.py @@ -0,0 +1,313 @@ +import cv2 +import torch +import numpy as np + +from pytorch3d.renderer import ( + PerspectiveCameras, + TexturesVertex, + PointLights, + Materials, + RasterizationSettings, + MeshRenderer, + MeshRasterizer, + SoftPhongShader, +) +from pytorch3d.structures import Meshes +from pytorch3d.structures.meshes import join_meshes_as_scene +from pytorch3d.renderer.cameras import look_at_rotation + +from .tools import get_colors, checkerboard_geometry + + +def overlay_image_onto_background(image, mask, bbox, background): + if isinstance(image, torch.Tensor): + image = image.detach().cpu().numpy() + if isinstance(mask, torch.Tensor): + mask = mask.detach().cpu().numpy() + + out_image = background.copy() + bbox = bbox[0].int().cpu().numpy().copy() + roi_image = out_image[bbox[1]:bbox[3], bbox[0]:bbox[2]] + + roi_image[mask] = image[mask] + out_image[bbox[1]:bbox[3], bbox[0]:bbox[2]] = roi_image + + return out_image + + +def update_intrinsics_from_bbox(K_org, bbox): + device, dtype = K_org.device, K_org.dtype + + K = torch.zeros((K_org.shape[0], 4, 4) + ).to(device=device, dtype=dtype) + K[:, :3, :3] = K_org.clone() + K[:, 2, 2] = 0 + K[:, 2, -1] = 1 + K[:, -1, 2] = 1 + + image_sizes = [] + for idx, bbox in enumerate(bbox): + left, upper, right, lower = bbox + cx, cy = K[idx, 0, 2], K[idx, 1, 2] + + new_cx = cx - left + new_cy = cy - upper + new_height = max(lower - upper, 1) + new_width = max(right - left, 1) + new_cx = new_width - new_cx + new_cy = new_height - new_cy + + K[idx, 0, 2] = new_cx + K[idx, 1, 2] = new_cy + image_sizes.append((int(new_height), int(new_width))) + + return K, image_sizes + + +def perspective_projection(x3d, K, R=None, T=None): + if R != None: + x3d = torch.matmul(R, x3d.transpose(1, 2)).transpose(1, 2) + if T != None: + x3d = x3d + T.transpose(1, 2) + + x2d = torch.div(x3d, x3d[..., 2:]) + x2d = torch.matmul(K, x2d.transpose(-1, -2)).transpose(-1, -2)[..., :2] + return x2d + + +def compute_bbox_from_points(X, img_w, img_h, scaleFactor=1.2): + left = torch.clamp(X.min(1)[0][:, 0], min=0, max=img_w) + right = torch.clamp(X.max(1)[0][:, 0], min=0, max=img_w) + top = torch.clamp(X.min(1)[0][:, 1], min=0, max=img_h) + bottom = torch.clamp(X.max(1)[0][:, 1], min=0, max=img_h) + + cx = (left + right) / 2 + cy = (top + bottom) / 2 + width = (right - left) + height = (bottom - top) + + new_left = torch.clamp(cx - width/2 * scaleFactor, min=0, max=img_w-1) + new_right = torch.clamp(cx + width/2 * scaleFactor, min=1, max=img_w) + new_top = torch.clamp(cy - height / 2 * scaleFactor, min=0, max=img_h-1) + new_bottom = torch.clamp(cy + height / 2 * scaleFactor, min=1, max=img_h) + + bbox = torch.stack((new_left.detach(), new_top.detach(), + new_right.detach(), new_bottom.detach())).int().float().T + + return bbox + + +class Renderer(): + def __init__(self, width, height, focal_length, device, faces=None): + + self.width = width + self.height = height + self.focal_length = focal_length + + self.device = device + if faces is not None: + self.faces = torch.from_numpy( + (faces).astype('int') + ).unsqueeze(0).to(self.device) + + self.initialize_camera_params() + self.lights = PointLights(device=device, location=[[0.0, 0.0, -10.0]]) + self.create_renderer() + + def create_renderer(self): + self.renderer = MeshRenderer( + rasterizer=MeshRasterizer( + raster_settings=RasterizationSettings( + image_size=self.image_sizes[0], + blur_radius=1e-5), + ), + shader=SoftPhongShader( + device=self.device, + lights=self.lights, + ) + ) + + def create_camera(self, R=None, T=None): + if R is not None: + self.R = R.clone().view(1, 3, 3).to(self.device) + if T is not None: + self.T = T.clone().view(1, 3).to(self.device) + + return PerspectiveCameras( + device=self.device, + R=self.R.mT, + T=self.T, + K=self.K_full, + image_size=self.image_sizes, + in_ndc=False) + + + def initialize_camera_params(self): + """Hard coding for camera parameters + TODO: Do some soft coding""" + + # Extrinsics + self.R = torch.diag( + torch.tensor([1, 1, 1]) + ).float().to(self.device).unsqueeze(0) + + self.T = torch.tensor( + [0, 0, 0] + ).unsqueeze(0).float().to(self.device) + + # Intrinsics + self.K = torch.tensor( + [[self.focal_length, 0, self.width/2], + [0, self.focal_length, self.height/2], + [0, 0, 1]] + ).unsqueeze(0).float().to(self.device) + self.bboxes = torch.tensor([[0, 0, self.width, self.height]]).float() + self.K_full, self.image_sizes = update_intrinsics_from_bbox(self.K, self.bboxes) + self.cameras = self.create_camera() + + + def set_ground(self, length, center_x, center_z): + device = self.device + v, f, vc, fc = map(torch.from_numpy, checkerboard_geometry(length=length, c1=center_x, c2=center_z, up="y")) + v, f, vc = v.to(device), f.to(device), vc.to(device) + self.ground_geometry = [v, f, vc] + + + def update_bbox(self, x3d, scale=2.0, mask=None): + """ Update bbox of cameras from the given 3d points + + x3d: input 3D keypoints (or vertices), (num_frames, num_points, 3) + """ + + if x3d.size(-1) != 3: + x2d = x3d.unsqueeze(0) + else: + x2d = perspective_projection(x3d.unsqueeze(0), self.K, self.R, self.T.reshape(1, 3, 1)) + + if mask is not None: + x2d = x2d[:, ~mask] + + bbox = compute_bbox_from_points(x2d, self.width, self.height, scale) + self.bboxes = bbox + + self.K_full, self.image_sizes = update_intrinsics_from_bbox(self.K, bbox) + self.cameras = self.create_camera() + self.create_renderer() + + def reset_bbox(self,): + bbox = torch.zeros((1, 4)).float().to(self.device) + bbox[0, 2] = self.width + bbox[0, 3] = self.height + self.bboxes = bbox + + self.K_full, self.image_sizes = update_intrinsics_from_bbox(self.K, bbox) + self.cameras = self.create_camera() + self.create_renderer() + + def render_mesh(self, vertices, background, colors=[0.8, 0.8, 0.8]): + self.update_bbox(vertices[::50], scale=1.2) + vertices = vertices.unsqueeze(0) + + if colors[0] > 1: colors = [c / 255. for c in colors] + verts_features = torch.tensor(colors).reshape(1, 1, 3).to(device=vertices.device, dtype=vertices.dtype) + verts_features = verts_features.repeat(1, vertices.shape[1], 1) + textures = TexturesVertex(verts_features=verts_features) + + mesh = Meshes(verts=vertices, + faces=self.faces, + textures=textures,) + + materials = Materials( + device=self.device, + specular_color=(colors, ), + shininess=0 + ) + + results = torch.flip( + self.renderer(mesh, materials=materials, cameras=self.cameras, lights=self.lights), + [1, 2] + ) + image = results[0, ..., :3] * 255 + mask = results[0, ..., -1] > 1e-3 + + image = overlay_image_onto_background(image, mask, self.bboxes, background.copy()) + self.reset_bbox() + return image + + + def render_with_ground(self, verts, faces, colors, cameras, lights): + """ + :param verts (B, V, 3) + :param faces (F, 3) + :param colors (B, 3) + """ + + # (B, V, 3), (B, F, 3), (B, V, 3) + verts, faces, colors = prep_shared_geometry(verts, faces, colors) + # (V, 3), (F, 3), (V, 3) + gv, gf, gc = self.ground_geometry + verts = list(torch.unbind(verts, dim=0)) + [gv] + faces = list(torch.unbind(faces, dim=0)) + [gf] + colors = list(torch.unbind(colors, dim=0)) + [gc[..., :3]] + mesh = create_meshes(verts, faces, colors) + + materials = Materials( + device=self.device, + shininess=0 + ) + + results = self.renderer(mesh, cameras=cameras, lights=lights, materials=materials) + image = (results[0, ..., :3].cpu().numpy() * 255).astype(np.uint8) + + return image + + +def prep_shared_geometry(verts, faces, colors): + """ + :param verts (B, V, 3) + :param faces (F, 3) + :param colors (B, 4) + """ + B, V, _ = verts.shape + F, _ = faces.shape + colors = colors.unsqueeze(1).expand(B, V, -1)[..., :3] + faces = faces.unsqueeze(0).expand(B, F, -1) + return verts, faces, colors + + +def create_meshes(verts, faces, colors): + """ + :param verts (B, V, 3) + :param faces (B, F, 3) + :param colors (B, V, 3) + """ + textures = TexturesVertex(verts_features=colors) + meshes = Meshes(verts=verts, faces=faces, textures=textures) + return join_meshes_as_scene(meshes) + + +def get_global_cameras(verts, device, distance=5, position=(-5.0, 5.0, 0.0)): + positions = torch.tensor([position]).repeat(len(verts), 1) + targets = verts.mean(1) + + directions = targets - positions + directions = directions / torch.norm(directions, dim=-1).unsqueeze(-1) * distance + positions = targets - directions + + rotation = look_at_rotation(positions, targets, ).mT + translation = -(rotation @ positions.unsqueeze(-1)).squeeze(-1) + + lights = PointLights(device=device, location=[position]) + return rotation, translation, lights + + +def _get_global_cameras(verts, device, min_distance=3, chunk_size=100): + + # split into smaller chunks to visualize + start_idxs = list(range(0, len(verts), chunk_size)) + end_idxs = [min(start_idx + chunk_size, len(verts)) for start_idx in start_idxs] + + Rs, Ts = [], [] + for start_idx, end_idx in zip(start_idxs, end_idxs): + vert = verts[start_idx:end_idx].clone() + import pdb; pdb.set_trace() \ No newline at end of file diff --git a/lib/vis/run_vis.py b/lib/vis/run_vis.py new file mode 100644 index 0000000000000000000000000000000000000000..d940a624ab5109b43fb732292f99b75c5461e684 --- /dev/null +++ b/lib/vis/run_vis.py @@ -0,0 +1,92 @@ +import os +import os.path as osp + +import cv2 +import torch +import imageio +import numpy as np +from progress.bar import Bar + +from lib.vis.renderer import Renderer, get_global_cameras + +def run_vis_on_demo(cfg, video, results, output_pth, smpl, vis_global=True): + # to torch tensor + tt = lambda x: torch.from_numpy(x).float().to(cfg.DEVICE) + + cap = cv2.VideoCapture(video) + fps = cap.get(cv2.CAP_PROP_FPS) + length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + width, height = cap.get(cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT) + + # create renderer with cliff focal length estimation + focal_length = (width ** 2 + height ** 2) ** 0.5 + renderer = Renderer(width, height, focal_length, cfg.DEVICE, smpl.faces) + + if vis_global: + # setup global coordinate subject + # current implementation only visualize the subject appeared longest + n_frames = {k: len(results[k]['frame_ids']) for k in results.keys()} + sid = max(n_frames, key=n_frames.get) + global_output = smpl.get_output( + body_pose=tt(results[sid]['pose_world'][:, 3:]), + global_orient=tt(results[sid]['pose_world'][:, :3]), + betas=tt(results[sid]['betas']), + transl=tt(results[sid]['trans_world'])) + verts_glob = global_output.vertices.cpu() + verts_glob[..., 1] = verts_glob[..., 1] - verts_glob[..., 1].min() + cx, cz = (verts_glob.mean(1).max(0)[0] + verts_glob.mean(1).min(0)[0])[[0, 2]] / 2.0 + sx, sz = (verts_glob.mean(1).max(0)[0] - verts_glob.mean(1).min(0)[0])[[0, 2]] + scale = max(sx.item(), sz.item()) * 1.5 + + # set default ground + renderer.set_ground(scale, cx.item(), cz.item()) + + # build global camera + global_R, global_T, global_lights = get_global_cameras(verts_glob, cfg.DEVICE) + + # build default camera + default_R, default_T = torch.eye(3), torch.zeros(3) + + writer = imageio.get_writer( + osp.join(output_pth, 'output.mp4'), + fps=fps, mode='I', format='FFMPEG', macro_block_size=1 + ) + bar = Bar('Rendering results ...', fill='#', max=length) + + frame_i = 0 + _global_R, _global_T = None, None + # run rendering + while (cap.isOpened()): + flag, org_img = cap.read() + if not flag: break + img = org_img[..., ::-1].copy() + + # render onto the input video + renderer.create_camera(default_R, default_T) + for _id, val in results.items(): + # render onto the image + frame_i2 = np.where(val['frame_ids'] == frame_i)[0] + if len(frame_i2) == 0: continue + frame_i2 = frame_i2[0] + img = renderer.render_mesh(torch.from_numpy(val['verts'][frame_i2]).to(cfg.DEVICE), img) + + if vis_global: + # render the global coordinate + if frame_i in results[sid]['frame_ids']: + frame_i3 = np.where(results[sid]['frame_ids'] == frame_i)[0] + verts = verts_glob[[frame_i3]].to(cfg.DEVICE) + faces = renderer.faces.clone().squeeze(0) + colors = torch.ones((1, 4)).float().to(cfg.DEVICE); colors[..., :3] *= 0.9 + + if _global_R is None: + _global_R = global_R[frame_i3].clone(); _global_T = global_T[frame_i3].clone() + cameras = renderer.create_camera(global_R[frame_i3], global_T[frame_i3]) + img_glob = renderer.render_with_ground(verts, faces, colors, cameras, global_lights) + + try: img = np.concatenate((img, img_glob), axis=1) + except: img = np.concatenate((img, np.ones_like(img) * 255), axis=1) + + writer.append_data(img) + bar.next() + frame_i += 1 + writer.close() \ No newline at end of file diff --git a/lib/vis/tools.py b/lib/vis/tools.py new file mode 100644 index 0000000000000000000000000000000000000000..503e4377bcaa18d4d541381963fdd4458b81a7c1 --- /dev/null +++ b/lib/vis/tools.py @@ -0,0 +1,822 @@ +import os +import cv2 +import numpy as np +import torch +from PIL import Image + + +def read_image(path, scale=1): + im = Image.open(path) + if scale == 1: + return np.array(im) + W, H = im.size + w, h = int(scale * W), int(scale * H) + return np.array(im.resize((w, h), Image.ANTIALIAS)) + + +def transform_torch3d(T_c2w): + """ + :param T_c2w (*, 4, 4) + returns (*, 3, 3), (*, 3) + """ + R1 = torch.tensor( + [[-1.0, 0.0, 0.0], [0.0, -1.0, 0.0], [0.0, 0.0, 1.0],], device=T_c2w.device, + ) + R2 = torch.tensor( + [[1.0, 0.0, 0.0], [0.0, -1.0, 0.0], [0.0, 0.0, -1.0],], device=T_c2w.device, + ) + cam_R, cam_t = T_c2w[..., :3, :3], T_c2w[..., :3, 3] + cam_R = torch.einsum("...ij,jk->...ik", cam_R, R1) + cam_t = torch.einsum("ij,...j->...i", R2, cam_t) + return cam_R, cam_t + + +def transform_pyrender(T_c2w): + """ + :param T_c2w (*, 4, 4) + """ + T_vis = torch.tensor( + [ + [1.0, 0.0, 0.0, 0.0], + [0.0, -1.0, 0.0, 0.0], + [0.0, 0.0, -1.0, 0.0], + [0.0, 0.0, 0.0, 1.0], + ], + device=T_c2w.device, + ) + return torch.einsum( + "...ij,jk->...ik", torch.einsum("ij,...jk->...ik", T_vis, T_c2w), T_vis + ) + + +def smpl_to_geometry(verts, faces, vis_mask=None, track_ids=None): + """ + :param verts (B, T, V, 3) + :param faces (F, 3) + :param vis_mask (optional) (B, T) visibility of each person + :param track_ids (optional) (B,) + returns list of T verts (B, V, 3), faces (F, 3), colors (B, 3) + where B is different depending on the visibility of the people + """ + B, T = verts.shape[:2] + device = verts.device + + # (B, 3) + colors = ( + track_to_colors(track_ids) + if track_ids is not None + else torch.ones(B, 3, device) * 0.5 + ) + + # list T (B, V, 3), T (B, 3), T (F, 3) + return filter_visible_meshes(verts, colors, faces, vis_mask) + + +def filter_visible_meshes(verts, colors, faces, vis_mask=None, vis_opacity=False): + """ + :param verts (B, T, V, 3) + :param colors (B, 3) + :param faces (F, 3) + :param vis_mask (optional tensor, default None) (B, T) ternary mask + -1 if not in frame + 0 if temporarily occluded + 1 if visible + :param vis_opacity (optional bool, default False) + if True, make occluded people alpha=0.5, otherwise alpha=1 + returns a list of T lists verts (Bi, V, 3), colors (Bi, 4), faces (F, 3) + """ + # import ipdb; ipdb.set_trace() + B, T = verts.shape[:2] + faces = [faces for t in range(T)] + if vis_mask is None: + verts = [verts[:, t] for t in range(T)] + colors = [colors for t in range(T)] + return verts, colors, faces + + # render occluded and visible, but not removed + vis_mask = vis_mask >= 0 + if vis_opacity: + alpha = 0.5 * (vis_mask[..., None] + 1) + else: + alpha = (vis_mask[..., None] >= 0).float() + vert_list = [verts[vis_mask[:, t], t] for t in range(T)] + colors = [ + torch.cat([colors[vis_mask[:, t]], alpha[vis_mask[:, t], t]], dim=-1) + for t in range(T) + ] + bounds = get_bboxes(verts, vis_mask) + return vert_list, colors, faces, bounds + + +def get_bboxes(verts, vis_mask): + """ + return bb_min, bb_max, and mean for each track (B, 3) over entire trajectory + :param verts (B, T, V, 3) + :param vis_mask (B, T) + """ + B, T, *_ = verts.shape + bb_min, bb_max, mean = [], [], [] + for b in range(B): + v = verts[b, vis_mask[b, :T]] # (Tb, V, 3) + bb_min.append(v.amin(dim=(0, 1))) + bb_max.append(v.amax(dim=(0, 1))) + mean.append(v.mean(dim=(0, 1))) + bb_min = torch.stack(bb_min, dim=0) + bb_max = torch.stack(bb_max, dim=0) + mean = torch.stack(mean, dim=0) + # point to a track that's long and close to the camera + zs = mean[:, 2] + counts = vis_mask[:, :T].sum(dim=-1) # (B,) + mask = counts < 0.8 * T + zs[mask] = torch.inf + sel = torch.argmin(zs) + return bb_min.amin(dim=0), bb_max.amax(dim=0), mean[sel] + + +def track_to_colors(track_ids): + """ + :param track_ids (B) + """ + color_map = torch.from_numpy(get_colors()).to(track_ids) + return color_map[track_ids] / 255 # (B, 3) + + +def get_colors(): + # color_file = os.path.abspath(os.path.join(__file__, "../colors_phalp.txt")) + color_file = os.path.abspath(os.path.join(__file__, "../colors.txt")) + RGB_tuples = np.vstack( + [ + np.loadtxt(color_file, skiprows=0), + # np.loadtxt(color_file, skiprows=1), + np.random.uniform(0, 255, size=(10000, 3)), + [[0, 0, 0]], + ] + ) + b = np.where(RGB_tuples == 0) + RGB_tuples[b] = 1 + return RGB_tuples.astype(np.float32) + + +def checkerboard_geometry( + length=12.0, + color0=[0.8, 0.9, 0.9], + color1=[0.6, 0.7, 0.7], + tile_width=0.5, + alpha=1.0, + up="y", + c1=0.0, + c2=0.0, +): + assert up == "y" or up == "z" + color0 = np.array(color0 + [alpha]) + color1 = np.array(color1 + [alpha]) + radius = length / 2.0 + num_rows = num_cols = max(2, int(length / tile_width)) + vertices = [] + vert_colors = [] + faces = [] + face_colors = [] + for i in range(num_rows): + for j in range(num_cols): + u0, v0 = j * tile_width - radius, i * tile_width - radius + us = np.array([u0, u0, u0 + tile_width, u0 + tile_width]) + vs = np.array([v0, v0 + tile_width, v0 + tile_width, v0]) + zs = np.zeros(4) + if up == "y": + cur_verts = np.stack([us, zs, vs], axis=-1) # (4, 3) + cur_verts[:, 0] += c1 + cur_verts[:, 2] += c2 + else: + cur_verts = np.stack([us, vs, zs], axis=-1) # (4, 3) + cur_verts[:, 0] += c1 + cur_verts[:, 1] += c2 + + cur_faces = np.array( + [[0, 1, 3], [1, 2, 3], [0, 3, 1], [1, 3, 2]], dtype=np.int64 + ) + cur_faces += 4 * (i * num_cols + j) # the number of previously added verts + use_color0 = (i % 2 == 0 and j % 2 == 0) or (i % 2 == 1 and j % 2 == 1) + cur_color = color0 if use_color0 else color1 + cur_colors = np.array([cur_color, cur_color, cur_color, cur_color]) + + vertices.append(cur_verts) + faces.append(cur_faces) + vert_colors.append(cur_colors) + face_colors.append(cur_colors) + + vertices = np.concatenate(vertices, axis=0).astype(np.float32) + vert_colors = np.concatenate(vert_colors, axis=0).astype(np.float32) + faces = np.concatenate(faces, axis=0).astype(np.float32) + face_colors = np.concatenate(face_colors, axis=0).astype(np.float32) + + return vertices, faces, vert_colors, face_colors + + +def camera_marker_geometry(radius, height, up): + assert up == "y" or up == "z" + if up == "y": + vertices = np.array( + [ + [-radius, -radius, 0], + [radius, -radius, 0], + [radius, radius, 0], + [-radius, radius, 0], + [0, 0, height], + ] + ) + else: + vertices = np.array( + [ + [-radius, 0, -radius], + [radius, 0, -radius], + [radius, 0, radius], + [-radius, 0, radius], + [0, -height, 0], + ] + ) + + faces = np.array( + [[0, 3, 1], [1, 3, 2], [0, 1, 4], [1, 2, 4], [2, 3, 4], [3, 0, 4],] + ) + + face_colors = np.array( + [ + [1.0, 1.0, 1.0, 1.0], + [1.0, 1.0, 1.0, 1.0], + [0.0, 1.0, 0.0, 1.0], + [1.0, 0.0, 0.0, 1.0], + [0.0, 1.0, 0.0, 1.0], + [1.0, 0.0, 0.0, 1.0], + ] + ) + return vertices, faces, face_colors + + +def vis_keypoints( + keypts_list, + img_size, + radius=6, + thickness=3, + kpt_score_thr=0.3, + dataset="TopDownCocoDataset", +): + """ + Visualize keypoints + From ViTPose/mmpose/apis/inference.py + """ + palette = np.array( + [ + [255, 128, 0], + [255, 153, 51], + [255, 178, 102], + [230, 230, 0], + [255, 153, 255], + [153, 204, 255], + [255, 102, 255], + [255, 51, 255], + [102, 178, 255], + [51, 153, 255], + [255, 153, 153], + [255, 102, 102], + [255, 51, 51], + [153, 255, 153], + [102, 255, 102], + [51, 255, 51], + [0, 255, 0], + [0, 0, 255], + [255, 0, 0], + [255, 255, 255], + ] + ) + + if dataset in ( + "TopDownCocoDataset", + "BottomUpCocoDataset", + "TopDownOCHumanDataset", + "AnimalMacaqueDataset", + ): + # show the results + skeleton = [ + [15, 13], + [13, 11], + [16, 14], + [14, 12], + [11, 12], + [5, 11], + [6, 12], + [5, 6], + [5, 7], + [6, 8], + [7, 9], + [8, 10], + [1, 2], + [0, 1], + [0, 2], + [1, 3], + [2, 4], + [3, 5], + [4, 6], + ] + + pose_link_color = palette[ + [0, 0, 0, 0, 7, 7, 7, 9, 9, 9, 9, 9, 16, 16, 16, 16, 16, 16, 16] + ] + pose_kpt_color = palette[ + [16, 16, 16, 16, 16, 9, 9, 9, 9, 9, 9, 0, 0, 0, 0, 0, 0] + ] + + elif dataset == "TopDownCocoWholeBodyDataset": + # show the results + skeleton = [ + [15, 13], + [13, 11], + [16, 14], + [14, 12], + [11, 12], + [5, 11], + [6, 12], + [5, 6], + [5, 7], + [6, 8], + [7, 9], + [8, 10], + [1, 2], + [0, 1], + [0, 2], + [1, 3], + [2, 4], + [3, 5], + [4, 6], + [15, 17], + [15, 18], + [15, 19], + [16, 20], + [16, 21], + [16, 22], + [91, 92], + [92, 93], + [93, 94], + [94, 95], + [91, 96], + [96, 97], + [97, 98], + [98, 99], + [91, 100], + [100, 101], + [101, 102], + [102, 103], + [91, 104], + [104, 105], + [105, 106], + [106, 107], + [91, 108], + [108, 109], + [109, 110], + [110, 111], + [112, 113], + [113, 114], + [114, 115], + [115, 116], + [112, 117], + [117, 118], + [118, 119], + [119, 120], + [112, 121], + [121, 122], + [122, 123], + [123, 124], + [112, 125], + [125, 126], + [126, 127], + [127, 128], + [112, 129], + [129, 130], + [130, 131], + [131, 132], + ] + + pose_link_color = palette[ + [0, 0, 0, 0, 7, 7, 7, 9, 9, 9, 9, 9, 16, 16, 16, 16, 16, 16, 16] + + [16, 16, 16, 16, 16, 16] + + [0, 0, 0, 0, 4, 4, 4, 4, 8, 8, 8, 8, 12, 12, 12, 12, 16, 16, 16, 16] + + [0, 0, 0, 0, 4, 4, 4, 4, 8, 8, 8, 8, 12, 12, 12, 12, 16, 16, 16, 16] + ] + pose_kpt_color = palette[ + [16, 16, 16, 16, 16, 9, 9, 9, 9, 9, 9, 0, 0, 0, 0, 0, 0] + + [0, 0, 0, 0, 0, 0] + + [19] * (68 + 42) + ] + + elif dataset == "TopDownAicDataset": + skeleton = [ + [2, 1], + [1, 0], + [0, 13], + [13, 3], + [3, 4], + [4, 5], + [8, 7], + [7, 6], + [6, 9], + [9, 10], + [10, 11], + [12, 13], + [0, 6], + [3, 9], + ] + + pose_link_color = palette[[9, 9, 9, 9, 9, 9, 16, 16, 16, 16, 16, 0, 7, 7]] + pose_kpt_color = palette[[9, 9, 9, 9, 9, 9, 16, 16, 16, 16, 16, 16, 0, 0]] + + elif dataset == "TopDownMpiiDataset": + skeleton = [ + [0, 1], + [1, 2], + [2, 6], + [6, 3], + [3, 4], + [4, 5], + [6, 7], + [7, 8], + [8, 9], + [8, 12], + [12, 11], + [11, 10], + [8, 13], + [13, 14], + [14, 15], + ] + + pose_link_color = palette[[16, 16, 16, 16, 16, 16, 7, 7, 0, 9, 9, 9, 9, 9, 9]] + pose_kpt_color = palette[[16, 16, 16, 16, 16, 16, 7, 7, 0, 0, 9, 9, 9, 9, 9, 9]] + + elif dataset == "TopDownMpiiTrbDataset": + skeleton = [ + [12, 13], + [13, 0], + [13, 1], + [0, 2], + [1, 3], + [2, 4], + [3, 5], + [0, 6], + [1, 7], + [6, 7], + [6, 8], + [7, 9], + [8, 10], + [9, 11], + [14, 15], + [16, 17], + [18, 19], + [20, 21], + [22, 23], + [24, 25], + [26, 27], + [28, 29], + [30, 31], + [32, 33], + [34, 35], + [36, 37], + [38, 39], + ] + + pose_link_color = palette[[16] * 14 + [19] * 13] + pose_kpt_color = palette[[16] * 14 + [0] * 26] + + elif dataset in ("OneHand10KDataset", "FreiHandDataset", "PanopticDataset"): + skeleton = [ + [0, 1], + [1, 2], + [2, 3], + [3, 4], + [0, 5], + [5, 6], + [6, 7], + [7, 8], + [0, 9], + [9, 10], + [10, 11], + [11, 12], + [0, 13], + [13, 14], + [14, 15], + [15, 16], + [0, 17], + [17, 18], + [18, 19], + [19, 20], + ] + + pose_link_color = palette[ + [0, 0, 0, 0, 4, 4, 4, 4, 8, 8, 8, 8, 12, 12, 12, 12, 16, 16, 16, 16] + ] + pose_kpt_color = palette[ + [0, 0, 0, 0, 0, 4, 4, 4, 4, 8, 8, 8, 8, 12, 12, 12, 12, 16, 16, 16, 16] + ] + + elif dataset == "InterHand2DDataset": + skeleton = [ + [0, 1], + [1, 2], + [2, 3], + [4, 5], + [5, 6], + [6, 7], + [8, 9], + [9, 10], + [10, 11], + [12, 13], + [13, 14], + [14, 15], + [16, 17], + [17, 18], + [18, 19], + [3, 20], + [7, 20], + [11, 20], + [15, 20], + [19, 20], + ] + + pose_link_color = palette[ + [0, 0, 0, 4, 4, 4, 8, 8, 8, 12, 12, 12, 16, 16, 16, 0, 4, 8, 12, 16] + ] + pose_kpt_color = palette[ + [0, 0, 0, 0, 4, 4, 4, 4, 8, 8, 8, 8, 12, 12, 12, 12, 16, 16, 16, 16, 0] + ] + + elif dataset == "Face300WDataset": + # show the results + skeleton = [] + + pose_link_color = palette[[]] + pose_kpt_color = palette[[19] * 68] + kpt_score_thr = 0 + + elif dataset == "FaceAFLWDataset": + # show the results + skeleton = [] + + pose_link_color = palette[[]] + pose_kpt_color = palette[[19] * 19] + kpt_score_thr = 0 + + elif dataset == "FaceCOFWDataset": + # show the results + skeleton = [] + + pose_link_color = palette[[]] + pose_kpt_color = palette[[19] * 29] + kpt_score_thr = 0 + + elif dataset == "FaceWFLWDataset": + # show the results + skeleton = [] + + pose_link_color = palette[[]] + pose_kpt_color = palette[[19] * 98] + kpt_score_thr = 0 + + elif dataset == "AnimalHorse10Dataset": + skeleton = [ + [0, 1], + [1, 12], + [12, 16], + [16, 21], + [21, 17], + [17, 11], + [11, 10], + [10, 8], + [8, 9], + [9, 12], + [2, 3], + [3, 4], + [5, 6], + [6, 7], + [13, 14], + [14, 15], + [18, 19], + [19, 20], + ] + + pose_link_color = palette[[4] * 10 + [6] * 2 + [6] * 2 + [7] * 2 + [7] * 2] + pose_kpt_color = palette[ + [4, 4, 6, 6, 6, 6, 6, 6, 4, 4, 4, 4, 4, 7, 7, 7, 4, 4, 7, 7, 7, 4] + ] + + elif dataset == "AnimalFlyDataset": + skeleton = [ + [1, 0], + [2, 0], + [3, 0], + [4, 3], + [5, 4], + [7, 6], + [8, 7], + [9, 8], + [11, 10], + [12, 11], + [13, 12], + [15, 14], + [16, 15], + [17, 16], + [19, 18], + [20, 19], + [21, 20], + [23, 22], + [24, 23], + [25, 24], + [27, 26], + [28, 27], + [29, 28], + [30, 3], + [31, 3], + ] + + pose_link_color = palette[[0] * 25] + pose_kpt_color = palette[[0] * 32] + + elif dataset == "AnimalLocustDataset": + skeleton = [ + [1, 0], + [2, 1], + [3, 2], + [4, 3], + [6, 5], + [7, 6], + [9, 8], + [10, 9], + [11, 10], + [13, 12], + [14, 13], + [15, 14], + [17, 16], + [18, 17], + [19, 18], + [21, 20], + [22, 21], + [24, 23], + [25, 24], + [26, 25], + [28, 27], + [29, 28], + [30, 29], + [32, 31], + [33, 32], + [34, 33], + ] + + pose_link_color = palette[[0] * 26] + pose_kpt_color = palette[[0] * 35] + + elif dataset == "AnimalZebraDataset": + skeleton = [[1, 0], [2, 1], [3, 2], [4, 2], [5, 7], [6, 7], [7, 2], [8, 7]] + + pose_link_color = palette[[0] * 8] + pose_kpt_color = palette[[0] * 9] + + elif dataset in "AnimalPoseDataset": + skeleton = [ + [0, 1], + [0, 2], + [1, 3], + [0, 4], + [1, 4], + [4, 5], + [5, 7], + [6, 7], + [5, 8], + [8, 12], + [12, 16], + [5, 9], + [9, 13], + [13, 17], + [6, 10], + [10, 14], + [14, 18], + [6, 11], + [11, 15], + [15, 19], + ] + + pose_link_color = palette[[0] * 20] + pose_kpt_color = palette[[0] * 20] + else: + NotImplementedError() + + img_w, img_h = img_size + img = 255 * np.ones((img_h, img_w, 3), dtype=np.uint8) + img = imshow_keypoints( + img, + keypts_list, + skeleton, + kpt_score_thr, + pose_kpt_color, + pose_link_color, + radius, + thickness, + ) + alpha = 255 * (img != 255).any(axis=-1, keepdims=True).astype(np.uint8) + return np.concatenate([img, alpha], axis=-1) + + +def imshow_keypoints( + img, + pose_result, + skeleton=None, + kpt_score_thr=0.3, + pose_kpt_color=None, + pose_link_color=None, + radius=4, + thickness=1, + show_keypoint_weight=False, +): + """Draw keypoints and links on an image. + From ViTPose/mmpose/core/visualization/image.py + + Args: + img (H, W, 3) array + pose_result (list[kpts]): The poses to draw. Each element kpts is + a set of K keypoints as an Kx3 numpy.ndarray, where each + keypoint is represented as x, y, score. + kpt_score_thr (float, optional): Minimum score of keypoints + to be shown. Default: 0.3. + pose_kpt_color (np.array[Nx3]`): Color of N keypoints. If None, + the keypoint will not be drawn. + pose_link_color (np.array[Mx3]): Color of M links. If None, the + links will not be drawn. + thickness (int): Thickness of lines. + show_keypoint_weight (bool): If True, opacity indicates keypoint score + """ + img_h, img_w, _ = img.shape + idcs = [0, 16, 15, 18, 17, 5, 2, 6, 3, 7, 4, 12, 9, 13, 10, 14, 11] + for kpts in pose_result: + kpts = np.array(kpts, copy=False)[idcs] + + # draw each point on image + if pose_kpt_color is not None: + assert len(pose_kpt_color) == len(kpts) + for kid, kpt in enumerate(kpts): + x_coord, y_coord, kpt_score = int(kpt[0]), int(kpt[1]), kpt[2] + if kpt_score > kpt_score_thr: + color = tuple(int(c) for c in pose_kpt_color[kid]) + if show_keypoint_weight: + img_copy = img.copy() + cv2.circle( + img_copy, (int(x_coord), int(y_coord)), radius, color, -1 + ) + transparency = max(0, min(1, kpt_score)) + cv2.addWeighted( + img_copy, transparency, img, 1 - transparency, 0, dst=img + ) + else: + cv2.circle(img, (int(x_coord), int(y_coord)), radius, color, -1) + + # draw links + if skeleton is not None and pose_link_color is not None: + assert len(pose_link_color) == len(skeleton) + for sk_id, sk in enumerate(skeleton): + pos1 = (int(kpts[sk[0], 0]), int(kpts[sk[0], 1])) + pos2 = (int(kpts[sk[1], 0]), int(kpts[sk[1], 1])) + if ( + pos1[0] > 0 + and pos1[0] < img_w + and pos1[1] > 0 + and pos1[1] < img_h + and pos2[0] > 0 + and pos2[0] < img_w + and pos2[1] > 0 + and pos2[1] < img_h + and kpts[sk[0], 2] > kpt_score_thr + and kpts[sk[1], 2] > kpt_score_thr + ): + color = tuple(int(c) for c in pose_link_color[sk_id]) + if show_keypoint_weight: + img_copy = img.copy() + X = (pos1[0], pos2[0]) + Y = (pos1[1], pos2[1]) + mX = np.mean(X) + mY = np.mean(Y) + length = ((Y[0] - Y[1]) ** 2 + (X[0] - X[1]) ** 2) ** 0.5 + angle = math.degrees(math.atan2(Y[0] - Y[1], X[0] - X[1])) + stickwidth = 2 + polygon = cv2.ellipse2Poly( + (int(mX), int(mY)), + (int(length / 2), int(stickwidth)), + int(angle), + 0, + 360, + 1, + ) + cv2.fillConvexPoly(img_copy, polygon, color) + transparency = max( + 0, min(1, 0.5 * (kpts[sk[0], 2] + kpts[sk[1], 2])) + ) + cv2.addWeighted( + img_copy, transparency, img, 1 - transparency, 0, dst=img + ) + else: + cv2.line(img, pos1, pos2, color, thickness=thickness) + + return img \ No newline at end of file diff --git a/output/demo/test19/output.mp4 b/output/demo/test19/output.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..2b0ff61514173f1d0dbc688a9905a8ec42ec7888 Binary files /dev/null and b/output/demo/test19/output.mp4 differ diff --git a/output/demo/test19/slam_results.pth b/output/demo/test19/slam_results.pth new file mode 100644 index 0000000000000000000000000000000000000000..30960920c35d27cbaf981575ce81156881d3013d --- /dev/null +++ b/output/demo/test19/slam_results.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:eb6e0b47809fe94bdc26bc99318f9eb9beccb005ec81c407887a5bd7223b5b81 +size 2353 diff --git a/output/demo/test19/tracking_results.pth b/output/demo/test19/tracking_results.pth new file mode 100644 index 0000000000000000000000000000000000000000..69c2c5d2621b19fe51fdbb09bab84294e96cbb30 --- /dev/null +++ b/output/demo/test19/tracking_results.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3d1b3d6e23597e07daaa1b124cba63a1ea91d3909fe2c903c3c9b2b2819ce140 +size 333898 diff --git a/output/demo/test19/wham_output.pkl b/output/demo/test19/wham_output.pkl new file mode 100644 index 0000000000000000000000000000000000000000..d994b69baefb5b73245454edb6618eb76c157a77 --- /dev/null +++ b/output/demo/test19/wham_output.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1484cca6cf2774c3c0cbefa3d47ed7f2dd04db96b05ebdd14e5a11610a415b3e +size 3167067 diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..e9a73a55cf7ecb136437c7a4aacf8cc6bf517250 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,21 @@ +chumpy @ git+https://github.com/mattloper/chumpy +numpy==1.22.3 +yacs +joblib +scikit-image +opencv-python +imageio[ffmpeg] +matplotlib +tensorboard +smplx +progress +einops +mmcv==1.3.9 +timm==0.4.9 +munkres +xtcocotools>=1.8 +loguru +setuptools==59.5.0 +tqdm +ultralytics +gdown==4.6.0 \ No newline at end of file diff --git a/test.py b/test.py new file mode 100644 index 0000000000000000000000000000000000000000..d83a39fa183dc9ee60a25f37b05e7807b066152c --- /dev/null +++ b/test.py @@ -0,0 +1,230 @@ +import os +import argparse +import os.path as osp +from glob import glob +from collections import defaultdict + +import cv2 +import torch +import joblib +import numpy as np +from loguru import logger +from progress.bar import Bar + +from configs.config import get_cfg_defaults +from lib.data.datasets import CustomDataset +from lib.utils.imutils import avg_preds +from lib.utils.transforms import matrix_to_axis_angle +from lib.models import build_network, build_body_model +from lib.models.preproc.detector import DetectionModel +from lib.models.preproc.extractor import FeatureExtractor +from lib.models.smplify import TemporalSMPLify + +try: + from lib.models.preproc.slam import SLAMModel + + _run_global = True +except: + logger.info('DPVO is not properly installed. Only estimate in local coordinates !') + _run_global = False + + +def run(cfg, + video, + output_pth, + network, + calib=None, + run_global=True, + save_pkl=False, + visualize=False, + run_smplify=False): + cap = cv2.VideoCapture(video) + assert cap.isOpened(), f'Failed to load video file {video}' + fps = cap.get(cv2.CAP_PROP_FPS) + length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + width, height = cap.get(cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT) + + # Whether or not estimating motion in global coordinates + run_global = run_global and _run_global + + # Preprocess + with torch.no_grad(): + if not (osp.exists(osp.join(output_pth, 'tracking_results.pth')) and + osp.exists(osp.join(output_pth, 'slam_results.pth'))): + + detector = DetectionModel(cfg.DEVICE.lower()) + extractor = FeatureExtractor(cfg.DEVICE.lower(), cfg.FLIP_EVAL) + + if run_global: + slam = SLAMModel(video, output_pth, width, height, calib) + else: + slam = None + + bar = Bar('Preprocess: 2D detection and SLAM', fill='#', max=length) + while (cap.isOpened()): + flag, img = cap.read() + if not flag: break + + # 2D detection and tracking + detector.track(img, fps, length) + + # SLAM + if slam is not None: + slam.track() + + bar.next() + + tracking_results = detector.process(fps) + + if slam is not None: + slam_results = slam.process() + else: + slam_results = np.zeros((length, 7)) + slam_results[:, 3] = 1.0 # Unit quaternion + + # Extract image features + # TODO: Merge this into the previous while loop with an online bbox smoothing. + tracking_results = extractor.run(video, tracking_results) + logger.info('Complete Data preprocessing!') + + # Save the processed data + joblib.dump(tracking_results, osp.join(output_pth, 'tracking_results.pth')) + joblib.dump(slam_results, osp.join(output_pth, 'slam_results.pth')) + logger.info(f'Save processed data at {output_pth}') + + # If the processed data already exists, load the processed data + else: + tracking_results = joblib.load(osp.join(output_pth, 'tracking_results.pth')) + slam_results = joblib.load(osp.join(output_pth, 'slam_results.pth')) + logger.info(f'Already processed data exists at {output_pth} ! Load the data .') + + # Build dataset + dataset = CustomDataset(cfg, tracking_results, slam_results, width, height, fps) + + # run WHAM + results = defaultdict(dict) + + n_subjs = len(dataset) + for subj in range(n_subjs): + + with torch.no_grad(): + if cfg.FLIP_EVAL: + # Forward pass with flipped input + flipped_batch = dataset.load_data(subj, True) + _id, x, inits, features, mask, init_root, cam_angvel, frame_id, kwargs = flipped_batch + flipped_pred = network(x, inits, features, mask=mask, init_root=init_root, cam_angvel=cam_angvel, + return_y_up=True, **kwargs) + + # Forward pass with normal input + batch = dataset.load_data(subj) + _id, x, inits, features, mask, init_root, cam_angvel, frame_id, kwargs = batch + pred = network(x, inits, features, mask=mask, init_root=init_root, cam_angvel=cam_angvel, + return_y_up=True, **kwargs) + + # Merge two predictions + flipped_pose, flipped_shape = flipped_pred['pose'].squeeze(0), flipped_pred['betas'].squeeze(0) + pose, shape = pred['pose'].squeeze(0), pred['betas'].squeeze(0) + flipped_pose, pose = flipped_pose.reshape(-1, 24, 6), pose.reshape(-1, 24, 6) + avg_pose, avg_shape = avg_preds(pose, shape, flipped_pose, flipped_shape) + avg_pose = avg_pose.reshape(-1, 144) + avg_contact = (flipped_pred['contact'][..., [2, 3, 0, 1]] + pred['contact']) / 2 + + # Refine trajectory with merged prediction + network.pred_pose = avg_pose.view_as(network.pred_pose) + network.pred_shape = avg_shape.view_as(network.pred_shape) + network.pred_contact = avg_contact.view_as(network.pred_contact) + output = network.forward_smpl(**kwargs) + pred = network.refine_trajectory(output, cam_angvel, return_y_up=True) + + else: + # data + batch = dataset.load_data(subj) + _id, x, inits, features, mask, init_root, cam_angvel, frame_id, kwargs = batch + + # inference + pred = network(x, inits, features, mask=mask, init_root=init_root, cam_angvel=cam_angvel, + return_y_up=True, **kwargs) + + # if False: + if run_smplify: + smplify = TemporalSMPLify(smpl, img_w=width, img_h=height, device=cfg.DEVICE) + input_keypoints = dataset.tracking_results[_id]['keypoints'] + pred = smplify.fit(pred, input_keypoints, **kwargs) + + with torch.no_grad(): + network.pred_pose = pred['pose'] + network.pred_shape = pred['betas'] + network.pred_cam = pred['cam'] + output = network.forward_smpl(**kwargs) + pred = network.refine_trajectory(output, cam_angvel, return_y_up=True) + + # ========= Store results ========= # + pred_body_pose = matrix_to_axis_angle(pred['poses_body']).cpu().numpy().reshape(-1, 69) + pred_root = matrix_to_axis_angle(pred['poses_root_cam']).cpu().numpy().reshape(-1, 3) + pred_root_world = matrix_to_axis_angle(pred['poses_root_world']).cpu().numpy().reshape(-1, 3) + pred_pose = np.concatenate((pred_root, pred_body_pose), axis=-1) + pred_pose_world = np.concatenate((pred_root_world, pred_body_pose), axis=-1) + pred_trans = (pred['trans_cam'] - network.output.offset).cpu().numpy() + + results[_id]['pose'] = pred_pose + results[_id]['trans'] = pred_trans + results[_id]['pose_world'] = pred_pose_world + results[_id]['trans_world'] = pred['trans_world'].cpu().squeeze(0).numpy() + results[_id]['betas'] = pred['betas'].cpu().squeeze(0).numpy() + results[_id]['verts'] = (pred['verts_cam'] + pred['trans_cam'].unsqueeze(1)).cpu().numpy() + results[_id]['frame_ids'] = frame_id + + if save_pkl: + joblib.dump(results, osp.join(output_pth, "wham_output.pkl")) + + # Visualize + if visualize: + from lib.vis.run_vis import run_vis_on_demo + with torch.no_grad(): + run_vis_on_demo(cfg, video, results, output_pth, network.smpl, vis_global=run_global) + + +if __name__ == '__main__': + + VIDEO_PATH = "examples/test19.mov" + OUTPUT_PATH = "output/demo" + CALIB_PATH = None + ESTIMATE_LOCAL_ONLY = False + VISUALIZE = True + SAVE_PKL = True + RUN_SMPLIFY = False + GENDER = 'male' + + + cfg = get_cfg_defaults() + cfg.merge_from_file('configs/yamls/demo.yaml') + + logger.info(f'GPU name -> {torch.cuda.get_device_name()}') + logger.info(f'GPU feat -> {torch.cuda.get_device_properties("cuda")}') + + # ========= Load WHAM ========= # + smpl_batch_size = cfg.TRAIN.BATCH_SIZE * cfg.DATASET.SEQLEN + smpl = build_body_model(device=cfg.DEVICE, gender=GENDER, batch_size=smpl_batch_size) + network = build_network(cfg, smpl) + network.eval() + + # Output folder + sequence = '.'.join(VIDEO_PATH.split('/')[-1].split('.')[:-1]) + output_pth = osp.join(OUTPUT_PATH, sequence) + os.makedirs(output_pth, exist_ok=True) + + faces_np = network.smpl.get_faces() + np.save(osp.join(output_pth, f'faces_{GENDER}.npy'), faces_np) + + run(cfg, + VIDEO_PATH, + output_pth, + network, + CALIB_PATH, + run_global=not ESTIMATE_LOCAL_ONLY, + save_pkl=SAVE_PKL, + visualize=VISUALIZE, + run_smplify=RUN_SMPLIFY) + + print() + logger.info('Done !') diff --git a/third-party/DPVO/DPRetrieval/CMakeLists.txt b/third-party/DPVO/DPRetrieval/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..8c78c643246f899e4a2ee6c6ff3fd412b9c87132 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/CMakeLists.txt @@ -0,0 +1,17 @@ +cmake_minimum_required(VERSION 3.4...3.18) +project(dpretrieval) +find_package(OpenCV REQUIRED) +include_directories(${OpenCV_INCLUDE_DIRS}) + +find_package(DBoW2 REQUIRED) +include_directories(${DBoW2_INCLUDE_DIRS}) + + +add_subdirectory(pybind11) +pybind11_add_module(dpretrieval src/main.cpp) +target_link_libraries(dpretrieval PRIVATE ${OpenCV_LIBS} ${DBoW2_LIBS} ) + +# EXAMPLE_VERSION_INFO is defined by setup.py and passed into the C++ code as a +# define (VERSION_INFO) here. +target_compile_definitions(dpretrieval + PRIVATE VERSION_INFO=${EXAMPLE_VERSION_INFO}) \ No newline at end of file diff --git a/third-party/DPVO/DPRetrieval/pybind11/.appveyor.yml b/third-party/DPVO/DPRetrieval/pybind11/.appveyor.yml new file mode 100644 index 0000000000000000000000000000000000000000..0eed11a3473353796b6fd81edb0b220b55a358e7 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/.appveyor.yml @@ -0,0 +1,37 @@ +version: 1.0.{build} +image: +- Visual Studio 2015 +test: off +skip_branch_with_pr: true +build: + parallel: true +platform: +- x86 +environment: + matrix: + - PYTHON: 36 + CONFIG: Debug + - PYTHON: 27 + CONFIG: Debug +install: +- ps: | + $env:CMAKE_GENERATOR = "Visual Studio 14 2015" + if ($env:PLATFORM -eq "x64") { $env:PYTHON = "$env:PYTHON-x64" } + $env:PATH = "C:\Python$env:PYTHON\;C:\Python$env:PYTHON\Scripts\;$env:PATH" + python -W ignore -m pip install --upgrade pip wheel + python -W ignore -m pip install pytest numpy --no-warn-script-location pytest-timeout +- ps: | + Start-FileDownload 'https://gitlab.com/libeigen/eigen/-/archive/3.3.7/eigen-3.3.7.zip' + 7z x eigen-3.3.7.zip -y > $null + $env:CMAKE_INCLUDE_PATH = "eigen-3.3.7;$env:CMAKE_INCLUDE_PATH" +build_script: +- cmake -G "%CMAKE_GENERATOR%" -A "%CMAKE_ARCH%" + -DCMAKE_CXX_STANDARD=14 + -DPYBIND11_WERROR=ON + -DDOWNLOAD_CATCH=ON + -DCMAKE_SUPPRESS_REGENERATION=1 + . +- set MSBuildLogger="C:\Program Files\AppVeyor\BuildAgent\Appveyor.MSBuildLogger.dll" +- cmake --build . --config %CONFIG% --target pytest -- /m /v:m /logger:%MSBuildLogger% +- cmake --build . --config %CONFIG% --target cpptest -- /m /v:m /logger:%MSBuildLogger% +on_failure: if exist "tests\test_cmake_build" type tests\test_cmake_build\*.log* diff --git a/third-party/DPVO/DPRetrieval/pybind11/.clang-format b/third-party/DPVO/DPRetrieval/pybind11/.clang-format new file mode 100644 index 0000000000000000000000000000000000000000..7ec1c91bf1a83f14af90f67d99ca2dfb12df638d --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/.clang-format @@ -0,0 +1,38 @@ +--- +# See all possible options and defaults with: +# clang-format --style=llvm --dump-config +BasedOnStyle: LLVM +AccessModifierOffset: -4 +AllowShortLambdasOnASingleLine: true +AlwaysBreakTemplateDeclarations: Yes +BinPackArguments: false +BinPackParameters: false +BreakBeforeBinaryOperators: All +BreakConstructorInitializers: BeforeColon +ColumnLimit: 99 +CommentPragmas: 'NOLINT:.*|^ IWYU pragma:' +IncludeBlocks: Regroup +IndentCaseLabels: true +IndentPPDirectives: AfterHash +IndentWidth: 4 +Language: Cpp +SpaceAfterCStyleCast: true +Standard: Cpp11 +StatementMacros: ['PyObject_HEAD'] +TabWidth: 4 +IncludeCategories: + - Regex: '' + Priority: 4 + - Regex: '.*' + Priority: 5 +... diff --git a/third-party/DPVO/DPRetrieval/pybind11/.clang-tidy b/third-party/DPVO/DPRetrieval/pybind11/.clang-tidy new file mode 100644 index 0000000000000000000000000000000000000000..340d4f5965d8706deb3d077ff713608f6a937856 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/.clang-tidy @@ -0,0 +1,72 @@ +FormatStyle: file + +Checks: ' +*bugprone*, +clang-analyzer-optin.performance.Padding, +clang-analyzer-optin.cplusplus.VirtualCall, +cppcoreguidelines-init-variables, +cppcoreguidelines-prefer-member-initializer, +cppcoreguidelines-pro-type-static-cast-downcast, +cppcoreguidelines-slicing, +google-explicit-constructor, +llvm-namespace-comment, +misc-misplaced-const, +misc-non-copyable-objects, +misc-static-assert, +misc-throw-by-value-catch-by-reference, +misc-uniqueptr-reset-release, +misc-unused-parameters, +modernize-avoid-bind, +modernize-make-shared, +modernize-redundant-void-arg, +modernize-replace-auto-ptr, +modernize-replace-disallow-copy-and-assign-macro, +modernize-replace-random-shuffle, +modernize-shrink-to-fit, +modernize-use-auto, +modernize-use-bool-literals, +modernize-use-equals-default, +modernize-use-equals-delete, +modernize-use-default-member-init, +modernize-use-noexcept, +modernize-use-emplace, +modernize-use-override, +modernize-use-using, +*performance*, +readability-avoid-const-params-in-decls, +readability-braces-around-statements, +readability-const-return-type, +readability-container-size-empty, +readability-delete-null-pointer, +readability-else-after-return, +readability-implicit-bool-conversion, +readability-inconsistent-declaration-parameter-name, +readability-make-member-function-const, +readability-misplaced-array-index, +readability-non-const-parameter, +readability-qualified-auto, +readability-redundant-function-ptr-dereference, +readability-redundant-smartptr-get, +readability-redundant-string-cstr, +readability-simplify-subscript-expr, +readability-static-accessed-through-instance, +readability-static-definition-in-anonymous-namespace, +readability-string-compare, +readability-suspicious-call-argument, +readability-uniqueptr-delete-release, +-bugprone-exception-escape, +-bugprone-reserved-identifier, +-bugprone-unused-raii, +' + +CheckOptions: +- key: performance-for-range-copy.WarnOnAllAutoCopies + value: true +- key: performance-unnecessary-value-param.AllowedTypes + value: 'exception_ptr$;' +- key: readability-implicit-bool-conversion.AllowPointerConditions + value: true + +HeaderFilterRegex: 'pybind11/.*h' + +WarningsAsErrors: '*' diff --git a/third-party/DPVO/DPRetrieval/pybind11/.cmake-format.yaml b/third-party/DPVO/DPRetrieval/pybind11/.cmake-format.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2371b63a853bf9b83965a1c1c15e3619942e7f21 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/.cmake-format.yaml @@ -0,0 +1,73 @@ +parse: + additional_commands: + pybind11_add_module: + flags: + - THIN_LTO + - MODULE + - SHARED + - NO_EXTRAS + - EXCLUDE_FROM_ALL + - SYSTEM + +format: + line_width: 99 + tab_size: 2 + + # If an argument group contains more than this many sub-groups + # (parg or kwarg groups) then force it to a vertical layout. + max_subgroups_hwrap: 2 + + # If a positional argument group contains more than this many + # arguments, then force it to a vertical layout. + max_pargs_hwrap: 6 + + # If a cmdline positional group consumes more than this many + # lines without nesting, then invalidate the layout (and nest) + max_rows_cmdline: 2 + separate_ctrl_name_with_space: false + separate_fn_name_with_space: false + dangle_parens: false + + # If the trailing parenthesis must be 'dangled' on its on + # 'line, then align it to this reference: `prefix`: the start' + # 'of the statement, `prefix-indent`: the start of the' + # 'statement, plus one indentation level, `child`: align to' + # the column of the arguments + dangle_align: prefix + # If the statement spelling length (including space and + # parenthesis) is smaller than this amount, then force reject + # nested layouts. + min_prefix_chars: 4 + + # If the statement spelling length (including space and + # parenthesis) is larger than the tab width by more than this + # amount, then force reject un-nested layouts. + max_prefix_chars: 10 + + # If a candidate layout is wrapped horizontally but it exceeds + # this many lines, then reject the layout. + max_lines_hwrap: 2 + + line_ending: unix + + # Format command names consistently as 'lower' or 'upper' case + command_case: canonical + + # Format keywords consistently as 'lower' or 'upper' case + # unchanged is valid too + keyword_case: 'upper' + + # A list of command names which should always be wrapped + always_wrap: [] + + # If true, the argument lists which are known to be sortable + # will be sorted lexicographically + enable_sort: true + + # If true, the parsers may infer whether or not an argument + # list is sortable (without annotation). + autosort: false + +# Causes a few issues - can be solved later, possibly. +markup: + enable_markup: false diff --git a/third-party/DPVO/DPRetrieval/pybind11/.pre-commit-config.yaml b/third-party/DPVO/DPRetrieval/pybind11/.pre-commit-config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..44a1adb5fd7c4f7fe557031f2ab424bf6253a8f1 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/.pre-commit-config.yaml @@ -0,0 +1,147 @@ +# To use: +# +# pre-commit run -a +# +# Or: +# +# pre-commit install # (runs every time you commit in git) +# +# To update this file: +# +# pre-commit autoupdate +# +# See https://github.com/pre-commit/pre-commit + +repos: +# Standard hooks +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.1.0 + hooks: + - id: check-added-large-files + - id: check-case-conflict + - id: check-docstring-first + - id: check-merge-conflict + - id: check-symlinks + - id: check-toml + - id: check-yaml + - id: debug-statements + - id: end-of-file-fixer + - id: mixed-line-ending + - id: requirements-txt-fixer + - id: trailing-whitespace + - id: fix-encoding-pragma + exclude: ^noxfile.py$ + +- repo: https://github.com/asottile/pyupgrade + rev: v2.31.0 + hooks: + - id: pyupgrade + +- repo: https://github.com/PyCQA/isort + rev: 5.10.1 + hooks: + - id: isort + +# Black, the code formatter, natively supports pre-commit +- repo: https://github.com/psf/black + rev: 21.12b0 # Keep in sync with blacken-docs + hooks: + - id: black + +- repo: https://github.com/asottile/blacken-docs + rev: v1.12.0 + hooks: + - id: blacken-docs + additional_dependencies: + - black==21.12b0 # keep in sync with black hook + +# Changes tabs to spaces +- repo: https://github.com/Lucas-C/pre-commit-hooks + rev: v1.1.10 + hooks: + - id: remove-tabs + +# Autoremoves unused imports +- repo: https://github.com/hadialqattan/pycln + rev: v1.1.0 + hooks: + - id: pycln + +- repo: https://github.com/pre-commit/pygrep-hooks + rev: v1.9.0 + hooks: + - id: python-check-blanket-noqa + - id: python-check-blanket-type-ignore + - id: python-no-log-warn + - id: rst-backticks + - id: rst-directive-colons + - id: rst-inline-touching-normal + +# Flake8 also supports pre-commit natively (same author) +- repo: https://github.com/PyCQA/flake8 + rev: 4.0.1 + hooks: + - id: flake8 + additional_dependencies: &flake8_dependencies + - flake8-bugbear + - pep8-naming + exclude: ^(docs/.*|tools/.*)$ + +- repo: https://github.com/asottile/yesqa + rev: v1.3.0 + hooks: + - id: yesqa + additional_dependencies: *flake8_dependencies + +# CMake formatting +- repo: https://github.com/cheshirekow/cmake-format-precommit + rev: v0.6.13 + hooks: + - id: cmake-format + additional_dependencies: [pyyaml] + types: [file] + files: (\.cmake|CMakeLists.txt)(.in)?$ + +# Check static types with mypy +- repo: https://github.com/pre-commit/mirrors-mypy + rev: v0.931 + hooks: + - id: mypy + # Running per-file misbehaves a bit, so just run on all files, it's fast + pass_filenames: false + additional_dependencies: [typed_ast] + +# Checks the manifest for missing files (native support) +- repo: https://github.com/mgedmin/check-manifest + rev: "0.47" + hooks: + - id: check-manifest + # This is a slow hook, so only run this if --hook-stage manual is passed + stages: [manual] + additional_dependencies: [cmake, ninja] + +- repo: https://github.com/codespell-project/codespell + rev: v2.1.0 + hooks: + - id: codespell + exclude: ".supp$" + args: ["-L", "nd,ot,thist"] + +- repo: https://github.com/shellcheck-py/shellcheck-py + rev: v0.8.0.3 + hooks: + - id: shellcheck + +# The original pybind11 checks for a few C++ style items +- repo: local + hooks: + - id: disallow-caps + name: Disallow improper capitalization + language: pygrep + entry: PyBind|Numpy|Cmake|CCache|PyTest + exclude: .pre-commit-config.yaml + +- repo: https://github.com/pre-commit/mirrors-clang-format + rev: "v13.0.0" + hooks: + - id: clang-format diff --git a/third-party/DPVO/DPRetrieval/pybind11/.readthedocs.yml b/third-party/DPVO/DPRetrieval/pybind11/.readthedocs.yml new file mode 100644 index 0000000000000000000000000000000000000000..cf4c6d05049872ee5e8c66c871d1b7aa8ee03876 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/.readthedocs.yml @@ -0,0 +1,3 @@ +python: + version: 3 +requirements_file: docs/requirements.txt diff --git a/third-party/DPVO/DPRetrieval/pybind11/CMakeLists.txt b/third-party/DPVO/DPRetrieval/pybind11/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..ca678c22440ab42bd1ff19621d54ee9b26e269b8 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/CMakeLists.txt @@ -0,0 +1,299 @@ +# CMakeLists.txt -- Build system for the pybind11 modules +# +# Copyright (c) 2015 Wenzel Jakob +# +# All rights reserved. Use of this source code is governed by a +# BSD-style license that can be found in the LICENSE file. + +cmake_minimum_required(VERSION 3.4) + +# The `cmake_minimum_required(VERSION 3.4...3.22)` syntax does not work with +# some versions of VS that have a patched CMake 3.11. This forces us to emulate +# the behavior using the following workaround: +if(${CMAKE_VERSION} VERSION_LESS 3.22) + cmake_policy(VERSION ${CMAKE_MAJOR_VERSION}.${CMAKE_MINOR_VERSION}) +else() + cmake_policy(VERSION 3.22) +endif() + +# Avoid infinite recursion if tests include this as a subdirectory +if(DEFINED PYBIND11_MASTER_PROJECT) + return() +endif() + +# Extract project version from source +file(STRINGS "${CMAKE_CURRENT_SOURCE_DIR}/include/pybind11/detail/common.h" + pybind11_version_defines REGEX "#define PYBIND11_VERSION_(MAJOR|MINOR|PATCH) ") + +foreach(ver ${pybind11_version_defines}) + if(ver MATCHES [[#define PYBIND11_VERSION_(MAJOR|MINOR|PATCH) +([^ ]+)$]]) + set(PYBIND11_VERSION_${CMAKE_MATCH_1} "${CMAKE_MATCH_2}") + endif() +endforeach() + +if(PYBIND11_VERSION_PATCH MATCHES [[\.([a-zA-Z0-9]+)$]]) + set(pybind11_VERSION_TYPE "${CMAKE_MATCH_1}") +endif() +string(REGEX MATCH "^[0-9]+" PYBIND11_VERSION_PATCH "${PYBIND11_VERSION_PATCH}") + +project( + pybind11 + LANGUAGES CXX + VERSION "${PYBIND11_VERSION_MAJOR}.${PYBIND11_VERSION_MINOR}.${PYBIND11_VERSION_PATCH}") + +# Standard includes +include(GNUInstallDirs) +include(CMakePackageConfigHelpers) +include(CMakeDependentOption) + +if(NOT pybind11_FIND_QUIETLY) + message(STATUS "pybind11 v${pybind11_VERSION} ${pybind11_VERSION_TYPE}") +endif() + +# Check if pybind11 is being used directly or via add_subdirectory +if(CMAKE_SOURCE_DIR STREQUAL PROJECT_SOURCE_DIR) + ### Warn if not an out-of-source builds + if(CMAKE_CURRENT_SOURCE_DIR STREQUAL CMAKE_CURRENT_BINARY_DIR) + set(lines + "You are building in-place. If that is not what you intended to " + "do, you can clean the source directory with:\n" + "rm -r CMakeCache.txt CMakeFiles/ cmake_uninstall.cmake pybind11Config.cmake " + "pybind11ConfigVersion.cmake tests/CMakeFiles/\n") + message(AUTHOR_WARNING ${lines}) + endif() + + set(PYBIND11_MASTER_PROJECT ON) + + if(OSX AND CMAKE_VERSION VERSION_LESS 3.7) + # Bug in macOS CMake < 3.7 is unable to download catch + message(WARNING "CMAKE 3.7+ needed on macOS to download catch, and newer HIGHLY recommended") + elseif(WINDOWS AND CMAKE_VERSION VERSION_LESS 3.8) + # Only tested with 3.8+ in CI. + message(WARNING "CMAKE 3.8+ tested on Windows, previous versions untested") + endif() + + message(STATUS "CMake ${CMAKE_VERSION}") + + if(CMAKE_CXX_STANDARD) + set(CMAKE_CXX_EXTENSIONS OFF) + set(CMAKE_CXX_STANDARD_REQUIRED ON) + endif() + + set(pybind11_system "") + + set_property(GLOBAL PROPERTY USE_FOLDERS ON) +else() + set(PYBIND11_MASTER_PROJECT OFF) + set(pybind11_system SYSTEM) +endif() + +# Options +option(PYBIND11_INSTALL "Install pybind11 header files?" ${PYBIND11_MASTER_PROJECT}) +option(PYBIND11_TEST "Build pybind11 test suite?" ${PYBIND11_MASTER_PROJECT}) +option(PYBIND11_NOPYTHON "Disable search for Python" OFF) +set(PYBIND11_INTERNALS_VERSION + "" + CACHE STRING "Override the ABI version, may be used to enable the unstable ABI.") + +cmake_dependent_option( + USE_PYTHON_INCLUDE_DIR + "Install pybind11 headers in Python include directory instead of default installation prefix" + OFF "PYBIND11_INSTALL" OFF) + +cmake_dependent_option(PYBIND11_FINDPYTHON "Force new FindPython" OFF + "NOT CMAKE_VERSION VERSION_LESS 3.12" OFF) + +# NB: when adding a header don't forget to also add it to setup.py +set(PYBIND11_HEADERS + include/pybind11/detail/class.h + include/pybind11/detail/common.h + include/pybind11/detail/descr.h + include/pybind11/detail/init.h + include/pybind11/detail/internals.h + include/pybind11/detail/type_caster_base.h + include/pybind11/detail/typeid.h + include/pybind11/attr.h + include/pybind11/buffer_info.h + include/pybind11/cast.h + include/pybind11/chrono.h + include/pybind11/common.h + include/pybind11/complex.h + include/pybind11/options.h + include/pybind11/eigen.h + include/pybind11/embed.h + include/pybind11/eval.h + include/pybind11/gil.h + include/pybind11/iostream.h + include/pybind11/functional.h + include/pybind11/numpy.h + include/pybind11/operators.h + include/pybind11/pybind11.h + include/pybind11/pytypes.h + include/pybind11/stl.h + include/pybind11/stl_bind.h + include/pybind11/stl/filesystem.h) + +# Compare with grep and warn if mismatched +if(PYBIND11_MASTER_PROJECT AND NOT CMAKE_VERSION VERSION_LESS 3.12) + file( + GLOB_RECURSE _pybind11_header_check + LIST_DIRECTORIES false + RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" + CONFIGURE_DEPENDS "include/pybind11/*.h") + set(_pybind11_here_only ${PYBIND11_HEADERS}) + set(_pybind11_disk_only ${_pybind11_header_check}) + list(REMOVE_ITEM _pybind11_here_only ${_pybind11_header_check}) + list(REMOVE_ITEM _pybind11_disk_only ${PYBIND11_HEADERS}) + if(_pybind11_here_only) + message(AUTHOR_WARNING "PYBIND11_HEADERS has extra files:" ${_pybind11_here_only}) + endif() + if(_pybind11_disk_only) + message(AUTHOR_WARNING "PYBIND11_HEADERS is missing files:" ${_pybind11_disk_only}) + endif() +endif() + +# CMake 3.12 added list(TRANSFORM PREPEND +# But we can't use it yet +string(REPLACE "include/" "${CMAKE_CURRENT_SOURCE_DIR}/include/" PYBIND11_HEADERS + "${PYBIND11_HEADERS}") + +# Cache variable so this can be used in parent projects +set(pybind11_INCLUDE_DIR + "${CMAKE_CURRENT_LIST_DIR}/include" + CACHE INTERNAL "Directory where pybind11 headers are located") + +# Backward compatible variable for add_subdirectory mode +if(NOT PYBIND11_MASTER_PROJECT) + set(PYBIND11_INCLUDE_DIR + "${pybind11_INCLUDE_DIR}" + CACHE INTERNAL "") +endif() + +# Note: when creating targets, you cannot use if statements at configure time - +# you need generator expressions, because those will be placed in the target file. +# You can also place ifs *in* the Config.in, but not here. + +# This section builds targets, but does *not* touch Python +# Non-IMPORT targets cannot be defined twice +if(NOT TARGET pybind11_headers) + # Build the headers-only target (no Python included): + # (long name used here to keep this from clashing in subdirectory mode) + add_library(pybind11_headers INTERFACE) + add_library(pybind11::pybind11_headers ALIAS pybind11_headers) # to match exported target + add_library(pybind11::headers ALIAS pybind11_headers) # easier to use/remember + + target_include_directories( + pybind11_headers ${pybind11_system} INTERFACE $ + $) + + target_compile_features(pybind11_headers INTERFACE cxx_inheriting_constructors cxx_user_literals + cxx_right_angle_brackets) + if(NOT "${PYBIND11_INTERNALS_VERSION}" STREQUAL "") + target_compile_definitions( + pybind11_headers INTERFACE "PYBIND11_INTERNALS_VERSION=${PYBIND11_INTERNALS_VERSION}") + endif() +else() + # It is invalid to install a target twice, too. + set(PYBIND11_INSTALL OFF) +endif() + +include("${CMAKE_CURRENT_SOURCE_DIR}/tools/pybind11Common.cmake") + +# Relative directory setting +if(USE_PYTHON_INCLUDE_DIR AND DEFINED Python_INCLUDE_DIRS) + file(RELATIVE_PATH CMAKE_INSTALL_INCLUDEDIR ${CMAKE_INSTALL_PREFIX} ${Python_INCLUDE_DIRS}) +elseif(USE_PYTHON_INCLUDE_DIR AND DEFINED PYTHON_INCLUDE_DIR) + file(RELATIVE_PATH CMAKE_INSTALL_INCLUDEDIR ${CMAKE_INSTALL_PREFIX} ${PYTHON_INCLUDE_DIRS}) +endif() + +if(PYBIND11_INSTALL) + install(DIRECTORY ${pybind11_INCLUDE_DIR}/pybind11 DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}) + set(PYBIND11_CMAKECONFIG_INSTALL_DIR + "${CMAKE_INSTALL_DATAROOTDIR}/cmake/${PROJECT_NAME}" + CACHE STRING "install path for pybind11Config.cmake") + + if(IS_ABSOLUTE "${CMAKE_INSTALL_INCLUDEDIR}") + set(pybind11_INCLUDEDIR "${CMAKE_INSTALL_FULL_INCLUDEDIR}") + else() + set(pybind11_INCLUDEDIR "\$\{PACKAGE_PREFIX_DIR\}/${CMAKE_INSTALL_INCLUDEDIR}") + endif() + + configure_package_config_file( + tools/${PROJECT_NAME}Config.cmake.in "${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}Config.cmake" + INSTALL_DESTINATION ${PYBIND11_CMAKECONFIG_INSTALL_DIR}) + + if(CMAKE_VERSION VERSION_LESS 3.14) + # Remove CMAKE_SIZEOF_VOID_P from ConfigVersion.cmake since the library does + # not depend on architecture specific settings or libraries. + set(_PYBIND11_CMAKE_SIZEOF_VOID_P ${CMAKE_SIZEOF_VOID_P}) + unset(CMAKE_SIZEOF_VOID_P) + + write_basic_package_version_file( + ${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}ConfigVersion.cmake + VERSION ${PROJECT_VERSION} + COMPATIBILITY AnyNewerVersion) + + set(CMAKE_SIZEOF_VOID_P ${_PYBIND11_CMAKE_SIZEOF_VOID_P}) + else() + # CMake 3.14+ natively supports header-only libraries + write_basic_package_version_file( + ${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}ConfigVersion.cmake + VERSION ${PROJECT_VERSION} + COMPATIBILITY AnyNewerVersion ARCH_INDEPENDENT) + endif() + + install( + FILES ${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}Config.cmake + ${CMAKE_CURRENT_BINARY_DIR}/${PROJECT_NAME}ConfigVersion.cmake + tools/FindPythonLibsNew.cmake + tools/pybind11Common.cmake + tools/pybind11Tools.cmake + tools/pybind11NewTools.cmake + DESTINATION ${PYBIND11_CMAKECONFIG_INSTALL_DIR}) + + if(NOT PYBIND11_EXPORT_NAME) + set(PYBIND11_EXPORT_NAME "${PROJECT_NAME}Targets") + endif() + + install(TARGETS pybind11_headers EXPORT "${PYBIND11_EXPORT_NAME}") + + install( + EXPORT "${PYBIND11_EXPORT_NAME}" + NAMESPACE "pybind11::" + DESTINATION ${PYBIND11_CMAKECONFIG_INSTALL_DIR}) + + # Uninstall target + if(PYBIND11_MASTER_PROJECT) + configure_file("${CMAKE_CURRENT_SOURCE_DIR}/tools/cmake_uninstall.cmake.in" + "${CMAKE_CURRENT_BINARY_DIR}/cmake_uninstall.cmake" IMMEDIATE @ONLY) + + add_custom_target(uninstall COMMAND ${CMAKE_COMMAND} -P + ${CMAKE_CURRENT_BINARY_DIR}/cmake_uninstall.cmake) + endif() +endif() + +# BUILD_TESTING takes priority, but only if this is the master project +if(PYBIND11_MASTER_PROJECT AND DEFINED BUILD_TESTING) + if(BUILD_TESTING) + if(_pybind11_nopython) + message(FATAL_ERROR "Cannot activate tests in NOPYTHON mode") + else() + add_subdirectory(tests) + endif() + endif() +else() + if(PYBIND11_TEST) + if(_pybind11_nopython) + message(FATAL_ERROR "Cannot activate tests in NOPYTHON mode") + else() + add_subdirectory(tests) + endif() + endif() +endif() + +# Better symmetry with find_package(pybind11 CONFIG) mode. +if(NOT PYBIND11_MASTER_PROJECT) + set(pybind11_FOUND + TRUE + CACHE INTERNAL "True if pybind11 and all required components found on the system") +endif() diff --git a/third-party/DPVO/DPRetrieval/pybind11/LICENSE b/third-party/DPVO/DPRetrieval/pybind11/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..46b7deda3da0daac2b9c2547237aa38cf449f6b1 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/LICENSE @@ -0,0 +1,29 @@ +Copyright (c) 2016 Wenzel Jakob , All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its contributors + may be used to endorse or promote products derived from this software + without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +Please also refer to the file .github/CONTRIBUTING.md, which clarifies licensing of +external contributions to this project including patches, pull requests, etc. diff --git a/third-party/DPVO/DPRetrieval/pybind11/MANIFEST.in b/third-party/DPVO/DPRetrieval/pybind11/MANIFEST.in new file mode 100644 index 0000000000000000000000000000000000000000..f2502e15cc831c1909d7bcd5a1b3b86936d70a83 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/MANIFEST.in @@ -0,0 +1,6 @@ +recursive-include pybind11/include/pybind11 *.h +recursive-include pybind11 *.py +recursive-include pybind11 py.typed +recursive-include pybind11 *.pyi +include pybind11/share/cmake/pybind11/*.cmake +include LICENSE README.rst pyproject.toml setup.py setup.cfg diff --git a/third-party/DPVO/DPRetrieval/pybind11/README.rst b/third-party/DPVO/DPRetrieval/pybind11/README.rst new file mode 100644 index 0000000000000000000000000000000000000000..0f0ed4af2b96abe1dc0e15655bbb0aace117e351 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/README.rst @@ -0,0 +1,180 @@ +.. figure:: https://github.com/pybind/pybind11/raw/master/docs/pybind11-logo.png + :alt: pybind11 logo + +**pybind11 — Seamless operability between C++11 and Python** + +|Latest Documentation Status| |Stable Documentation Status| |Gitter chat| |GitHub Discussions| |CI| |Build status| + +|Repology| |PyPI package| |Conda-forge| |Python Versions| + +`Setuptools example `_ +• `Scikit-build example `_ +• `CMake example `_ + +.. start + + +**pybind11** is a lightweight header-only library that exposes C++ types +in Python and vice versa, mainly to create Python bindings of existing +C++ code. Its goals and syntax are similar to the excellent +`Boost.Python `_ +library by David Abrahams: to minimize boilerplate code in traditional +extension modules by inferring type information using compile-time +introspection. + +The main issue with Boost.Python—and the reason for creating such a +similar project—is Boost. Boost is an enormously large and complex suite +of utility libraries that works with almost every C++ compiler in +existence. This compatibility has its cost: arcane template tricks and +workarounds are necessary to support the oldest and buggiest of compiler +specimens. Now that C++11-compatible compilers are widely available, +this heavy machinery has become an excessively large and unnecessary +dependency. + +Think of this library as a tiny self-contained version of Boost.Python +with everything stripped away that isn’t relevant for binding +generation. Without comments, the core header files only require ~4K +lines of code and depend on Python (2.7 or 3.5+, or PyPy) and the C++ +standard library. This compact implementation was possible thanks to +some of the new C++11 language features (specifically: tuples, lambda +functions and variadic templates). Since its creation, this library has +grown beyond Boost.Python in many ways, leading to dramatically simpler +binding code in many common situations. + +Tutorial and reference documentation is provided at +`pybind11.readthedocs.io `_. +A PDF version of the manual is available +`here `_. +And the source code is always available at +`github.com/pybind/pybind11 `_. + + +Core features +------------- + + +pybind11 can map the following core C++ features to Python: + +- Functions accepting and returning custom data structures per value, + reference, or pointer +- Instance methods and static methods +- Overloaded functions +- Instance attributes and static attributes +- Arbitrary exception types +- Enumerations +- Callbacks +- Iterators and ranges +- Custom operators +- Single and multiple inheritance +- STL data structures +- Smart pointers with reference counting like ``std::shared_ptr`` +- Internal references with correct reference counting +- C++ classes with virtual (and pure virtual) methods can be extended + in Python + +Goodies +------- + +In addition to the core functionality, pybind11 provides some extra +goodies: + +- Python 2.7, 3.5+, and PyPy/PyPy3 7.3 are supported with an + implementation-agnostic interface. + +- It is possible to bind C++11 lambda functions with captured + variables. The lambda capture data is stored inside the resulting + Python function object. + +- pybind11 uses C++11 move constructors and move assignment operators + whenever possible to efficiently transfer custom data types. + +- It’s easy to expose the internal storage of custom data types through + Pythons’ buffer protocols. This is handy e.g. for fast conversion + between C++ matrix classes like Eigen and NumPy without expensive + copy operations. + +- pybind11 can automatically vectorize functions so that they are + transparently applied to all entries of one or more NumPy array + arguments. + +- Python's slice-based access and assignment operations can be + supported with just a few lines of code. + +- Everything is contained in just a few header files; there is no need + to link against any additional libraries. + +- Binaries are generally smaller by a factor of at least 2 compared to + equivalent bindings generated by Boost.Python. A recent pybind11 + conversion of PyRosetta, an enormous Boost.Python binding project, + `reported `_ + a binary size reduction of **5.4x** and compile time reduction by + **5.8x**. + +- Function signatures are precomputed at compile time (using + ``constexpr``), leading to smaller binaries. + +- With little extra effort, C++ types can be pickled and unpickled + similar to regular Python objects. + +Supported compilers +------------------- + +1. Clang/LLVM 3.3 or newer (for Apple Xcode’s clang, this is 5.0.0 or + newer) +2. GCC 4.8 or newer +3. Microsoft Visual Studio 2015 Update 3 or newer +4. Intel classic C++ compiler 18 or newer (ICC 20.2 tested in CI) +5. Cygwin/GCC (previously tested on 2.5.1) +6. NVCC (CUDA 11.0 tested in CI) +7. NVIDIA PGI (20.9 tested in CI) + +About +----- + +This project was created by `Wenzel +Jakob `_. Significant features and/or +improvements to the code were contributed by Jonas Adler, Lori A. Burns, +Sylvain Corlay, Eric Cousineau, Aaron Gokaslan, Ralf Grosse-Kunstleve, Trent Houliston, Axel +Huebl, @hulucc, Yannick Jadoul, Sergey Lyskov Johan Mabille, Tomasz Miąsko, +Dean Moldovan, Ben Pritchard, Jason Rhinelander, Boris Schäling, Pim +Schellart, Henry Schreiner, Ivan Smirnov, Boris Staletic, and Patrick Stewart. + +We thank Google for a generous financial contribution to the continuous +integration infrastructure used by this project. + + +Contributing +~~~~~~~~~~~~ + +See the `contributing +guide `_ +for information on building and contributing to pybind11. + +License +~~~~~~~ + +pybind11 is provided under a BSD-style license that can be found in the +`LICENSE `_ +file. By using, distributing, or contributing to this project, you agree +to the terms and conditions of this license. + +.. |Latest Documentation Status| image:: https://readthedocs.org/projects/pybind11/badge?version=latest + :target: http://pybind11.readthedocs.org/en/latest +.. |Stable Documentation Status| image:: https://img.shields.io/badge/docs-stable-blue.svg + :target: http://pybind11.readthedocs.org/en/stable +.. |Gitter chat| image:: https://img.shields.io/gitter/room/gitterHQ/gitter.svg + :target: https://gitter.im/pybind/Lobby +.. |CI| image:: https://github.com/pybind/pybind11/workflows/CI/badge.svg + :target: https://github.com/pybind/pybind11/actions +.. |Build status| image:: https://ci.appveyor.com/api/projects/status/riaj54pn4h08xy40?svg=true + :target: https://ci.appveyor.com/project/wjakob/pybind11 +.. |PyPI package| image:: https://img.shields.io/pypi/v/pybind11.svg + :target: https://pypi.org/project/pybind11/ +.. |Conda-forge| image:: https://img.shields.io/conda/vn/conda-forge/pybind11.svg + :target: https://github.com/conda-forge/pybind11-feedstock +.. |Repology| image:: https://repology.org/badge/latest-versions/python:pybind11.svg + :target: https://repology.org/project/python:pybind11/versions +.. |Python Versions| image:: https://img.shields.io/pypi/pyversions/pybind11.svg + :target: https://pypi.org/project/pybind11/ +.. |GitHub Discussions| image:: https://img.shields.io/static/v1?label=Discussions&message=Ask&color=blue&logo=github + :target: https://github.com/pybind/pybind11/discussions diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/Doxyfile b/third-party/DPVO/DPRetrieval/pybind11/docs/Doxyfile new file mode 100644 index 0000000000000000000000000000000000000000..3dcbc5bfa5bfe77e2325e33a40c3bce720618fcb --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/Doxyfile @@ -0,0 +1,22 @@ +PROJECT_NAME = pybind11 +INPUT = ../include/pybind11/ +RECURSIVE = YES + +GENERATE_HTML = NO +GENERATE_LATEX = NO +GENERATE_XML = YES +XML_OUTPUT = .build/doxygenxml +XML_PROGRAMLISTING = YES + +MACRO_EXPANSION = YES +EXPAND_ONLY_PREDEF = YES +EXPAND_AS_DEFINED = PYBIND11_RUNTIME_EXCEPTION + +ALIASES = "rst=\verbatim embed:rst" +ALIASES += "endrst=\endverbatim" + +QUIET = YES +WARNINGS = YES +WARN_IF_UNDOCUMENTED = NO +PREDEFINED = PY_MAJOR_VERSION=3 \ + PYBIND11_NOINLINE diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/_static/theme_overrides.css b/third-party/DPVO/DPRetrieval/pybind11/docs/_static/theme_overrides.css new file mode 100644 index 0000000000000000000000000000000000000000..8021731bdee9066aea8ebd1a32ab0e0b569a231d --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/_static/theme_overrides.css @@ -0,0 +1,11 @@ +.wy-table-responsive table td, +.wy-table-responsive table th { + white-space: initial !important; +} +.rst-content table.docutils td { + vertical-align: top !important; +} +div[class^='highlight'] pre { + white-space: pre; + white-space: pre-wrap; +} diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/chrono.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/chrono.rst new file mode 100644 index 0000000000000000000000000000000000000000..6adcc9fd3fa7c65bac94ff584a3188bf98490feb --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/chrono.rst @@ -0,0 +1,81 @@ +Chrono +====== + +When including the additional header file :file:`pybind11/chrono.h` conversions +from C++11 chrono datatypes to python datetime objects are automatically enabled. +This header also enables conversions of python floats (often from sources such +as ``time.monotonic()``, ``time.perf_counter()`` and ``time.process_time()``) +into durations. + +An overview of clocks in C++11 +------------------------------ + +A point of confusion when using these conversions is the differences between +clocks provided in C++11. There are three clock types defined by the C++11 +standard and users can define their own if needed. Each of these clocks have +different properties and when converting to and from python will give different +results. + +The first clock defined by the standard is ``std::chrono::system_clock``. This +clock measures the current date and time. However, this clock changes with to +updates to the operating system time. For example, if your time is synchronised +with a time server this clock will change. This makes this clock a poor choice +for timing purposes but good for measuring the wall time. + +The second clock defined in the standard is ``std::chrono::steady_clock``. +This clock ticks at a steady rate and is never adjusted. This makes it excellent +for timing purposes, however the value in this clock does not correspond to the +current date and time. Often this clock will be the amount of time your system +has been on, although it does not have to be. This clock will never be the same +clock as the system clock as the system clock can change but steady clocks +cannot. + +The third clock defined in the standard is ``std::chrono::high_resolution_clock``. +This clock is the clock that has the highest resolution out of the clocks in the +system. It is normally a typedef to either the system clock or the steady clock +but can be its own independent clock. This is important as when using these +conversions as the types you get in python for this clock might be different +depending on the system. +If it is a typedef of the system clock, python will get datetime objects, but if +it is a different clock they will be timedelta objects. + +Provided conversions +-------------------- + +.. rubric:: C++ to Python + +- ``std::chrono::system_clock::time_point`` → ``datetime.datetime`` + System clock times are converted to python datetime instances. They are + in the local timezone, but do not have any timezone information attached + to them (they are naive datetime objects). + +- ``std::chrono::duration`` → ``datetime.timedelta`` + Durations are converted to timedeltas, any precision in the duration + greater than microseconds is lost by rounding towards zero. + +- ``std::chrono::[other_clocks]::time_point`` → ``datetime.timedelta`` + Any clock time that is not the system clock is converted to a time delta. + This timedelta measures the time from the clocks epoch to now. + +.. rubric:: Python to C++ + +- ``datetime.datetime`` or ``datetime.date`` or ``datetime.time`` → ``std::chrono::system_clock::time_point`` + Date/time objects are converted into system clock timepoints. Any + timezone information is ignored and the type is treated as a naive + object. + +- ``datetime.timedelta`` → ``std::chrono::duration`` + Time delta are converted into durations with microsecond precision. + +- ``datetime.timedelta`` → ``std::chrono::[other_clocks]::time_point`` + Time deltas that are converted into clock timepoints are treated as + the amount of time from the start of the clocks epoch. + +- ``float`` → ``std::chrono::duration`` + Floats that are passed to C++ as durations be interpreted as a number of + seconds. These will be converted to the duration using ``duration_cast`` + from the float. + +- ``float`` → ``std::chrono::[other_clocks]::time_point`` + Floats that are passed to C++ as time points will be interpreted as the + number of seconds from the start of the clocks epoch. diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/custom.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/custom.rst new file mode 100644 index 0000000000000000000000000000000000000000..fee178a0d0d24e7e9d8d9a84bfc9013f65181f84 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/custom.rst @@ -0,0 +1,93 @@ +Custom type casters +=================== + +In very rare cases, applications may require custom type casters that cannot be +expressed using the abstractions provided by pybind11, thus requiring raw +Python C API calls. This is fairly advanced usage and should only be pursued by +experts who are familiar with the intricacies of Python reference counting. + +The following snippets demonstrate how this works for a very simple ``inty`` +type that that should be convertible from Python types that provide a +``__int__(self)`` method. + +.. code-block:: cpp + + struct inty { long long_value; }; + + void print(inty s) { + std::cout << s.long_value << std::endl; + } + +The following Python snippet demonstrates the intended usage from the Python side: + +.. code-block:: python + + class A: + def __int__(self): + return 123 + + + from example import print + + print(A()) + +To register the necessary conversion routines, it is necessary to add an +instantiation of the ``pybind11::detail::type_caster`` template. +Although this is an implementation detail, adding an instantiation of this +type is explicitly allowed. + +.. code-block:: cpp + + namespace pybind11 { namespace detail { + template <> struct type_caster { + public: + /** + * This macro establishes the name 'inty' in + * function signatures and declares a local variable + * 'value' of type inty + */ + PYBIND11_TYPE_CASTER(inty, const_name("inty")); + + /** + * Conversion part 1 (Python->C++): convert a PyObject into a inty + * instance or return false upon failure. The second argument + * indicates whether implicit conversions should be applied. + */ + bool load(handle src, bool) { + /* Extract PyObject from handle */ + PyObject *source = src.ptr(); + /* Try converting into a Python integer value */ + PyObject *tmp = PyNumber_Long(source); + if (!tmp) + return false; + /* Now try to convert into a C++ int */ + value.long_value = PyLong_AsLong(tmp); + Py_DECREF(tmp); + /* Ensure return code was OK (to avoid out-of-range errors etc) */ + return !(value.long_value == -1 && !PyErr_Occurred()); + } + + /** + * Conversion part 2 (C++ -> Python): convert an inty instance into + * a Python object. The second and third arguments are used to + * indicate the return value policy and parent object (for + * ``return_value_policy::reference_internal``) and are generally + * ignored by implicit casters. + */ + static handle cast(inty src, return_value_policy /* policy */, handle /* parent */) { + return PyLong_FromLong(src.long_value); + } + }; + }} // namespace pybind11::detail + +.. note:: + + A ``type_caster`` defined with ``PYBIND11_TYPE_CASTER(T, ...)`` requires + that ``T`` is default-constructible (``value`` is first default constructed + and then ``load()`` assigns to it). + +.. warning:: + + When using custom type casters, it's important to declare them consistently + in every compilation unit of the Python extension module. Otherwise, + undefined behavior can ensue. diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/eigen.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/eigen.rst new file mode 100644 index 0000000000000000000000000000000000000000..0e7fdb169019497e2e5226492f92483f656c3bb3 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/eigen.rst @@ -0,0 +1,310 @@ +Eigen +##### + +`Eigen `_ is C++ header-based library for dense and +sparse linear algebra. Due to its popularity and widespread adoption, pybind11 +provides transparent conversion and limited mapping support between Eigen and +Scientific Python linear algebra data types. + +To enable the built-in Eigen support you must include the optional header file +:file:`pybind11/eigen.h`. + +Pass-by-value +============= + +When binding a function with ordinary Eigen dense object arguments (for +example, ``Eigen::MatrixXd``), pybind11 will accept any input value that is +already (or convertible to) a ``numpy.ndarray`` with dimensions compatible with +the Eigen type, copy its values into a temporary Eigen variable of the +appropriate type, then call the function with this temporary variable. + +Sparse matrices are similarly copied to or from +``scipy.sparse.csr_matrix``/``scipy.sparse.csc_matrix`` objects. + +Pass-by-reference +================= + +One major limitation of the above is that every data conversion implicitly +involves a copy, which can be both expensive (for large matrices) and disallows +binding functions that change their (Matrix) arguments. Pybind11 allows you to +work around this by using Eigen's ``Eigen::Ref`` class much as you +would when writing a function taking a generic type in Eigen itself (subject to +some limitations discussed below). + +When calling a bound function accepting a ``Eigen::Ref`` +type, pybind11 will attempt to avoid copying by using an ``Eigen::Map`` object +that maps into the source ``numpy.ndarray`` data: this requires both that the +data types are the same (e.g. ``dtype='float64'`` and ``MatrixType::Scalar`` is +``double``); and that the storage is layout compatible. The latter limitation +is discussed in detail in the section below, and requires careful +consideration: by default, numpy matrices and Eigen matrices are *not* storage +compatible. + +If the numpy matrix cannot be used as is (either because its types differ, e.g. +passing an array of integers to an Eigen parameter requiring doubles, or +because the storage is incompatible), pybind11 makes a temporary copy and +passes the copy instead. + +When a bound function parameter is instead ``Eigen::Ref`` (note the +lack of ``const``), pybind11 will only allow the function to be called if it +can be mapped *and* if the numpy array is writeable (that is +``a.flags.writeable`` is true). Any access (including modification) made to +the passed variable will be transparently carried out directly on the +``numpy.ndarray``. + +This means you can write code such as the following and have it work as +expected: + +.. code-block:: cpp + + void scale_by_2(Eigen::Ref v) { + v *= 2; + } + +Note, however, that you will likely run into limitations due to numpy and +Eigen's difference default storage order for data; see the below section on +:ref:`storage_orders` for details on how to bind code that won't run into such +limitations. + +.. note:: + + Passing by reference is not supported for sparse types. + +Returning values to Python +========================== + +When returning an ordinary dense Eigen matrix type to numpy (e.g. +``Eigen::MatrixXd`` or ``Eigen::RowVectorXf``) pybind11 keeps the matrix and +returns a numpy array that directly references the Eigen matrix: no copy of the +data is performed. The numpy array will have ``array.flags.owndata`` set to +``False`` to indicate that it does not own the data, and the lifetime of the +stored Eigen matrix will be tied to the returned ``array``. + +If you bind a function with a non-reference, ``const`` return type (e.g. +``const Eigen::MatrixXd``), the same thing happens except that pybind11 also +sets the numpy array's ``writeable`` flag to false. + +If you return an lvalue reference or pointer, the usual pybind11 rules apply, +as dictated by the binding function's return value policy (see the +documentation on :ref:`return_value_policies` for full details). That means, +without an explicit return value policy, lvalue references will be copied and +pointers will be managed by pybind11. In order to avoid copying, you should +explicitly specify an appropriate return value policy, as in the following +example: + +.. code-block:: cpp + + class MyClass { + Eigen::MatrixXd big_mat = Eigen::MatrixXd::Zero(10000, 10000); + public: + Eigen::MatrixXd &getMatrix() { return big_mat; } + const Eigen::MatrixXd &viewMatrix() { return big_mat; } + }; + + // Later, in binding code: + py::class_(m, "MyClass") + .def(py::init<>()) + .def("copy_matrix", &MyClass::getMatrix) // Makes a copy! + .def("get_matrix", &MyClass::getMatrix, py::return_value_policy::reference_internal) + .def("view_matrix", &MyClass::viewMatrix, py::return_value_policy::reference_internal) + ; + +.. code-block:: python + + a = MyClass() + m = a.get_matrix() # flags.writeable = True, flags.owndata = False + v = a.view_matrix() # flags.writeable = False, flags.owndata = False + c = a.copy_matrix() # flags.writeable = True, flags.owndata = True + # m[5,6] and v[5,6] refer to the same element, c[5,6] does not. + +Note in this example that ``py::return_value_policy::reference_internal`` is +used to tie the life of the MyClass object to the life of the returned arrays. + +You may also return an ``Eigen::Ref``, ``Eigen::Map`` or other map-like Eigen +object (for example, the return value of ``matrix.block()`` and related +methods) that map into a dense Eigen type. When doing so, the default +behaviour of pybind11 is to simply reference the returned data: you must take +care to ensure that this data remains valid! You may ask pybind11 to +explicitly *copy* such a return value by using the +``py::return_value_policy::copy`` policy when binding the function. You may +also use ``py::return_value_policy::reference_internal`` or a +``py::keep_alive`` to ensure the data stays valid as long as the returned numpy +array does. + +When returning such a reference of map, pybind11 additionally respects the +readonly-status of the returned value, marking the numpy array as non-writeable +if the reference or map was itself read-only. + +.. note:: + + Sparse types are always copied when returned. + +.. _storage_orders: + +Storage orders +============== + +Passing arguments via ``Eigen::Ref`` has some limitations that you must be +aware of in order to effectively pass matrices by reference. First and +foremost is that the default ``Eigen::Ref`` class requires +contiguous storage along columns (for column-major types, the default in Eigen) +or rows if ``MatrixType`` is specifically an ``Eigen::RowMajor`` storage type. +The former, Eigen's default, is incompatible with ``numpy``'s default row-major +storage, and so you will not be able to pass numpy arrays to Eigen by reference +without making one of two changes. + +(Note that this does not apply to vectors (or column or row matrices): for such +types the "row-major" and "column-major" distinction is meaningless). + +The first approach is to change the use of ``Eigen::Ref`` to the +more general ``Eigen::Ref>`` (or similar type with a fully dynamic stride type in the +third template argument). Since this is a rather cumbersome type, pybind11 +provides a ``py::EigenDRef`` type alias for your convenience (along +with EigenDMap for the equivalent Map, and EigenDStride for just the stride +type). + +This type allows Eigen to map into any arbitrary storage order. This is not +the default in Eigen for performance reasons: contiguous storage allows +vectorization that cannot be done when storage is not known to be contiguous at +compile time. The default ``Eigen::Ref`` stride type allows non-contiguous +storage along the outer dimension (that is, the rows of a column-major matrix +or columns of a row-major matrix), but not along the inner dimension. + +This type, however, has the added benefit of also being able to map numpy array +slices. For example, the following (contrived) example uses Eigen with a numpy +slice to multiply by 2 all coefficients that are both on even rows (0, 2, 4, +...) and in columns 2, 5, or 8: + +.. code-block:: cpp + + m.def("scale", [](py::EigenDRef m, double c) { m *= c; }); + +.. code-block:: python + + # a = np.array(...) + scale_by_2(myarray[0::2, 2:9:3]) + +The second approach to avoid copying is more intrusive: rearranging the +underlying data types to not run into the non-contiguous storage problem in the +first place. In particular, that means using matrices with ``Eigen::RowMajor`` +storage, where appropriate, such as: + +.. code-block:: cpp + + using RowMatrixXd = Eigen::Matrix; + // Use RowMatrixXd instead of MatrixXd + +Now bound functions accepting ``Eigen::Ref`` arguments will be +callable with numpy's (default) arrays without involving a copying. + +You can, alternatively, change the storage order that numpy arrays use by +adding the ``order='F'`` option when creating an array: + +.. code-block:: python + + myarray = np.array(source, order="F") + +Such an object will be passable to a bound function accepting an +``Eigen::Ref`` (or similar column-major Eigen type). + +One major caveat with this approach, however, is that it is not entirely as +easy as simply flipping all Eigen or numpy usage from one to the other: some +operations may alter the storage order of a numpy array. For example, ``a2 = +array.transpose()`` results in ``a2`` being a view of ``array`` that references +the same data, but in the opposite storage order! + +While this approach allows fully optimized vectorized calculations in Eigen, it +cannot be used with array slices, unlike the first approach. + +When *returning* a matrix to Python (either a regular matrix, a reference via +``Eigen::Ref<>``, or a map/block into a matrix), no special storage +consideration is required: the created numpy array will have the required +stride that allows numpy to properly interpret the array, whatever its storage +order. + +Failing rather than copying +=========================== + +The default behaviour when binding ``Eigen::Ref`` Eigen +references is to copy matrix values when passed a numpy array that does not +conform to the element type of ``MatrixType`` or does not have a compatible +stride layout. If you want to explicitly avoid copying in such a case, you +should bind arguments using the ``py::arg().noconvert()`` annotation (as +described in the :ref:`nonconverting_arguments` documentation). + +The following example shows an example of arguments that don't allow data +copying to take place: + +.. code-block:: cpp + + // The method and function to be bound: + class MyClass { + // ... + double some_method(const Eigen::Ref &matrix) { /* ... */ } + }; + float some_function(const Eigen::Ref &big, + const Eigen::Ref &small) { + // ... + } + + // The associated binding code: + using namespace pybind11::literals; // for "arg"_a + py::class_(m, "MyClass") + // ... other class definitions + .def("some_method", &MyClass::some_method, py::arg().noconvert()); + + m.def("some_function", &some_function, + "big"_a.noconvert(), // <- Don't allow copying for this arg + "small"_a // <- This one can be copied if needed + ); + +With the above binding code, attempting to call the the ``some_method(m)`` +method on a ``MyClass`` object, or attempting to call ``some_function(m, m2)`` +will raise a ``RuntimeError`` rather than making a temporary copy of the array. +It will, however, allow the ``m2`` argument to be copied into a temporary if +necessary. + +Note that explicitly specifying ``.noconvert()`` is not required for *mutable* +Eigen references (e.g. ``Eigen::Ref`` without ``const`` on the +``MatrixXd``): mutable references will never be called with a temporary copy. + +Vectors versus column/row matrices +================================== + +Eigen and numpy have fundamentally different notions of a vector. In Eigen, a +vector is simply a matrix with the number of columns or rows set to 1 at +compile time (for a column vector or row vector, respectively). NumPy, in +contrast, has comparable 2-dimensional 1xN and Nx1 arrays, but *also* has +1-dimensional arrays of size N. + +When passing a 2-dimensional 1xN or Nx1 array to Eigen, the Eigen type must +have matching dimensions: That is, you cannot pass a 2-dimensional Nx1 numpy +array to an Eigen value expecting a row vector, or a 1xN numpy array as a +column vector argument. + +On the other hand, pybind11 allows you to pass 1-dimensional arrays of length N +as Eigen parameters. If the Eigen type can hold a column vector of length N it +will be passed as such a column vector. If not, but the Eigen type constraints +will accept a row vector, it will be passed as a row vector. (The column +vector takes precedence when both are supported, for example, when passing a +1D numpy array to a MatrixXd argument). Note that the type need not be +explicitly a vector: it is permitted to pass a 1D numpy array of size 5 to an +Eigen ``Matrix``: you would end up with a 1x5 Eigen matrix. +Passing the same to an ``Eigen::MatrixXd`` would result in a 5x1 Eigen matrix. + +When returning an Eigen vector to numpy, the conversion is ambiguous: a row +vector of length 4 could be returned as either a 1D array of length 4, or as a +2D array of size 1x4. When encountering such a situation, pybind11 compromises +by considering the returned Eigen type: if it is a compile-time vector--that +is, the type has either the number of rows or columns set to 1 at compile +time--pybind11 converts to a 1D numpy array when returning the value. For +instances that are a vector only at run-time (e.g. ``MatrixXd``, +``Matrix``), pybind11 returns the vector as a 2D array to +numpy. If this isn't want you want, you can use ``array.reshape(...)`` to get +a view of the same data in the desired dimensions. + +.. seealso:: + + The file :file:`tests/test_eigen.cpp` contains a complete example that + shows how to pass Eigen sparse and dense data types in more detail. diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/functional.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/functional.rst new file mode 100644 index 0000000000000000000000000000000000000000..8b301895cca9dc4ab997c32d77a5d8622325a25a --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/functional.rst @@ -0,0 +1,109 @@ +Functional +########## + +The following features must be enabled by including :file:`pybind11/functional.h`. + + +Callbacks and passing anonymous functions +========================================= + +The C++11 standard brought lambda functions and the generic polymorphic +function wrapper ``std::function<>`` to the C++ programming language, which +enable powerful new ways of working with functions. Lambda functions come in +two flavors: stateless lambda function resemble classic function pointers that +link to an anonymous piece of code, while stateful lambda functions +additionally depend on captured variables that are stored in an anonymous +*lambda closure object*. + +Here is a simple example of a C++ function that takes an arbitrary function +(stateful or stateless) with signature ``int -> int`` as an argument and runs +it with the value 10. + +.. code-block:: cpp + + int func_arg(const std::function &f) { + return f(10); + } + +The example below is more involved: it takes a function of signature ``int -> int`` +and returns another function of the same kind. The return value is a stateful +lambda function, which stores the value ``f`` in the capture object and adds 1 to +its return value upon execution. + +.. code-block:: cpp + + std::function func_ret(const std::function &f) { + return [f](int i) { + return f(i) + 1; + }; + } + +This example demonstrates using python named parameters in C++ callbacks which +requires using ``py::cpp_function`` as a wrapper. Usage is similar to defining +methods of classes: + +.. code-block:: cpp + + py::cpp_function func_cpp() { + return py::cpp_function([](int i) { return i+1; }, + py::arg("number")); + } + +After including the extra header file :file:`pybind11/functional.h`, it is almost +trivial to generate binding code for all of these functions. + +.. code-block:: cpp + + #include + + PYBIND11_MODULE(example, m) { + m.def("func_arg", &func_arg); + m.def("func_ret", &func_ret); + m.def("func_cpp", &func_cpp); + } + +The following interactive session shows how to call them from Python. + +.. code-block:: pycon + + $ python + >>> import example + >>> def square(i): + ... return i * i + ... + >>> example.func_arg(square) + 100L + >>> square_plus_1 = example.func_ret(square) + >>> square_plus_1(4) + 17L + >>> plus_1 = func_cpp() + >>> plus_1(number=43) + 44L + +.. warning:: + + Keep in mind that passing a function from C++ to Python (or vice versa) + will instantiate a piece of wrapper code that translates function + invocations between the two languages. Naturally, this translation + increases the computational cost of each function call somewhat. A + problematic situation can arise when a function is copied back and forth + between Python and C++ many times in a row, in which case the underlying + wrappers will accumulate correspondingly. The resulting long sequence of + C++ -> Python -> C++ -> ... roundtrips can significantly decrease + performance. + + There is one exception: pybind11 detects case where a stateless function + (i.e. a function pointer or a lambda function without captured variables) + is passed as an argument to another C++ function exposed in Python. In this + case, there is no overhead. Pybind11 will extract the underlying C++ + function pointer from the wrapped function to sidestep a potential C++ -> + Python -> C++ roundtrip. This is demonstrated in :file:`tests/test_callbacks.cpp`. + +.. note:: + + This functionality is very useful when generating bindings for callbacks in + C++ libraries (e.g. GUI libraries, asynchronous networking libraries, etc.). + + The file :file:`tests/test_callbacks.cpp` contains a complete example + that demonstrates how to work with callbacks and anonymous functions in + more detail. diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/index.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..336aed113a16acc963e7587a43b1dae79b07917f --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/index.rst @@ -0,0 +1,43 @@ +.. _type-conversions: + +Type conversions +################ + +Apart from enabling cross-language function calls, a fundamental problem +that a binding tool like pybind11 must address is to provide access to +native Python types in C++ and vice versa. There are three fundamentally +different ways to do this—which approach is preferable for a particular type +depends on the situation at hand. + +1. Use a native C++ type everywhere. In this case, the type must be wrapped + using pybind11-generated bindings so that Python can interact with it. + +2. Use a native Python type everywhere. It will need to be wrapped so that + C++ functions can interact with it. + +3. Use a native C++ type on the C++ side and a native Python type on the + Python side. pybind11 refers to this as a *type conversion*. + + Type conversions are the most "natural" option in the sense that native + (non-wrapped) types are used everywhere. The main downside is that a copy + of the data must be made on every Python ↔ C++ transition: this is + needed since the C++ and Python versions of the same type generally won't + have the same memory layout. + + pybind11 can perform many kinds of conversions automatically. An overview + is provided in the table ":ref:`conversion_table`". + +The following subsections discuss the differences between these options in more +detail. The main focus in this section is on type conversions, which represent +the last case of the above list. + +.. toctree:: + :maxdepth: 1 + + overview + strings + stl + functional + chrono + eigen + custom diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/overview.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/overview.rst new file mode 100644 index 0000000000000000000000000000000000000000..7c512a6051b73c4693492efe8f0898efca53490d --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/overview.rst @@ -0,0 +1,171 @@ +Overview +######## + +.. rubric:: 1. Native type in C++, wrapper in Python + +Exposing a custom C++ type using :class:`py::class_` was covered in detail +in the :doc:`/classes` section. There, the underlying data structure is +always the original C++ class while the :class:`py::class_` wrapper provides +a Python interface. Internally, when an object like this is sent from C++ to +Python, pybind11 will just add the outer wrapper layer over the native C++ +object. Getting it back from Python is just a matter of peeling off the +wrapper. + +.. rubric:: 2. Wrapper in C++, native type in Python + +This is the exact opposite situation. Now, we have a type which is native to +Python, like a ``tuple`` or a ``list``. One way to get this data into C++ is +with the :class:`py::object` family of wrappers. These are explained in more +detail in the :doc:`/advanced/pycpp/object` section. We'll just give a quick +example here: + +.. code-block:: cpp + + void print_list(py::list my_list) { + for (auto item : my_list) + std::cout << item << " "; + } + +.. code-block:: pycon + + >>> print_list([1, 2, 3]) + 1 2 3 + +The Python ``list`` is not converted in any way -- it's just wrapped in a C++ +:class:`py::list` class. At its core it's still a Python object. Copying a +:class:`py::list` will do the usual reference-counting like in Python. +Returning the object to Python will just remove the thin wrapper. + +.. rubric:: 3. Converting between native C++ and Python types + +In the previous two cases we had a native type in one language and a wrapper in +the other. Now, we have native types on both sides and we convert between them. + +.. code-block:: cpp + + void print_vector(const std::vector &v) { + for (auto item : v) + std::cout << item << "\n"; + } + +.. code-block:: pycon + + >>> print_vector([1, 2, 3]) + 1 2 3 + +In this case, pybind11 will construct a new ``std::vector`` and copy each +element from the Python ``list``. The newly constructed object will be passed +to ``print_vector``. The same thing happens in the other direction: a new +``list`` is made to match the value returned from C++. + +Lots of these conversions are supported out of the box, as shown in the table +below. They are very convenient, but keep in mind that these conversions are +fundamentally based on copying data. This is perfectly fine for small immutable +types but it may become quite expensive for large data structures. This can be +avoided by overriding the automatic conversion with a custom wrapper (i.e. the +above-mentioned approach 1). This requires some manual effort and more details +are available in the :ref:`opaque` section. + +.. _conversion_table: + +List of all builtin conversions +------------------------------- + +The following basic data types are supported out of the box (some may require +an additional extension header to be included). To pass other data structures +as arguments and return values, refer to the section on binding :ref:`classes`. + ++------------------------------------+---------------------------+-----------------------------------+ +| Data type | Description | Header file | ++====================================+===========================+===================================+ +| ``int8_t``, ``uint8_t`` | 8-bit integers | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``int16_t``, ``uint16_t`` | 16-bit integers | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``int32_t``, ``uint32_t`` | 32-bit integers | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``int64_t``, ``uint64_t`` | 64-bit integers | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``ssize_t``, ``size_t`` | Platform-dependent size | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``float``, ``double`` | Floating point types | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``bool`` | Two-state Boolean type | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``char`` | Character literal | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``char16_t`` | UTF-16 character literal | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``char32_t`` | UTF-32 character literal | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``wchar_t`` | Wide character literal | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``const char *`` | UTF-8 string literal | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``const char16_t *`` | UTF-16 string literal | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``const char32_t *`` | UTF-32 string literal | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``const wchar_t *`` | Wide string literal | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::string`` | STL dynamic UTF-8 string | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::u16string`` | STL dynamic UTF-16 string | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::u32string`` | STL dynamic UTF-32 string | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::wstring`` | STL dynamic wide string | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::string_view``, | STL C++17 string views | :file:`pybind11/pybind11.h` | +| ``std::u16string_view``, etc. | | | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::pair`` | Pair of two custom types | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::tuple<...>`` | Arbitrary tuple of types | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::reference_wrapper<...>`` | Reference type wrapper | :file:`pybind11/pybind11.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::complex`` | Complex numbers | :file:`pybind11/complex.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::array`` | STL static array | :file:`pybind11/stl.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::vector`` | STL dynamic array | :file:`pybind11/stl.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::deque`` | STL double-ended queue | :file:`pybind11/stl.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::valarray`` | STL value array | :file:`pybind11/stl.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::list`` | STL linked list | :file:`pybind11/stl.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::map`` | STL ordered map | :file:`pybind11/stl.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::unordered_map`` | STL unordered map | :file:`pybind11/stl.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::set`` | STL ordered set | :file:`pybind11/stl.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::unordered_set`` | STL unordered set | :file:`pybind11/stl.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::optional`` | STL optional type (C++17) | :file:`pybind11/stl.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::experimental::optional`` | STL optional type (exp.) | :file:`pybind11/stl.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::variant<...>`` | Type-safe union (C++17) | :file:`pybind11/stl.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::filesystem::path`` | STL path (C++17) [#]_ | :file:`pybind11/stl/filesystem.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::function<...>`` | STL polymorphic function | :file:`pybind11/functional.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::chrono::duration<...>`` | STL time duration | :file:`pybind11/chrono.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``std::chrono::time_point<...>`` | STL date/time | :file:`pybind11/chrono.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``Eigen::Matrix<...>`` | Eigen: dense matrix | :file:`pybind11/eigen.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``Eigen::Map<...>`` | Eigen: mapped memory | :file:`pybind11/eigen.h` | ++------------------------------------+---------------------------+-----------------------------------+ +| ``Eigen::SparseMatrix<...>`` | Eigen: sparse matrix | :file:`pybind11/eigen.h` | ++------------------------------------+---------------------------+-----------------------------------+ + +.. [#] ``std::filesystem::path`` is converted to ``pathlib.Path`` and + ``os.PathLike`` is converted to ``std::filesystem::path``, but this requires + Python 3.6 (for ``__fspath__`` support). diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/stl.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/stl.rst new file mode 100644 index 0000000000000000000000000000000000000000..99099c7237c1f29b82ce02dd2f419068baf6a3fa --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/stl.rst @@ -0,0 +1,251 @@ +STL containers +############## + +Automatic conversion +==================== + +When including the additional header file :file:`pybind11/stl.h`, conversions +between ``std::vector<>``/``std::deque<>``/``std::list<>``/``std::array<>``/``std::valarray<>``, +``std::set<>``/``std::unordered_set<>``, and +``std::map<>``/``std::unordered_map<>`` and the Python ``list``, ``set`` and +``dict`` data structures are automatically enabled. The types ``std::pair<>`` +and ``std::tuple<>`` are already supported out of the box with just the core +:file:`pybind11/pybind11.h` header. + +The major downside of these implicit conversions is that containers must be +converted (i.e. copied) on every Python->C++ and C++->Python transition, which +can have implications on the program semantics and performance. Please read the +next sections for more details and alternative approaches that avoid this. + +.. note:: + + Arbitrary nesting of any of these types is possible. + +.. seealso:: + + The file :file:`tests/test_stl.cpp` contains a complete + example that demonstrates how to pass STL data types in more detail. + +.. _cpp17_container_casters: + +C++17 library containers +======================== + +The :file:`pybind11/stl.h` header also includes support for ``std::optional<>`` +and ``std::variant<>``. These require a C++17 compiler and standard library. +In C++14 mode, ``std::experimental::optional<>`` is supported if available. + +Various versions of these containers also exist for C++11 (e.g. in Boost). +pybind11 provides an easy way to specialize the ``type_caster`` for such +types: + +.. code-block:: cpp + + // `boost::optional` as an example -- can be any `std::optional`-like container + namespace pybind11 { namespace detail { + template + struct type_caster> : optional_caster> {}; + }} + +The above should be placed in a header file and included in all translation units +where automatic conversion is needed. Similarly, a specialization can be provided +for custom variant types: + +.. code-block:: cpp + + // `boost::variant` as an example -- can be any `std::variant`-like container + namespace pybind11 { namespace detail { + template + struct type_caster> : variant_caster> {}; + + // Specifies the function used to visit the variant -- `apply_visitor` instead of `visit` + template <> + struct visit_helper { + template + static auto call(Args &&...args) -> decltype(boost::apply_visitor(args...)) { + return boost::apply_visitor(args...); + } + }; + }} // namespace pybind11::detail + +The ``visit_helper`` specialization is not required if your ``name::variant`` provides +a ``name::visit()`` function. For any other function name, the specialization must be +included to tell pybind11 how to visit the variant. + +.. warning:: + + When converting a ``variant`` type, pybind11 follows the same rules as when + determining which function overload to call (:ref:`overload_resolution`), and + so the same caveats hold. In particular, the order in which the ``variant``'s + alternatives are listed is important, since pybind11 will try conversions in + this order. This means that, for example, when converting ``variant``, + the ``bool`` variant will never be selected, as any Python ``bool`` is already + an ``int`` and is convertible to a C++ ``int``. Changing the order of alternatives + (and using ``variant``, in this example) provides a solution. + +.. note:: + + pybind11 only supports the modern implementation of ``boost::variant`` + which makes use of variadic templates. This requires Boost 1.56 or newer. + Additionally, on Windows, MSVC 2017 is required because ``boost::variant`` + falls back to the old non-variadic implementation on MSVC 2015. + +.. _opaque: + +Making opaque types +=================== + +pybind11 heavily relies on a template matching mechanism to convert parameters +and return values that are constructed from STL data types such as vectors, +linked lists, hash tables, etc. This even works in a recursive manner, for +instance to deal with lists of hash maps of pairs of elementary and custom +types, etc. + +However, a fundamental limitation of this approach is that internal conversions +between Python and C++ types involve a copy operation that prevents +pass-by-reference semantics. What does this mean? + +Suppose we bind the following function + +.. code-block:: cpp + + void append_1(std::vector &v) { + v.push_back(1); + } + +and call it from Python, the following happens: + +.. code-block:: pycon + + >>> v = [5, 6] + >>> append_1(v) + >>> print(v) + [5, 6] + +As you can see, when passing STL data structures by reference, modifications +are not propagated back the Python side. A similar situation arises when +exposing STL data structures using the ``def_readwrite`` or ``def_readonly`` +functions: + +.. code-block:: cpp + + /* ... definition ... */ + + class MyClass { + std::vector contents; + }; + + /* ... binding code ... */ + + py::class_(m, "MyClass") + .def(py::init<>()) + .def_readwrite("contents", &MyClass::contents); + +In this case, properties can be read and written in their entirety. However, an +``append`` operation involving such a list type has no effect: + +.. code-block:: pycon + + >>> m = MyClass() + >>> m.contents = [5, 6] + >>> print(m.contents) + [5, 6] + >>> m.contents.append(7) + >>> print(m.contents) + [5, 6] + +Finally, the involved copy operations can be costly when dealing with very +large lists. To deal with all of the above situations, pybind11 provides a +macro named ``PYBIND11_MAKE_OPAQUE(T)`` that disables the template-based +conversion machinery of types, thus rendering them *opaque*. The contents of +opaque objects are never inspected or extracted, hence they *can* be passed by +reference. For instance, to turn ``std::vector`` into an opaque type, add +the declaration + +.. code-block:: cpp + + PYBIND11_MAKE_OPAQUE(std::vector); + +before any binding code (e.g. invocations to ``class_::def()``, etc.). This +macro must be specified at the top level (and outside of any namespaces), since +it adds a template instantiation of ``type_caster``. If your binding code consists of +multiple compilation units, it must be present in every file (typically via a +common header) preceding any usage of ``std::vector``. Opaque types must +also have a corresponding ``class_`` declaration to associate them with a name +in Python, and to define a set of available operations, e.g.: + +.. code-block:: cpp + + py::class_>(m, "IntVector") + .def(py::init<>()) + .def("clear", &std::vector::clear) + .def("pop_back", &std::vector::pop_back) + .def("__len__", [](const std::vector &v) { return v.size(); }) + .def("__iter__", [](std::vector &v) { + return py::make_iterator(v.begin(), v.end()); + }, py::keep_alive<0, 1>()) /* Keep vector alive while iterator is used */ + // .... + +.. seealso:: + + The file :file:`tests/test_opaque_types.cpp` contains a complete + example that demonstrates how to create and expose opaque types using + pybind11 in more detail. + +.. _stl_bind: + +Binding STL containers +====================== + +The ability to expose STL containers as native Python objects is a fairly +common request, hence pybind11 also provides an optional header file named +:file:`pybind11/stl_bind.h` that does exactly this. The mapped containers try +to match the behavior of their native Python counterparts as much as possible. + +The following example showcases usage of :file:`pybind11/stl_bind.h`: + +.. code-block:: cpp + + // Don't forget this + #include + + PYBIND11_MAKE_OPAQUE(std::vector); + PYBIND11_MAKE_OPAQUE(std::map); + + // ... + + // later in binding code: + py::bind_vector>(m, "VectorInt"); + py::bind_map>(m, "MapStringDouble"); + +When binding STL containers pybind11 considers the types of the container's +elements to decide whether the container should be confined to the local module +(via the :ref:`module_local` feature). If the container element types are +anything other than already-bound custom types bound without +``py::module_local()`` the container binding will have ``py::module_local()`` +applied. This includes converting types such as numeric types, strings, Eigen +types; and types that have not yet been bound at the time of the stl container +binding. This module-local binding is designed to avoid potential conflicts +between module bindings (for example, from two separate modules each attempting +to bind ``std::vector`` as a python type). + +It is possible to override this behavior to force a definition to be either +module-local or global. To do so, you can pass the attributes +``py::module_local()`` (to make the binding module-local) or +``py::module_local(false)`` (to make the binding global) into the +``py::bind_vector`` or ``py::bind_map`` arguments: + +.. code-block:: cpp + + py::bind_vector>(m, "VectorInt", py::module_local(false)); + +Note, however, that such a global binding would make it impossible to load this +module at the same time as any other pybind module that also attempts to bind +the same container type (``std::vector`` in the above example). + +See :ref:`module_local` for more details on module-local bindings. + +.. seealso:: + + The file :file:`tests/test_stl_binders.cpp` shows how to use the + convenience STL container wrappers. diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/strings.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/strings.rst new file mode 100644 index 0000000000000000000000000000000000000000..0834de98002eca3e95ddadf4962927b3e94b1060 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/cast/strings.rst @@ -0,0 +1,305 @@ +Strings, bytes and Unicode conversions +###################################### + +.. note:: + + This section discusses string handling in terms of Python 3 strings. For + Python 2.7, replace all occurrences of ``str`` with ``unicode`` and + ``bytes`` with ``str``. Python 2.7 users may find it best to use ``from + __future__ import unicode_literals`` to avoid unintentionally using ``str`` + instead of ``unicode``. + +Passing Python strings to C++ +============================= + +When a Python ``str`` is passed from Python to a C++ function that accepts +``std::string`` or ``char *`` as arguments, pybind11 will encode the Python +string to UTF-8. All Python ``str`` can be encoded in UTF-8, so this operation +does not fail. + +The C++ language is encoding agnostic. It is the responsibility of the +programmer to track encodings. It's often easiest to simply `use UTF-8 +everywhere `_. + +.. code-block:: c++ + + m.def("utf8_test", + [](const std::string &s) { + cout << "utf-8 is icing on the cake.\n"; + cout << s; + } + ); + m.def("utf8_charptr", + [](const char *s) { + cout << "My favorite food is\n"; + cout << s; + } + ); + +.. code-block:: pycon + + >>> utf8_test("🎂") + utf-8 is icing on the cake. + 🎂 + + >>> utf8_charptr("🍕") + My favorite food is + 🍕 + +.. note:: + + Some terminal emulators do not support UTF-8 or emoji fonts and may not + display the example above correctly. + +The results are the same whether the C++ function accepts arguments by value or +reference, and whether or not ``const`` is used. + +Passing bytes to C++ +-------------------- + +A Python ``bytes`` object will be passed to C++ functions that accept +``std::string`` or ``char*`` *without* conversion. On Python 3, in order to +make a function *only* accept ``bytes`` (and not ``str``), declare it as taking +a ``py::bytes`` argument. + + +Returning C++ strings to Python +=============================== + +When a C++ function returns a ``std::string`` or ``char*`` to a Python caller, +**pybind11 will assume that the string is valid UTF-8** and will decode it to a +native Python ``str``, using the same API as Python uses to perform +``bytes.decode('utf-8')``. If this implicit conversion fails, pybind11 will +raise a ``UnicodeDecodeError``. + +.. code-block:: c++ + + m.def("std_string_return", + []() { + return std::string("This string needs to be UTF-8 encoded"); + } + ); + +.. code-block:: pycon + + >>> isinstance(example.std_string_return(), str) + True + + +Because UTF-8 is inclusive of pure ASCII, there is never any issue with +returning a pure ASCII string to Python. If there is any possibility that the +string is not pure ASCII, it is necessary to ensure the encoding is valid +UTF-8. + +.. warning:: + + Implicit conversion assumes that a returned ``char *`` is null-terminated. + If there is no null terminator a buffer overrun will occur. + +Explicit conversions +-------------------- + +If some C++ code constructs a ``std::string`` that is not a UTF-8 string, one +can perform a explicit conversion and return a ``py::str`` object. Explicit +conversion has the same overhead as implicit conversion. + +.. code-block:: c++ + + // This uses the Python C API to convert Latin-1 to Unicode + m.def("str_output", + []() { + std::string s = "Send your r\xe9sum\xe9 to Alice in HR"; // Latin-1 + py::str py_s = PyUnicode_DecodeLatin1(s.data(), s.length()); + return py_s; + } + ); + +.. code-block:: pycon + + >>> str_output() + 'Send your résumé to Alice in HR' + +The `Python C API +`_ provides +several built-in codecs. + + +One could also use a third party encoding library such as libiconv to transcode +to UTF-8. + +Return C++ strings without conversion +------------------------------------- + +If the data in a C++ ``std::string`` does not represent text and should be +returned to Python as ``bytes``, then one can return the data as a +``py::bytes`` object. + +.. code-block:: c++ + + m.def("return_bytes", + []() { + std::string s("\xba\xd0\xba\xd0"); // Not valid UTF-8 + return py::bytes(s); // Return the data without transcoding + } + ); + +.. code-block:: pycon + + >>> example.return_bytes() + b'\xba\xd0\xba\xd0' + + +Note the asymmetry: pybind11 will convert ``bytes`` to ``std::string`` without +encoding, but cannot convert ``std::string`` back to ``bytes`` implicitly. + +.. code-block:: c++ + + m.def("asymmetry", + [](std::string s) { // Accepts str or bytes from Python + return s; // Looks harmless, but implicitly converts to str + } + ); + +.. code-block:: pycon + + >>> isinstance(example.asymmetry(b"have some bytes"), str) + True + + >>> example.asymmetry(b"\xba\xd0\xba\xd0") # invalid utf-8 as bytes + UnicodeDecodeError: 'utf-8' codec can't decode byte 0xba in position 0: invalid start byte + + +Wide character strings +====================== + +When a Python ``str`` is passed to a C++ function expecting ``std::wstring``, +``wchar_t*``, ``std::u16string`` or ``std::u32string``, the ``str`` will be +encoded to UTF-16 or UTF-32 depending on how the C++ compiler implements each +type, in the platform's native endianness. When strings of these types are +returned, they are assumed to contain valid UTF-16 or UTF-32, and will be +decoded to Python ``str``. + +.. code-block:: c++ + + #define UNICODE + #include + + m.def("set_window_text", + [](HWND hwnd, std::wstring s) { + // Call SetWindowText with null-terminated UTF-16 string + ::SetWindowText(hwnd, s.c_str()); + } + ); + m.def("get_window_text", + [](HWND hwnd) { + const int buffer_size = ::GetWindowTextLength(hwnd) + 1; + auto buffer = std::make_unique< wchar_t[] >(buffer_size); + + ::GetWindowText(hwnd, buffer.data(), buffer_size); + + std::wstring text(buffer.get()); + + // wstring will be converted to Python str + return text; + } + ); + +.. warning:: + + Wide character strings may not work as described on Python 2.7 or Python + 3.3 compiled with ``--enable-unicode=ucs2``. + +Strings in multibyte encodings such as Shift-JIS must transcoded to a +UTF-8/16/32 before being returned to Python. + + +Character literals +================== + +C++ functions that accept character literals as input will receive the first +character of a Python ``str`` as their input. If the string is longer than one +Unicode character, trailing characters will be ignored. + +When a character literal is returned from C++ (such as a ``char`` or a +``wchar_t``), it will be converted to a ``str`` that represents the single +character. + +.. code-block:: c++ + + m.def("pass_char", [](char c) { return c; }); + m.def("pass_wchar", [](wchar_t w) { return w; }); + +.. code-block:: pycon + + >>> example.pass_char("A") + 'A' + +While C++ will cast integers to character types (``char c = 0x65;``), pybind11 +does not convert Python integers to characters implicitly. The Python function +``chr()`` can be used to convert integers to characters. + +.. code-block:: pycon + + >>> example.pass_char(0x65) + TypeError + + >>> example.pass_char(chr(0x65)) + 'A' + +If the desire is to work with an 8-bit integer, use ``int8_t`` or ``uint8_t`` +as the argument type. + +Grapheme clusters +----------------- + +A single grapheme may be represented by two or more Unicode characters. For +example 'é' is usually represented as U+00E9 but can also be expressed as the +combining character sequence U+0065 U+0301 (that is, the letter 'e' followed by +a combining acute accent). The combining character will be lost if the +two-character sequence is passed as an argument, even though it renders as a +single grapheme. + +.. code-block:: pycon + + >>> example.pass_wchar("é") + 'é' + + >>> combining_e_acute = "e" + "\u0301" + + >>> combining_e_acute + 'é' + + >>> combining_e_acute == "é" + False + + >>> example.pass_wchar(combining_e_acute) + 'e' + +Normalizing combining characters before passing the character literal to C++ +may resolve *some* of these issues: + +.. code-block:: pycon + + >>> example.pass_wchar(unicodedata.normalize("NFC", combining_e_acute)) + 'é' + +In some languages (Thai for example), there are `graphemes that cannot be +expressed as a single Unicode code point +`_, so there is +no way to capture them in a C++ character type. + + +C++17 string views +================== + +C++17 string views are automatically supported when compiling in C++17 mode. +They follow the same rules for encoding and decoding as the corresponding STL +string type (for example, a ``std::u16string_view`` argument will be passed +UTF-16-encoded data, and a returned ``std::string_view`` will be decoded as +UTF-8). + +References +========== + +* `The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets (No Excuses!) `_ +* `C++ - Using STL Strings at Win32 API Boundaries `_ diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/classes.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/classes.rst new file mode 100644 index 0000000000000000000000000000000000000000..64322e94bc7e955cafd5edb4a2ca7cbe596fd08f --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/classes.rst @@ -0,0 +1,1349 @@ +Classes +####### + +This section presents advanced binding code for classes and it is assumed +that you are already familiar with the basics from :doc:`/classes`. + +.. _overriding_virtuals: + +Overriding virtual functions in Python +====================================== + +Suppose that a C++ class or interface has a virtual function that we'd like +to override from within Python (we'll focus on the class ``Animal``; ``Dog`` is +given as a specific example of how one would do this with traditional C++ +code). + +.. code-block:: cpp + + class Animal { + public: + virtual ~Animal() { } + virtual std::string go(int n_times) = 0; + }; + + class Dog : public Animal { + public: + std::string go(int n_times) override { + std::string result; + for (int i=0; igo(3); + } + +Normally, the binding code for these classes would look as follows: + +.. code-block:: cpp + + PYBIND11_MODULE(example, m) { + py::class_(m, "Animal") + .def("go", &Animal::go); + + py::class_(m, "Dog") + .def(py::init<>()); + + m.def("call_go", &call_go); + } + +However, these bindings are impossible to extend: ``Animal`` is not +constructible, and we clearly require some kind of "trampoline" that +redirects virtual calls back to Python. + +Defining a new type of ``Animal`` from within Python is possible but requires a +helper class that is defined as follows: + +.. code-block:: cpp + + class PyAnimal : public Animal { + public: + /* Inherit the constructors */ + using Animal::Animal; + + /* Trampoline (need one for each virtual function) */ + std::string go(int n_times) override { + PYBIND11_OVERRIDE_PURE( + std::string, /* Return type */ + Animal, /* Parent class */ + go, /* Name of function in C++ (must match Python name) */ + n_times /* Argument(s) */ + ); + } + }; + +The macro :c:macro:`PYBIND11_OVERRIDE_PURE` should be used for pure virtual +functions, and :c:macro:`PYBIND11_OVERRIDE` should be used for functions which have +a default implementation. There are also two alternate macros +:c:macro:`PYBIND11_OVERRIDE_PURE_NAME` and :c:macro:`PYBIND11_OVERRIDE_NAME` which +take a string-valued name argument between the *Parent class* and *Name of the +function* slots, which defines the name of function in Python. This is required +when the C++ and Python versions of the +function have different names, e.g. ``operator()`` vs ``__call__``. + +The binding code also needs a few minor adaptations (highlighted): + +.. code-block:: cpp + :emphasize-lines: 2,3 + + PYBIND11_MODULE(example, m) { + py::class_(m, "Animal") + .def(py::init<>()) + .def("go", &Animal::go); + + py::class_(m, "Dog") + .def(py::init<>()); + + m.def("call_go", &call_go); + } + +Importantly, pybind11 is made aware of the trampoline helper class by +specifying it as an extra template argument to :class:`class_`. (This can also +be combined with other template arguments such as a custom holder type; the +order of template types does not matter). Following this, we are able to +define a constructor as usual. + +Bindings should be made against the actual class, not the trampoline helper class. + +.. code-block:: cpp + :emphasize-lines: 3 + + py::class_(m, "Animal"); + .def(py::init<>()) + .def("go", &PyAnimal::go); /* <--- THIS IS WRONG, use &Animal::go */ + +Note, however, that the above is sufficient for allowing python classes to +extend ``Animal``, but not ``Dog``: see :ref:`virtual_and_inheritance` for the +necessary steps required to providing proper overriding support for inherited +classes. + +The Python session below shows how to override ``Animal::go`` and invoke it via +a virtual method call. + +.. code-block:: pycon + + >>> from example import * + >>> d = Dog() + >>> call_go(d) + u'woof! woof! woof! ' + >>> class Cat(Animal): + ... def go(self, n_times): + ... return "meow! " * n_times + ... + >>> c = Cat() + >>> call_go(c) + u'meow! meow! meow! ' + +If you are defining a custom constructor in a derived Python class, you *must* +ensure that you explicitly call the bound C++ constructor using ``__init__``, +*regardless* of whether it is a default constructor or not. Otherwise, the +memory for the C++ portion of the instance will be left uninitialized, which +will generally leave the C++ instance in an invalid state and cause undefined +behavior if the C++ instance is subsequently used. + +.. versionchanged:: 2.6 + The default pybind11 metaclass will throw a ``TypeError`` when it detects + that ``__init__`` was not called by a derived class. + +Here is an example: + +.. code-block:: python + + class Dachshund(Dog): + def __init__(self, name): + Dog.__init__(self) # Without this, a TypeError is raised. + self.name = name + + def bark(self): + return "yap!" + +Note that a direct ``__init__`` constructor *should be called*, and ``super()`` +should not be used. For simple cases of linear inheritance, ``super()`` +may work, but once you begin mixing Python and C++ multiple inheritance, +things will fall apart due to differences between Python's MRO and C++'s +mechanisms. + +Please take a look at the :ref:`macro_notes` before using this feature. + +.. note:: + + When the overridden type returns a reference or pointer to a type that + pybind11 converts from Python (for example, numeric values, std::string, + and other built-in value-converting types), there are some limitations to + be aware of: + + - because in these cases there is no C++ variable to reference (the value + is stored in the referenced Python variable), pybind11 provides one in + the PYBIND11_OVERRIDE macros (when needed) with static storage duration. + Note that this means that invoking the overridden method on *any* + instance will change the referenced value stored in *all* instances of + that type. + + - Attempts to modify a non-const reference will not have the desired + effect: it will change only the static cache variable, but this change + will not propagate to underlying Python instance, and the change will be + replaced the next time the override is invoked. + +.. warning:: + + The :c:macro:`PYBIND11_OVERRIDE` and accompanying macros used to be called + ``PYBIND11_OVERLOAD`` up until pybind11 v2.5.0, and :func:`get_override` + used to be called ``get_overload``. This naming was corrected and the older + macro and function names may soon be deprecated, in order to reduce + confusion with overloaded functions and methods and ``py::overload_cast`` + (see :ref:`classes`). + +.. seealso:: + + The file :file:`tests/test_virtual_functions.cpp` contains a complete + example that demonstrates how to override virtual functions using pybind11 + in more detail. + +.. _virtual_and_inheritance: + +Combining virtual functions and inheritance +=========================================== + +When combining virtual methods with inheritance, you need to be sure to provide +an override for each method for which you want to allow overrides from derived +python classes. For example, suppose we extend the above ``Animal``/``Dog`` +example as follows: + +.. code-block:: cpp + + class Animal { + public: + virtual std::string go(int n_times) = 0; + virtual std::string name() { return "unknown"; } + }; + class Dog : public Animal { + public: + std::string go(int n_times) override { + std::string result; + for (int i=0; i class PyAnimal : public AnimalBase { + public: + using AnimalBase::AnimalBase; // Inherit constructors + std::string go(int n_times) override { PYBIND11_OVERRIDE_PURE(std::string, AnimalBase, go, n_times); } + std::string name() override { PYBIND11_OVERRIDE(std::string, AnimalBase, name, ); } + }; + template class PyDog : public PyAnimal { + public: + using PyAnimal::PyAnimal; // Inherit constructors + // Override PyAnimal's pure virtual go() with a non-pure one: + std::string go(int n_times) override { PYBIND11_OVERRIDE(std::string, DogBase, go, n_times); } + std::string bark() override { PYBIND11_OVERRIDE(std::string, DogBase, bark, ); } + }; + +This technique has the advantage of requiring just one trampoline method to be +declared per virtual method and pure virtual method override. It does, +however, require the compiler to generate at least as many methods (and +possibly more, if both pure virtual and overridden pure virtual methods are +exposed, as above). + +The classes are then registered with pybind11 using: + +.. code-block:: cpp + + py::class_> animal(m, "Animal"); + py::class_> dog(m, "Dog"); + py::class_> husky(m, "Husky"); + // ... add animal, dog, husky definitions + +Note that ``Husky`` did not require a dedicated trampoline template class at +all, since it neither declares any new virtual methods nor provides any pure +virtual method implementations. + +With either the repeated-virtuals or templated trampoline methods in place, you +can now create a python class that inherits from ``Dog``: + +.. code-block:: python + + class ShihTzu(Dog): + def bark(self): + return "yip!" + +.. seealso:: + + See the file :file:`tests/test_virtual_functions.cpp` for complete examples + using both the duplication and templated trampoline approaches. + +.. _extended_aliases: + +Extended trampoline class functionality +======================================= + +.. _extended_class_functionality_forced_trampoline: + +Forced trampoline class initialisation +-------------------------------------- +The trampoline classes described in the previous sections are, by default, only +initialized when needed. More specifically, they are initialized when a python +class actually inherits from a registered type (instead of merely creating an +instance of the registered type), or when a registered constructor is only +valid for the trampoline class but not the registered class. This is primarily +for performance reasons: when the trampoline class is not needed for anything +except virtual method dispatching, not initializing the trampoline class +improves performance by avoiding needing to do a run-time check to see if the +inheriting python instance has an overridden method. + +Sometimes, however, it is useful to always initialize a trampoline class as an +intermediate class that does more than just handle virtual method dispatching. +For example, such a class might perform extra class initialization, extra +destruction operations, and might define new members and methods to enable a +more python-like interface to a class. + +In order to tell pybind11 that it should *always* initialize the trampoline +class when creating new instances of a type, the class constructors should be +declared using ``py::init_alias()`` instead of the usual +``py::init()``. This forces construction via the trampoline class, +ensuring member initialization and (eventual) destruction. + +.. seealso:: + + See the file :file:`tests/test_virtual_functions.cpp` for complete examples + showing both normal and forced trampoline instantiation. + +Different method signatures +--------------------------- +The macro's introduced in :ref:`overriding_virtuals` cover most of the standard +use cases when exposing C++ classes to Python. Sometimes it is hard or unwieldy +to create a direct one-on-one mapping between the arguments and method return +type. + +An example would be when the C++ signature contains output arguments using +references (See also :ref:`faq_reference_arguments`). Another way of solving +this is to use the method body of the trampoline class to do conversions to the +input and return of the Python method. + +The main building block to do so is the :func:`get_override`, this function +allows retrieving a method implemented in Python from within the trampoline's +methods. Consider for example a C++ method which has the signature +``bool myMethod(int32_t& value)``, where the return indicates whether +something should be done with the ``value``. This can be made convenient on the +Python side by allowing the Python function to return ``None`` or an ``int``: + +.. code-block:: cpp + + bool MyClass::myMethod(int32_t& value) + { + pybind11::gil_scoped_acquire gil; // Acquire the GIL while in this scope. + // Try to look up the overridden method on the Python side. + pybind11::function override = pybind11::get_override(this, "myMethod"); + if (override) { // method is found + auto obj = override(value); // Call the Python function. + if (py::isinstance(obj)) { // check if it returned a Python integer type + value = obj.cast(); // Cast it and assign it to the value. + return true; // Return true; value should be used. + } else { + return false; // Python returned none, return false. + } + } + return false; // Alternatively return MyClass::myMethod(value); + } + + +.. _custom_constructors: + +Custom constructors +=================== + +The syntax for binding constructors was previously introduced, but it only +works when a constructor of the appropriate arguments actually exists on the +C++ side. To extend this to more general cases, pybind11 makes it possible +to bind factory functions as constructors. For example, suppose you have a +class like this: + +.. code-block:: cpp + + class Example { + private: + Example(int); // private constructor + public: + // Factory function: + static Example create(int a) { return Example(a); } + }; + + py::class_(m, "Example") + .def(py::init(&Example::create)); + +While it is possible to create a straightforward binding of the static +``create`` method, it may sometimes be preferable to expose it as a constructor +on the Python side. This can be accomplished by calling ``.def(py::init(...))`` +with the function reference returning the new instance passed as an argument. +It is also possible to use this approach to bind a function returning a new +instance by raw pointer or by the holder (e.g. ``std::unique_ptr``). + +The following example shows the different approaches: + +.. code-block:: cpp + + class Example { + private: + Example(int); // private constructor + public: + // Factory function - returned by value: + static Example create(int a) { return Example(a); } + + // These constructors are publicly callable: + Example(double); + Example(int, int); + Example(std::string); + }; + + py::class_(m, "Example") + // Bind the factory function as a constructor: + .def(py::init(&Example::create)) + // Bind a lambda function returning a pointer wrapped in a holder: + .def(py::init([](std::string arg) { + return std::unique_ptr(new Example(arg)); + })) + // Return a raw pointer: + .def(py::init([](int a, int b) { return new Example(a, b); })) + // You can mix the above with regular C++ constructor bindings as well: + .def(py::init()) + ; + +When the constructor is invoked from Python, pybind11 will call the factory +function and store the resulting C++ instance in the Python instance. + +When combining factory functions constructors with :ref:`virtual function +trampolines ` there are two approaches. The first is to +add a constructor to the alias class that takes a base value by +rvalue-reference. If such a constructor is available, it will be used to +construct an alias instance from the value returned by the factory function. +The second option is to provide two factory functions to ``py::init()``: the +first will be invoked when no alias class is required (i.e. when the class is +being used but not inherited from in Python), and the second will be invoked +when an alias is required. + +You can also specify a single factory function that always returns an alias +instance: this will result in behaviour similar to ``py::init_alias<...>()``, +as described in the :ref:`extended trampoline class documentation +`. + +The following example shows the different factory approaches for a class with +an alias: + +.. code-block:: cpp + + #include + class Example { + public: + // ... + virtual ~Example() = default; + }; + class PyExample : public Example { + public: + using Example::Example; + PyExample(Example &&base) : Example(std::move(base)) {} + }; + py::class_(m, "Example") + // Returns an Example pointer. If a PyExample is needed, the Example + // instance will be moved via the extra constructor in PyExample, above. + .def(py::init([]() { return new Example(); })) + // Two callbacks: + .def(py::init([]() { return new Example(); } /* no alias needed */, + []() { return new PyExample(); } /* alias needed */)) + // *Always* returns an alias instance (like py::init_alias<>()) + .def(py::init([]() { return new PyExample(); })) + ; + +Brace initialization +-------------------- + +``pybind11::init<>`` internally uses C++11 brace initialization to call the +constructor of the target class. This means that it can be used to bind +*implicit* constructors as well: + +.. code-block:: cpp + + struct Aggregate { + int a; + std::string b; + }; + + py::class_(m, "Aggregate") + .def(py::init()); + +.. note:: + + Note that brace initialization preferentially invokes constructor overloads + taking a ``std::initializer_list``. In the rare event that this causes an + issue, you can work around it by using ``py::init(...)`` with a lambda + function that constructs the new object as desired. + +.. _classes_with_non_public_destructors: + +Non-public destructors +====================== + +If a class has a private or protected destructor (as might e.g. be the case in +a singleton pattern), a compile error will occur when creating bindings via +pybind11. The underlying issue is that the ``std::unique_ptr`` holder type that +is responsible for managing the lifetime of instances will reference the +destructor even if no deallocations ever take place. In order to expose classes +with private or protected destructors, it is possible to override the holder +type via a holder type argument to ``class_``. Pybind11 provides a helper class +``py::nodelete`` that disables any destructor invocations. In this case, it is +crucial that instances are deallocated on the C++ side to avoid memory leaks. + +.. code-block:: cpp + + /* ... definition ... */ + + class MyClass { + private: + ~MyClass() { } + }; + + /* ... binding code ... */ + + py::class_>(m, "MyClass") + .def(py::init<>()) + +.. _destructors_that_call_python: + +Destructors that call Python +============================ + +If a Python function is invoked from a C++ destructor, an exception may be thrown +of type :class:`error_already_set`. If this error is thrown out of a class destructor, +``std::terminate()`` will be called, terminating the process. Class destructors +must catch all exceptions of type :class:`error_already_set` to discard the Python +exception using :func:`error_already_set::discard_as_unraisable`. + +Every Python function should be treated as *possibly throwing*. When a Python generator +stops yielding items, Python will throw a ``StopIteration`` exception, which can pass +though C++ destructors if the generator's stack frame holds the last reference to C++ +objects. + +For more information, see :ref:`the documentation on exceptions `. + +.. code-block:: cpp + + class MyClass { + public: + ~MyClass() { + try { + py::print("Even printing is dangerous in a destructor"); + py::exec("raise ValueError('This is an unraisable exception')"); + } catch (py::error_already_set &e) { + // error_context should be information about where/why the occurred, + // e.g. use __func__ to get the name of the current function + e.discard_as_unraisable(__func__); + } + } + }; + +.. note:: + + pybind11 does not support C++ destructors marked ``noexcept(false)``. + +.. versionadded:: 2.6 + +.. _implicit_conversions: + +Implicit conversions +==================== + +Suppose that instances of two types ``A`` and ``B`` are used in a project, and +that an ``A`` can easily be converted into an instance of type ``B`` (examples of this +could be a fixed and an arbitrary precision number type). + +.. code-block:: cpp + + py::class_(m, "A") + /// ... members ... + + py::class_(m, "B") + .def(py::init()) + /// ... members ... + + m.def("func", + [](const B &) { /* .... */ } + ); + +To invoke the function ``func`` using a variable ``a`` containing an ``A`` +instance, we'd have to write ``func(B(a))`` in Python. On the other hand, C++ +will automatically apply an implicit type conversion, which makes it possible +to directly write ``func(a)``. + +In this situation (i.e. where ``B`` has a constructor that converts from +``A``), the following statement enables similar implicit conversions on the +Python side: + +.. code-block:: cpp + + py::implicitly_convertible(); + +.. note:: + + Implicit conversions from ``A`` to ``B`` only work when ``B`` is a custom + data type that is exposed to Python via pybind11. + + To prevent runaway recursion, implicit conversions are non-reentrant: an + implicit conversion invoked as part of another implicit conversion of the + same type (i.e. from ``A`` to ``B``) will fail. + +.. _static_properties: + +Static properties +================= + +The section on :ref:`properties` discussed the creation of instance properties +that are implemented in terms of C++ getters and setters. + +Static properties can also be created in a similar way to expose getters and +setters of static class attributes. Note that the implicit ``self`` argument +also exists in this case and is used to pass the Python ``type`` subclass +instance. This parameter will often not be needed by the C++ side, and the +following example illustrates how to instantiate a lambda getter function +that ignores it: + +.. code-block:: cpp + + py::class_(m, "Foo") + .def_property_readonly_static("foo", [](py::object /* self */) { return Foo(); }); + +Operator overloading +==================== + +Suppose that we're given the following ``Vector2`` class with a vector addition +and scalar multiplication operation, all implemented using overloaded operators +in C++. + +.. code-block:: cpp + + class Vector2 { + public: + Vector2(float x, float y) : x(x), y(y) { } + + Vector2 operator+(const Vector2 &v) const { return Vector2(x + v.x, y + v.y); } + Vector2 operator*(float value) const { return Vector2(x * value, y * value); } + Vector2& operator+=(const Vector2 &v) { x += v.x; y += v.y; return *this; } + Vector2& operator*=(float v) { x *= v; y *= v; return *this; } + + friend Vector2 operator*(float f, const Vector2 &v) { + return Vector2(f * v.x, f * v.y); + } + + std::string toString() const { + return "[" + std::to_string(x) + ", " + std::to_string(y) + "]"; + } + private: + float x, y; + }; + +The following snippet shows how the above operators can be conveniently exposed +to Python. + +.. code-block:: cpp + + #include + + PYBIND11_MODULE(example, m) { + py::class_(m, "Vector2") + .def(py::init()) + .def(py::self + py::self) + .def(py::self += py::self) + .def(py::self *= float()) + .def(float() * py::self) + .def(py::self * float()) + .def(-py::self) + .def("__repr__", &Vector2::toString); + } + +Note that a line like + +.. code-block:: cpp + + .def(py::self * float()) + +is really just short hand notation for + +.. code-block:: cpp + + .def("__mul__", [](const Vector2 &a, float b) { + return a * b; + }, py::is_operator()) + +This can be useful for exposing additional operators that don't exist on the +C++ side, or to perform other types of customization. The ``py::is_operator`` +flag marker is needed to inform pybind11 that this is an operator, which +returns ``NotImplemented`` when invoked with incompatible arguments rather than +throwing a type error. + +.. note:: + + To use the more convenient ``py::self`` notation, the additional + header file :file:`pybind11/operators.h` must be included. + +.. seealso:: + + The file :file:`tests/test_operator_overloading.cpp` contains a + complete example that demonstrates how to work with overloaded operators in + more detail. + +.. _pickling: + +Pickling support +================ + +Python's ``pickle`` module provides a powerful facility to serialize and +de-serialize a Python object graph into a binary data stream. To pickle and +unpickle C++ classes using pybind11, a ``py::pickle()`` definition must be +provided. Suppose the class in question has the following signature: + +.. code-block:: cpp + + class Pickleable { + public: + Pickleable(const std::string &value) : m_value(value) { } + const std::string &value() const { return m_value; } + + void setExtra(int extra) { m_extra = extra; } + int extra() const { return m_extra; } + private: + std::string m_value; + int m_extra = 0; + }; + +Pickling support in Python is enabled by defining the ``__setstate__`` and +``__getstate__`` methods [#f3]_. For pybind11 classes, use ``py::pickle()`` +to bind these two functions: + +.. code-block:: cpp + + py::class_(m, "Pickleable") + .def(py::init()) + .def("value", &Pickleable::value) + .def("extra", &Pickleable::extra) + .def("setExtra", &Pickleable::setExtra) + .def(py::pickle( + [](const Pickleable &p) { // __getstate__ + /* Return a tuple that fully encodes the state of the object */ + return py::make_tuple(p.value(), p.extra()); + }, + [](py::tuple t) { // __setstate__ + if (t.size() != 2) + throw std::runtime_error("Invalid state!"); + + /* Create a new C++ instance */ + Pickleable p(t[0].cast()); + + /* Assign any additional state */ + p.setExtra(t[1].cast()); + + return p; + } + )); + +The ``__setstate__`` part of the ``py::pickle()`` definition follows the same +rules as the single-argument version of ``py::init()``. The return type can be +a value, pointer or holder type. See :ref:`custom_constructors` for details. + +An instance can now be pickled as follows: + +.. code-block:: python + + try: + import cPickle as pickle # Use cPickle on Python 2.7 + except ImportError: + import pickle + + p = Pickleable("test_value") + p.setExtra(15) + data = pickle.dumps(p, 2) + + +.. note:: + Note that only the cPickle module is supported on Python 2.7. + + The second argument to ``dumps`` is also crucial: it selects the pickle + protocol version 2, since the older version 1 is not supported. Newer + versions are also fine—for instance, specify ``-1`` to always use the + latest available version. Beware: failure to follow these instructions + will cause important pybind11 memory allocation routines to be skipped + during unpickling, which will likely lead to memory corruption and/or + segmentation faults. + +.. seealso:: + + The file :file:`tests/test_pickling.cpp` contains a complete example + that demonstrates how to pickle and unpickle types using pybind11 in more + detail. + +.. [#f3] http://docs.python.org/3/library/pickle.html#pickling-class-instances + +Deepcopy support +================ + +Python normally uses references in assignments. Sometimes a real copy is needed +to prevent changing all copies. The ``copy`` module [#f5]_ provides these +capabilities. + +On Python 3, a class with pickle support is automatically also (deep)copy +compatible. However, performance can be improved by adding custom +``__copy__`` and ``__deepcopy__`` methods. With Python 2.7, these custom methods +are mandatory for (deep)copy compatibility, because pybind11 only supports +cPickle. + +For simple classes (deep)copy can be enabled by using the copy constructor, +which should look as follows: + +.. code-block:: cpp + + py::class_(m, "Copyable") + .def("__copy__", [](const Copyable &self) { + return Copyable(self); + }) + .def("__deepcopy__", [](const Copyable &self, py::dict) { + return Copyable(self); + }, "memo"_a); + +.. note:: + + Dynamic attributes will not be copied in this example. + +.. [#f5] https://docs.python.org/3/library/copy.html + +Multiple Inheritance +==================== + +pybind11 can create bindings for types that derive from multiple base types +(aka. *multiple inheritance*). To do so, specify all bases in the template +arguments of the ``class_`` declaration: + +.. code-block:: cpp + + py::class_(m, "MyType") + ... + +The base types can be specified in arbitrary order, and they can even be +interspersed with alias types and holder types (discussed earlier in this +document)---pybind11 will automatically find out which is which. The only +requirement is that the first template argument is the type to be declared. + +It is also permitted to inherit multiply from exported C++ classes in Python, +as well as inheriting from multiple Python and/or pybind11-exported classes. + +There is one caveat regarding the implementation of this feature: + +When only one base type is specified for a C++ type that actually has multiple +bases, pybind11 will assume that it does not participate in multiple +inheritance, which can lead to undefined behavior. In such cases, add the tag +``multiple_inheritance`` to the class constructor: + +.. code-block:: cpp + + py::class_(m, "MyType", py::multiple_inheritance()); + +The tag is redundant and does not need to be specified when multiple base types +are listed. + +.. _module_local: + +Module-local class bindings +=========================== + +When creating a binding for a class, pybind11 by default makes that binding +"global" across modules. What this means is that a type defined in one module +can be returned from any module resulting in the same Python type. For +example, this allows the following: + +.. code-block:: cpp + + // In the module1.cpp binding code for module1: + py::class_(m, "Pet") + .def(py::init()) + .def_readonly("name", &Pet::name); + +.. code-block:: cpp + + // In the module2.cpp binding code for module2: + m.def("create_pet", [](std::string name) { return new Pet(name); }); + +.. code-block:: pycon + + >>> from module1 import Pet + >>> from module2 import create_pet + >>> pet1 = Pet("Kitty") + >>> pet2 = create_pet("Doggy") + >>> pet2.name() + 'Doggy' + +When writing binding code for a library, this is usually desirable: this +allows, for example, splitting up a complex library into multiple Python +modules. + +In some cases, however, this can cause conflicts. For example, suppose two +unrelated modules make use of an external C++ library and each provide custom +bindings for one of that library's classes. This will result in an error when +a Python program attempts to import both modules (directly or indirectly) +because of conflicting definitions on the external type: + +.. code-block:: cpp + + // dogs.cpp + + // Binding for external library class: + py::class(m, "Pet") + .def("name", &pets::Pet::name); + + // Binding for local extension class: + py::class(m, "Dog") + .def(py::init()); + +.. code-block:: cpp + + // cats.cpp, in a completely separate project from the above dogs.cpp. + + // Binding for external library class: + py::class(m, "Pet") + .def("get_name", &pets::Pet::name); + + // Binding for local extending class: + py::class(m, "Cat") + .def(py::init()); + +.. code-block:: pycon + + >>> import cats + >>> import dogs + Traceback (most recent call last): + File "", line 1, in + ImportError: generic_type: type "Pet" is already registered! + +To get around this, you can tell pybind11 to keep the external class binding +localized to the module by passing the ``py::module_local()`` attribute into +the ``py::class_`` constructor: + +.. code-block:: cpp + + // Pet binding in dogs.cpp: + py::class(m, "Pet", py::module_local()) + .def("name", &pets::Pet::name); + +.. code-block:: cpp + + // Pet binding in cats.cpp: + py::class(m, "Pet", py::module_local()) + .def("get_name", &pets::Pet::name); + +This makes the Python-side ``dogs.Pet`` and ``cats.Pet`` into distinct classes, +avoiding the conflict and allowing both modules to be loaded. C++ code in the +``dogs`` module that casts or returns a ``Pet`` instance will result in a +``dogs.Pet`` Python instance, while C++ code in the ``cats`` module will result +in a ``cats.Pet`` Python instance. + +This does come with two caveats, however: First, external modules cannot return +or cast a ``Pet`` instance to Python (unless they also provide their own local +bindings). Second, from the Python point of view they are two distinct classes. + +Note that the locality only applies in the C++ -> Python direction. When +passing such a ``py::module_local`` type into a C++ function, the module-local +classes are still considered. This means that if the following function is +added to any module (including but not limited to the ``cats`` and ``dogs`` +modules above) it will be callable with either a ``dogs.Pet`` or ``cats.Pet`` +argument: + +.. code-block:: cpp + + m.def("pet_name", [](const pets::Pet &pet) { return pet.name(); }); + +For example, suppose the above function is added to each of ``cats.cpp``, +``dogs.cpp`` and ``frogs.cpp`` (where ``frogs.cpp`` is some other module that +does *not* bind ``Pets`` at all). + +.. code-block:: pycon + + >>> import cats, dogs, frogs # No error because of the added py::module_local() + >>> mycat, mydog = cats.Cat("Fluffy"), dogs.Dog("Rover") + >>> (cats.pet_name(mycat), dogs.pet_name(mydog)) + ('Fluffy', 'Rover') + >>> (cats.pet_name(mydog), dogs.pet_name(mycat), frogs.pet_name(mycat)) + ('Rover', 'Fluffy', 'Fluffy') + +It is possible to use ``py::module_local()`` registrations in one module even +if another module registers the same type globally: within the module with the +module-local definition, all C++ instances will be cast to the associated bound +Python type. In other modules any such values are converted to the global +Python type created elsewhere. + +.. note:: + + STL bindings (as provided via the optional :file:`pybind11/stl_bind.h` + header) apply ``py::module_local`` by default when the bound type might + conflict with other modules; see :ref:`stl_bind` for details. + +.. note:: + + The localization of the bound types is actually tied to the shared object + or binary generated by the compiler/linker. For typical modules created + with ``PYBIND11_MODULE()``, this distinction is not significant. It is + possible, however, when :ref:`embedding` to embed multiple modules in the + same binary (see :ref:`embedding_modules`). In such a case, the + localization will apply across all embedded modules within the same binary. + +.. seealso:: + + The file :file:`tests/test_local_bindings.cpp` contains additional examples + that demonstrate how ``py::module_local()`` works. + +Binding protected member functions +================================== + +It's normally not possible to expose ``protected`` member functions to Python: + +.. code-block:: cpp + + class A { + protected: + int foo() const { return 42; } + }; + + py::class_(m, "A") + .def("foo", &A::foo); // error: 'foo' is a protected member of 'A' + +On one hand, this is good because non-``public`` members aren't meant to be +accessed from the outside. But we may want to make use of ``protected`` +functions in derived Python classes. + +The following pattern makes this possible: + +.. code-block:: cpp + + class A { + protected: + int foo() const { return 42; } + }; + + class Publicist : public A { // helper type for exposing protected functions + public: + using A::foo; // inherited with different access modifier + }; + + py::class_(m, "A") // bind the primary class + .def("foo", &Publicist::foo); // expose protected methods via the publicist + +This works because ``&Publicist::foo`` is exactly the same function as +``&A::foo`` (same signature and address), just with a different access +modifier. The only purpose of the ``Publicist`` helper class is to make +the function name ``public``. + +If the intent is to expose ``protected`` ``virtual`` functions which can be +overridden in Python, the publicist pattern can be combined with the previously +described trampoline: + +.. code-block:: cpp + + class A { + public: + virtual ~A() = default; + + protected: + virtual int foo() const { return 42; } + }; + + class Trampoline : public A { + public: + int foo() const override { PYBIND11_OVERRIDE(int, A, foo, ); } + }; + + class Publicist : public A { + public: + using A::foo; + }; + + py::class_(m, "A") // <-- `Trampoline` here + .def("foo", &Publicist::foo); // <-- `Publicist` here, not `Trampoline`! + +.. note:: + + MSVC 2015 has a compiler bug (fixed in version 2017) which + requires a more explicit function binding in the form of + ``.def("foo", static_cast(&Publicist::foo));`` + where ``int (A::*)() const`` is the type of ``A::foo``. + +Binding final classes +===================== + +Some classes may not be appropriate to inherit from. In C++11, classes can +use the ``final`` specifier to ensure that a class cannot be inherited from. +The ``py::is_final`` attribute can be used to ensure that Python classes +cannot inherit from a specified type. The underlying C++ type does not need +to be declared final. + +.. code-block:: cpp + + class IsFinal final {}; + + py::class_(m, "IsFinal", py::is_final()); + +When you try to inherit from such a class in Python, you will now get this +error: + +.. code-block:: pycon + + >>> class PyFinalChild(IsFinal): + ... pass + ... + TypeError: type 'IsFinal' is not an acceptable base type + +.. note:: This attribute is currently ignored on PyPy + +.. versionadded:: 2.6 + +Binding classes with template parameters +======================================== + +pybind11 can also wrap classes that have template parameters. Consider these classes: + +.. code-block:: cpp + + struct Cat {}; + struct Dog {}; + + template + struct Cage { + Cage(PetType& pet); + PetType& get(); + }; + +C++ templates may only be instantiated at compile time, so pybind11 can only +wrap instantiated templated classes. You cannot wrap a non-instantiated template: + +.. code-block:: cpp + + // BROKEN (this will not compile) + py::class_(m, "Cage"); + .def("get", &Cage::get); + +You must explicitly specify each template/type combination that you want to +wrap separately. + +.. code-block:: cpp + + // ok + py::class_>(m, "CatCage") + .def("get", &Cage::get); + + // ok + py::class_>(m, "DogCage") + .def("get", &Cage::get); + +If your class methods have template parameters you can wrap those as well, +but once again each instantiation must be explicitly specified: + +.. code-block:: cpp + + typename + struct MyClass { + template + T fn(V v); + }; + + py::class>(m, "MyClassT") + .def("fn", &MyClass::fn); + +Custom automatic downcasters +============================ + +As explained in :ref:`inheritance`, pybind11 comes with built-in +understanding of the dynamic type of polymorphic objects in C++; that +is, returning a Pet to Python produces a Python object that knows it's +wrapping a Dog, if Pet has virtual methods and pybind11 knows about +Dog and this Pet is in fact a Dog. Sometimes, you might want to +provide this automatic downcasting behavior when creating bindings for +a class hierarchy that does not use standard C++ polymorphism, such as +LLVM [#f4]_. As long as there's some way to determine at runtime +whether a downcast is safe, you can proceed by specializing the +``pybind11::polymorphic_type_hook`` template: + +.. code-block:: cpp + + enum class PetKind { Cat, Dog, Zebra }; + struct Pet { // Not polymorphic: has no virtual methods + const PetKind kind; + int age = 0; + protected: + Pet(PetKind _kind) : kind(_kind) {} + }; + struct Dog : Pet { + Dog() : Pet(PetKind::Dog) {} + std::string sound = "woof!"; + std::string bark() const { return sound; } + }; + + namespace pybind11 { + template<> struct polymorphic_type_hook { + static const void *get(const Pet *src, const std::type_info*& type) { + // note that src may be nullptr + if (src && src->kind == PetKind::Dog) { + type = &typeid(Dog); + return static_cast(src); + } + return src; + } + }; + } // namespace pybind11 + +When pybind11 wants to convert a C++ pointer of type ``Base*`` to a +Python object, it calls ``polymorphic_type_hook::get()`` to +determine if a downcast is possible. The ``get()`` function should use +whatever runtime information is available to determine if its ``src`` +parameter is in fact an instance of some class ``Derived`` that +inherits from ``Base``. If it finds such a ``Derived``, it sets ``type += &typeid(Derived)`` and returns a pointer to the ``Derived`` object +that contains ``src``. Otherwise, it just returns ``src``, leaving +``type`` at its default value of nullptr. If you set ``type`` to a +type that pybind11 doesn't know about, no downcasting will occur, and +the original ``src`` pointer will be used with its static type +``Base*``. + +It is critical that the returned pointer and ``type`` argument of +``get()`` agree with each other: if ``type`` is set to something +non-null, the returned pointer must point to the start of an object +whose type is ``type``. If the hierarchy being exposed uses only +single inheritance, a simple ``return src;`` will achieve this just +fine, but in the general case, you must cast ``src`` to the +appropriate derived-class pointer (e.g. using +``static_cast(src)``) before allowing it to be returned as a +``void*``. + +.. [#f4] https://llvm.org/docs/HowToSetUpLLVMStyleRTTI.html + +.. note:: + + pybind11's standard support for downcasting objects whose types + have virtual methods is implemented using + ``polymorphic_type_hook`` too, using the standard C++ ability to + determine the most-derived type of a polymorphic object using + ``typeid()`` and to cast a base pointer to that most-derived type + (even if you don't know what it is) using ``dynamic_cast``. + +.. seealso:: + + The file :file:`tests/test_tagbased_polymorphic.cpp` contains a + more complete example, including a demonstration of how to provide + automatic downcasting for an entire class hierarchy without + writing one get() function for each class. + +Accessing the type object +========================= + +You can get the type object from a C++ class that has already been registered using: + +.. code-block:: cpp + + py::type T_py = py::type::of(); + +You can directly use ``py::type::of(ob)`` to get the type object from any python +object, just like ``type(ob)`` in Python. + +.. note:: + + Other types, like ``py::type::of()``, do not work, see :ref:`type-conversions`. + +.. versionadded:: 2.6 + +Custom type setup +================= + +For advanced use cases, such as enabling garbage collection support, you may +wish to directly manipulate the ``PyHeapTypeObject`` corresponding to a +``py::class_`` definition. + +You can do that using ``py::custom_type_setup``: + +.. code-block:: cpp + + struct OwnsPythonObjects { + py::object value = py::none(); + }; + py::class_ cls( + m, "OwnsPythonObjects", py::custom_type_setup([](PyHeapTypeObject *heap_type) { + auto *type = &heap_type->ht_type; + type->tp_flags |= Py_TPFLAGS_HAVE_GC; + type->tp_traverse = [](PyObject *self_base, visitproc visit, void *arg) { + auto &self = py::cast(py::handle(self_base)); + Py_VISIT(self.value.ptr()); + return 0; + }; + type->tp_clear = [](PyObject *self_base) { + auto &self = py::cast(py::handle(self_base)); + self.value = py::none(); + return 0; + }; + })); + cls.def(py::init<>()); + cls.def_readwrite("value", &OwnsPythonObjects::value); + +.. versionadded:: 2.8 diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/embedding.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/embedding.rst new file mode 100644 index 0000000000000000000000000000000000000000..78a03e7dc02550f010e1ef795eacb8139a3be6ba --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/embedding.rst @@ -0,0 +1,262 @@ +.. _embedding: + +Embedding the interpreter +######################### + +While pybind11 is mainly focused on extending Python using C++, it's also +possible to do the reverse: embed the Python interpreter into a C++ program. +All of the other documentation pages still apply here, so refer to them for +general pybind11 usage. This section will cover a few extra things required +for embedding. + +Getting started +=============== + +A basic executable with an embedded interpreter can be created with just a few +lines of CMake and the ``pybind11::embed`` target, as shown below. For more +information, see :doc:`/compiling`. + +.. code-block:: cmake + + cmake_minimum_required(VERSION 3.4) + project(example) + + find_package(pybind11 REQUIRED) # or `add_subdirectory(pybind11)` + + add_executable(example main.cpp) + target_link_libraries(example PRIVATE pybind11::embed) + +The essential structure of the ``main.cpp`` file looks like this: + +.. code-block:: cpp + + #include // everything needed for embedding + namespace py = pybind11; + + int main() { + py::scoped_interpreter guard{}; // start the interpreter and keep it alive + + py::print("Hello, World!"); // use the Python API + } + +The interpreter must be initialized before using any Python API, which includes +all the functions and classes in pybind11. The RAII guard class ``scoped_interpreter`` +takes care of the interpreter lifetime. After the guard is destroyed, the interpreter +shuts down and clears its memory. No Python functions can be called after this. + +Executing Python code +===================== + +There are a few different ways to run Python code. One option is to use ``eval``, +``exec`` or ``eval_file``, as explained in :ref:`eval`. Here is a quick example in +the context of an executable with an embedded interpreter: + +.. code-block:: cpp + + #include + namespace py = pybind11; + + int main() { + py::scoped_interpreter guard{}; + + py::exec(R"( + kwargs = dict(name="World", number=42) + message = "Hello, {name}! The answer is {number}".format(**kwargs) + print(message) + )"); + } + +Alternatively, similar results can be achieved using pybind11's API (see +:doc:`/advanced/pycpp/index` for more details). + +.. code-block:: cpp + + #include + namespace py = pybind11; + using namespace py::literals; + + int main() { + py::scoped_interpreter guard{}; + + auto kwargs = py::dict("name"_a="World", "number"_a=42); + auto message = "Hello, {name}! The answer is {number}"_s.format(**kwargs); + py::print(message); + } + +The two approaches can also be combined: + +.. code-block:: cpp + + #include + #include + + namespace py = pybind11; + using namespace py::literals; + + int main() { + py::scoped_interpreter guard{}; + + auto locals = py::dict("name"_a="World", "number"_a=42); + py::exec(R"( + message = "Hello, {name}! The answer is {number}".format(**locals()) + )", py::globals(), locals); + + auto message = locals["message"].cast(); + std::cout << message; + } + +Importing modules +================= + +Python modules can be imported using ``module_::import()``: + +.. code-block:: cpp + + py::module_ sys = py::module_::import("sys"); + py::print(sys.attr("path")); + +For convenience, the current working directory is included in ``sys.path`` when +embedding the interpreter. This makes it easy to import local Python files: + +.. code-block:: python + + """calc.py located in the working directory""" + + + def add(i, j): + return i + j + + +.. code-block:: cpp + + py::module_ calc = py::module_::import("calc"); + py::object result = calc.attr("add")(1, 2); + int n = result.cast(); + assert(n == 3); + +Modules can be reloaded using ``module_::reload()`` if the source is modified e.g. +by an external process. This can be useful in scenarios where the application +imports a user defined data processing script which needs to be updated after +changes by the user. Note that this function does not reload modules recursively. + +.. _embedding_modules: + +Adding embedded modules +======================= + +Embedded binary modules can be added using the ``PYBIND11_EMBEDDED_MODULE`` macro. +Note that the definition must be placed at global scope. They can be imported +like any other module. + +.. code-block:: cpp + + #include + namespace py = pybind11; + + PYBIND11_EMBEDDED_MODULE(fast_calc, m) { + // `m` is a `py::module_` which is used to bind functions and classes + m.def("add", [](int i, int j) { + return i + j; + }); + } + + int main() { + py::scoped_interpreter guard{}; + + auto fast_calc = py::module_::import("fast_calc"); + auto result = fast_calc.attr("add")(1, 2).cast(); + assert(result == 3); + } + +Unlike extension modules where only a single binary module can be created, on +the embedded side an unlimited number of modules can be added using multiple +``PYBIND11_EMBEDDED_MODULE`` definitions (as long as they have unique names). + +These modules are added to Python's list of builtins, so they can also be +imported in pure Python files loaded by the interpreter. Everything interacts +naturally: + +.. code-block:: python + + """py_module.py located in the working directory""" + import cpp_module + + a = cpp_module.a + b = a + 1 + + +.. code-block:: cpp + + #include + namespace py = pybind11; + + PYBIND11_EMBEDDED_MODULE(cpp_module, m) { + m.attr("a") = 1; + } + + int main() { + py::scoped_interpreter guard{}; + + auto py_module = py::module_::import("py_module"); + + auto locals = py::dict("fmt"_a="{} + {} = {}", **py_module.attr("__dict__")); + assert(locals["a"].cast() == 1); + assert(locals["b"].cast() == 2); + + py::exec(R"( + c = a + b + message = fmt.format(a, b, c) + )", py::globals(), locals); + + assert(locals["c"].cast() == 3); + assert(locals["message"].cast() == "1 + 2 = 3"); + } + + +Interpreter lifetime +==================== + +The Python interpreter shuts down when ``scoped_interpreter`` is destroyed. After +this, creating a new instance will restart the interpreter. Alternatively, the +``initialize_interpreter`` / ``finalize_interpreter`` pair of functions can be used +to directly set the state at any time. + +Modules created with pybind11 can be safely re-initialized after the interpreter +has been restarted. However, this may not apply to third-party extension modules. +The issue is that Python itself cannot completely unload extension modules and +there are several caveats with regard to interpreter restarting. In short, not +all memory may be freed, either due to Python reference cycles or user-created +global data. All the details can be found in the CPython documentation. + +.. warning:: + + Creating two concurrent ``scoped_interpreter`` guards is a fatal error. So is + calling ``initialize_interpreter`` for a second time after the interpreter + has already been initialized. + + Do not use the raw CPython API functions ``Py_Initialize`` and + ``Py_Finalize`` as these do not properly handle the lifetime of + pybind11's internal data. + + +Sub-interpreter support +======================= + +Creating multiple copies of ``scoped_interpreter`` is not possible because it +represents the main Python interpreter. Sub-interpreters are something different +and they do permit the existence of multiple interpreters. This is an advanced +feature of the CPython API and should be handled with care. pybind11 does not +currently offer a C++ interface for sub-interpreters, so refer to the CPython +documentation for all the details regarding this feature. + +We'll just mention a couple of caveats the sub-interpreters support in pybind11: + + 1. Sub-interpreters will not receive independent copies of embedded modules. + Instead, these are shared and modifications in one interpreter may be + reflected in another. + + 2. Managing multiple threads, multiple interpreters and the GIL can be + challenging and there are several caveats here, even within the pure + CPython API (please refer to the Python docs for details). As for + pybind11, keep in mind that ``gil_scoped_release`` and ``gil_scoped_acquire`` + do not take sub-interpreters into account. diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/exceptions.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/exceptions.rst new file mode 100644 index 0000000000000000000000000000000000000000..7fac473263902f2df3d6c3f4098b70b81a033028 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/exceptions.rst @@ -0,0 +1,398 @@ +Exceptions +########## + +Built-in C++ to Python exception translation +============================================ + +When Python calls C++ code through pybind11, pybind11 provides a C++ exception handler +that will trap C++ exceptions, translate them to the corresponding Python exception, +and raise them so that Python code can handle them. + +pybind11 defines translations for ``std::exception`` and its standard +subclasses, and several special exception classes that translate to specific +Python exceptions. Note that these are not actually Python exceptions, so they +cannot be examined using the Python C API. Instead, they are pure C++ objects +that pybind11 will translate the corresponding Python exception when they arrive +at its exception handler. + +.. tabularcolumns:: |p{0.5\textwidth}|p{0.45\textwidth}| + ++--------------------------------------+--------------------------------------+ +| Exception thrown by C++ | Translated to Python exception type | ++======================================+======================================+ +| :class:`std::exception` | ``RuntimeError`` | ++--------------------------------------+--------------------------------------+ +| :class:`std::bad_alloc` | ``MemoryError`` | ++--------------------------------------+--------------------------------------+ +| :class:`std::domain_error` | ``ValueError`` | ++--------------------------------------+--------------------------------------+ +| :class:`std::invalid_argument` | ``ValueError`` | ++--------------------------------------+--------------------------------------+ +| :class:`std::length_error` | ``ValueError`` | ++--------------------------------------+--------------------------------------+ +| :class:`std::out_of_range` | ``IndexError`` | ++--------------------------------------+--------------------------------------+ +| :class:`std::range_error` | ``ValueError`` | ++--------------------------------------+--------------------------------------+ +| :class:`std::overflow_error` | ``OverflowError`` | ++--------------------------------------+--------------------------------------+ +| :class:`pybind11::stop_iteration` | ``StopIteration`` (used to implement | +| | custom iterators) | ++--------------------------------------+--------------------------------------+ +| :class:`pybind11::index_error` | ``IndexError`` (used to indicate out | +| | of bounds access in ``__getitem__``, | +| | ``__setitem__``, etc.) | ++--------------------------------------+--------------------------------------+ +| :class:`pybind11::key_error` | ``KeyError`` (used to indicate out | +| | of bounds access in ``__getitem__``, | +| | ``__setitem__`` in dict-like | +| | objects, etc.) | ++--------------------------------------+--------------------------------------+ +| :class:`pybind11::value_error` | ``ValueError`` (used to indicate | +| | wrong value passed in | +| | ``container.remove(...)``) | ++--------------------------------------+--------------------------------------+ +| :class:`pybind11::type_error` | ``TypeError`` | ++--------------------------------------+--------------------------------------+ +| :class:`pybind11::buffer_error` | ``BufferError`` | ++--------------------------------------+--------------------------------------+ +| :class:`pybind11::import_error` | ``ImportError`` | ++--------------------------------------+--------------------------------------+ +| :class:`pybind11::attribute_error` | ``AttributeError`` | ++--------------------------------------+--------------------------------------+ +| Any other exception | ``RuntimeError`` | ++--------------------------------------+--------------------------------------+ + +Exception translation is not bidirectional. That is, *catching* the C++ +exceptions defined above will not trap exceptions that originate from +Python. For that, catch :class:`pybind11::error_already_set`. See :ref:`below +` for further details. + +There is also a special exception :class:`cast_error` that is thrown by +:func:`handle::call` when the input arguments cannot be converted to Python +objects. + +Registering custom translators +============================== + +If the default exception conversion policy described above is insufficient, +pybind11 also provides support for registering custom exception translators. +Similar to pybind11 classes, exception translators can be local to the module +they are defined in or global to the entire python session. To register a simple +exception conversion that translates a C++ exception into a new Python exception +using the C++ exception's ``what()`` method, a helper function is available: + +.. code-block:: cpp + + py::register_exception(module, "PyExp"); + +This call creates a Python exception class with the name ``PyExp`` in the given +module and automatically converts any encountered exceptions of type ``CppExp`` +into Python exceptions of type ``PyExp``. + +A matching function is available for registering a local exception translator: + +.. code-block:: cpp + + py::register_local_exception(module, "PyExp"); + + +It is possible to specify base class for the exception using the third +parameter, a ``handle``: + +.. code-block:: cpp + + py::register_exception(module, "PyExp", PyExc_RuntimeError); + py::register_local_exception(module, "PyExp", PyExc_RuntimeError); + +Then ``PyExp`` can be caught both as ``PyExp`` and ``RuntimeError``. + +The class objects of the built-in Python exceptions are listed in the Python +documentation on `Standard Exceptions `_. +The default base class is ``PyExc_Exception``. + +When more advanced exception translation is needed, the functions +``py::register_exception_translator(translator)`` and +``py::register_local_exception_translator(translator)`` can be used to register +functions that can translate arbitrary exception types (and which may include +additional logic to do so). The functions takes a stateless callable (e.g. a +function pointer or a lambda function without captured variables) with the call +signature ``void(std::exception_ptr)``. + +When a C++ exception is thrown, the registered exception translators are tried +in reverse order of registration (i.e. the last registered translator gets the +first shot at handling the exception). All local translators will be tried +before a global translator is tried. + +Inside the translator, ``std::rethrow_exception`` should be used within +a try block to re-throw the exception. One or more catch clauses to catch +the appropriate exceptions should then be used with each clause using +``PyErr_SetString`` to set a Python exception or ``ex(string)`` to set +the python exception to a custom exception type (see below). + +To declare a custom Python exception type, declare a ``py::exception`` variable +and use this in the associated exception translator (note: it is often useful +to make this a static declaration when using it inside a lambda expression +without requiring capturing). + +The following example demonstrates this for a hypothetical exception classes +``MyCustomException`` and ``OtherException``: the first is translated to a +custom python exception ``MyCustomError``, while the second is translated to a +standard python RuntimeError: + +.. code-block:: cpp + + static py::exception exc(m, "MyCustomError"); + py::register_exception_translator([](std::exception_ptr p) { + try { + if (p) std::rethrow_exception(p); + } catch (const MyCustomException &e) { + exc(e.what()); + } catch (const OtherException &e) { + PyErr_SetString(PyExc_RuntimeError, e.what()); + } + }); + +Multiple exceptions can be handled by a single translator, as shown in the +example above. If the exception is not caught by the current translator, the +previously registered one gets a chance. + +If none of the registered exception translators is able to handle the +exception, it is handled by the default converter as described in the previous +section. + +.. seealso:: + + The file :file:`tests/test_exceptions.cpp` contains examples + of various custom exception translators and custom exception types. + +.. note:: + + Call either ``PyErr_SetString`` or a custom exception's call + operator (``exc(string)``) for every exception caught in a custom exception + translator. Failure to do so will cause Python to crash with ``SystemError: + error return without exception set``. + + Exceptions that you do not plan to handle should simply not be caught, or + may be explicitly (re-)thrown to delegate it to the other, + previously-declared existing exception translators. + + Note that ``libc++`` and ``libstdc++`` `behave differently `_ + with ``-fvisibility=hidden``. Therefore exceptions that are used across ABI boundaries need to be explicitly exported, as exercised in ``tests/test_exceptions.h``. + See also: "Problems with C++ exceptions" under `GCC Wiki `_. + + +Local vs Global Exception Translators +===================================== + +When a global exception translator is registered, it will be applied across all +modules in the reverse order of registration. This can create behavior where the +order of module import influences how exceptions are translated. + +If module1 has the following translator: + +.. code-block:: cpp + + py::register_exception_translator([](std::exception_ptr p) { + try { + if (p) std::rethrow_exception(p); + } catch (const std::invalid_argument &e) { + PyErr_SetString("module1 handled this") + } + } + +and module2 has the following similar translator: + +.. code-block:: cpp + + py::register_exception_translator([](std::exception_ptr p) { + try { + if (p) std::rethrow_exception(p); + } catch (const std::invalid_argument &e) { + PyErr_SetString("module2 handled this") + } + } + +then which translator handles the invalid_argument will be determined by the +order that module1 and module2 are imported. Since exception translators are +applied in the reverse order of registration, which ever module was imported +last will "win" and that translator will be applied. + +If there are multiple pybind11 modules that share exception types (either +standard built-in or custom) loaded into a single python instance and +consistent error handling behavior is needed, then local translators should be +used. + +Changing the previous example to use ``register_local_exception_translator`` +would mean that when invalid_argument is thrown in the module2 code, the +module2 translator will always handle it, while in module1, the module1 +translator will do the same. + +.. _handling_python_exceptions_cpp: + +Handling exceptions from Python in C++ +====================================== + +When C++ calls Python functions, such as in a callback function or when +manipulating Python objects, and Python raises an ``Exception``, pybind11 +converts the Python exception into a C++ exception of type +:class:`pybind11::error_already_set` whose payload contains a C++ string textual +summary and the actual Python exception. ``error_already_set`` is used to +propagate Python exception back to Python (or possibly, handle them in C++). + +.. tabularcolumns:: |p{0.5\textwidth}|p{0.45\textwidth}| + ++--------------------------------------+--------------------------------------+ +| Exception raised in Python | Thrown as C++ exception type | ++======================================+======================================+ +| Any Python ``Exception`` | :class:`pybind11::error_already_set` | ++--------------------------------------+--------------------------------------+ + +For example: + +.. code-block:: cpp + + try { + // open("missing.txt", "r") + auto file = py::module_::import("io").attr("open")("missing.txt", "r"); + auto text = file.attr("read")(); + file.attr("close")(); + } catch (py::error_already_set &e) { + if (e.matches(PyExc_FileNotFoundError)) { + py::print("missing.txt not found"); + } else if (e.matches(PyExc_PermissionError)) { + py::print("missing.txt found but not accessible"); + } else { + throw; + } + } + +Note that C++ to Python exception translation does not apply here, since that is +a method for translating C++ exceptions to Python, not vice versa. The error raised +from Python is always ``error_already_set``. + +This example illustrates this behavior: + +.. code-block:: cpp + + try { + py::eval("raise ValueError('The Ring')"); + } catch (py::value_error &boromir) { + // Boromir never gets the ring + assert(false); + } catch (py::error_already_set &frodo) { + // Frodo gets the ring + py::print("I will take the ring"); + } + + try { + // py::value_error is a request for pybind11 to raise a Python exception + throw py::value_error("The ball"); + } catch (py::error_already_set &cat) { + // cat won't catch the ball since + // py::value_error is not a Python exception + assert(false); + } catch (py::value_error &dog) { + // dog will catch the ball + py::print("Run Spot run"); + throw; // Throw it again (pybind11 will raise ValueError) + } + +Handling errors from the Python C API +===================================== + +Where possible, use :ref:`pybind11 wrappers ` instead of calling +the Python C API directly. When calling the Python C API directly, in +addition to manually managing reference counts, one must follow the pybind11 +error protocol, which is outlined here. + +After calling the Python C API, if Python returns an error, +``throw py::error_already_set();``, which allows pybind11 to deal with the +exception and pass it back to the Python interpreter. This includes calls to +the error setting functions such as ``PyErr_SetString``. + +.. code-block:: cpp + + PyErr_SetString(PyExc_TypeError, "C API type error demo"); + throw py::error_already_set(); + + // But it would be easier to simply... + throw py::type_error("pybind11 wrapper type error"); + +Alternately, to ignore the error, call `PyErr_Clear +`_. + +Any Python error must be thrown or cleared, or Python/pybind11 will be left in +an invalid state. + +Chaining exceptions ('raise from') +================================== + +In Python 3.3 a mechanism for indicating that exceptions were caused by other +exceptions was introduced: + +.. code-block:: py + + try: + print(1 / 0) + except Exception as exc: + raise RuntimeError("could not divide by zero") from exc + +To do a similar thing in pybind11, you can use the ``py::raise_from`` function. It +sets the current python error indicator, so to continue propagating the exception +you should ``throw py::error_already_set()`` (Python 3 only). + +.. code-block:: cpp + + try { + py::eval("print(1 / 0")); + } catch (py::error_already_set &e) { + py::raise_from(e, PyExc_RuntimeError, "could not divide by zero"); + throw py::error_already_set(); + } + +.. versionadded:: 2.8 + +.. _unraisable_exceptions: + +Handling unraisable exceptions +============================== + +If a Python function invoked from a C++ destructor or any function marked +``noexcept(true)`` (collectively, "noexcept functions") throws an exception, there +is no way to propagate the exception, as such functions may not throw. +Should they throw or fail to catch any exceptions in their call graph, +the C++ runtime calls ``std::terminate()`` to abort immediately. + +Similarly, Python exceptions raised in a class's ``__del__`` method do not +propagate, but are logged by Python as an unraisable error. In Python 3.8+, a +`system hook is triggered +`_ +and an auditing event is logged. + +Any noexcept function should have a try-catch block that traps +class:`error_already_set` (or any other exception that can occur). Note that +pybind11 wrappers around Python exceptions such as +:class:`pybind11::value_error` are *not* Python exceptions; they are C++ +exceptions that pybind11 catches and converts to Python exceptions. Noexcept +functions cannot propagate these exceptions either. A useful approach is to +convert them to Python exceptions and then ``discard_as_unraisable`` as shown +below. + +.. code-block:: cpp + + void nonthrowing_func() noexcept(true) { + try { + // ... + } catch (py::error_already_set &eas) { + // Discard the Python error using Python APIs, using the C++ magic + // variable __func__. Python already knows the type and value and of the + // exception object. + eas.discard_as_unraisable(__func__); + } catch (const std::exception &e) { + // Log and discard C++ exceptions. + third_party::log(e); + } + } + +.. versionadded:: 2.6 diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/functions.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/functions.rst new file mode 100644 index 0000000000000000000000000000000000000000..5c53fd941c28a751fad92b8e3561b78260601f33 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/functions.rst @@ -0,0 +1,615 @@ +Functions +######### + +Before proceeding with this section, make sure that you are already familiar +with the basics of binding functions and classes, as explained in :doc:`/basics` +and :doc:`/classes`. The following guide is applicable to both free and member +functions, i.e. *methods* in Python. + +.. _return_value_policies: + +Return value policies +===================== + +Python and C++ use fundamentally different ways of managing the memory and +lifetime of objects managed by them. This can lead to issues when creating +bindings for functions that return a non-trivial type. Just by looking at the +type information, it is not clear whether Python should take charge of the +returned value and eventually free its resources, or if this is handled on the +C++ side. For this reason, pybind11 provides a several *return value policy* +annotations that can be passed to the :func:`module_::def` and +:func:`class_::def` functions. The default policy is +:enum:`return_value_policy::automatic`. + +Return value policies are tricky, and it's very important to get them right. +Just to illustrate what can go wrong, consider the following simple example: + +.. code-block:: cpp + + /* Function declaration */ + Data *get_data() { return _data; /* (pointer to a static data structure) */ } + ... + + /* Binding code */ + m.def("get_data", &get_data); // <-- KABOOM, will cause crash when called from Python + +What's going on here? When ``get_data()`` is called from Python, the return +value (a native C++ type) must be wrapped to turn it into a usable Python type. +In this case, the default return value policy (:enum:`return_value_policy::automatic`) +causes pybind11 to assume ownership of the static ``_data`` instance. + +When Python's garbage collector eventually deletes the Python +wrapper, pybind11 will also attempt to delete the C++ instance (via ``operator +delete()``) due to the implied ownership. At this point, the entire application +will come crashing down, though errors could also be more subtle and involve +silent data corruption. + +In the above example, the policy :enum:`return_value_policy::reference` should have +been specified so that the global data instance is only *referenced* without any +implied transfer of ownership, i.e.: + +.. code-block:: cpp + + m.def("get_data", &get_data, py::return_value_policy::reference); + +On the other hand, this is not the right policy for many other situations, +where ignoring ownership could lead to resource leaks. +As a developer using pybind11, it's important to be familiar with the different +return value policies, including which situation calls for which one of them. +The following table provides an overview of available policies: + +.. tabularcolumns:: |p{0.5\textwidth}|p{0.45\textwidth}| + ++--------------------------------------------------+----------------------------------------------------------------------------+ +| Return value policy | Description | ++==================================================+============================================================================+ +| :enum:`return_value_policy::take_ownership` | Reference an existing object (i.e. do not create a new copy) and take | +| | ownership. Python will call the destructor and delete operator when the | +| | object's reference count reaches zero. Undefined behavior ensues when the | +| | C++ side does the same, or when the data was not dynamically allocated. | ++--------------------------------------------------+----------------------------------------------------------------------------+ +| :enum:`return_value_policy::copy` | Create a new copy of the returned object, which will be owned by Python. | +| | This policy is comparably safe because the lifetimes of the two instances | +| | are decoupled. | ++--------------------------------------------------+----------------------------------------------------------------------------+ +| :enum:`return_value_policy::move` | Use ``std::move`` to move the return value contents into a new instance | +| | that will be owned by Python. This policy is comparably safe because the | +| | lifetimes of the two instances (move source and destination) are decoupled.| ++--------------------------------------------------+----------------------------------------------------------------------------+ +| :enum:`return_value_policy::reference` | Reference an existing object, but do not take ownership. The C++ side is | +| | responsible for managing the object's lifetime and deallocating it when | +| | it is no longer used. Warning: undefined behavior will ensue when the C++ | +| | side deletes an object that is still referenced and used by Python. | ++--------------------------------------------------+----------------------------------------------------------------------------+ +| :enum:`return_value_policy::reference_internal` | Indicates that the lifetime of the return value is tied to the lifetime | +| | of a parent object, namely the implicit ``this``, or ``self`` argument of | +| | the called method or property. Internally, this policy works just like | +| | :enum:`return_value_policy::reference` but additionally applies a | +| | ``keep_alive<0, 1>`` *call policy* (described in the next section) that | +| | prevents the parent object from being garbage collected as long as the | +| | return value is referenced by Python. This is the default policy for | +| | property getters created via ``def_property``, ``def_readwrite``, etc. | ++--------------------------------------------------+----------------------------------------------------------------------------+ +| :enum:`return_value_policy::automatic` | This policy falls back to the policy | +| | :enum:`return_value_policy::take_ownership` when the return value is a | +| | pointer. Otherwise, it uses :enum:`return_value_policy::move` or | +| | :enum:`return_value_policy::copy` for rvalue and lvalue references, | +| | respectively. See above for a description of what all of these different | +| | policies do. This is the default policy for ``py::class_``-wrapped types. | ++--------------------------------------------------+----------------------------------------------------------------------------+ +| :enum:`return_value_policy::automatic_reference` | As above, but use policy :enum:`return_value_policy::reference` when the | +| | return value is a pointer. This is the default conversion policy for | +| | function arguments when calling Python functions manually from C++ code | +| | (i.e. via ``handle::operator()``) and the casters in ``pybind11/stl.h``. | +| | You probably won't need to use this explicitly. | ++--------------------------------------------------+----------------------------------------------------------------------------+ + +Return value policies can also be applied to properties: + +.. code-block:: cpp + + class_(m, "MyClass") + .def_property("data", &MyClass::getData, &MyClass::setData, + py::return_value_policy::copy); + +Technically, the code above applies the policy to both the getter and the +setter function, however, the setter doesn't really care about *return* +value policies which makes this a convenient terse syntax. Alternatively, +targeted arguments can be passed through the :class:`cpp_function` constructor: + +.. code-block:: cpp + + class_(m, "MyClass") + .def_property("data", + py::cpp_function(&MyClass::getData, py::return_value_policy::copy), + py::cpp_function(&MyClass::setData) + ); + +.. warning:: + + Code with invalid return value policies might access uninitialized memory or + free data structures multiple times, which can lead to hard-to-debug + non-determinism and segmentation faults, hence it is worth spending the + time to understand all the different options in the table above. + +.. note:: + + One important aspect of the above policies is that they only apply to + instances which pybind11 has *not* seen before, in which case the policy + clarifies essential questions about the return value's lifetime and + ownership. When pybind11 knows the instance already (as identified by its + type and address in memory), it will return the existing Python object + wrapper rather than creating a new copy. + +.. note:: + + The next section on :ref:`call_policies` discusses *call policies* that can be + specified *in addition* to a return value policy from the list above. Call + policies indicate reference relationships that can involve both return values + and parameters of functions. + +.. note:: + + As an alternative to elaborate call policies and lifetime management logic, + consider using smart pointers (see the section on :ref:`smart_pointers` for + details). Smart pointers can tell whether an object is still referenced from + C++ or Python, which generally eliminates the kinds of inconsistencies that + can lead to crashes or undefined behavior. For functions returning smart + pointers, it is not necessary to specify a return value policy. + +.. _call_policies: + +Additional call policies +======================== + +In addition to the above return value policies, further *call policies* can be +specified to indicate dependencies between parameters or ensure a certain state +for the function call. + +Keep alive +---------- + +In general, this policy is required when the C++ object is any kind of container +and another object is being added to the container. ``keep_alive`` +indicates that the argument with index ``Patient`` should be kept alive at least +until the argument with index ``Nurse`` is freed by the garbage collector. Argument +indices start at one, while zero refers to the return value. For methods, index +``1`` refers to the implicit ``this`` pointer, while regular arguments begin at +index ``2``. Arbitrarily many call policies can be specified. When a ``Nurse`` +with value ``None`` is detected at runtime, the call policy does nothing. + +When the nurse is not a pybind11-registered type, the implementation internally +relies on the ability to create a *weak reference* to the nurse object. When +the nurse object is not a pybind11-registered type and does not support weak +references, an exception will be thrown. + +If you use an incorrect argument index, you will get a ``RuntimeError`` saying +``Could not activate keep_alive!``. You should review the indices you're using. + +Consider the following example: here, the binding code for a list append +operation ties the lifetime of the newly added element to the underlying +container: + +.. code-block:: cpp + + py::class_(m, "List") + .def("append", &List::append, py::keep_alive<1, 2>()); + +For consistency, the argument indexing is identical for constructors. Index +``1`` still refers to the implicit ``this`` pointer, i.e. the object which is +being constructed. Index ``0`` refers to the return type which is presumed to +be ``void`` when a constructor is viewed like a function. The following example +ties the lifetime of the constructor element to the constructed object: + +.. code-block:: cpp + + py::class_(m, "Nurse") + .def(py::init(), py::keep_alive<1, 2>()); + +.. note:: + + ``keep_alive`` is analogous to the ``with_custodian_and_ward`` (if Nurse, + Patient != 0) and ``with_custodian_and_ward_postcall`` (if Nurse/Patient == + 0) policies from Boost.Python. + +Call guard +---------- + +The ``call_guard`` policy allows any scope guard type ``T`` to be placed +around the function call. For example, this definition: + +.. code-block:: cpp + + m.def("foo", foo, py::call_guard()); + +is equivalent to the following pseudocode: + +.. code-block:: cpp + + m.def("foo", [](args...) { + T scope_guard; + return foo(args...); // forwarded arguments + }); + +The only requirement is that ``T`` is default-constructible, but otherwise any +scope guard will work. This is very useful in combination with ``gil_scoped_release``. +See :ref:`gil`. + +Multiple guards can also be specified as ``py::call_guard``. The +constructor order is left to right and destruction happens in reverse. + +.. seealso:: + + The file :file:`tests/test_call_policies.cpp` contains a complete example + that demonstrates using `keep_alive` and `call_guard` in more detail. + +.. _python_objects_as_args: + +Python objects as arguments +=========================== + +pybind11 exposes all major Python types using thin C++ wrapper classes. These +wrapper classes can also be used as parameters of functions in bindings, which +makes it possible to directly work with native Python types on the C++ side. +For instance, the following statement iterates over a Python ``dict``: + +.. code-block:: cpp + + void print_dict(const py::dict& dict) { + /* Easily interact with Python types */ + for (auto item : dict) + std::cout << "key=" << std::string(py::str(item.first)) << ", " + << "value=" << std::string(py::str(item.second)) << std::endl; + } + +It can be exported: + +.. code-block:: cpp + + m.def("print_dict", &print_dict); + +And used in Python as usual: + +.. code-block:: pycon + + >>> print_dict({"foo": 123, "bar": "hello"}) + key=foo, value=123 + key=bar, value=hello + +For more information on using Python objects in C++, see :doc:`/advanced/pycpp/index`. + +Accepting \*args and \*\*kwargs +=============================== + +Python provides a useful mechanism to define functions that accept arbitrary +numbers of arguments and keyword arguments: + +.. code-block:: python + + def generic(*args, **kwargs): + ... # do something with args and kwargs + +Such functions can also be created using pybind11: + +.. code-block:: cpp + + void generic(py::args args, const py::kwargs& kwargs) { + /// .. do something with args + if (kwargs) + /// .. do something with kwargs + } + + /// Binding code + m.def("generic", &generic); + +The class ``py::args`` derives from ``py::tuple`` and ``py::kwargs`` derives +from ``py::dict``. + +You may also use just one or the other, and may combine these with other +arguments. Note, however, that ``py::kwargs`` must always be the last argument +of the function, and ``py::args`` implies that any further arguments are +keyword-only (see :ref:`keyword_only_arguments`). + +Please refer to the other examples for details on how to iterate over these, +and on how to cast their entries into C++ objects. A demonstration is also +available in ``tests/test_kwargs_and_defaults.cpp``. + +.. note:: + + When combining \*args or \*\*kwargs with :ref:`keyword_args` you should + *not* include ``py::arg`` tags for the ``py::args`` and ``py::kwargs`` + arguments. + +Default arguments revisited +=========================== + +The section on :ref:`default_args` previously discussed basic usage of default +arguments using pybind11. One noteworthy aspect of their implementation is that +default arguments are converted to Python objects right at declaration time. +Consider the following example: + +.. code-block:: cpp + + py::class_("MyClass") + .def("myFunction", py::arg("arg") = SomeType(123)); + +In this case, pybind11 must already be set up to deal with values of the type +``SomeType`` (via a prior instantiation of ``py::class_``), or an +exception will be thrown. + +Another aspect worth highlighting is that the "preview" of the default argument +in the function signature is generated using the object's ``__repr__`` method. +If not available, the signature may not be very helpful, e.g.: + +.. code-block:: pycon + + FUNCTIONS + ... + | myFunction(...) + | Signature : (MyClass, arg : SomeType = ) -> NoneType + ... + +The first way of addressing this is by defining ``SomeType.__repr__``. +Alternatively, it is possible to specify the human-readable preview of the +default argument manually using the ``arg_v`` notation: + +.. code-block:: cpp + + py::class_("MyClass") + .def("myFunction", py::arg_v("arg", SomeType(123), "SomeType(123)")); + +Sometimes it may be necessary to pass a null pointer value as a default +argument. In this case, remember to cast it to the underlying type in question, +like so: + +.. code-block:: cpp + + py::class_("MyClass") + .def("myFunction", py::arg("arg") = static_cast(nullptr)); + +.. _keyword_only_arguments: + +Keyword-only arguments +====================== + +Python 3 introduced keyword-only arguments by specifying an unnamed ``*`` +argument in a function definition: + +.. code-block:: python + + def f(a, *, b): # a can be positional or via keyword; b must be via keyword + pass + + + f(a=1, b=2) # good + f(b=2, a=1) # good + f(1, b=2) # good + f(1, 2) # TypeError: f() takes 1 positional argument but 2 were given + +Pybind11 provides a ``py::kw_only`` object that allows you to implement +the same behaviour by specifying the object between positional and keyword-only +argument annotations when registering the function: + +.. code-block:: cpp + + m.def("f", [](int a, int b) { /* ... */ }, + py::arg("a"), py::kw_only(), py::arg("b")); + +Note that you currently cannot combine this with a ``py::args`` argument. This +feature does *not* require Python 3 to work. + +.. versionadded:: 2.6 + +As of pybind11 2.9, a ``py::args`` argument implies that any following arguments +are keyword-only, as if ``py::kw_only()`` had been specified in the same +relative location of the argument list as the ``py::args`` argument. The +``py::kw_only()`` may be included to be explicit about this, but is not +required. (Prior to 2.9 ``py::args`` may only occur at the end of the argument +list, or immediately before a ``py::kwargs`` argument at the end). + +.. versionadded:: 2.9 + +Positional-only arguments +========================= + +Python 3.8 introduced a new positional-only argument syntax, using ``/`` in the +function definition (note that this has been a convention for CPython +positional arguments, such as in ``pow()``, since Python 2). You can +do the same thing in any version of Python using ``py::pos_only()``: + +.. code-block:: cpp + + m.def("f", [](int a, int b) { /* ... */ }, + py::arg("a"), py::pos_only(), py::arg("b")); + +You now cannot give argument ``a`` by keyword. This can be combined with +keyword-only arguments, as well. + +.. versionadded:: 2.6 + +.. _nonconverting_arguments: + +Non-converting arguments +======================== + +Certain argument types may support conversion from one type to another. Some +examples of conversions are: + +* :ref:`implicit_conversions` declared using ``py::implicitly_convertible()`` +* Calling a method accepting a double with an integer argument +* Calling a ``std::complex`` argument with a non-complex python type + (for example, with a float). (Requires the optional ``pybind11/complex.h`` + header). +* Calling a function taking an Eigen matrix reference with a numpy array of the + wrong type or of an incompatible data layout. (Requires the optional + ``pybind11/eigen.h`` header). + +This behaviour is sometimes undesirable: the binding code may prefer to raise +an error rather than convert the argument. This behaviour can be obtained +through ``py::arg`` by calling the ``.noconvert()`` method of the ``py::arg`` +object, such as: + +.. code-block:: cpp + + m.def("floats_only", [](double f) { return 0.5 * f; }, py::arg("f").noconvert()); + m.def("floats_preferred", [](double f) { return 0.5 * f; }, py::arg("f")); + +Attempting the call the second function (the one without ``.noconvert()``) with +an integer will succeed, but attempting to call the ``.noconvert()`` version +will fail with a ``TypeError``: + +.. code-block:: pycon + + >>> floats_preferred(4) + 2.0 + >>> floats_only(4) + Traceback (most recent call last): + File "", line 1, in + TypeError: floats_only(): incompatible function arguments. The following argument types are supported: + 1. (f: float) -> float + + Invoked with: 4 + +You may, of course, combine this with the :var:`_a` shorthand notation (see +:ref:`keyword_args`) and/or :ref:`default_args`. It is also permitted to omit +the argument name by using the ``py::arg()`` constructor without an argument +name, i.e. by specifying ``py::arg().noconvert()``. + +.. note:: + + When specifying ``py::arg`` options it is necessary to provide the same + number of options as the bound function has arguments. Thus if you want to + enable no-convert behaviour for just one of several arguments, you will + need to specify a ``py::arg()`` annotation for each argument with the + no-convert argument modified to ``py::arg().noconvert()``. + +.. _none_arguments: + +Allow/Prohibiting None arguments +================================ + +When a C++ type registered with :class:`py::class_` is passed as an argument to +a function taking the instance as pointer or shared holder (e.g. ``shared_ptr`` +or a custom, copyable holder as described in :ref:`smart_pointers`), pybind +allows ``None`` to be passed from Python which results in calling the C++ +function with ``nullptr`` (or an empty holder) for the argument. + +To explicitly enable or disable this behaviour, using the +``.none`` method of the :class:`py::arg` object: + +.. code-block:: cpp + + py::class_(m, "Dog").def(py::init<>()); + py::class_(m, "Cat").def(py::init<>()); + m.def("bark", [](Dog *dog) -> std::string { + if (dog) return "woof!"; /* Called with a Dog instance */ + else return "(no dog)"; /* Called with None, dog == nullptr */ + }, py::arg("dog").none(true)); + m.def("meow", [](Cat *cat) -> std::string { + // Can't be called with None argument + return "meow"; + }, py::arg("cat").none(false)); + +With the above, the Python call ``bark(None)`` will return the string ``"(no +dog)"``, while attempting to call ``meow(None)`` will raise a ``TypeError``: + +.. code-block:: pycon + + >>> from animals import Dog, Cat, bark, meow + >>> bark(Dog()) + 'woof!' + >>> meow(Cat()) + 'meow' + >>> bark(None) + '(no dog)' + >>> meow(None) + Traceback (most recent call last): + File "", line 1, in + TypeError: meow(): incompatible function arguments. The following argument types are supported: + 1. (cat: animals.Cat) -> str + + Invoked with: None + +The default behaviour when the tag is unspecified is to allow ``None``. + +.. note:: + + Even when ``.none(true)`` is specified for an argument, ``None`` will be converted to a + ``nullptr`` *only* for custom and :ref:`opaque ` types. Pointers to built-in types + (``double *``, ``int *``, ...) and STL types (``std::vector *``, ...; if ``pybind11/stl.h`` + is included) are copied when converted to C++ (see :doc:`/advanced/cast/overview`) and will + not allow ``None`` as argument. To pass optional argument of these copied types consider + using ``std::optional`` + +.. _overload_resolution: + +Overload resolution order +========================= + +When a function or method with multiple overloads is called from Python, +pybind11 determines which overload to call in two passes. The first pass +attempts to call each overload without allowing argument conversion (as if +every argument had been specified as ``py::arg().noconvert()`` as described +above). + +If no overload succeeds in the no-conversion first pass, a second pass is +attempted in which argument conversion is allowed (except where prohibited via +an explicit ``py::arg().noconvert()`` attribute in the function definition). + +If the second pass also fails a ``TypeError`` is raised. + +Within each pass, overloads are tried in the order they were registered with +pybind11. If the ``py::prepend()`` tag is added to the definition, a function +can be placed at the beginning of the overload sequence instead, allowing user +overloads to proceed built in functions. + +What this means in practice is that pybind11 will prefer any overload that does +not require conversion of arguments to an overload that does, but otherwise +prefers earlier-defined overloads to later-defined ones. + +.. note:: + + pybind11 does *not* further prioritize based on the number/pattern of + overloaded arguments. That is, pybind11 does not prioritize a function + requiring one conversion over one requiring three, but only prioritizes + overloads requiring no conversion at all to overloads that require + conversion of at least one argument. + +.. versionadded:: 2.6 + + The ``py::prepend()`` tag. + +Binding functions with template parameters +========================================== + +You can bind functions that have template parameters. Here's a function: + +.. code-block:: cpp + + template + void set(T t); + +C++ templates cannot be instantiated at runtime, so you cannot bind the +non-instantiated function: + +.. code-block:: cpp + + // BROKEN (this will not compile) + m.def("set", &set); + +You must bind each instantiated function template separately. You may bind +each instantiation with the same name, which will be treated the same as +an overloaded function: + +.. code-block:: cpp + + m.def("set", &set); + m.def("set", &set); + +Sometimes it's more clear to bind them with separate names, which is also +an option: + +.. code-block:: cpp + + m.def("setInt", &set); + m.def("setString", &set); diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/misc.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/misc.rst new file mode 100644 index 0000000000000000000000000000000000000000..25187070677eafdaca3f001248565a86aebbe762 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/misc.rst @@ -0,0 +1,337 @@ +Miscellaneous +############# + +.. _macro_notes: + +General notes regarding convenience macros +========================================== + +pybind11 provides a few convenience macros such as +:func:`PYBIND11_DECLARE_HOLDER_TYPE` and ``PYBIND11_OVERRIDE_*``. Since these +are "just" macros that are evaluated in the preprocessor (which has no concept +of types), they *will* get confused by commas in a template argument; for +example, consider: + +.. code-block:: cpp + + PYBIND11_OVERRIDE(MyReturnType, Class, func) + +The limitation of the C preprocessor interprets this as five arguments (with new +arguments beginning after each comma) rather than three. To get around this, +there are two alternatives: you can use a type alias, or you can wrap the type +using the ``PYBIND11_TYPE`` macro: + +.. code-block:: cpp + + // Version 1: using a type alias + using ReturnType = MyReturnType; + using ClassType = Class; + PYBIND11_OVERRIDE(ReturnType, ClassType, func); + + // Version 2: using the PYBIND11_TYPE macro: + PYBIND11_OVERRIDE(PYBIND11_TYPE(MyReturnType), + PYBIND11_TYPE(Class), func) + +The ``PYBIND11_MAKE_OPAQUE`` macro does *not* require the above workarounds. + +.. _gil: + +Global Interpreter Lock (GIL) +============================= + +When calling a C++ function from Python, the GIL is always held. +The classes :class:`gil_scoped_release` and :class:`gil_scoped_acquire` can be +used to acquire and release the global interpreter lock in the body of a C++ +function call. In this way, long-running C++ code can be parallelized using +multiple Python threads. Taking :ref:`overriding_virtuals` as an example, this +could be realized as follows (important changes highlighted): + +.. code-block:: cpp + :emphasize-lines: 8,9,31,32 + + class PyAnimal : public Animal { + public: + /* Inherit the constructors */ + using Animal::Animal; + + /* Trampoline (need one for each virtual function) */ + std::string go(int n_times) { + /* Acquire GIL before calling Python code */ + py::gil_scoped_acquire acquire; + + PYBIND11_OVERRIDE_PURE( + std::string, /* Return type */ + Animal, /* Parent class */ + go, /* Name of function */ + n_times /* Argument(s) */ + ); + } + }; + + PYBIND11_MODULE(example, m) { + py::class_ animal(m, "Animal"); + animal + .def(py::init<>()) + .def("go", &Animal::go); + + py::class_(m, "Dog", animal) + .def(py::init<>()); + + m.def("call_go", [](Animal *animal) -> std::string { + /* Release GIL before calling into (potentially long-running) C++ code */ + py::gil_scoped_release release; + return call_go(animal); + }); + } + +The ``call_go`` wrapper can also be simplified using the ``call_guard`` policy +(see :ref:`call_policies`) which yields the same result: + +.. code-block:: cpp + + m.def("call_go", &call_go, py::call_guard()); + + +Binding sequence data types, iterators, the slicing protocol, etc. +================================================================== + +Please refer to the supplemental example for details. + +.. seealso:: + + The file :file:`tests/test_sequences_and_iterators.cpp` contains a + complete example that shows how to bind a sequence data type, including + length queries (``__len__``), iterators (``__iter__``), the slicing + protocol and other kinds of useful operations. + + +Partitioning code over multiple extension modules +================================================= + +It's straightforward to split binding code over multiple extension modules, +while referencing types that are declared elsewhere. Everything "just" works +without any special precautions. One exception to this rule occurs when +extending a type declared in another extension module. Recall the basic example +from Section :ref:`inheritance`. + +.. code-block:: cpp + + py::class_ pet(m, "Pet"); + pet.def(py::init()) + .def_readwrite("name", &Pet::name); + + py::class_(m, "Dog", pet /* <- specify parent */) + .def(py::init()) + .def("bark", &Dog::bark); + +Suppose now that ``Pet`` bindings are defined in a module named ``basic``, +whereas the ``Dog`` bindings are defined somewhere else. The challenge is of +course that the variable ``pet`` is not available anymore though it is needed +to indicate the inheritance relationship to the constructor of ``class_``. +However, it can be acquired as follows: + +.. code-block:: cpp + + py::object pet = (py::object) py::module_::import("basic").attr("Pet"); + + py::class_(m, "Dog", pet) + .def(py::init()) + .def("bark", &Dog::bark); + +Alternatively, you can specify the base class as a template parameter option to +``class_``, which performs an automated lookup of the corresponding Python +type. Like the above code, however, this also requires invoking the ``import`` +function once to ensure that the pybind11 binding code of the module ``basic`` +has been executed: + +.. code-block:: cpp + + py::module_::import("basic"); + + py::class_(m, "Dog") + .def(py::init()) + .def("bark", &Dog::bark); + +Naturally, both methods will fail when there are cyclic dependencies. + +Note that pybind11 code compiled with hidden-by-default symbol visibility (e.g. +via the command line flag ``-fvisibility=hidden`` on GCC/Clang), which is +required for proper pybind11 functionality, can interfere with the ability to +access types defined in another extension module. Working around this requires +manually exporting types that are accessed by multiple extension modules; +pybind11 provides a macro to do just this: + +.. code-block:: cpp + + class PYBIND11_EXPORT Dog : public Animal { + ... + }; + +Note also that it is possible (although would rarely be required) to share arbitrary +C++ objects between extension modules at runtime. Internal library data is shared +between modules using capsule machinery [#f6]_ which can be also utilized for +storing, modifying and accessing user-defined data. Note that an extension module +will "see" other extensions' data if and only if they were built with the same +pybind11 version. Consider the following example: + +.. code-block:: cpp + + auto data = reinterpret_cast(py::get_shared_data("mydata")); + if (!data) + data = static_cast(py::set_shared_data("mydata", new MyData(42))); + +If the above snippet was used in several separately compiled extension modules, +the first one to be imported would create a ``MyData`` instance and associate +a ``"mydata"`` key with a pointer to it. Extensions that are imported later +would be then able to access the data behind the same pointer. + +.. [#f6] https://docs.python.org/3/extending/extending.html#using-capsules + +Module Destructors +================== + +pybind11 does not provide an explicit mechanism to invoke cleanup code at +module destruction time. In rare cases where such functionality is required, it +is possible to emulate it using Python capsules or weak references with a +destruction callback. + +.. code-block:: cpp + + auto cleanup_callback = []() { + // perform cleanup here -- this function is called with the GIL held + }; + + m.add_object("_cleanup", py::capsule(cleanup_callback)); + +This approach has the potential downside that instances of classes exposed +within the module may still be alive when the cleanup callback is invoked +(whether this is acceptable will generally depend on the application). + +Alternatively, the capsule may also be stashed within a type object, which +ensures that it not called before all instances of that type have been +collected: + +.. code-block:: cpp + + auto cleanup_callback = []() { /* ... */ }; + m.attr("BaseClass").attr("_cleanup") = py::capsule(cleanup_callback); + +Both approaches also expose a potentially dangerous ``_cleanup`` attribute in +Python, which may be undesirable from an API standpoint (a premature explicit +call from Python might lead to undefined behavior). Yet another approach that +avoids this issue involves weak reference with a cleanup callback: + +.. code-block:: cpp + + // Register a callback function that is invoked when the BaseClass object is collected + py::cpp_function cleanup_callback( + [](py::handle weakref) { + // perform cleanup here -- this function is called with the GIL held + + weakref.dec_ref(); // release weak reference + } + ); + + // Create a weak reference with a cleanup callback and initially leak it + (void) py::weakref(m.attr("BaseClass"), cleanup_callback).release(); + +.. note:: + + PyPy does not garbage collect objects when the interpreter exits. An alternative + approach (which also works on CPython) is to use the :py:mod:`atexit` module [#f7]_, + for example: + + .. code-block:: cpp + + auto atexit = py::module_::import("atexit"); + atexit.attr("register")(py::cpp_function([]() { + // perform cleanup here -- this function is called with the GIL held + })); + + .. [#f7] https://docs.python.org/3/library/atexit.html + + +Generating documentation using Sphinx +===================================== + +Sphinx [#f4]_ has the ability to inspect the signatures and documentation +strings in pybind11-based extension modules to automatically generate beautiful +documentation in a variety formats. The python_example repository [#f5]_ contains a +simple example repository which uses this approach. + +There are two potential gotchas when using this approach: first, make sure that +the resulting strings do not contain any :kbd:`TAB` characters, which break the +docstring parsing routines. You may want to use C++11 raw string literals, +which are convenient for multi-line comments. Conveniently, any excess +indentation will be automatically be removed by Sphinx. However, for this to +work, it is important that all lines are indented consistently, i.e.: + +.. code-block:: cpp + + // ok + m.def("foo", &foo, R"mydelimiter( + The foo function + + Parameters + ---------- + )mydelimiter"); + + // *not ok* + m.def("foo", &foo, R"mydelimiter(The foo function + + Parameters + ---------- + )mydelimiter"); + +By default, pybind11 automatically generates and prepends a signature to the docstring of a function +registered with ``module_::def()`` and ``class_::def()``. Sometimes this +behavior is not desirable, because you want to provide your own signature or remove +the docstring completely to exclude the function from the Sphinx documentation. +The class ``options`` allows you to selectively suppress auto-generated signatures: + +.. code-block:: cpp + + PYBIND11_MODULE(example, m) { + py::options options; + options.disable_function_signatures(); + + m.def("add", [](int a, int b) { return a + b; }, "A function which adds two numbers"); + } + +Note that changes to the settings affect only function bindings created during the +lifetime of the ``options`` instance. When it goes out of scope at the end of the module's init function, +the default settings are restored to prevent unwanted side effects. + +.. [#f4] http://www.sphinx-doc.org +.. [#f5] http://github.com/pybind/python_example + +.. _avoiding-cpp-types-in-docstrings: + +Avoiding C++ types in docstrings +================================ + +Docstrings are generated at the time of the declaration, e.g. when ``.def(...)`` is called. +At this point parameter and return types should be known to pybind11. +If a custom type is not exposed yet through a ``py::class_`` constructor or a custom type caster, +its C++ type name will be used instead to generate the signature in the docstring: + +.. code-block:: text + + | __init__(...) + | __init__(self: example.Foo, arg0: ns::Bar) -> None + ^^^^^^^ + + +This limitation can be circumvented by ensuring that C++ classes are registered with pybind11 +before they are used as a parameter or return type of a function: + +.. code-block:: cpp + + PYBIND11_MODULE(example, m) { + + auto pyFoo = py::class_(m, "Foo"); + auto pyBar = py::class_(m, "Bar"); + + pyFoo.def(py::init()); + pyBar.def(py::init()); + } diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/pycpp/index.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/pycpp/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..b87d4d60824c05e44ff9c2b0a030299e4d122d9e --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/pycpp/index.rst @@ -0,0 +1,13 @@ +Python C++ interface +#################### + +pybind11 exposes Python types and functions using thin C++ wrappers, which +makes it possible to conveniently call Python code from C++ without resorting +to Python's C API. + +.. toctree:: + :maxdepth: 2 + + object + numpy + utilities diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/pycpp/numpy.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/pycpp/numpy.rst new file mode 100644 index 0000000000000000000000000000000000000000..848ef37e0803f5a38d3fe6890f863542bdc0b291 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/pycpp/numpy.rst @@ -0,0 +1,463 @@ +.. _numpy: + +NumPy +##### + +Buffer protocol +=============== + +Python supports an extremely general and convenient approach for exchanging +data between plugin libraries. Types can expose a buffer view [#f2]_, which +provides fast direct access to the raw internal data representation. Suppose we +want to bind the following simplistic Matrix class: + +.. code-block:: cpp + + class Matrix { + public: + Matrix(size_t rows, size_t cols) : m_rows(rows), m_cols(cols) { + m_data = new float[rows*cols]; + } + float *data() { return m_data; } + size_t rows() const { return m_rows; } + size_t cols() const { return m_cols; } + private: + size_t m_rows, m_cols; + float *m_data; + }; + +The following binding code exposes the ``Matrix`` contents as a buffer object, +making it possible to cast Matrices into NumPy arrays. It is even possible to +completely avoid copy operations with Python expressions like +``np.array(matrix_instance, copy = False)``. + +.. code-block:: cpp + + py::class_(m, "Matrix", py::buffer_protocol()) + .def_buffer([](Matrix &m) -> py::buffer_info { + return py::buffer_info( + m.data(), /* Pointer to buffer */ + sizeof(float), /* Size of one scalar */ + py::format_descriptor::format(), /* Python struct-style format descriptor */ + 2, /* Number of dimensions */ + { m.rows(), m.cols() }, /* Buffer dimensions */ + { sizeof(float) * m.cols(), /* Strides (in bytes) for each index */ + sizeof(float) } + ); + }); + +Supporting the buffer protocol in a new type involves specifying the special +``py::buffer_protocol()`` tag in the ``py::class_`` constructor and calling the +``def_buffer()`` method with a lambda function that creates a +``py::buffer_info`` description record on demand describing a given matrix +instance. The contents of ``py::buffer_info`` mirror the Python buffer protocol +specification. + +.. code-block:: cpp + + struct buffer_info { + void *ptr; + py::ssize_t itemsize; + std::string format; + py::ssize_t ndim; + std::vector shape; + std::vector strides; + }; + +To create a C++ function that can take a Python buffer object as an argument, +simply use the type ``py::buffer`` as one of its arguments. Buffers can exist +in a great variety of configurations, hence some safety checks are usually +necessary in the function body. Below, you can see a basic example on how to +define a custom constructor for the Eigen double precision matrix +(``Eigen::MatrixXd``) type, which supports initialization from compatible +buffer objects (e.g. a NumPy matrix). + +.. code-block:: cpp + + /* Bind MatrixXd (or some other Eigen type) to Python */ + typedef Eigen::MatrixXd Matrix; + + typedef Matrix::Scalar Scalar; + constexpr bool rowMajor = Matrix::Flags & Eigen::RowMajorBit; + + py::class_(m, "Matrix", py::buffer_protocol()) + .def(py::init([](py::buffer b) { + typedef Eigen::Stride Strides; + + /* Request a buffer descriptor from Python */ + py::buffer_info info = b.request(); + + /* Some sanity checks ... */ + if (info.format != py::format_descriptor::format()) + throw std::runtime_error("Incompatible format: expected a double array!"); + + if (info.ndim != 2) + throw std::runtime_error("Incompatible buffer dimension!"); + + auto strides = Strides( + info.strides[rowMajor ? 0 : 1] / (py::ssize_t)sizeof(Scalar), + info.strides[rowMajor ? 1 : 0] / (py::ssize_t)sizeof(Scalar)); + + auto map = Eigen::Map( + static_cast(info.ptr), info.shape[0], info.shape[1], strides); + + return Matrix(map); + })); + +For reference, the ``def_buffer()`` call for this Eigen data type should look +as follows: + +.. code-block:: cpp + + .def_buffer([](Matrix &m) -> py::buffer_info { + return py::buffer_info( + m.data(), /* Pointer to buffer */ + sizeof(Scalar), /* Size of one scalar */ + py::format_descriptor::format(), /* Python struct-style format descriptor */ + 2, /* Number of dimensions */ + { m.rows(), m.cols() }, /* Buffer dimensions */ + { sizeof(Scalar) * (rowMajor ? m.cols() : 1), + sizeof(Scalar) * (rowMajor ? 1 : m.rows()) } + /* Strides (in bytes) for each index */ + ); + }) + +For a much easier approach of binding Eigen types (although with some +limitations), refer to the section on :doc:`/advanced/cast/eigen`. + +.. seealso:: + + The file :file:`tests/test_buffers.cpp` contains a complete example + that demonstrates using the buffer protocol with pybind11 in more detail. + +.. [#f2] http://docs.python.org/3/c-api/buffer.html + +Arrays +====== + +By exchanging ``py::buffer`` with ``py::array`` in the above snippet, we can +restrict the function so that it only accepts NumPy arrays (rather than any +type of Python object satisfying the buffer protocol). + +In many situations, we want to define a function which only accepts a NumPy +array of a certain data type. This is possible via the ``py::array_t`` +template. For instance, the following function requires the argument to be a +NumPy array containing double precision values. + +.. code-block:: cpp + + void f(py::array_t array); + +When it is invoked with a different type (e.g. an integer or a list of +integers), the binding code will attempt to cast the input into a NumPy array +of the requested type. This feature requires the :file:`pybind11/numpy.h` +header to be included. Note that :file:`pybind11/numpy.h` does not depend on +the NumPy headers, and thus can be used without declaring a build-time +dependency on NumPy; NumPy>=1.7.0 is a runtime dependency. + +Data in NumPy arrays is not guaranteed to packed in a dense manner; +furthermore, entries can be separated by arbitrary column and row strides. +Sometimes, it can be useful to require a function to only accept dense arrays +using either the C (row-major) or Fortran (column-major) ordering. This can be +accomplished via a second template argument with values ``py::array::c_style`` +or ``py::array::f_style``. + +.. code-block:: cpp + + void f(py::array_t array); + +The ``py::array::forcecast`` argument is the default value of the second +template parameter, and it ensures that non-conforming arguments are converted +into an array satisfying the specified requirements instead of trying the next +function overload. + +There are several methods on arrays; the methods listed below under references +work, as well as the following functions based on the NumPy API: + +- ``.dtype()`` returns the type of the contained values. + +- ``.strides()`` returns a pointer to the strides of the array (optionally pass + an integer axis to get a number). + +- ``.flags()`` returns the flag settings. ``.writable()`` and ``.owndata()`` + are directly available. + +- ``.offset_at()`` returns the offset (optionally pass indices). + +- ``.squeeze()`` returns a view with length-1 axes removed. + +- ``.view(dtype)`` returns a view of the array with a different dtype. + +- ``.reshape({i, j, ...})`` returns a view of the array with a different shape. + ``.resize({...})`` is also available. + +- ``.index_at(i, j, ...)`` gets the count from the beginning to a given index. + + +There are also several methods for getting references (described below). + +Structured types +================ + +In order for ``py::array_t`` to work with structured (record) types, we first +need to register the memory layout of the type. This can be done via +``PYBIND11_NUMPY_DTYPE`` macro, called in the plugin definition code, which +expects the type followed by field names: + +.. code-block:: cpp + + struct A { + int x; + double y; + }; + + struct B { + int z; + A a; + }; + + // ... + PYBIND11_MODULE(test, m) { + // ... + + PYBIND11_NUMPY_DTYPE(A, x, y); + PYBIND11_NUMPY_DTYPE(B, z, a); + /* now both A and B can be used as template arguments to py::array_t */ + } + +The structure should consist of fundamental arithmetic types, ``std::complex``, +previously registered substructures, and arrays of any of the above. Both C++ +arrays and ``std::array`` are supported. While there is a static assertion to +prevent many types of unsupported structures, it is still the user's +responsibility to use only "plain" structures that can be safely manipulated as +raw memory without violating invariants. + +Vectorizing functions +===================== + +Suppose we want to bind a function with the following signature to Python so +that it can process arbitrary NumPy array arguments (vectors, matrices, general +N-D arrays) in addition to its normal arguments: + +.. code-block:: cpp + + double my_func(int x, float y, double z); + +After including the ``pybind11/numpy.h`` header, this is extremely simple: + +.. code-block:: cpp + + m.def("vectorized_func", py::vectorize(my_func)); + +Invoking the function like below causes 4 calls to be made to ``my_func`` with +each of the array elements. The significant advantage of this compared to +solutions like ``numpy.vectorize()`` is that the loop over the elements runs +entirely on the C++ side and can be crunched down into a tight, optimized loop +by the compiler. The result is returned as a NumPy array of type +``numpy.dtype.float64``. + +.. code-block:: pycon + + >>> x = np.array([[1, 3], [5, 7]]) + >>> y = np.array([[2, 4], [6, 8]]) + >>> z = 3 + >>> result = vectorized_func(x, y, z) + +The scalar argument ``z`` is transparently replicated 4 times. The input +arrays ``x`` and ``y`` are automatically converted into the right types (they +are of type ``numpy.dtype.int64`` but need to be ``numpy.dtype.int32`` and +``numpy.dtype.float32``, respectively). + +.. note:: + + Only arithmetic, complex, and POD types passed by value or by ``const &`` + reference are vectorized; all other arguments are passed through as-is. + Functions taking rvalue reference arguments cannot be vectorized. + +In cases where the computation is too complicated to be reduced to +``vectorize``, it will be necessary to create and access the buffer contents +manually. The following snippet contains a complete example that shows how this +works (the code is somewhat contrived, since it could have been done more +simply using ``vectorize``). + +.. code-block:: cpp + + #include + #include + + namespace py = pybind11; + + py::array_t add_arrays(py::array_t input1, py::array_t input2) { + py::buffer_info buf1 = input1.request(), buf2 = input2.request(); + + if (buf1.ndim != 1 || buf2.ndim != 1) + throw std::runtime_error("Number of dimensions must be one"); + + if (buf1.size != buf2.size) + throw std::runtime_error("Input shapes must match"); + + /* No pointer is passed, so NumPy will allocate the buffer */ + auto result = py::array_t(buf1.size); + + py::buffer_info buf3 = result.request(); + + double *ptr1 = static_cast(buf1.ptr); + double *ptr2 = static_cast(buf2.ptr); + double *ptr3 = static_cast(buf3.ptr); + + for (size_t idx = 0; idx < buf1.shape[0]; idx++) + ptr3[idx] = ptr1[idx] + ptr2[idx]; + + return result; + } + + PYBIND11_MODULE(test, m) { + m.def("add_arrays", &add_arrays, "Add two NumPy arrays"); + } + +.. seealso:: + + The file :file:`tests/test_numpy_vectorize.cpp` contains a complete + example that demonstrates using :func:`vectorize` in more detail. + +Direct access +============= + +For performance reasons, particularly when dealing with very large arrays, it +is often desirable to directly access array elements without internal checking +of dimensions and bounds on every access when indices are known to be already +valid. To avoid such checks, the ``array`` class and ``array_t`` template +class offer an unchecked proxy object that can be used for this unchecked +access through the ``unchecked`` and ``mutable_unchecked`` methods, +where ``N`` gives the required dimensionality of the array: + +.. code-block:: cpp + + m.def("sum_3d", [](py::array_t x) { + auto r = x.unchecked<3>(); // x must have ndim = 3; can be non-writeable + double sum = 0; + for (py::ssize_t i = 0; i < r.shape(0); i++) + for (py::ssize_t j = 0; j < r.shape(1); j++) + for (py::ssize_t k = 0; k < r.shape(2); k++) + sum += r(i, j, k); + return sum; + }); + m.def("increment_3d", [](py::array_t x) { + auto r = x.mutable_unchecked<3>(); // Will throw if ndim != 3 or flags.writeable is false + for (py::ssize_t i = 0; i < r.shape(0); i++) + for (py::ssize_t j = 0; j < r.shape(1); j++) + for (py::ssize_t k = 0; k < r.shape(2); k++) + r(i, j, k) += 1.0; + }, py::arg().noconvert()); + +To obtain the proxy from an ``array`` object, you must specify both the data +type and number of dimensions as template arguments, such as ``auto r = +myarray.mutable_unchecked()``. + +If the number of dimensions is not known at compile time, you can omit the +dimensions template parameter (i.e. calling ``arr_t.unchecked()`` or +``arr.unchecked()``. This will give you a proxy object that works in the +same way, but results in less optimizable code and thus a small efficiency +loss in tight loops. + +Note that the returned proxy object directly references the array's data, and +only reads its shape, strides, and writeable flag when constructed. You must +take care to ensure that the referenced array is not destroyed or reshaped for +the duration of the returned object, typically by limiting the scope of the +returned instance. + +The returned proxy object supports some of the same methods as ``py::array`` so +that it can be used as a drop-in replacement for some existing, index-checked +uses of ``py::array``: + +- ``.ndim()`` returns the number of dimensions + +- ``.data(1, 2, ...)`` and ``r.mutable_data(1, 2, ...)``` returns a pointer to + the ``const T`` or ``T`` data, respectively, at the given indices. The + latter is only available to proxies obtained via ``a.mutable_unchecked()``. + +- ``.itemsize()`` returns the size of an item in bytes, i.e. ``sizeof(T)``. + +- ``.ndim()`` returns the number of dimensions. + +- ``.shape(n)`` returns the size of dimension ``n`` + +- ``.size()`` returns the total number of elements (i.e. the product of the shapes). + +- ``.nbytes()`` returns the number of bytes used by the referenced elements + (i.e. ``itemsize()`` times ``size()``). + +.. seealso:: + + The file :file:`tests/test_numpy_array.cpp` contains additional examples + demonstrating the use of this feature. + +Ellipsis +======== + +Python 3 provides a convenient ``...`` ellipsis notation that is often used to +slice multidimensional arrays. For instance, the following snippet extracts the +middle dimensions of a tensor with the first and last index set to zero. +In Python 2, the syntactic sugar ``...`` is not available, but the singleton +``Ellipsis`` (of type ``ellipsis``) can still be used directly. + +.. code-block:: python + + a = ... # a NumPy array + b = a[0, ..., 0] + +The function ``py::ellipsis()`` function can be used to perform the same +operation on the C++ side: + +.. code-block:: cpp + + py::array a = /* A NumPy array */; + py::array b = a[py::make_tuple(0, py::ellipsis(), 0)]; + +.. versionchanged:: 2.6 + ``py::ellipsis()`` is now also available in Python 2. + +Memory view +=========== + +For a case when we simply want to provide a direct accessor to C/C++ buffer +without a concrete class object, we can return a ``memoryview`` object. Suppose +we wish to expose a ``memoryview`` for 2x4 uint8_t array, we can do the +following: + +.. code-block:: cpp + + const uint8_t buffer[] = { + 0, 1, 2, 3, + 4, 5, 6, 7 + }; + m.def("get_memoryview2d", []() { + return py::memoryview::from_buffer( + buffer, // buffer pointer + { 2, 4 }, // shape (rows, cols) + { sizeof(uint8_t) * 4, sizeof(uint8_t) } // strides in bytes + ); + }) + +This approach is meant for providing a ``memoryview`` for a C/C++ buffer not +managed by Python. The user is responsible for managing the lifetime of the +buffer. Using a ``memoryview`` created in this way after deleting the buffer in +C++ side results in undefined behavior. + +We can also use ``memoryview::from_memory`` for a simple 1D contiguous buffer: + +.. code-block:: cpp + + m.def("get_memoryview1d", []() { + return py::memoryview::from_memory( + buffer, // buffer pointer + sizeof(uint8_t) * 8 // buffer size + ); + }) + +.. note:: + + ``memoryview::from_memory`` is not available in Python 2. + +.. versionchanged:: 2.6 + ``memoryview::from_memory`` added. diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/pycpp/object.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/pycpp/object.rst new file mode 100644 index 0000000000000000000000000000000000000000..b8cb949d52a06fd177da022f3400dc33eac15727 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/pycpp/object.rst @@ -0,0 +1,286 @@ +Python types +############ + +.. _wrappers: + +Available wrappers +================== + +All major Python types are available as thin C++ wrapper classes. These +can also be used as function parameters -- see :ref:`python_objects_as_args`. + +Available types include :class:`handle`, :class:`object`, :class:`bool_`, +:class:`int_`, :class:`float_`, :class:`str`, :class:`bytes`, :class:`tuple`, +:class:`list`, :class:`dict`, :class:`slice`, :class:`none`, :class:`capsule`, +:class:`iterable`, :class:`iterator`, :class:`function`, :class:`buffer`, +:class:`array`, and :class:`array_t`. + +.. warning:: + + Be sure to review the :ref:`pytypes_gotchas` before using this heavily in + your C++ API. + +.. _instantiating_compound_types: + +Instantiating compound Python types from C++ +============================================ + +Dictionaries can be initialized in the :class:`dict` constructor: + +.. code-block:: cpp + + using namespace pybind11::literals; // to bring in the `_a` literal + py::dict d("spam"_a=py::none(), "eggs"_a=42); + +A tuple of python objects can be instantiated using :func:`py::make_tuple`: + +.. code-block:: cpp + + py::tuple tup = py::make_tuple(42, py::none(), "spam"); + +Each element is converted to a supported Python type. + +A `simple namespace`_ can be instantiated using + +.. code-block:: cpp + + using namespace pybind11::literals; // to bring in the `_a` literal + py::object SimpleNamespace = py::module_::import("types").attr("SimpleNamespace"); + py::object ns = SimpleNamespace("spam"_a=py::none(), "eggs"_a=42); + +Attributes on a namespace can be modified with the :func:`py::delattr`, +:func:`py::getattr`, and :func:`py::setattr` functions. Simple namespaces can +be useful as lightweight stand-ins for class instances. + +.. _simple namespace: https://docs.python.org/3/library/types.html#types.SimpleNamespace + +.. _casting_back_and_forth: + +Casting back and forth +====================== + +In this kind of mixed code, it is often necessary to convert arbitrary C++ +types to Python, which can be done using :func:`py::cast`: + +.. code-block:: cpp + + MyClass *cls = ...; + py::object obj = py::cast(cls); + +The reverse direction uses the following syntax: + +.. code-block:: cpp + + py::object obj = ...; + MyClass *cls = obj.cast(); + +When conversion fails, both directions throw the exception :class:`cast_error`. + +.. _python_libs: + +Accessing Python libraries from C++ +=================================== + +It is also possible to import objects defined in the Python standard +library or available in the current Python environment (``sys.path``) and work +with these in C++. + +This example obtains a reference to the Python ``Decimal`` class. + +.. code-block:: cpp + + // Equivalent to "from decimal import Decimal" + py::object Decimal = py::module_::import("decimal").attr("Decimal"); + +.. code-block:: cpp + + // Try to import scipy + py::object scipy = py::module_::import("scipy"); + return scipy.attr("__version__"); + + +.. _calling_python_functions: + +Calling Python functions +======================== + +It is also possible to call Python classes, functions and methods +via ``operator()``. + +.. code-block:: cpp + + // Construct a Python object of class Decimal + py::object pi = Decimal("3.14159"); + +.. code-block:: cpp + + // Use Python to make our directories + py::object os = py::module_::import("os"); + py::object makedirs = os.attr("makedirs"); + makedirs("/tmp/path/to/somewhere"); + +One can convert the result obtained from Python to a pure C++ version +if a ``py::class_`` or type conversion is defined. + +.. code-block:: cpp + + py::function f = <...>; + py::object result_py = f(1234, "hello", some_instance); + MyClass &result = result_py.cast(); + +.. _calling_python_methods: + +Calling Python methods +======================== + +To call an object's method, one can again use ``.attr`` to obtain access to the +Python method. + +.. code-block:: cpp + + // Calculate e^π in decimal + py::object exp_pi = pi.attr("exp")(); + py::print(py::str(exp_pi)); + +In the example above ``pi.attr("exp")`` is a *bound method*: it will always call +the method for that same instance of the class. Alternately one can create an +*unbound method* via the Python class (instead of instance) and pass the ``self`` +object explicitly, followed by other arguments. + +.. code-block:: cpp + + py::object decimal_exp = Decimal.attr("exp"); + + // Compute the e^n for n=0..4 + for (int n = 0; n < 5; n++) { + py::print(decimal_exp(Decimal(n)); + } + +Keyword arguments +================= + +Keyword arguments are also supported. In Python, there is the usual call syntax: + +.. code-block:: python + + def f(number, say, to): + ... # function code + + + f(1234, say="hello", to=some_instance) # keyword call in Python + +In C++, the same call can be made using: + +.. code-block:: cpp + + using namespace pybind11::literals; // to bring in the `_a` literal + f(1234, "say"_a="hello", "to"_a=some_instance); // keyword call in C++ + +Unpacking arguments +=================== + +Unpacking of ``*args`` and ``**kwargs`` is also possible and can be mixed with +other arguments: + +.. code-block:: cpp + + // * unpacking + py::tuple args = py::make_tuple(1234, "hello", some_instance); + f(*args); + + // ** unpacking + py::dict kwargs = py::dict("number"_a=1234, "say"_a="hello", "to"_a=some_instance); + f(**kwargs); + + // mixed keywords, * and ** unpacking + py::tuple args = py::make_tuple(1234); + py::dict kwargs = py::dict("to"_a=some_instance); + f(*args, "say"_a="hello", **kwargs); + +Generalized unpacking according to PEP448_ is also supported: + +.. code-block:: cpp + + py::dict kwargs1 = py::dict("number"_a=1234); + py::dict kwargs2 = py::dict("to"_a=some_instance); + f(**kwargs1, "say"_a="hello", **kwargs2); + +.. seealso:: + + The file :file:`tests/test_pytypes.cpp` contains a complete + example that demonstrates passing native Python types in more detail. The + file :file:`tests/test_callbacks.cpp` presents a few examples of calling + Python functions from C++, including keywords arguments and unpacking. + +.. _PEP448: https://www.python.org/dev/peps/pep-0448/ + +.. _implicit_casting: + +Implicit casting +================ + +When using the C++ interface for Python types, or calling Python functions, +objects of type :class:`object` are returned. It is possible to invoke implicit +conversions to subclasses like :class:`dict`. The same holds for the proxy objects +returned by ``operator[]`` or ``obj.attr()``. +Casting to subtypes improves code readability and allows values to be passed to +C++ functions that require a specific subtype rather than a generic :class:`object`. + +.. code-block:: cpp + + #include + using namespace pybind11::literals; + + py::module_ os = py::module_::import("os"); + py::module_ path = py::module_::import("os.path"); // like 'import os.path as path' + py::module_ np = py::module_::import("numpy"); // like 'import numpy as np' + + py::str curdir_abs = path.attr("abspath")(path.attr("curdir")); + py::print(py::str("Current directory: ") + curdir_abs); + py::dict environ = os.attr("environ"); + py::print(environ["HOME"]); + py::array_t arr = np.attr("ones")(3, "dtype"_a="float32"); + py::print(py::repr(arr + py::int_(1))); + +These implicit conversions are available for subclasses of :class:`object`; there +is no need to call ``obj.cast()`` explicitly as for custom classes, see +:ref:`casting_back_and_forth`. + +.. note:: + If a trivial conversion via move constructor is not possible, both implicit and + explicit casting (calling ``obj.cast()``) will attempt a "rich" conversion. + For instance, ``py::list env = os.attr("environ");`` will succeed and is + equivalent to the Python code ``env = list(os.environ)`` that produces a + list of the dict keys. + +.. TODO: Adapt text once PR #2349 has landed + +Handling exceptions +=================== + +Python exceptions from wrapper classes will be thrown as a ``py::error_already_set``. +See :ref:`Handling exceptions from Python in C++ +` for more information on handling exceptions +raised when calling C++ wrapper classes. + +.. _pytypes_gotchas: + +Gotchas +======= + +Default-Constructed Wrappers +---------------------------- + +When a wrapper type is default-constructed, it is **not** a valid Python object (i.e. it is not ``py::none()``). It is simply the same as +``PyObject*`` null pointer. To check for this, use +``static_cast(my_wrapper)``. + +Assigning py::none() to wrappers +-------------------------------- + +You may be tempted to use types like ``py::str`` and ``py::dict`` in C++ +signatures (either pure C++, or in bound signatures), and assign them default +values of ``py::none()``. However, in a best case scenario, it will fail fast +because ``None`` is not convertible to that type (e.g. ``py::dict``), or in a +worse case scenario, it will silently work but corrupt the types you want to +work with (e.g. ``py::str(py::none())`` will yield ``"None"`` in Python). diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/pycpp/utilities.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/pycpp/utilities.rst new file mode 100644 index 0000000000000000000000000000000000000000..5b4511614fd094d149397f1aa9e47b54b91a98c5 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/pycpp/utilities.rst @@ -0,0 +1,155 @@ +Utilities +######### + +Using Python's print function in C++ +==================================== + +The usual way to write output in C++ is using ``std::cout`` while in Python one +would use ``print``. Since these methods use different buffers, mixing them can +lead to output order issues. To resolve this, pybind11 modules can use the +:func:`py::print` function which writes to Python's ``sys.stdout`` for consistency. + +Python's ``print`` function is replicated in the C++ API including optional +keyword arguments ``sep``, ``end``, ``file``, ``flush``. Everything works as +expected in Python: + +.. code-block:: cpp + + py::print(1, 2.0, "three"); // 1 2.0 three + py::print(1, 2.0, "three", "sep"_a="-"); // 1-2.0-three + + auto args = py::make_tuple("unpacked", true); + py::print("->", *args, "end"_a="<-"); // -> unpacked True <- + +.. _ostream_redirect: + +Capturing standard output from ostream +====================================== + +Often, a library will use the streams ``std::cout`` and ``std::cerr`` to print, +but this does not play well with Python's standard ``sys.stdout`` and ``sys.stderr`` +redirection. Replacing a library's printing with ``py::print `` may not +be feasible. This can be fixed using a guard around the library function that +redirects output to the corresponding Python streams: + +.. code-block:: cpp + + #include + + ... + + // Add a scoped redirect for your noisy code + m.def("noisy_func", []() { + py::scoped_ostream_redirect stream( + std::cout, // std::ostream& + py::module_::import("sys").attr("stdout") // Python output + ); + call_noisy_func(); + }); + +.. warning:: + + The implementation in ``pybind11/iostream.h`` is NOT thread safe. Multiple + threads writing to a redirected ostream concurrently cause data races + and potentially buffer overflows. Therefore it is currently a requirement + that all (possibly) concurrent redirected ostream writes are protected by + a mutex. #HelpAppreciated: Work on iostream.h thread safety. For more + background see the discussions under + `PR #2982 `_ and + `PR #2995 `_. + +This method respects flushes on the output streams and will flush if needed +when the scoped guard is destroyed. This allows the output to be redirected in +real time, such as to a Jupyter notebook. The two arguments, the C++ stream and +the Python output, are optional, and default to standard output if not given. An +extra type, ``py::scoped_estream_redirect ``, is identical +except for defaulting to ``std::cerr`` and ``sys.stderr``; this can be useful with +``py::call_guard``, which allows multiple items, but uses the default constructor: + +.. code-block:: cpp + + // Alternative: Call single function using call guard + m.def("noisy_func", &call_noisy_function, + py::call_guard()); + +The redirection can also be done in Python with the addition of a context +manager, using the ``py::add_ostream_redirect() `` function: + +.. code-block:: cpp + + py::add_ostream_redirect(m, "ostream_redirect"); + +The name in Python defaults to ``ostream_redirect`` if no name is passed. This +creates the following context manager in Python: + +.. code-block:: python + + with ostream_redirect(stdout=True, stderr=True): + noisy_function() + +It defaults to redirecting both streams, though you can use the keyword +arguments to disable one of the streams if needed. + +.. note:: + + The above methods will not redirect C-level output to file descriptors, such + as ``fprintf``. For those cases, you'll need to redirect the file + descriptors either directly in C or with Python's ``os.dup2`` function + in an operating-system dependent way. + +.. _eval: + +Evaluating Python expressions from strings and files +==================================================== + +pybind11 provides the ``eval``, ``exec`` and ``eval_file`` functions to evaluate +Python expressions and statements. The following example illustrates how they +can be used. + +.. code-block:: cpp + + // At beginning of file + #include + + ... + + // Evaluate in scope of main module + py::object scope = py::module_::import("__main__").attr("__dict__"); + + // Evaluate an isolated expression + int result = py::eval("my_variable + 10", scope).cast(); + + // Evaluate a sequence of statements + py::exec( + "print('Hello')\n" + "print('world!');", + scope); + + // Evaluate the statements in an separate Python file on disk + py::eval_file("script.py", scope); + +C++11 raw string literals are also supported and quite handy for this purpose. +The only requirement is that the first statement must be on a new line following +the raw string delimiter ``R"(``, ensuring all lines have common leading indent: + +.. code-block:: cpp + + py::exec(R"( + x = get_answer() + if x == 42: + print('Hello World!') + else: + print('Bye!') + )", scope + ); + +.. note:: + + `eval` and `eval_file` accept a template parameter that describes how the + string/file should be interpreted. Possible choices include ``eval_expr`` + (isolated expression), ``eval_single_statement`` (a single statement, return + value is always ``none``), and ``eval_statements`` (sequence of statements, + return value is always ``none``). `eval` defaults to ``eval_expr``, + `eval_file` defaults to ``eval_statements`` and `exec` is just a shortcut + for ``eval``. diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/smart_ptrs.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/smart_ptrs.rst new file mode 100644 index 0000000000000000000000000000000000000000..984167968fa6b27b6c15090cd17cc6b8d21787f0 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/advanced/smart_ptrs.rst @@ -0,0 +1,174 @@ +Smart pointers +############## + +std::unique_ptr +=============== + +Given a class ``Example`` with Python bindings, it's possible to return +instances wrapped in C++11 unique pointers, like so + +.. code-block:: cpp + + std::unique_ptr create_example() { return std::unique_ptr(new Example()); } + +.. code-block:: cpp + + m.def("create_example", &create_example); + +In other words, there is nothing special that needs to be done. While returning +unique pointers in this way is allowed, it is *illegal* to use them as function +arguments. For instance, the following function signature cannot be processed +by pybind11. + +.. code-block:: cpp + + void do_something_with_example(std::unique_ptr ex) { ... } + +The above signature would imply that Python needs to give up ownership of an +object that is passed to this function, which is generally not possible (for +instance, the object might be referenced elsewhere). + +std::shared_ptr +=============== + +The binding generator for classes, :class:`class_`, can be passed a template +type that denotes a special *holder* type that is used to manage references to +the object. If no such holder type template argument is given, the default for +a type named ``Type`` is ``std::unique_ptr``, which means that the object +is deallocated when Python's reference count goes to zero. + +It is possible to switch to other types of reference counting wrappers or smart +pointers, which is useful in codebases that rely on them. For instance, the +following snippet causes ``std::shared_ptr`` to be used instead. + +.. code-block:: cpp + + py::class_ /* <- holder type */> obj(m, "Example"); + +Note that any particular class can only be associated with a single holder type. + +One potential stumbling block when using holder types is that they need to be +applied consistently. Can you guess what's broken about the following binding +code? + +.. code-block:: cpp + + class Child { }; + + class Parent { + public: + Parent() : child(std::make_shared()) { } + Child *get_child() { return child.get(); } /* Hint: ** DON'T DO THIS ** */ + private: + std::shared_ptr child; + }; + + PYBIND11_MODULE(example, m) { + py::class_>(m, "Child"); + + py::class_>(m, "Parent") + .def(py::init<>()) + .def("get_child", &Parent::get_child); + } + +The following Python code will cause undefined behavior (and likely a +segmentation fault). + +.. code-block:: python + + from example import Parent + + print(Parent().get_child()) + +The problem is that ``Parent::get_child()`` returns a pointer to an instance of +``Child``, but the fact that this instance is already managed by +``std::shared_ptr<...>`` is lost when passing raw pointers. In this case, +pybind11 will create a second independent ``std::shared_ptr<...>`` that also +claims ownership of the pointer. In the end, the object will be freed **twice** +since these shared pointers have no way of knowing about each other. + +There are two ways to resolve this issue: + +1. For types that are managed by a smart pointer class, never use raw pointers + in function arguments or return values. In other words: always consistently + wrap pointers into their designated holder types (such as + ``std::shared_ptr<...>``). In this case, the signature of ``get_child()`` + should be modified as follows: + +.. code-block:: cpp + + std::shared_ptr get_child() { return child; } + +2. Adjust the definition of ``Child`` by specifying + ``std::enable_shared_from_this`` (see cppreference_ for details) as a + base class. This adds a small bit of information to ``Child`` that allows + pybind11 to realize that there is already an existing + ``std::shared_ptr<...>`` and communicate with it. In this case, the + declaration of ``Child`` should look as follows: + +.. _cppreference: http://en.cppreference.com/w/cpp/memory/enable_shared_from_this + +.. code-block:: cpp + + class Child : public std::enable_shared_from_this { }; + +.. _smart_pointers: + +Custom smart pointers +===================== + +pybind11 supports ``std::unique_ptr`` and ``std::shared_ptr`` right out of the +box. For any other custom smart pointer, transparent conversions can be enabled +using a macro invocation similar to the following. It must be declared at the +top namespace level before any binding code: + +.. code-block:: cpp + + PYBIND11_DECLARE_HOLDER_TYPE(T, SmartPtr); + +The first argument of :func:`PYBIND11_DECLARE_HOLDER_TYPE` should be a +placeholder name that is used as a template parameter of the second argument. +Thus, feel free to use any identifier, but use it consistently on both sides; +also, don't use the name of a type that already exists in your codebase. + +The macro also accepts a third optional boolean parameter that is set to false +by default. Specify + +.. code-block:: cpp + + PYBIND11_DECLARE_HOLDER_TYPE(T, SmartPtr, true); + +if ``SmartPtr`` can always be initialized from a ``T*`` pointer without the +risk of inconsistencies (such as multiple independent ``SmartPtr`` instances +believing that they are the sole owner of the ``T*`` pointer). A common +situation where ``true`` should be passed is when the ``T`` instances use +*intrusive* reference counting. + +Please take a look at the :ref:`macro_notes` before using this feature. + +By default, pybind11 assumes that your custom smart pointer has a standard +interface, i.e. provides a ``.get()`` member function to access the underlying +raw pointer. If this is not the case, pybind11's ``holder_helper`` must be +specialized: + +.. code-block:: cpp + + // Always needed for custom holder types + PYBIND11_DECLARE_HOLDER_TYPE(T, SmartPtr); + + // Only needed if the type's `.get()` goes by another name + namespace pybind11 { namespace detail { + template + struct holder_helper> { // <-- specialization + static const T *get(const SmartPtr &p) { return p.getPointer(); } + }; + }} + +The above specialization informs pybind11 that the custom ``SmartPtr`` class +provides ``.get()`` functionality via ``.getPointer()``. + +.. seealso:: + + The file :file:`tests/test_smart_ptr.cpp` contains a complete example + that demonstrates how to work with custom reference-counting holder types + in more detail. diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/basics.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/basics.rst new file mode 100644 index 0000000000000000000000000000000000000000..69a5abd56223d843bc3afebeb5dec7562562e867 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/basics.rst @@ -0,0 +1,308 @@ +.. _basics: + +First steps +########### + +This sections demonstrates the basic features of pybind11. Before getting +started, make sure that development environment is set up to compile the +included set of test cases. + + +Compiling the test cases +======================== + +Linux/macOS +----------- + +On Linux you'll need to install the **python-dev** or **python3-dev** packages as +well as **cmake**. On macOS, the included python version works out of the box, +but **cmake** must still be installed. + +After installing the prerequisites, run + +.. code-block:: bash + + mkdir build + cd build + cmake .. + make check -j 4 + +The last line will both compile and run the tests. + +Windows +------- + +On Windows, only **Visual Studio 2015** and newer are supported since pybind11 relies +on various C++11 language features that break older versions of Visual Studio. + +.. Note:: + + To use the C++17 in Visual Studio 2017 (MSVC 14.1), pybind11 requires the flag + ``/permissive-`` to be passed to the compiler `to enforce standard conformance`_. When + building with Visual Studio 2019, this is not strictly necessary, but still advised. + +.. _`to enforce standard conformance`: https://docs.microsoft.com/en-us/cpp/build/reference/permissive-standards-conformance?view=vs-2017 + +To compile and run the tests: + +.. code-block:: batch + + mkdir build + cd build + cmake .. + cmake --build . --config Release --target check + +This will create a Visual Studio project, compile and run the target, all from the +command line. + +.. Note:: + + If all tests fail, make sure that the Python binary and the testcases are compiled + for the same processor type and bitness (i.e. either **i386** or **x86_64**). You + can specify **x86_64** as the target architecture for the generated Visual Studio + project using ``cmake -A x64 ..``. + +.. seealso:: + + Advanced users who are already familiar with Boost.Python may want to skip + the tutorial and look at the test cases in the :file:`tests` directory, + which exercise all features of pybind11. + +Header and namespace conventions +================================ + +For brevity, all code examples assume that the following two lines are present: + +.. code-block:: cpp + + #include + + namespace py = pybind11; + +Some features may require additional headers, but those will be specified as needed. + +.. _simple_example: + +Creating bindings for a simple function +======================================= + +Let's start by creating Python bindings for an extremely simple function, which +adds two numbers and returns their result: + +.. code-block:: cpp + + int add(int i, int j) { + return i + j; + } + +For simplicity [#f1]_, we'll put both this function and the binding code into +a file named :file:`example.cpp` with the following contents: + +.. code-block:: cpp + + #include + + int add(int i, int j) { + return i + j; + } + + PYBIND11_MODULE(example, m) { + m.doc() = "pybind11 example plugin"; // optional module docstring + + m.def("add", &add, "A function that adds two numbers"); + } + +.. [#f1] In practice, implementation and binding code will generally be located + in separate files. + +The :func:`PYBIND11_MODULE` macro creates a function that will be called when an +``import`` statement is issued from within Python. The module name (``example``) +is given as the first macro argument (it should not be in quotes). The second +argument (``m``) defines a variable of type :class:`py::module_ ` which +is the main interface for creating bindings. The method :func:`module_::def` +generates binding code that exposes the ``add()`` function to Python. + +.. note:: + + Notice how little code was needed to expose our function to Python: all + details regarding the function's parameters and return value were + automatically inferred using template metaprogramming. This overall + approach and the used syntax are borrowed from Boost.Python, though the + underlying implementation is very different. + +pybind11 is a header-only library, hence it is not necessary to link against +any special libraries and there are no intermediate (magic) translation steps. +On Linux, the above example can be compiled using the following command: + +.. code-block:: bash + + $ c++ -O3 -Wall -shared -std=c++11 -fPIC $(python3 -m pybind11 --includes) example.cpp -o example$(python3-config --extension-suffix) + +.. note:: + + If you used :ref:`include_as_a_submodule` to get the pybind11 source, then + use ``$(python3-config --includes) -Iextern/pybind11/include`` instead of + ``$(python3 -m pybind11 --includes)`` in the above compilation, as + explained in :ref:`building_manually`. + +For more details on the required compiler flags on Linux and macOS, see +:ref:`building_manually`. For complete cross-platform compilation instructions, +refer to the :ref:`compiling` page. + +The `python_example`_ and `cmake_example`_ repositories are also a good place +to start. They are both complete project examples with cross-platform build +systems. The only difference between the two is that `python_example`_ uses +Python's ``setuptools`` to build the module, while `cmake_example`_ uses CMake +(which may be preferable for existing C++ projects). + +.. _python_example: https://github.com/pybind/python_example +.. _cmake_example: https://github.com/pybind/cmake_example + +Building the above C++ code will produce a binary module file that can be +imported to Python. Assuming that the compiled module is located in the +current directory, the following interactive Python session shows how to +load and execute the example: + +.. code-block:: pycon + + $ python + Python 2.7.10 (default, Aug 22 2015, 20:33:39) + [GCC 4.2.1 Compatible Apple LLVM 7.0.0 (clang-700.0.59.1)] on darwin + Type "help", "copyright", "credits" or "license" for more information. + >>> import example + >>> example.add(1, 2) + 3L + >>> + +.. _keyword_args: + +Keyword arguments +================= + +With a simple code modification, it is possible to inform Python about the +names of the arguments ("i" and "j" in this case). + +.. code-block:: cpp + + m.def("add", &add, "A function which adds two numbers", + py::arg("i"), py::arg("j")); + +:class:`arg` is one of several special tag classes which can be used to pass +metadata into :func:`module_::def`. With this modified binding code, we can now +call the function using keyword arguments, which is a more readable alternative +particularly for functions taking many parameters: + +.. code-block:: pycon + + >>> import example + >>> example.add(i=1, j=2) + 3L + +The keyword names also appear in the function signatures within the documentation. + +.. code-block:: pycon + + >>> help(example) + + .... + + FUNCTIONS + add(...) + Signature : (i: int, j: int) -> int + + A function which adds two numbers + +A shorter notation for named arguments is also available: + +.. code-block:: cpp + + // regular notation + m.def("add1", &add, py::arg("i"), py::arg("j")); + // shorthand + using namespace pybind11::literals; + m.def("add2", &add, "i"_a, "j"_a); + +The :var:`_a` suffix forms a C++11 literal which is equivalent to :class:`arg`. +Note that the literal operator must first be made visible with the directive +``using namespace pybind11::literals``. This does not bring in anything else +from the ``pybind11`` namespace except for literals. + +.. _default_args: + +Default arguments +================= + +Suppose now that the function to be bound has default arguments, e.g.: + +.. code-block:: cpp + + int add(int i = 1, int j = 2) { + return i + j; + } + +Unfortunately, pybind11 cannot automatically extract these parameters, since they +are not part of the function's type information. However, they are simple to specify +using an extension of :class:`arg`: + +.. code-block:: cpp + + m.def("add", &add, "A function which adds two numbers", + py::arg("i") = 1, py::arg("j") = 2); + +The default values also appear within the documentation. + +.. code-block:: pycon + + >>> help(example) + + .... + + FUNCTIONS + add(...) + Signature : (i: int = 1, j: int = 2) -> int + + A function which adds two numbers + +The shorthand notation is also available for default arguments: + +.. code-block:: cpp + + // regular notation + m.def("add1", &add, py::arg("i") = 1, py::arg("j") = 2); + // shorthand + m.def("add2", &add, "i"_a=1, "j"_a=2); + +Exporting variables +=================== + +To expose a value from C++, use the ``attr`` function to register it in a +module as shown below. Built-in types and general objects (more on that later) +are automatically converted when assigned as attributes, and can be explicitly +converted using the function ``py::cast``. + +.. code-block:: cpp + + PYBIND11_MODULE(example, m) { + m.attr("the_answer") = 42; + py::object world = py::cast("World"); + m.attr("what") = world; + } + +These are then accessible from Python: + +.. code-block:: pycon + + >>> import example + >>> example.the_answer + 42 + >>> example.what + 'World' + +.. _supported_types: + +Supported data types +==================== + +A large number of data types are supported out of the box and can be used +seamlessly as functions arguments, return values or with ``py::cast`` in general. +For a full overview, see the :doc:`advanced/cast/index` section. diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/benchmark.py b/third-party/DPVO/DPRetrieval/pybind11/docs/benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..9992118c4020b1cd833356a2fba22bf7b5d75948 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/benchmark.py @@ -0,0 +1,91 @@ +# -*- coding: utf-8 -*- +import datetime as dt +import os +import random + +nfns = 4 # Functions per class +nargs = 4 # Arguments per function + + +def generate_dummy_code_pybind11(nclasses=10): + decl = "" + bindings = "" + + for cl in range(nclasses): + decl += "class cl%03i;\n" % cl + decl += "\n" + + for cl in range(nclasses): + decl += "class cl%03i {\n" % cl + decl += "public:\n" + bindings += ' py::class_(m, "cl%03i")\n' % (cl, cl) + for fn in range(nfns): + ret = random.randint(0, nclasses - 1) + params = [random.randint(0, nclasses - 1) for i in range(nargs)] + decl += " cl%03i *fn_%03i(" % (ret, fn) + decl += ", ".join("cl%03i *" % p for p in params) + decl += ");\n" + bindings += ' .def("fn_%03i", &cl%03i::fn_%03i)\n' % (fn, cl, fn) + decl += "};\n\n" + bindings += " ;\n" + + result = "#include \n\n" + result += "namespace py = pybind11;\n\n" + result += decl + "\n" + result += "PYBIND11_MODULE(example, m) {\n" + result += bindings + result += "}" + return result + + +def generate_dummy_code_boost(nclasses=10): + decl = "" + bindings = "" + + for cl in range(nclasses): + decl += "class cl%03i;\n" % cl + decl += "\n" + + for cl in range(nclasses): + decl += "class cl%03i {\n" % cl + decl += "public:\n" + bindings += ' py::class_("cl%03i")\n' % (cl, cl) + for fn in range(nfns): + ret = random.randint(0, nclasses - 1) + params = [random.randint(0, nclasses - 1) for i in range(nargs)] + decl += " cl%03i *fn_%03i(" % (ret, fn) + decl += ", ".join("cl%03i *" % p for p in params) + decl += ");\n" + bindings += ( + ' .def("fn_%03i", &cl%03i::fn_%03i, py::return_value_policy())\n' + % (fn, cl, fn) + ) + decl += "};\n\n" + bindings += " ;\n" + + result = "#include \n\n" + result += "namespace py = boost::python;\n\n" + result += decl + "\n" + result += "BOOST_PYTHON_MODULE(example) {\n" + result += bindings + result += "}" + return result + + +for codegen in [generate_dummy_code_pybind11, generate_dummy_code_boost]: + print("{") + for i in range(0, 10): + nclasses = 2 ** i + with open("test.cpp", "w") as f: + f.write(codegen(nclasses)) + n1 = dt.datetime.now() + os.system( + "g++ -Os -shared -rdynamic -undefined dynamic_lookup " + "-fvisibility=hidden -std=c++14 test.cpp -I include " + "-I /System/Library/Frameworks/Python.framework/Headers -o test.so" + ) + n2 = dt.datetime.now() + elapsed = (n2 - n1).total_seconds() + size = os.stat("test.so").st_size + print(" {%i, %f, %i}," % (nclasses * nfns, elapsed, size)) + print("}") diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/benchmark.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/benchmark.rst new file mode 100644 index 0000000000000000000000000000000000000000..02d142e6225955467c04d7573de144cab2ebe07b --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/benchmark.rst @@ -0,0 +1,95 @@ +Benchmark +========= + +The following is the result of a synthetic benchmark comparing both compilation +time and module size of pybind11 against Boost.Python. A detailed report about a +Boost.Python to pybind11 conversion of a real project is available here: [#f1]_. + +.. [#f1] http://graylab.jhu.edu/RosettaCon2016/PyRosetta-4.pdf + +Setup +----- + +A python script (see the ``docs/benchmark.py`` file) was used to generate a set +of files with dummy classes whose count increases for each successive benchmark +(between 1 and 2048 classes in powers of two). Each class has four methods with +a randomly generated signature with a return value and four arguments. (There +was no particular reason for this setup other than the desire to generate many +unique function signatures whose count could be controlled in a simple way.) + +Here is an example of the binding code for one class: + +.. code-block:: cpp + + ... + class cl034 { + public: + cl279 *fn_000(cl084 *, cl057 *, cl065 *, cl042 *); + cl025 *fn_001(cl098 *, cl262 *, cl414 *, cl121 *); + cl085 *fn_002(cl445 *, cl297 *, cl145 *, cl421 *); + cl470 *fn_003(cl200 *, cl323 *, cl332 *, cl492 *); + }; + ... + + PYBIND11_MODULE(example, m) { + ... + py::class_(m, "cl034") + .def("fn_000", &cl034::fn_000) + .def("fn_001", &cl034::fn_001) + .def("fn_002", &cl034::fn_002) + .def("fn_003", &cl034::fn_003) + ... + } + +The Boost.Python version looks almost identical except that a return value +policy had to be specified as an argument to ``def()``. For both libraries, +compilation was done with + +.. code-block:: bash + + Apple LLVM version 7.0.2 (clang-700.1.81) + +and the following compilation flags + +.. code-block:: bash + + g++ -Os -shared -rdynamic -undefined dynamic_lookup -fvisibility=hidden -std=c++14 + +Compilation time +---------------- + +The following log-log plot shows how the compilation time grows for an +increasing number of class and function declarations. pybind11 includes many +fewer headers, which initially leads to shorter compilation times, but the +performance is ultimately fairly similar (pybind11 is 19.8 seconds faster for +the largest largest file with 2048 classes and a total of 8192 methods -- a +modest **1.2x** speedup relative to Boost.Python, which required 116.35 +seconds). + +.. only:: not latex + + .. image:: pybind11_vs_boost_python1.svg + +.. only:: latex + + .. image:: pybind11_vs_boost_python1.png + +Module size +----------- + +Differences between the two libraries become much more pronounced when +considering the file size of the generated Python plugin: for the largest file, +the binary generated by Boost.Python required 16.8 MiB, which was **2.17 +times** / **9.1 megabytes** larger than the output generated by pybind11. For +very small inputs, Boost.Python has an edge in the plot below -- however, note +that it stores many definitions in an external library, whose size was not +included here, hence the comparison is slightly shifted in Boost.Python's +favor. + +.. only:: not latex + + .. image:: pybind11_vs_boost_python2.svg + +.. only:: latex + + .. image:: pybind11_vs_boost_python2.png diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/changelog.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/changelog.rst new file mode 100644 index 0000000000000000000000000000000000000000..e43a94281e53d770d4fcd88f728d769019413c13 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/changelog.rst @@ -0,0 +1,2327 @@ +.. _changelog: + +Changelog +######### + +Starting with version 1.8.0, pybind11 releases use a `semantic versioning +`_ policy. + +Changes will be added here periodically from the "Suggested changelog entry" +block in pull request descriptions. + +IN DEVELOPMENT +-------------- + +Removed support for Python 2.7, Python 3.5, and MSVC 2015. Support for MSVC +2017 is limited due to availability of CI runners; we highly recommend MSVC +2019 or 2022 be used. + +New features: + +* ``type_caster`` was added. ``std::monostate`` is a tag type + that allows ``std::variant`` to act as an optional, or allows default + construction of a ``std::variant`` holding a non-default constructible type. + `#3818 `_ + +* Support bytearray casting to string. + `#3707 `_ + +Changes: + +* Python 2 support was removed completely. + `#3688 `_ + +* The minimum version for MSVC is now 2017. + `#3722 `_ + +* Improve exception handling in python ``str`` bindings. + `#3826 `_ + +* The bindings for capsules now have more consistent exception handling. + `#3825 `_ + +* Fix exception handling when ``pybind11::weakref()`` fails. + `#3739 `_ + + +Bug fixes: + +* ``PYBIND11_OBJECT_CVT`` and ``PYBIND11_OBJECT_CVT_DEFAULT`` macro can be used + to define classes in namespaces other than pybind11. + `#3797 `_ + +Build system improvements: + +* Add MSVC builds in debug mode to CI. + `#3784 `_ + +* MSVC 2022 C++20 coverage was added to GitHub Actions, including Eigen. + `#3732 `_, + `#3741 `_ + +* Avoid ``setup.py `` usage in internal tests. + `#3734 `_ + + +Backend and tidying up: + +* Remove idioms in code comments. Use inclusive language. + `#3809 `_ + + +Version 2.9.2 (Mar 29, 2022) +---------------------------- + +Changes: + +* Enum now has an ``__index__`` method on Python <3.8 too. + `#3700 `_ + +* Local internals are now cleared after finalizing the interpreter. + `#3744 `_ + +Bug fixes: + +* Better support for Python 3.11 alphas. + `#3694 `_ + +* ``PYBIND11_TYPE_CASTER`` now uses fully qualified symbols, so it can be used + outside of ``pybind11::detail``. + `#3758 `_ + +* Some fixes for PyPy 3.9. + `#3768 `_ + +* Fixed a potential memleak in PyPy in ``get_type_override``. + `#3774 `_ + +* Fix usage of ``VISIBILITY_INLINES_HIDDEN``. + `#3721 `_ + + +Build system improvements: + +* Uses ``sysconfig`` module to determine installation locations on Python >= + 3.10, instead of ``distutils`` which has been deprecated. + `#3764 `_ + +* Support Catch 2.13.5+ (supporting GLIBC 2.34+). + `#3679 `_ + +* Fix test failures with numpy 1.22 by ignoring whitespace when comparing + ``str()`` of dtypes. + `#3682 `_ + + +Backend and tidying up: + +* clang-tidy: added ``readability-qualified-auto``, + ``readability-braces-around-statements``, + ``cppcoreguidelines-prefer-member-initializer``, + ``clang-analyzer-optin.performance.Padding``, + ``cppcoreguidelines-pro-type-static-cast-downcast``, and + ``readability-inconsistent-declaration-parameter-name``. + `#3702 `_, + `#3699 `_, + `#3716 `_, + `#3709 `_ + +* clang-format was added to the pre-commit actions, and the entire code base + automatically reformatted (after several iterations preparing for this leap). + `#3713 `_ + + +Version 2.9.1 (Feb 2, 2022) +--------------------------- + +Changes: + +* If possible, attach Python exception with ``py::raise_from`` to ``TypeError`` + when casting from C++ to Python. This will give additional info if Python + exceptions occur in the caster. Adds a test case of trying to convert a set + from C++ to Python when the hash function is not defined in Python. + `#3605 `_ + +* Add a mapping of C++11 nested exceptions to their Python exception + equivalent using ``py::raise_from``. This attaches the nested exceptions in + Python using the ``__cause__`` field. + `#3608 `_ + +* Propagate Python exception traceback using ``raise_from`` if a pybind11 + function runs out of overloads. + `#3671 `_ + +* ``py::multiple_inheritance`` is now only needed when C++ bases are hidden + from pybind11. + `#3650 `_ and + `#3659 `_ + + +Bug fixes: + +* Remove a boolean cast in ``numpy.h`` that causes MSVC C4800 warnings when + compiling against Python 3.10 or newer. + `#3669 `_ + +* Render ``py::bool_`` and ``py::float_`` as ``bool`` and ``float`` + respectively. + `#3622 `_ + +Build system improvements: + +* Fix CMake extension suffix computation on Python 3.10+. + `#3663 `_ + +* Allow ``CMAKE_ARGS`` to override CMake args in pybind11's own ``setup.py``. + `#3577 `_ + +* Remove a few deprecated c-headers. + `#3610 `_ + +* More uniform handling of test targets. + `#3590 `_ + +* Add clang-tidy readability check to catch potentially swapped function args. + `#3611 `_ + + +Version 2.9.0 (Dec 28, 2021) +---------------------------- + +This is the last version to support Python 2.7 and 3.5. + +New Features: + +* Allow ``py::args`` to be followed by other arguments; the remaining arguments + are implicitly keyword-only, as if a ``py::kw_only{}`` annotation had been + used. + `#3402 `_ + +Changes: + +* Make str/bytes/memoryview more interoperable with ``std::string_view``. + `#3521 `_ + +* Replace ``_`` with ``const_name`` in internals, avoid defining ``pybind::_`` + if ``_`` defined as macro (common gettext usage) + `#3423 `_ + + +Bug fixes: + +* Fix a rare warning about extra copy in an Eigen constructor. + `#3486 `_ + +* Fix caching of the C++ overrides. + `#3465 `_ + +* Add missing ``std::forward`` calls to some ``cpp_function`` overloads. + `#3443 `_ + +* Support PyPy 7.3.7 and the PyPy3.8 beta. Test python-3.11 on PRs with the + ``python dev`` label. + `#3419 `_ + +* Replace usage of deprecated ``Eigen::MappedSparseMatrix`` with + ``Eigen::Map>`` for Eigen 3.3+. + `#3499 `_ + +* Tweaks to support Microsoft Visual Studio 2022. + `#3497 `_ + +Build system improvements: + +* Nicer CMake printout and IDE organisation for pybind11's own tests. + `#3479 `_ + +* CMake: report version type as part of the version string to avoid a spurious + space in the package status message. + `#3472 `_ + +* Flags starting with ``-g`` in ``$CFLAGS`` and ``$CPPFLAGS`` are no longer + overridden by ``.Pybind11Extension``. + `#3436 `_ + +* Ensure ThreadPool is closed in ``setup_helpers``. + `#3548 `_ + +* Avoid LTS on ``mips64`` and ``ppc64le`` (reported broken). + `#3557 `_ + + +v2.8.1 (Oct 27, 2021) +--------------------- + +Changes and additions: + +* The simple namespace creation shortcut added in 2.8.0 was deprecated due to + usage of CPython internal API, and will be removed soon. Use + ``py::module_::import("types").attr("SimpleNamespace")``. + `#3374 `_ + +* Add C++ Exception type to throw and catch ``AttributeError``. Useful for + defining custom ``__setattr__`` and ``__getattr__`` methods. + `#3387 `_ + +Fixes: + +* Fixed the potential for dangling references when using properties with + ``std::optional`` types. + `#3376 `_ + +* Modernize usage of ``PyCodeObject`` on Python 3.9+ (moving toward support for + Python 3.11a1) + `#3368 `_ + +* A long-standing bug in ``eigen.h`` was fixed (originally PR #3343). The bug + was unmasked by newly added ``static_assert``'s in the Eigen 3.4.0 release. + `#3352 `_ + +* Support multiple raw inclusion of CMake helper files (Conan.io does this for + multi-config generators). + `#3420 `_ + +* Fix harmless warning on upcoming CMake 3.22. + `#3368 `_ + +* Fix 2.8.0 regression with MSVC 2017 + C++17 mode + Python 3. + `#3407 `_ + +* Fix 2.8.0 regression that caused undefined behavior (typically + segfaults) in ``make_key_iterator``/``make_value_iterator`` if dereferencing + the iterator returned a temporary value instead of a reference. + `#3348 `_ + + +v2.8.0 (Oct 4, 2021) +-------------------- + +New features: + +* Added ``py::raise_from`` to enable chaining exceptions. + `#3215 `_ + +* Allow exception translators to be optionally registered local to a module + instead of applying globally across all pybind11 modules. Use + ``register_local_exception_translator(ExceptionTranslator&& translator)`` + instead of ``register_exception_translator(ExceptionTranslator&& + translator)`` to keep your exception remapping code local to the module. + `#2650 `_ + +* Add ``make_simple_namespace`` function for instantiating Python + ``SimpleNamespace`` objects. **Deprecated in 2.8.1.** + `#2840 `_ + +* ``pybind11::scoped_interpreter`` and ``initialize_interpreter`` have new + arguments to allow ``sys.argv`` initialization. + `#2341 `_ + +* Allow Python builtins to be used as callbacks in CPython. + `#1413 `_ + +* Added ``view`` to view arrays with a different datatype. + `#987 `_ + +* Implemented ``reshape`` on arrays. + `#984 `_ + +* Enable defining custom ``__new__`` methods on classes by fixing bug + preventing overriding methods if they have non-pybind11 siblings. + `#3265 `_ + +* Add ``make_value_iterator()``, and fix ``make_key_iterator()`` to return + references instead of copies. + `#3293 `_ + +* Improve the classes generated by ``bind_map``: `#3310 `_ + + * Change ``.items`` from an iterator to a dictionary view. + * Add ``.keys`` and ``.values`` (both dictionary views). + * Allow ``__contains__`` to take any object. + +* ``pybind11::custom_type_setup`` was added, for customizing the + ``PyHeapTypeObject`` corresponding to a class, which may be useful for + enabling garbage collection support, among other things. + `#3287 `_ + + +Changes: + +* Set ``__file__`` constant when running ``eval_file`` in an embedded interpreter. + `#3233 `_ + +* Python objects and (C++17) ``std::optional`` now accepted in ``py::slice`` + constructor. + `#1101 `_ + +* The pybind11 proxy types ``str``, ``bytes``, ``bytearray``, ``tuple``, + ``list`` now consistently support passing ``ssize_t`` values for sizes and + indexes. Previously, only ``size_t`` was accepted in several interfaces. + `#3219 `_ + +* Avoid evaluating ``PYBIND11_TLS_REPLACE_VALUE`` arguments more than once. + `#3290 `_ + +Fixes: + +* Bug fix: enum value's ``__int__`` returning non-int when underlying type is + bool or of char type. + `#1334 `_ + +* Fixes bug in setting error state in Capsule's pointer methods. + `#3261 `_ + +* A long-standing memory leak in ``py::cpp_function::initialize`` was fixed. + `#3229 `_ + +* Fixes thread safety for some ``pybind11::type_caster`` which require lifetime + extension, such as for ``std::string_view``. + `#3237 `_ + +* Restore compatibility with gcc 4.8.4 as distributed by ubuntu-trusty, linuxmint-17. + `#3270 `_ + + +Build system improvements: + +* Fix regression in CMake Python package config: improper use of absolute path. + `#3144 `_ + +* Cached Python version information could become stale when CMake was re-run + with a different Python version. The build system now detects this and + updates this information. + `#3299 `_ + +* Specified UTF8-encoding in setup.py calls of open(). + `#3137 `_ + +* Fix a harmless warning from CMake 3.21 with the classic Python discovery. + `#3220 `_ + +* Eigen repo and version can now be specified as cmake options. + `#3324 `_ + + +Backend and tidying up: + +* Reduced thread-local storage required for keeping alive temporary data for + type conversion to one key per ABI version, rather than one key per extension + module. This makes the total thread-local storage required by pybind11 2 + keys per ABI version. + `#3275 `_ + +* Optimize NumPy array construction with additional moves. + `#3183 `_ + +* Conversion to ``std::string`` and ``std::string_view`` now avoids making an + extra copy of the data on Python >= 3.3. + `#3257 `_ + +* Remove const modifier from certain C++ methods on Python collections + (``list``, ``set``, ``dict``) such as (``clear()``, ``append()``, + ``insert()``, etc...) and annotated them with ``py-non-const``. + +* Enable readability ``clang-tidy-const-return`` and remove useless consts. + `#3254 `_ + `#3194 `_ + +* The clang-tidy ``google-explicit-constructor`` option was enabled. + `#3250 `_ + +* Mark a pytype move constructor as noexcept (perf). + `#3236 `_ + +* Enable clang-tidy check to guard against inheritance slicing. + `#3210 `_ + +* Legacy warning suppression pragma were removed from eigen.h. On Unix + platforms, please use -isystem for Eigen include directories, to suppress + compiler warnings originating from Eigen headers. Note that CMake does this + by default. No adjustments are needed for Windows. + `#3198 `_ + +* Format pybind11 with isort consistent ordering of imports + `#3195 `_ + +* The warnings-suppression "pragma clamp" at the top/bottom of pybind11 was + removed, clearing the path to refactoring and IWYU cleanup. + `#3186 `_ + +* Enable most bugprone checks in clang-tidy and fix the found potential bugs + and poor coding styles. + `#3166 `_ + +* Add ``clang-tidy-readability`` rules to make boolean casts explicit improving + code readability. Also enabled other misc and readability clang-tidy checks. + `#3148 `_ + +* Move object in ``.pop()`` for list. + `#3116 `_ + + + + +v2.7.1 (Aug 3, 2021) +--------------------- + +Minor missing functionality added: + +* Allow Python builtins to be used as callbacks in CPython. + `#1413 `_ + +Bug fixes: + +* Fix regression in CMake Python package config: improper use of absolute path. + `#3144 `_ + +* Fix Mingw64 and add to the CI testing matrix. + `#3132 `_ + +* Specified UTF8-encoding in setup.py calls of open(). + `#3137 `_ + +* Add clang-tidy-readability rules to make boolean casts explicit improving + code readability. Also enabled other misc and readability clang-tidy checks. + `#3148 `_ + +* Move object in ``.pop()`` for list. + `#3116 `_ + +Backend and tidying up: + +* Removed and fixed warning suppressions. + `#3127 `_ + `#3129 `_ + `#3135 `_ + `#3141 `_ + `#3142 `_ + `#3150 `_ + `#3152 `_ + `#3160 `_ + `#3161 `_ + + +v2.7.0 (Jul 16, 2021) +--------------------- + +New features: + +* Enable ``py::implicitly_convertible`` for + ``py::class_``-wrapped types. + `#3059 `_ + +* Allow function pointer extraction from overloaded functions. + `#2944 `_ + +* NumPy: added ``.char_()`` to type which gives the NumPy public ``char`` + result, which also distinguishes types by bit length (unlike ``.kind()``). + `#2864 `_ + +* Add ``pybind11::bytearray`` to manipulate ``bytearray`` similar to ``bytes``. + `#2799 `_ + +* ``pybind11/stl/filesystem.h`` registers a type caster that, on C++17/Python + 3.6+, converts ``std::filesystem::path`` to ``pathlib.Path`` and any + ``os.PathLike`` to ``std::filesystem::path``. + `#2730 `_ + +* A ``PYBIND11_VERSION_HEX`` define was added, similar to ``PY_VERSION_HEX``. + `#3120 `_ + + + +Changes: + +* ``py::str`` changed to exclusively hold ``PyUnicodeObject``. Previously + ``py::str`` could also hold ``bytes``, which is probably surprising, was + never documented, and can mask bugs (e.g. accidental use of ``py::str`` + instead of ``py::bytes``). + `#2409 `_ + +* Add a safety guard to ensure that the Python GIL is held when C++ calls back + into Python via ``object_api<>::operator()`` (e.g. ``py::function`` + ``__call__``). (This feature is available for Python 3.6+ only.) + `#2919 `_ + +* Catch a missing ``self`` argument in calls to ``__init__()``. + `#2914 `_ + +* Use ``std::string_view`` if available to avoid a copy when passing an object + to a ``std::ostream``. + `#3042 `_ + +* An important warning about thread safety was added to the ``iostream.h`` + documentation; attempts to make ``py::scoped_ostream_redirect`` thread safe + have been removed, as it was only partially effective. + `#2995 `_ + + +Fixes: + +* Performance: avoid unnecessary strlen calls. + `#3058 `_ + +* Fix auto-generated documentation string when using ``const T`` in + ``pyarray_t``. + `#3020 `_ + +* Unify error messages thrown by ``simple_collector``/``unpacking_collector``. + `#3013 `_ + +* ``pybind11::builtin_exception`` is now explicitly exported, which means the + types included/defined in different modules are identical, and exceptions + raised in different modules can be caught correctly. The documentation was + updated to explain that custom exceptions that are used across module + boundaries need to be explicitly exported as well. + `#2999 `_ + +* Fixed exception when printing UTF-8 to a ``scoped_ostream_redirect``. + `#2982 `_ + +* Pickle support enhancement: ``setstate`` implementation will attempt to + ``setattr`` ``__dict__`` only if the unpickled ``dict`` object is not empty, + to not force use of ``py::dynamic_attr()`` unnecessarily. + `#2972 `_ + +* Allow negative timedelta values to roundtrip. + `#2870 `_ + +* Fix unchecked errors could potentially swallow signals/other exceptions. + `#2863 `_ + +* Add null pointer check with ``std::localtime``. + `#2846 `_ + +* Fix the ``weakref`` constructor from ``py::object`` to create a new + ``weakref`` on conversion. + `#2832 `_ + +* Avoid relying on exceptions in C++17 when getting a ``shared_ptr`` holder + from a ``shared_from_this`` class. + `#2819 `_ + +* Allow the codec's exception to be raised instead of :code:`RuntimeError` when + casting from :code:`py::str` to :code:`std::string`. + `#2903 `_ + + +Build system improvements: + +* In ``setup_helpers.py``, test for platforms that have some multiprocessing + features but lack semaphores, which ``ParallelCompile`` requires. + `#3043 `_ + +* Fix ``pybind11_INCLUDE_DIR`` in case ``CMAKE_INSTALL_INCLUDEDIR`` is + absolute. + `#3005 `_ + +* Fix bug not respecting ``WITH_SOABI`` or ``WITHOUT_SOABI`` to CMake. + `#2938 `_ + +* Fix the default ``Pybind11Extension`` compilation flags with a Mingw64 python. + `#2921 `_ + +* Clang on Windows: do not pass ``/MP`` (ignored flag). + `#2824 `_ + +* ``pybind11.setup_helpers.intree_extensions`` can be used to generate + ``Pybind11Extension`` instances from cpp files placed in the Python package + source tree. + `#2831 `_ + +Backend and tidying up: + +* Enable clang-tidy performance, readability, and modernization checks + throughout the codebase to enforce best coding practices. + `#3046 `_, + `#3049 `_, + `#3051 `_, + `#3052 `_, + `#3080 `_, and + `#3094 `_ + + +* Checks for common misspellings were added to the pre-commit hooks. + `#3076 `_ + +* Changed ``Werror`` to stricter ``Werror-all`` for Intel compiler and fixed + minor issues. + `#2948 `_ + +* Fixed compilation with GCC < 5 when the user defines ``_GLIBCXX_USE_CXX11_ABI``. + `#2956 `_ + +* Added nox support for easier local testing and linting of contributions. + `#3101 `_ and + `#3121 `_ + +* Avoid RTD style issue with docutils 0.17+. + `#3119 `_ + +* Support pipx run, such as ``pipx run pybind11 --include`` for a quick compile. + `#3117 `_ + + + +v2.6.2 (Jan 26, 2021) +--------------------- + +Minor missing functionality added: + +* enum: add missing Enum.value property. + `#2739 `_ + +* Allow thread termination to be avoided during shutdown for CPython 3.7+ via + ``.disarm`` for ``gil_scoped_acquire``/``gil_scoped_release``. + `#2657 `_ + +Fixed or improved behavior in a few special cases: + +* Fix bug where the constructor of ``object`` subclasses would not throw on + being passed a Python object of the wrong type. + `#2701 `_ + +* The ``type_caster`` for integers does not convert Python objects with + ``__int__`` anymore with ``noconvert`` or during the first round of trying + overloads. + `#2698 `_ + +* When casting to a C++ integer, ``__index__`` is always called and not + considered as conversion, consistent with Python 3.8+. + `#2801 `_ + +Build improvements: + +* Setup helpers: ``extra_compile_args`` and ``extra_link_args`` automatically set by + Pybind11Extension are now prepended, which allows them to be overridden + by user-set ``extra_compile_args`` and ``extra_link_args``. + `#2808 `_ + +* Setup helpers: Don't trigger unused parameter warning. + `#2735 `_ + +* CMake: Support running with ``--warn-uninitialized`` active. + `#2806 `_ + +* CMake: Avoid error if included from two submodule directories. + `#2804 `_ + +* CMake: Fix ``STATIC`` / ``SHARED`` being ignored in FindPython mode. + `#2796 `_ + +* CMake: Respect the setting for ``CMAKE_CXX_VISIBILITY_PRESET`` if defined. + `#2793 `_ + +* CMake: Fix issue with FindPython2/FindPython3 not working with ``pybind11::embed``. + `#2662 `_ + +* CMake: mixing local and installed pybind11's would prioritize the installed + one over the local one (regression in 2.6.0). + `#2716 `_ + + +Bug fixes: + +* Fixed segfault in multithreaded environments when using + ``scoped_ostream_redirect``. + `#2675 `_ + +* Leave docstring unset when all docstring-related options are disabled, rather + than set an empty string. + `#2745 `_ + +* The module key in builtins that pybind11 uses to store its internals changed + from std::string to a python str type (more natural on Python 2, no change on + Python 3). + `#2814 `_ + +* Fixed assertion error related to unhandled (later overwritten) exception in + CPython 3.8 and 3.9 debug builds. + `#2685 `_ + +* Fix ``py::gil_scoped_acquire`` assert with CPython 3.9 debug build. + `#2683 `_ + +* Fix issue with a test failing on pytest 6.2. + `#2741 `_ + +Warning fixes: + +* Fix warning modifying constructor parameter 'flag' that shadows a field of + 'set_flag' ``[-Wshadow-field-in-constructor-modified]``. + `#2780 `_ + +* Suppressed some deprecation warnings about old-style + ``__init__``/``__setstate__`` in the tests. + `#2759 `_ + +Valgrind work: + +* Fix invalid access when calling a pybind11 ``__init__`` on a non-pybind11 + class instance. + `#2755 `_ + +* Fixed various minor memory leaks in pybind11's test suite. + `#2758 `_ + +* Resolved memory leak in cpp_function initialization when exceptions occurred. + `#2756 `_ + +* Added a Valgrind build, checking for leaks and memory-related UB, to CI. + `#2746 `_ + +Compiler support: + +* Intel compiler was not activating C++14 support due to a broken define. + `#2679 `_ + +* Support ICC and NVIDIA HPC SDK in C++17 mode. + `#2729 `_ + +* Support Intel OneAPI compiler (ICC 20.2) and add to CI. + `#2573 `_ + + + +v2.6.1 (Nov 11, 2020) +--------------------- + +* ``py::exec``, ``py::eval``, and ``py::eval_file`` now add the builtins module + as ``"__builtins__"`` to their ``globals`` argument, better matching ``exec`` + and ``eval`` in pure Python. + `#2616 `_ + +* ``setup_helpers`` will no longer set a minimum macOS version higher than the + current version. + `#2622 `_ + +* Allow deleting static properties. + `#2629 `_ + +* Seal a leak in ``def_buffer``, cleaning up the ``capture`` object after the + ``class_`` object goes out of scope. + `#2634 `_ + +* ``pybind11_INCLUDE_DIRS`` was incorrect, potentially causing a regression if + it was expected to include ``PYTHON_INCLUDE_DIRS`` (please use targets + instead). + `#2636 `_ + +* Added parameter names to the ``py::enum_`` constructor and methods, avoiding + ``arg0`` in the generated docstrings. + `#2637 `_ + +* Added ``needs_recompile`` optional function to the ``ParallelCompiler`` + helper, to allow a recompile to be skipped based on a user-defined function. + `#2643 `_ + + +v2.6.0 (Oct 21, 2020) +--------------------- + +See :ref:`upgrade-guide-2.6` for help upgrading to the new version. + +New features: + +* Keyword-only arguments supported in Python 2 or 3 with ``py::kw_only()``. + `#2100 `_ + +* Positional-only arguments supported in Python 2 or 3 with ``py::pos_only()``. + `#2459 `_ + +* ``py::is_final()`` class modifier to block subclassing (CPython only). + `#2151 `_ + +* Added ``py::prepend()``, allowing a function to be placed at the beginning of + the overload chain. + `#1131 `_ + +* Access to the type object now provided with ``py::type::of()`` and + ``py::type::of(h)``. + `#2364 `_ + +* Perfect forwarding support for methods. + `#2048 `_ + +* Added ``py::error_already_set::discard_as_unraisable()``. + `#2372 `_ + +* ``py::hash`` is now public. + `#2217 `_ + +* ``py::class_`` is now supported. Note that writing to one data + member of the union and reading another (type punning) is UB in C++. Thus + pybind11-bound enums should never be used for such conversions. + `#2320 `_. + +* Classes now check local scope when registering members, allowing a subclass + to have a member with the same name as a parent (such as an enum). + `#2335 `_ + +Code correctness features: + +* Error now thrown when ``__init__`` is forgotten on subclasses. + `#2152 `_ + +* Throw error if conversion to a pybind11 type if the Python object isn't a + valid instance of that type, such as ``py::bytes(o)`` when ``py::object o`` + isn't a bytes instance. + `#2349 `_ + +* Throw if conversion to ``str`` fails. + `#2477 `_ + + +API changes: + +* ``py::module`` was renamed ``py::module_`` to avoid issues with C++20 when + used unqualified, but an alias ``py::module`` is provided for backward + compatibility. + `#2489 `_ + +* Public constructors for ``py::module_`` have been deprecated; please use + ``pybind11::module_::create_extension_module`` if you were using the public + constructor (fairly rare after ``PYBIND11_MODULE`` was introduced). + `#2552 `_ + +* ``PYBIND11_OVERLOAD*`` macros and ``get_overload`` function replaced by + correctly-named ``PYBIND11_OVERRIDE*`` and ``get_override``, fixing + inconsistencies in the presence of a closing ``;`` in these macros. + ``get_type_overload`` is deprecated. + `#2325 `_ + +Packaging / building improvements: + +* The Python package was reworked to be more powerful and useful. + `#2433 `_ + + * :ref:`build-setuptools` is easier thanks to a new + ``pybind11.setup_helpers`` module, which provides utilities to use + setuptools with pybind11. It can be used via PEP 518, ``setup_requires``, + or by directly importing or copying ``setup_helpers.py`` into your project. + + * CMake configuration files are now included in the Python package. Use + ``pybind11.get_cmake_dir()`` or ``python -m pybind11 --cmakedir`` to get + the directory with the CMake configuration files, or include the + site-packages location in your ``CMAKE_MODULE_PATH``. Or you can use the + new ``pybind11[global]`` extra when you install ``pybind11``, which + installs the CMake files and headers into your base environment in the + standard location. + + * ``pybind11-config`` is another way to write ``python -m pybind11`` if you + have your PATH set up. + + * Added external typing support to the helper module, code from + ``import pybind11`` can now be type checked. + `#2588 `_ + +* Minimum CMake required increased to 3.4. + `#2338 `_ and + `#2370 `_ + + * Full integration with CMake’s C++ standard system and compile features + replaces ``PYBIND11_CPP_STANDARD``. + + * Generated config file is now portable to different Python/compiler/CMake + versions. + + * Virtual environments prioritized if ``PYTHON_EXECUTABLE`` is not set + (``venv``, ``virtualenv``, and ``conda``) (similar to the new FindPython + mode). + + * Other CMake features now natively supported, like + ``CMAKE_INTERPROCEDURAL_OPTIMIZATION``, ``set(CMAKE_CXX_VISIBILITY_PRESET + hidden)``. + + * ``CUDA`` as a language is now supported. + + * Helper functions ``pybind11_strip``, ``pybind11_extension``, + ``pybind11_find_import`` added, see :doc:`cmake/index`. + + * Optional :ref:`find-python-mode` and :ref:`nopython-mode` with CMake. + `#2370 `_ + +* Uninstall target added. + `#2265 `_ and + `#2346 `_ + +* ``pybind11_add_module()`` now accepts an optional ``OPT_SIZE`` flag that + switches the binding target to size-based optimization if the global build + type can not always be fixed to ``MinSizeRel`` (except in debug mode, where + optimizations remain disabled). ``MinSizeRel`` or this flag reduces binary + size quite substantially (~25% on some platforms). + `#2463 `_ + +Smaller or developer focused features and fixes: + +* Moved ``mkdoc.py`` to a new repo, `pybind11-mkdoc`_. There are no longer + submodules in the main repo. + +* ``py::memoryview`` segfault fix and update, with new + ``py::memoryview::from_memory`` in Python 3, and documentation. + `#2223 `_ + +* Fix for ``buffer_info`` on Python 2. + `#2503 `_ + +* If ``__eq__`` defined but not ``__hash__``, ``__hash__`` is now set to + ``None``. + `#2291 `_ + +* ``py::ellipsis`` now also works on Python 2. + `#2360 `_ + +* Pointer to ``std::tuple`` & ``std::pair`` supported in cast. + `#2334 `_ + +* Small fixes in NumPy support. ``py::array`` now uses ``py::ssize_t`` as first + argument type. + `#2293 `_ + +* Added missing signature for ``py::array``. + `#2363 `_ + +* ``unchecked_mutable_reference`` has access to operator ``()`` and ``[]`` when + const. + `#2514 `_ + +* ``py::vectorize`` is now supported on functions that return void. + `#1969 `_ + +* ``py::capsule`` supports ``get_pointer`` and ``set_pointer``. + `#1131 `_ + +* Fix crash when different instances share the same pointer of the same type. + `#2252 `_ + +* Fix for ``py::len`` not clearing Python's error state when it fails and throws. + `#2575 `_ + +* Bugfixes related to more extensive testing, new GitHub Actions CI. + `#2321 `_ + +* Bug in timezone issue in Eastern hemisphere midnight fixed. + `#2438 `_ + +* ``std::chrono::time_point`` now works when the resolution is not the same as + the system. + `#2481 `_ + +* Bug fixed where ``py::array_t`` could accept arrays that did not match the + requested ordering. + `#2484 `_ + +* Avoid a segfault on some compilers when types are removed in Python. + `#2564 `_ + +* ``py::arg::none()`` is now also respected when passing keyword arguments. + `#2611 `_ + +* PyPy fixes, PyPy 7.3.x now supported, including PyPy3. (Known issue with + PyPy2 and Windows `#2596 `_). + `#2146 `_ + +* CPython 3.9.0 workaround for undefined behavior (macOS segfault). + `#2576 `_ + +* CPython 3.9 warning fixes. + `#2253 `_ + +* Improved C++20 support, now tested in CI. + `#2489 `_ + `#2599 `_ + +* Improved but still incomplete debug Python interpreter support. + `#2025 `_ + +* NVCC (CUDA 11) now supported and tested in CI. + `#2461 `_ + +* NVIDIA PGI compilers now supported and tested in CI. + `#2475 `_ + +* At least Intel 18 now explicitly required when compiling with Intel. + `#2577 `_ + +* Extensive style checking in CI, with `pre-commit`_ support. Code + modernization, checked by clang-tidy. + +* Expanded docs, including new main page, new installing section, and CMake + helpers page, along with over a dozen new sections on existing pages. + +* In GitHub, new docs for contributing and new issue templates. + +.. _pre-commit: https://pre-commit.com + +.. _pybind11-mkdoc: https://github.com/pybind/pybind11-mkdoc + +v2.5.0 (Mar 31, 2020) +----------------------------------------------------- + +* Use C++17 fold expressions in type casters, if available. This can + improve performance during overload resolution when functions have + multiple arguments. + `#2043 `_. + +* Changed include directory resolution in ``pybind11/__init__.py`` + and installation in ``setup.py``. This fixes a number of open issues + where pybind11 headers could not be found in certain environments. + `#1995 `_. + +* C++20 ``char8_t`` and ``u8string`` support. `#2026 + `_. + +* CMake: search for Python 3.9. `bb9c91 + `_. + +* Fixes for MSYS-based build environments. + `#2087 `_, + `#2053 `_. + +* STL bindings for ``std::vector<...>::clear``. `#2074 + `_. + +* Read-only flag for ``py::buffer``. `#1466 + `_. + +* Exception handling during module initialization. + `bf2b031 `_. + +* Support linking against a CPython debug build. + `#2025 `_. + +* Fixed issues involving the availability and use of aligned ``new`` and + ``delete``. `#1988 `_, + `759221 `_. + +* Fixed a resource leak upon interpreter shutdown. + `#2020 `_. + +* Fixed error handling in the boolean caster. + `#1976 `_. + +v2.4.3 (Oct 15, 2019) +----------------------------------------------------- + +* Adapt pybind11 to a C API convention change in Python 3.8. `#1950 + `_. + +v2.4.2 (Sep 21, 2019) +----------------------------------------------------- + +* Replaced usage of a C++14 only construct. `#1929 + `_. + +* Made an ifdef future-proof for Python >= 4. `f3109d + `_. + +v2.4.1 (Sep 20, 2019) +----------------------------------------------------- + +* Fixed a problem involving implicit conversion from enumerations to integers + on Python 3.8. `#1780 `_. + +v2.4.0 (Sep 19, 2019) +----------------------------------------------------- + +* Try harder to keep pybind11-internal data structures separate when there + are potential ABI incompatibilities. Fixes crashes that occurred when loading + multiple pybind11 extensions that were e.g. compiled by GCC (libstdc++) + and Clang (libc++). + `#1588 `_ and + `c9f5a `_. + +* Added support for ``__await__``, ``__aiter__``, and ``__anext__`` protocols. + `#1842 `_. + +* ``pybind11_add_module()``: don't strip symbols when compiling in + ``RelWithDebInfo`` mode. `#1980 + `_. + +* ``enum_``: Reproduce Python behavior when comparing against invalid values + (e.g. ``None``, strings, etc.). Add back support for ``__invert__()``. + `#1912 `_, + `#1907 `_. + +* List insertion operation for ``py::list``. + Added ``.empty()`` to all collection types. + Added ``py::set::contains()`` and ``py::dict::contains()``. + `#1887 `_, + `#1884 `_, + `#1888 `_. + +* ``py::details::overload_cast_impl`` is available in C++11 mode, can be used + like ``overload_cast`` with an additional set of parentheses. + `#1581 `_. + +* Fixed ``get_include()`` on Conda. + `#1877 `_. + +* ``stl_bind.h``: negative indexing support. + `#1882 `_. + +* Minor CMake fix to add MinGW compatibility. + `#1851 `_. + +* GIL-related fixes. + `#1836 `_, + `8b90b `_. + +* Other very minor/subtle fixes and improvements. + `#1329 `_, + `#1910 `_, + `#1863 `_, + `#1847 `_, + `#1890 `_, + `#1860 `_, + `#1848 `_, + `#1821 `_, + `#1837 `_, + `#1833 `_, + `#1748 `_, + `#1852 `_. + +v2.3.0 (June 11, 2019) +----------------------------------------------------- + +* Significantly reduced module binary size (10-20%) when compiled in C++11 mode + with GCC/Clang, or in any mode with MSVC. Function signatures are now always + precomputed at compile time (this was previously only available in C++14 mode + for non-MSVC compilers). + `#934 `_. + +* Add basic support for tag-based static polymorphism, where classes + provide a method to returns the desired type of an instance. + `#1326 `_. + +* Python type wrappers (``py::handle``, ``py::object``, etc.) + now support map Python's number protocol onto C++ arithmetic + operators such as ``operator+``, ``operator/=``, etc. + `#1511 `_. + +* A number of improvements related to enumerations: + + 1. The ``enum_`` implementation was rewritten from scratch to reduce + code bloat. Rather than instantiating a full implementation for each + enumeration, most code is now contained in a generic base class. + `#1511 `_. + + 2. The ``value()`` method of ``py::enum_`` now accepts an optional + docstring that will be shown in the documentation of the associated + enumeration. `#1160 `_. + + 3. check for already existing enum value and throw an error if present. + `#1453 `_. + +* Support for over-aligned type allocation via C++17's aligned ``new`` + statement. `#1582 `_. + +* Added ``py::ellipsis()`` method for slicing of multidimensional NumPy arrays + `#1502 `_. + +* Numerous Improvements to the ``mkdoc.py`` script for extracting documentation + from C++ header files. + `#1788 `_. + +* ``pybind11_add_module()``: allow including Python as a ``SYSTEM`` include path. + `#1416 `_. + +* ``pybind11/stl.h`` does not convert strings to ``vector`` anymore. + `#1258 `_. + +* Mark static methods as such to fix auto-generated Sphinx documentation. + `#1732 `_. + +* Re-throw forced unwind exceptions (e.g. during pthread termination). + `#1208 `_. + +* Added ``__contains__`` method to the bindings of maps (``std::map``, + ``std::unordered_map``). + `#1767 `_. + +* Improvements to ``gil_scoped_acquire``. + `#1211 `_. + +* Type caster support for ``std::deque``. + `#1609 `_. + +* Support for ``std::unique_ptr`` holders, whose deleters differ between a base and derived + class. `#1353 `_. + +* Construction of STL array/vector-like data structures from + iterators. Added an ``extend()`` operation. + `#1709 `_, + +* CMake build system improvements for projects that include non-C++ + files (e.g. plain C, CUDA) in ``pybind11_add_module`` et al. + `#1678 `_. + +* Fixed asynchronous invocation and deallocation of Python functions + wrapped in ``std::function``. + `#1595 `_. + +* Fixes regarding return value policy propagation in STL type casters. + `#1603 `_. + +* Fixed scoped enum comparisons. + `#1571 `_. + +* Fixed iostream redirection for code that releases the GIL. + `#1368 `_, + +* A number of CI-related fixes. + `#1757 `_, + `#1744 `_, + `#1670 `_. + +v2.2.4 (September 11, 2018) +----------------------------------------------------- + +* Use new Python 3.7 Thread Specific Storage (TSS) implementation if available. + `#1454 `_, + `#1517 `_. + +* Fixes for newer MSVC versions and C++17 mode. + `#1347 `_, + `#1462 `_. + +* Propagate return value policies to type-specific casters + when casting STL containers. + `#1455 `_. + +* Allow ostream-redirection of more than 1024 characters. + `#1479 `_. + +* Set ``Py_DEBUG`` define when compiling against a debug Python build. + `#1438 `_. + +* Untangle integer logic in number type caster to work for custom + types that may only be castable to a restricted set of builtin types. + `#1442 `_. + +* CMake build system: Remember Python version in cache file. + `#1434 `_. + +* Fix for custom smart pointers: use ``std::addressof`` to obtain holder + address instead of ``operator&``. + `#1435 `_. + +* Properly report exceptions thrown during module initialization. + `#1362 `_. + +* Fixed a segmentation fault when creating empty-shaped NumPy array. + `#1371 `_. + +* The version of Intel C++ compiler must be >= 2017, and this is now checked by + the header files. `#1363 `_. + +* A few minor typo fixes and improvements to the test suite, and + patches that silence compiler warnings. + +* Vectors now support construction from generators, as well as ``extend()`` from a + list or generator. + `#1496 `_. + + +v2.2.3 (April 29, 2018) +----------------------------------------------------- + +* The pybind11 header location detection was replaced by a new implementation + that no longer depends on ``pip`` internals (the recently released ``pip`` + 10 has restricted access to this API). + `#1190 `_. + +* Small adjustment to an implementation detail to work around a compiler segmentation fault in Clang 3.3/3.4. + `#1350 `_. + +* The minimal supported version of the Intel compiler was >= 17.0 since + pybind11 v2.1. This check is now explicit, and a compile-time error is raised + if the compiler meet the requirement. + `#1363 `_. + +* Fixed an endianness-related fault in the test suite. + `#1287 `_. + +v2.2.2 (February 7, 2018) +----------------------------------------------------- + +* Fixed a segfault when combining embedded interpreter + shutdown/reinitialization with external loaded pybind11 modules. + `#1092 `_. + +* Eigen support: fixed a bug where Nx1/1xN numpy inputs couldn't be passed as + arguments to Eigen vectors (which for Eigen are simply compile-time fixed + Nx1/1xN matrices). + `#1106 `_. + +* Clarified to license by moving the licensing of contributions from + ``LICENSE`` into ``CONTRIBUTING.md``: the licensing of contributions is not + actually part of the software license as distributed. This isn't meant to be + a substantial change in the licensing of the project, but addresses concerns + that the clause made the license non-standard. + `#1109 `_. + +* Fixed a regression introduced in 2.1 that broke binding functions with lvalue + character literal arguments. + `#1128 `_. + +* MSVC: fix for compilation failures under /permissive-, and added the flag to + the appveyor test suite. + `#1155 `_. + +* Fixed ``__qualname__`` generation, and in turn, fixes how class names + (especially nested class names) are shown in generated docstrings. + `#1171 `_. + +* Updated the FAQ with a suggested project citation reference. + `#1189 `_. + +* Added fixes for deprecation warnings when compiled under C++17 with + ``-Wdeprecated`` turned on, and add ``-Wdeprecated`` to the test suite + compilation flags. + `#1191 `_. + +* Fixed outdated PyPI URLs in ``setup.py``. + `#1213 `_. + +* Fixed a refcount leak for arguments that end up in a ``py::args`` argument + for functions with both fixed positional and ``py::args`` arguments. + `#1216 `_. + +* Fixed a potential segfault resulting from possible premature destruction of + ``py::args``/``py::kwargs`` arguments with overloaded functions. + `#1223 `_. + +* Fixed ``del map[item]`` for a ``stl_bind.h`` bound stl map. + `#1229 `_. + +* Fixed a regression from v2.1.x where the aggregate initialization could + unintentionally end up at a constructor taking a templated + ``std::initializer_list`` argument. + `#1249 `_. + +* Fixed an issue where calling a function with a keep_alive policy on the same + nurse/patient pair would cause the internal patient storage to needlessly + grow (unboundedly, if the nurse is long-lived). + `#1251 `_. + +* Various other minor fixes. + +v2.2.1 (September 14, 2017) +----------------------------------------------------- + +* Added ``py::module_::reload()`` member function for reloading a module. + `#1040 `_. + +* Fixed a reference leak in the number converter. + `#1078 `_. + +* Fixed compilation with Clang on host GCC < 5 (old libstdc++ which isn't fully + C++11 compliant). `#1062 `_. + +* Fixed a regression where the automatic ``std::vector`` caster would + fail to compile. The same fix also applies to any container which returns + element proxies instead of references. + `#1053 `_. + +* Fixed a regression where the ``py::keep_alive`` policy could not be applied + to constructors. `#1065 `_. + +* Fixed a nullptr dereference when loading a ``py::module_local`` type + that's only registered in an external module. + `#1058 `_. + +* Fixed implicit conversion of accessors to types derived from ``py::object``. + `#1076 `_. + +* The ``name`` in ``PYBIND11_MODULE(name, variable)`` can now be a macro. + `#1082 `_. + +* Relaxed overly strict ``py::pickle()`` check for matching get and set types. + `#1064 `_. + +* Conversion errors now try to be more informative when it's likely that + a missing header is the cause (e.g. forgetting ````). + `#1077 `_. + +v2.2.0 (August 31, 2017) +----------------------------------------------------- + +* Support for embedding the Python interpreter. See the + :doc:`documentation page ` for a + full overview of the new features. + `#774 `_, + `#889 `_, + `#892 `_, + `#920 `_. + + .. code-block:: cpp + + #include + namespace py = pybind11; + + int main() { + py::scoped_interpreter guard{}; // start the interpreter and keep it alive + + py::print("Hello, World!"); // use the Python API + } + +* Support for inheriting from multiple C++ bases in Python. + `#693 `_. + + .. code-block:: python + + from cpp_module import CppBase1, CppBase2 + + + class PyDerived(CppBase1, CppBase2): + def __init__(self): + CppBase1.__init__(self) # C++ bases must be initialized explicitly + CppBase2.__init__(self) + +* ``PYBIND11_MODULE`` is now the preferred way to create module entry points. + ``PYBIND11_PLUGIN`` is deprecated. See :ref:`macros` for details. + `#879 `_. + + .. code-block:: cpp + + // new + PYBIND11_MODULE(example, m) { + m.def("add", [](int a, int b) { return a + b; }); + } + + // old + PYBIND11_PLUGIN(example) { + py::module m("example"); + m.def("add", [](int a, int b) { return a + b; }); + return m.ptr(); + } + +* pybind11's headers and build system now more strictly enforce hidden symbol + visibility for extension modules. This should be seamless for most users, + but see the :doc:`upgrade` if you use a custom build system. + `#995 `_. + +* Support for ``py::module_local`` types which allow multiple modules to + export the same C++ types without conflicts. This is useful for opaque + types like ``std::vector``. ``py::bind_vector`` and ``py::bind_map`` + now default to ``py::module_local`` if their elements are builtins or + local types. See :ref:`module_local` for details. + `#949 `_, + `#981 `_, + `#995 `_, + `#997 `_. + +* Custom constructors can now be added very easily using lambdas or factory + functions which return a class instance by value, pointer or holder. This + supersedes the old placement-new ``__init__`` technique. + See :ref:`custom_constructors` for details. + `#805 `_, + `#1014 `_. + + .. code-block:: cpp + + struct Example { + Example(std::string); + }; + + py::class_(m, "Example") + .def(py::init()) // existing constructor + .def(py::init([](int n) { // custom constructor + return std::make_unique(std::to_string(n)); + })); + +* Similarly to custom constructors, pickling support functions are now bound + using the ``py::pickle()`` adaptor which improves type safety. See the + :doc:`upgrade` and :ref:`pickling` for details. + `#1038 `_. + +* Builtin support for converting C++17 standard library types and general + conversion improvements: + + 1. C++17 ``std::variant`` is supported right out of the box. C++11/14 + equivalents (e.g. ``boost::variant``) can also be added with a simple + user-defined specialization. See :ref:`cpp17_container_casters` for details. + `#811 `_, + `#845 `_, + `#989 `_. + + 2. Out-of-the-box support for C++17 ``std::string_view``. + `#906 `_. + + 3. Improved compatibility of the builtin ``optional`` converter. + `#874 `_. + + 4. The ``bool`` converter now accepts ``numpy.bool_`` and types which + define ``__bool__`` (Python 3.x) or ``__nonzero__`` (Python 2.7). + `#925 `_. + + 5. C++-to-Python casters are now more efficient and move elements out + of rvalue containers whenever possible. + `#851 `_, + `#936 `_, + `#938 `_. + + 6. Fixed ``bytes`` to ``std::string/char*`` conversion on Python 3. + `#817 `_. + + 7. Fixed lifetime of temporary C++ objects created in Python-to-C++ conversions. + `#924 `_. + +* Scope guard call policy for RAII types, e.g. ``py::call_guard()``, + ``py::call_guard()``. See :ref:`call_policies` for details. + `#740 `_. + +* Utility for redirecting C++ streams to Python (e.g. ``std::cout`` -> + ``sys.stdout``). Scope guard ``py::scoped_ostream_redirect`` in C++ and + a context manager in Python. See :ref:`ostream_redirect`. + `#1009 `_. + +* Improved handling of types and exceptions across module boundaries. + `#915 `_, + `#951 `_, + `#995 `_. + +* Fixed destruction order of ``py::keep_alive`` nurse/patient objects + in reference cycles. + `#856 `_. + +* NumPy and buffer protocol related improvements: + + 1. Support for negative strides in Python buffer objects/numpy arrays. This + required changing integers from unsigned to signed for the related C++ APIs. + Note: If you have compiler warnings enabled, you may notice some new conversion + warnings after upgrading. These can be resolved with ``static_cast``. + `#782 `_. + + 2. Support ``std::complex`` and arrays inside ``PYBIND11_NUMPY_DTYPE``. + `#831 `_, + `#832 `_. + + 3. Support for constructing ``py::buffer_info`` and ``py::arrays`` using + arbitrary containers or iterators instead of requiring a ``std::vector``. + `#788 `_, + `#822 `_, + `#860 `_. + + 4. Explicitly check numpy version and require >= 1.7.0. + `#819 `_. + +* Support for allowing/prohibiting ``None`` for specific arguments and improved + ``None`` overload resolution order. See :ref:`none_arguments` for details. + `#843 `_. + `#859 `_. + +* Added ``py::exec()`` as a shortcut for ``py::eval()`` + and support for C++11 raw string literals as input. See :ref:`eval`. + `#766 `_, + `#827 `_. + +* ``py::vectorize()`` ignores non-vectorizable arguments and supports + member functions. + `#762 `_. + +* Support for bound methods as callbacks (``pybind11/functional.h``). + `#815 `_. + +* Allow aliasing pybind11 methods: ``cls.attr("foo") = cls.attr("bar")``. + `#802 `_. + +* Don't allow mixed static/non-static overloads. + `#804 `_. + +* Fixed overriding static properties in derived classes. + `#784 `_. + +* Added support for write only properties. + `#1144 `_. + +* Improved deduction of member functions of a derived class when its bases + aren't registered with pybind11. + `#855 `_. + + .. code-block:: cpp + + struct Base { + int foo() { return 42; } + } + + struct Derived : Base {} + + // Now works, but previously required also binding `Base` + py::class_(m, "Derived") + .def("foo", &Derived::foo); // function is actually from `Base` + +* The implementation of ``py::init<>`` now uses C++11 brace initialization + syntax to construct instances, which permits binding implicit constructors of + aggregate types. `#1015 `_. + + .. code-block:: cpp + + struct Aggregate { + int a; + std::string b; + }; + + py::class_(m, "Aggregate") + .def(py::init()); + +* Fixed issues with multiple inheritance with offset base/derived pointers. + `#812 `_, + `#866 `_, + `#960 `_. + +* Fixed reference leak of type objects. + `#1030 `_. + +* Improved support for the ``/std:c++14`` and ``/std:c++latest`` modes + on MSVC 2017. + `#841 `_, + `#999 `_. + +* Fixed detection of private operator new on MSVC. + `#893 `_, + `#918 `_. + +* Intel C++ compiler compatibility fixes. + `#937 `_. + +* Fixed implicit conversion of ``py::enum_`` to integer types on Python 2.7. + `#821 `_. + +* Added ``py::hash`` to fetch the hash value of Python objects, and + ``.def(hash(py::self))`` to provide the C++ ``std::hash`` as the Python + ``__hash__`` method. + `#1034 `_. + +* Fixed ``__truediv__`` on Python 2 and ``__itruediv__`` on Python 3. + `#867 `_. + +* ``py::capsule`` objects now support the ``name`` attribute. This is useful + for interfacing with ``scipy.LowLevelCallable``. + `#902 `_. + +* Fixed ``py::make_iterator``'s ``__next__()`` for past-the-end calls. + `#897 `_. + +* Added ``error_already_set::matches()`` for checking Python exceptions. + `#772 `_. + +* Deprecated ``py::error_already_set::clear()``. It's no longer needed + following a simplification of the ``py::error_already_set`` class. + `#954 `_. + +* Deprecated ``py::handle::operator==()`` in favor of ``py::handle::is()`` + `#825 `_. + +* Deprecated ``py::object::borrowed``/``py::object::stolen``. + Use ``py::object::borrowed_t{}``/``py::object::stolen_t{}`` instead. + `#771 `_. + +* Changed internal data structure versioning to avoid conflicts between + modules compiled with different revisions of pybind11. + `#1012 `_. + +* Additional compile-time and run-time error checking and more informative messages. + `#786 `_, + `#794 `_, + `#803 `_. + +* Various minor improvements and fixes. + `#764 `_, + `#791 `_, + `#795 `_, + `#840 `_, + `#844 `_, + `#846 `_, + `#849 `_, + `#858 `_, + `#862 `_, + `#871 `_, + `#872 `_, + `#881 `_, + `#888 `_, + `#899 `_, + `#928 `_, + `#931 `_, + `#944 `_, + `#950 `_, + `#952 `_, + `#962 `_, + `#965 `_, + `#970 `_, + `#978 `_, + `#979 `_, + `#986 `_, + `#1020 `_, + `#1027 `_, + `#1037 `_. + +* Testing improvements. + `#798 `_, + `#882 `_, + `#898 `_, + `#900 `_, + `#921 `_, + `#923 `_, + `#963 `_. + +v2.1.1 (April 7, 2017) +----------------------------------------------------- + +* Fixed minimum version requirement for MSVC 2015u3 + `#773 `_. + +v2.1.0 (March 22, 2017) +----------------------------------------------------- + +* pybind11 now performs function overload resolution in two phases. The first + phase only considers exact type matches, while the second allows for implicit + conversions to take place. A special ``noconvert()`` syntax can be used to + completely disable implicit conversions for specific arguments. + `#643 `_, + `#634 `_, + `#650 `_. + +* Fixed a regression where static properties no longer worked with classes + using multiple inheritance. The ``py::metaclass`` attribute is no longer + necessary (and deprecated as of this release) when binding classes with + static properties. + `#679 `_, + +* Classes bound using ``pybind11`` can now use custom metaclasses. + `#679 `_, + +* ``py::args`` and ``py::kwargs`` can now be mixed with other positional + arguments when binding functions using pybind11. + `#611 `_. + +* Improved support for C++11 unicode string and character types; added + extensive documentation regarding pybind11's string conversion behavior. + `#624 `_, + `#636 `_, + `#715 `_. + +* pybind11 can now avoid expensive copies when converting Eigen arrays to NumPy + arrays (and vice versa). `#610 `_. + +* The "fast path" in ``py::vectorize`` now works for any full-size group of C or + F-contiguous arrays. The non-fast path is also faster since it no longer performs + copies of the input arguments (except when type conversions are necessary). + `#610 `_. + +* Added fast, unchecked access to NumPy arrays via a proxy object. + `#746 `_. + +* Transparent support for class-specific ``operator new`` and + ``operator delete`` implementations. + `#755 `_. + +* Slimmer and more efficient STL-compatible iterator interface for sequence types. + `#662 `_. + +* Improved custom holder type support. + `#607 `_. + +* ``nullptr`` to ``None`` conversion fixed in various builtin type casters. + `#732 `_. + +* ``enum_`` now exposes its members via a special ``__members__`` attribute. + `#666 `_. + +* ``std::vector`` bindings created using ``stl_bind.h`` can now optionally + implement the buffer protocol. `#488 `_. + +* Automated C++ reference documentation using doxygen and breathe. + `#598 `_. + +* Added minimum compiler version assertions. + `#727 `_. + +* Improved compatibility with C++1z. + `#677 `_. + +* Improved ``py::capsule`` API. Can be used to implement cleanup + callbacks that are involved at module destruction time. + `#752 `_. + +* Various minor improvements and fixes. + `#595 `_, + `#588 `_, + `#589 `_, + `#603 `_, + `#619 `_, + `#648 `_, + `#695 `_, + `#720 `_, + `#723 `_, + `#729 `_, + `#724 `_, + `#742 `_, + `#753 `_. + +v2.0.1 (Jan 4, 2017) +----------------------------------------------------- + +* Fix pointer to reference error in type_caster on MSVC + `#583 `_. + +* Fixed a segmentation in the test suite due to a typo + `cd7eac `_. + +v2.0.0 (Jan 1, 2017) +----------------------------------------------------- + +* Fixed a reference counting regression affecting types with custom metaclasses + (introduced in v2.0.0-rc1). + `#571 `_. + +* Quenched a CMake policy warning. + `#570 `_. + +v2.0.0-rc1 (Dec 23, 2016) +----------------------------------------------------- + +The pybind11 developers are excited to issue a release candidate of pybind11 +with a subsequent v2.0.0 release planned in early January next year. + +An incredible amount of effort by went into pybind11 over the last ~5 months, +leading to a release that is jam-packed with exciting new features and numerous +usability improvements. The following list links PRs or individual commits +whenever applicable. + +Happy Christmas! + +* Support for binding C++ class hierarchies that make use of multiple + inheritance. `#410 `_. + +* PyPy support: pybind11 now supports nightly builds of PyPy and will + interoperate with the future 5.7 release. No code changes are necessary, + everything "just" works as usual. Note that we only target the Python 2.7 + branch for now; support for 3.x will be added once its ``cpyext`` extension + support catches up. A few minor features remain unsupported for the time + being (notably dynamic attributes in custom types). + `#527 `_. + +* Significant work on the documentation -- in particular, the monolithic + ``advanced.rst`` file was restructured into a easier to read hierarchical + organization. `#448 `_. + +* Many NumPy-related improvements: + + 1. Object-oriented API to access and modify NumPy ``ndarray`` instances, + replicating much of the corresponding NumPy C API functionality. + `#402 `_. + + 2. NumPy array ``dtype`` array descriptors are now first-class citizens and + are exposed via a new class ``py::dtype``. + + 3. Structured dtypes can be registered using the ``PYBIND11_NUMPY_DTYPE()`` + macro. Special ``array`` constructors accepting dtype objects were also + added. + + One potential caveat involving this change: format descriptor strings + should now be accessed via ``format_descriptor::format()`` (however, for + compatibility purposes, the old syntax ``format_descriptor::value`` will + still work for non-structured data types). `#308 + `_. + + 4. Further improvements to support structured dtypes throughout the system. + `#472 `_, + `#474 `_, + `#459 `_, + `#453 `_, + `#452 `_, and + `#505 `_. + + 5. Fast access operators. `#497 `_. + + 6. Constructors for arrays whose storage is owned by another object. + `#440 `_. + + 7. Added constructors for ``array`` and ``array_t`` explicitly accepting shape + and strides; if strides are not provided, they are deduced assuming + C-contiguity. Also added simplified constructors for 1-dimensional case. + + 8. Added buffer/NumPy support for ``char[N]`` and ``std::array`` types. + + 9. Added ``memoryview`` wrapper type which is constructible from ``buffer_info``. + +* Eigen: many additional conversions and support for non-contiguous + arrays/slices. + `#427 `_, + `#315 `_, + `#316 `_, + `#312 `_, and + `#267 `_ + +* Incompatible changes in ``class_<...>::class_()``: + + 1. Declarations of types that provide access via the buffer protocol must + now include the ``py::buffer_protocol()`` annotation as an argument to + the ``class_`` constructor. + + 2. Declarations of types that require a custom metaclass (i.e. all classes + which include static properties via commands such as + ``def_readwrite_static()``) must now include the ``py::metaclass()`` + annotation as an argument to the ``class_`` constructor. + + These two changes were necessary to make type definitions in pybind11 + future-proof, and to support PyPy via its cpyext mechanism. `#527 + `_. + + + 3. This version of pybind11 uses a redesigned mechanism for instantiating + trampoline classes that are used to override virtual methods from within + Python. This led to the following user-visible syntax change: instead of + + .. code-block:: cpp + + py::class_("MyClass") + .alias() + .... + + write + + .. code-block:: cpp + + py::class_("MyClass") + .... + + Importantly, both the original and the trampoline class are now + specified as an arguments (in arbitrary order) to the ``py::class_`` + template, and the ``alias<..>()`` call is gone. The new scheme has zero + overhead in cases when Python doesn't override any functions of the + underlying C++ class. `rev. 86d825 + `_. + +* Added ``eval`` and ``eval_file`` functions for evaluating expressions and + statements from a string or file. `rev. 0d3fc3 + `_. + +* pybind11 can now create types with a modifiable dictionary. + `#437 `_ and + `#444 `_. + +* Support for translation of arbitrary C++ exceptions to Python counterparts. + `#296 `_ and + `#273 `_. + +* Report full backtraces through mixed C++/Python code, better reporting for + import errors, fixed GIL management in exception processing. + `#537 `_, + `#494 `_, + `rev. e72d95 `_, and + `rev. 099d6e `_. + +* Support for bit-level operations, comparisons, and serialization of C++ + enumerations. `#503 `_, + `#508 `_, + `#380 `_, + `#309 `_. + `#311 `_. + +* The ``class_`` constructor now accepts its template arguments in any order. + `#385 `_. + +* Attribute and item accessors now have a more complete interface which makes + it possible to chain attributes as in + ``obj.attr("a")[key].attr("b").attr("method")(1, 2, 3)``. `#425 + `_. + +* Major redesign of the default and conversion constructors in ``pytypes.h``. + `#464 `_. + +* Added built-in support for ``std::shared_ptr`` holder type. It is no longer + necessary to to include a declaration of the form + ``PYBIND11_DECLARE_HOLDER_TYPE(T, std::shared_ptr)`` (though continuing to + do so won't cause an error). + `#454 `_. + +* New ``py::overload_cast`` casting operator to select among multiple possible + overloads of a function. An example: + + .. code-block:: cpp + + py::class_(m, "Pet") + .def("set", py::overload_cast(&Pet::set), "Set the pet's age") + .def("set", py::overload_cast(&Pet::set), "Set the pet's name"); + + This feature only works on C++14-capable compilers. + `#541 `_. + +* C++ types are automatically cast to Python types, e.g. when assigning + them as an attribute. For instance, the following is now legal: + + .. code-block:: cpp + + py::module m = /* ... */ + m.attr("constant") = 123; + + (Previously, a ``py::cast`` call was necessary to avoid a compilation error.) + `#551 `_. + +* Redesigned ``pytest``-based test suite. `#321 `_. + +* Instance tracking to detect reference leaks in test suite. `#324 `_ + +* pybind11 can now distinguish between multiple different instances that are + located at the same memory address, but which have different types. + `#329 `_. + +* Improved logic in ``move`` return value policy. + `#510 `_, + `#297 `_. + +* Generalized unpacking API to permit calling Python functions from C++ using + notation such as ``foo(a1, a2, *args, "ka"_a=1, "kb"_a=2, **kwargs)``. `#372 `_. + +* ``py::print()`` function whose behavior matches that of the native Python + ``print()`` function. `#372 `_. + +* Added ``py::dict`` keyword constructor:``auto d = dict("number"_a=42, + "name"_a="World");``. `#372 `_. + +* Added ``py::str::format()`` method and ``_s`` literal: ``py::str s = "1 + 2 + = {}"_s.format(3);``. `#372 `_. + +* Added ``py::repr()`` function which is equivalent to Python's builtin + ``repr()``. `#333 `_. + +* Improved construction and destruction logic for holder types. It is now + possible to reference instances with smart pointer holder types without + constructing the holder if desired. The ``PYBIND11_DECLARE_HOLDER_TYPE`` + macro now accepts an optional second parameter to indicate whether the holder + type uses intrusive reference counting. + `#533 `_ and + `#561 `_. + +* Mapping a stateless C++ function to Python and back is now "for free" (i.e. + no extra indirections or argument conversion overheads). `rev. 954b79 + `_. + +* Bindings for ``std::valarray``. + `#545 `_. + +* Improved support for C++17 capable compilers. + `#562 `_. + +* Bindings for ``std::optional``. + `#475 `_, + `#476 `_, + `#479 `_, + `#499 `_, and + `#501 `_. + +* ``stl_bind.h``: general improvements and support for ``std::map`` and + ``std::unordered_map``. + `#490 `_, + `#282 `_, + `#235 `_. + +* The ``std::tuple``, ``std::pair``, ``std::list``, and ``std::vector`` type + casters now accept any Python sequence type as input. `rev. 107285 + `_. + +* Improved CMake Python detection on multi-architecture Linux. + `#532 `_. + +* Infrastructure to selectively disable or enable parts of the automatically + generated docstrings. `#486 `_. + +* ``reference`` and ``reference_internal`` are now the default return value + properties for static and non-static properties, respectively. `#473 + `_. (the previous defaults + were ``automatic``). `#473 `_. + +* Support for ``std::unique_ptr`` with non-default deleters or no deleter at + all (``py::nodelete``). `#384 `_. + +* Deprecated ``handle::call()`` method. The new syntax to call Python + functions is simply ``handle()``. It can also be invoked explicitly via + ``handle::operator()``, where ``X`` is an optional return value policy. + +* Print more informative error messages when ``make_tuple()`` or ``cast()`` + fail. `#262 `_. + +* Creation of holder types for classes deriving from + ``std::enable_shared_from_this<>`` now also works for ``const`` values. + `#260 `_. + +* ``make_iterator()`` improvements for better compatibility with various + types (now uses prefix increment operator); it now also accepts iterators + with different begin/end types as long as they are equality comparable. + `#247 `_. + +* ``arg()`` now accepts a wider range of argument types for default values. + `#244 `_. + +* Support ``keep_alive`` where the nurse object may be ``None``. `#341 + `_. + +* Added constructors for ``str`` and ``bytes`` from zero-terminated char + pointers, and from char pointers and length. Added constructors for ``str`` + from ``bytes`` and for ``bytes`` from ``str``, which will perform UTF-8 + decoding/encoding as required. + +* Many other improvements of library internals without user-visible changes + + +1.8.1 (July 12, 2016) +---------------------- +* Fixed a rare but potentially very severe issue when the garbage collector ran + during pybind11 type creation. + +1.8.0 (June 14, 2016) +---------------------- +* Redesigned CMake build system which exports a convenient + ``pybind11_add_module`` function to parent projects. +* ``std::vector<>`` type bindings analogous to Boost.Python's ``indexing_suite`` +* Transparent conversion of sparse and dense Eigen matrices and vectors (``eigen.h``) +* Added an ``ExtraFlags`` template argument to the NumPy ``array_t<>`` wrapper + to disable an enforced cast that may lose precision, e.g. to create overloads + for different precisions and complex vs real-valued matrices. +* Prevent implicit conversion of floating point values to integral types in + function arguments +* Fixed incorrect default return value policy for functions returning a shared + pointer +* Don't allow registering a type via ``class_`` twice +* Don't allow casting a ``None`` value into a C++ lvalue reference +* Fixed a crash in ``enum_::operator==`` that was triggered by the ``help()`` command +* Improved detection of whether or not custom C++ types can be copy/move-constructed +* Extended ``str`` type to also work with ``bytes`` instances +* Added a ``"name"_a`` user defined string literal that is equivalent to ``py::arg("name")``. +* When specifying function arguments via ``py::arg``, the test that verifies + the number of arguments now runs at compile time. +* Added ``[[noreturn]]`` attribute to ``pybind11_fail()`` to quench some + compiler warnings +* List function arguments in exception text when the dispatch code cannot find + a matching overload +* Added ``PYBIND11_OVERLOAD_NAME`` and ``PYBIND11_OVERLOAD_PURE_NAME`` macros which + can be used to override virtual methods whose name differs in C++ and Python + (e.g. ``__call__`` and ``operator()``) +* Various minor ``iterator`` and ``make_iterator()`` improvements +* Transparently support ``__bool__`` on Python 2.x and Python 3.x +* Fixed issue with destructor of unpickled object not being called +* Minor CMake build system improvements on Windows +* New ``pybind11::args`` and ``pybind11::kwargs`` types to create functions which + take an arbitrary number of arguments and keyword arguments +* New syntax to call a Python function from C++ using ``*args`` and ``*kwargs`` +* The functions ``def_property_*`` now correctly process docstring arguments (these + formerly caused a segmentation fault) +* Many ``mkdoc.py`` improvements (enumerations, template arguments, ``DOC()`` + macro accepts more arguments) +* Cygwin support +* Documentation improvements (pickling support, ``keep_alive``, macro usage) + +1.7 (April 30, 2016) +---------------------- +* Added a new ``move`` return value policy that triggers C++11 move semantics. + The automatic return value policy falls back to this case whenever a rvalue + reference is encountered +* Significantly more general GIL state routines that are used instead of + Python's troublesome ``PyGILState_Ensure`` and ``PyGILState_Release`` API +* Redesign of opaque types that drastically simplifies their usage +* Extended ability to pass values of type ``[const] void *`` +* ``keep_alive`` fix: don't fail when there is no patient +* ``functional.h``: acquire the GIL before calling a Python function +* Added Python RAII type wrappers ``none`` and ``iterable`` +* Added ``*args`` and ``*kwargs`` pass-through parameters to + ``pybind11.get_include()`` function +* Iterator improvements and fixes +* Documentation on return value policies and opaque types improved + +1.6 (April 30, 2016) +---------------------- +* Skipped due to upload to PyPI gone wrong and inability to recover + (https://github.com/pypa/packaging-problems/issues/74) + +1.5 (April 21, 2016) +---------------------- +* For polymorphic types, use RTTI to try to return the closest type registered with pybind11 +* Pickling support for serializing and unserializing C++ instances to a byte stream in Python +* Added a convenience routine ``make_iterator()`` which turns a range indicated + by a pair of C++ iterators into a iterable Python object +* Added ``len()`` and a variadic ``make_tuple()`` function +* Addressed a rare issue that could confuse the current virtual function + dispatcher and another that could lead to crashes in multi-threaded + applications +* Added a ``get_include()`` function to the Python module that returns the path + of the directory containing the installed pybind11 header files +* Documentation improvements: import issues, symbol visibility, pickling, limitations +* Added casting support for ``std::reference_wrapper<>`` + +1.4 (April 7, 2016) +-------------------------- +* Transparent type conversion for ``std::wstring`` and ``wchar_t`` +* Allow passing ``nullptr``-valued strings +* Transparent passing of ``void *`` pointers using capsules +* Transparent support for returning values wrapped in ``std::unique_ptr<>`` +* Improved docstring generation for compatibility with Sphinx +* Nicer debug error message when default parameter construction fails +* Support for "opaque" types that bypass the transparent conversion layer for STL containers +* Redesigned type casting interface to avoid ambiguities that could occasionally cause compiler errors +* Redesigned property implementation; fixes crashes due to an unfortunate default return value policy +* Anaconda package generation support + +1.3 (March 8, 2016) +-------------------------- + +* Added support for the Intel C++ compiler (v15+) +* Added support for the STL unordered set/map data structures +* Added support for the STL linked list data structure +* NumPy-style broadcasting support in ``pybind11::vectorize`` +* pybind11 now displays more verbose error messages when ``arg::operator=()`` fails +* pybind11 internal data structures now live in a version-dependent namespace to avoid ABI issues +* Many, many bugfixes involving corner cases and advanced usage + +1.2 (February 7, 2016) +-------------------------- + +* Optional: efficient generation of function signatures at compile time using C++14 +* Switched to a simpler and more general way of dealing with function default + arguments. Unused keyword arguments in function calls are now detected and + cause errors as expected +* New ``keep_alive`` call policy analogous to Boost.Python's ``with_custodian_and_ward`` +* New ``pybind11::base<>`` attribute to indicate a subclass relationship +* Improved interface for RAII type wrappers in ``pytypes.h`` +* Use RAII type wrappers consistently within pybind11 itself. This + fixes various potential refcount leaks when exceptions occur +* Added new ``bytes`` RAII type wrapper (maps to ``string`` in Python 2.7) +* Made handle and related RAII classes const correct, using them more + consistently everywhere now +* Got rid of the ugly ``__pybind11__`` attributes on the Python side---they are + now stored in a C++ hash table that is not visible in Python +* Fixed refcount leaks involving NumPy arrays and bound functions +* Vastly improved handling of shared/smart pointers +* Removed an unnecessary copy operation in ``pybind11::vectorize`` +* Fixed naming clashes when both pybind11 and NumPy headers are included +* Added conversions for additional exception types +* Documentation improvements (using multiple extension modules, smart pointers, + other minor clarifications) +* unified infrastructure for parsing variadic arguments in ``class_`` and cpp_function +* Fixed license text (was: ZLIB, should have been: 3-clause BSD) +* Python 3.2 compatibility +* Fixed remaining issues when accessing types in another plugin module +* Added enum comparison and casting methods +* Improved SFINAE-based detection of whether types are copy-constructible +* Eliminated many warnings about unused variables and the use of ``offsetof()`` +* Support for ``std::array<>`` conversions + +1.1 (December 7, 2015) +-------------------------- + +* Documentation improvements (GIL, wrapping functions, casting, fixed many typos) +* Generalized conversion of integer types +* Improved support for casting function objects +* Improved support for ``std::shared_ptr<>`` conversions +* Initial support for ``std::set<>`` conversions +* Fixed type resolution issue for types defined in a separate plugin module +* CMake build system improvements +* Factored out generic functionality to non-templated code (smaller code size) +* Added a code size / compile time benchmark vs Boost.Python +* Added an appveyor CI script + +1.0 (October 15, 2015) +------------------------ +* Initial release diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/classes.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/classes.rst new file mode 100644 index 0000000000000000000000000000000000000000..6495c4c1b1c9e756f714af7edafccb1973291dfa --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/classes.rst @@ -0,0 +1,542 @@ +.. _classes: + +Object-oriented code +#################### + +Creating bindings for a custom type +=================================== + +Let's now look at a more complex example where we'll create bindings for a +custom C++ data structure named ``Pet``. Its definition is given below: + +.. code-block:: cpp + + struct Pet { + Pet(const std::string &name) : name(name) { } + void setName(const std::string &name_) { name = name_; } + const std::string &getName() const { return name; } + + std::string name; + }; + +The binding code for ``Pet`` looks as follows: + +.. code-block:: cpp + + #include + + namespace py = pybind11; + + PYBIND11_MODULE(example, m) { + py::class_(m, "Pet") + .def(py::init()) + .def("setName", &Pet::setName) + .def("getName", &Pet::getName); + } + +:class:`class_` creates bindings for a C++ *class* or *struct*-style data +structure. :func:`init` is a convenience function that takes the types of a +constructor's parameters as template arguments and wraps the corresponding +constructor (see the :ref:`custom_constructors` section for details). An +interactive Python session demonstrating this example is shown below: + +.. code-block:: pycon + + % python + >>> import example + >>> p = example.Pet("Molly") + >>> print(p) + + >>> p.getName() + u'Molly' + >>> p.setName("Charly") + >>> p.getName() + u'Charly' + +.. seealso:: + + Static member functions can be bound in the same way using + :func:`class_::def_static`. + +Keyword and default arguments +============================= +It is possible to specify keyword and default arguments using the syntax +discussed in the previous chapter. Refer to the sections :ref:`keyword_args` +and :ref:`default_args` for details. + +Binding lambda functions +======================== + +Note how ``print(p)`` produced a rather useless summary of our data structure in the example above: + +.. code-block:: pycon + + >>> print(p) + + +To address this, we could bind a utility function that returns a human-readable +summary to the special method slot named ``__repr__``. Unfortunately, there is no +suitable functionality in the ``Pet`` data structure, and it would be nice if +we did not have to change it. This can easily be accomplished by binding a +Lambda function instead: + +.. code-block:: cpp + + py::class_(m, "Pet") + .def(py::init()) + .def("setName", &Pet::setName) + .def("getName", &Pet::getName) + .def("__repr__", + [](const Pet &a) { + return ""; + } + ); + +Both stateless [#f1]_ and stateful lambda closures are supported by pybind11. +With the above change, the same Python code now produces the following output: + +.. code-block:: pycon + + >>> print(p) + + +.. [#f1] Stateless closures are those with an empty pair of brackets ``[]`` as the capture object. + +.. _properties: + +Instance and static fields +========================== + +We can also directly expose the ``name`` field using the +:func:`class_::def_readwrite` method. A similar :func:`class_::def_readonly` +method also exists for ``const`` fields. + +.. code-block:: cpp + + py::class_(m, "Pet") + .def(py::init()) + .def_readwrite("name", &Pet::name) + // ... remainder ... + +This makes it possible to write + +.. code-block:: pycon + + >>> p = example.Pet("Molly") + >>> p.name + u'Molly' + >>> p.name = "Charly" + >>> p.name + u'Charly' + +Now suppose that ``Pet::name`` was a private internal variable +that can only be accessed via setters and getters. + +.. code-block:: cpp + + class Pet { + public: + Pet(const std::string &name) : name(name) { } + void setName(const std::string &name_) { name = name_; } + const std::string &getName() const { return name; } + private: + std::string name; + }; + +In this case, the method :func:`class_::def_property` +(:func:`class_::def_property_readonly` for read-only data) can be used to +provide a field-like interface within Python that will transparently call +the setter and getter functions: + +.. code-block:: cpp + + py::class_(m, "Pet") + .def(py::init()) + .def_property("name", &Pet::getName, &Pet::setName) + // ... remainder ... + +Write only properties can be defined by passing ``nullptr`` as the +input for the read function. + +.. seealso:: + + Similar functions :func:`class_::def_readwrite_static`, + :func:`class_::def_readonly_static` :func:`class_::def_property_static`, + and :func:`class_::def_property_readonly_static` are provided for binding + static variables and properties. Please also see the section on + :ref:`static_properties` in the advanced part of the documentation. + +Dynamic attributes +================== + +Native Python classes can pick up new attributes dynamically: + +.. code-block:: pycon + + >>> class Pet: + ... name = "Molly" + ... + >>> p = Pet() + >>> p.name = "Charly" # overwrite existing + >>> p.age = 2 # dynamically add a new attribute + +By default, classes exported from C++ do not support this and the only writable +attributes are the ones explicitly defined using :func:`class_::def_readwrite` +or :func:`class_::def_property`. + +.. code-block:: cpp + + py::class_(m, "Pet") + .def(py::init<>()) + .def_readwrite("name", &Pet::name); + +Trying to set any other attribute results in an error: + +.. code-block:: pycon + + >>> p = example.Pet() + >>> p.name = "Charly" # OK, attribute defined in C++ + >>> p.age = 2 # fail + AttributeError: 'Pet' object has no attribute 'age' + +To enable dynamic attributes for C++ classes, the :class:`py::dynamic_attr` tag +must be added to the :class:`py::class_` constructor: + +.. code-block:: cpp + + py::class_(m, "Pet", py::dynamic_attr()) + .def(py::init<>()) + .def_readwrite("name", &Pet::name); + +Now everything works as expected: + +.. code-block:: pycon + + >>> p = example.Pet() + >>> p.name = "Charly" # OK, overwrite value in C++ + >>> p.age = 2 # OK, dynamically add a new attribute + >>> p.__dict__ # just like a native Python class + {'age': 2} + +Note that there is a small runtime cost for a class with dynamic attributes. +Not only because of the addition of a ``__dict__``, but also because of more +expensive garbage collection tracking which must be activated to resolve +possible circular references. Native Python classes incur this same cost by +default, so this is not anything to worry about. By default, pybind11 classes +are more efficient than native Python classes. Enabling dynamic attributes +just brings them on par. + +.. _inheritance: + +Inheritance and automatic downcasting +===================================== + +Suppose now that the example consists of two data structures with an +inheritance relationship: + +.. code-block:: cpp + + struct Pet { + Pet(const std::string &name) : name(name) { } + std::string name; + }; + + struct Dog : Pet { + Dog(const std::string &name) : Pet(name) { } + std::string bark() const { return "woof!"; } + }; + +There are two different ways of indicating a hierarchical relationship to +pybind11: the first specifies the C++ base class as an extra template +parameter of the :class:`class_`: + +.. code-block:: cpp + + py::class_(m, "Pet") + .def(py::init()) + .def_readwrite("name", &Pet::name); + + // Method 1: template parameter: + py::class_(m, "Dog") + .def(py::init()) + .def("bark", &Dog::bark); + +Alternatively, we can also assign a name to the previously bound ``Pet`` +:class:`class_` object and reference it when binding the ``Dog`` class: + +.. code-block:: cpp + + py::class_ pet(m, "Pet"); + pet.def(py::init()) + .def_readwrite("name", &Pet::name); + + // Method 2: pass parent class_ object: + py::class_(m, "Dog", pet /* <- specify Python parent type */) + .def(py::init()) + .def("bark", &Dog::bark); + +Functionality-wise, both approaches are equivalent. Afterwards, instances will +expose fields and methods of both types: + +.. code-block:: pycon + + >>> p = example.Dog("Molly") + >>> p.name + u'Molly' + >>> p.bark() + u'woof!' + +The C++ classes defined above are regular non-polymorphic types with an +inheritance relationship. This is reflected in Python: + +.. code-block:: cpp + + // Return a base pointer to a derived instance + m.def("pet_store", []() { return std::unique_ptr(new Dog("Molly")); }); + +.. code-block:: pycon + + >>> p = example.pet_store() + >>> type(p) # `Dog` instance behind `Pet` pointer + Pet # no pointer downcasting for regular non-polymorphic types + >>> p.bark() + AttributeError: 'Pet' object has no attribute 'bark' + +The function returned a ``Dog`` instance, but because it's a non-polymorphic +type behind a base pointer, Python only sees a ``Pet``. In C++, a type is only +considered polymorphic if it has at least one virtual function and pybind11 +will automatically recognize this: + +.. code-block:: cpp + + struct PolymorphicPet { + virtual ~PolymorphicPet() = default; + }; + + struct PolymorphicDog : PolymorphicPet { + std::string bark() const { return "woof!"; } + }; + + // Same binding code + py::class_(m, "PolymorphicPet"); + py::class_(m, "PolymorphicDog") + .def(py::init<>()) + .def("bark", &PolymorphicDog::bark); + + // Again, return a base pointer to a derived instance + m.def("pet_store2", []() { return std::unique_ptr(new PolymorphicDog); }); + +.. code-block:: pycon + + >>> p = example.pet_store2() + >>> type(p) + PolymorphicDog # automatically downcast + >>> p.bark() + u'woof!' + +Given a pointer to a polymorphic base, pybind11 performs automatic downcasting +to the actual derived type. Note that this goes beyond the usual situation in +C++: we don't just get access to the virtual functions of the base, we get the +concrete derived type including functions and attributes that the base type may +not even be aware of. + +.. seealso:: + + For more information about polymorphic behavior see :ref:`overriding_virtuals`. + + +Overloaded methods +================== + +Sometimes there are several overloaded C++ methods with the same name taking +different kinds of input arguments: + +.. code-block:: cpp + + struct Pet { + Pet(const std::string &name, int age) : name(name), age(age) { } + + void set(int age_) { age = age_; } + void set(const std::string &name_) { name = name_; } + + std::string name; + int age; + }; + +Attempting to bind ``Pet::set`` will cause an error since the compiler does not +know which method the user intended to select. We can disambiguate by casting +them to function pointers. Binding multiple functions to the same Python name +automatically creates a chain of function overloads that will be tried in +sequence. + +.. code-block:: cpp + + py::class_(m, "Pet") + .def(py::init()) + .def("set", static_cast(&Pet::set), "Set the pet's age") + .def("set", static_cast(&Pet::set), "Set the pet's name"); + +The overload signatures are also visible in the method's docstring: + +.. code-block:: pycon + + >>> help(example.Pet) + + class Pet(__builtin__.object) + | Methods defined here: + | + | __init__(...) + | Signature : (Pet, str, int) -> NoneType + | + | set(...) + | 1. Signature : (Pet, int) -> NoneType + | + | Set the pet's age + | + | 2. Signature : (Pet, str) -> NoneType + | + | Set the pet's name + +If you have a C++14 compatible compiler [#cpp14]_, you can use an alternative +syntax to cast the overloaded function: + +.. code-block:: cpp + + py::class_(m, "Pet") + .def("set", py::overload_cast(&Pet::set), "Set the pet's age") + .def("set", py::overload_cast(&Pet::set), "Set the pet's name"); + +Here, ``py::overload_cast`` only requires the parameter types to be specified. +The return type and class are deduced. This avoids the additional noise of +``void (Pet::*)()`` as seen in the raw cast. If a function is overloaded based +on constness, the ``py::const_`` tag should be used: + +.. code-block:: cpp + + struct Widget { + int foo(int x, float y); + int foo(int x, float y) const; + }; + + py::class_(m, "Widget") + .def("foo_mutable", py::overload_cast(&Widget::foo)) + .def("foo_const", py::overload_cast(&Widget::foo, py::const_)); + +If you prefer the ``py::overload_cast`` syntax but have a C++11 compatible compiler only, +you can use ``py::detail::overload_cast_impl`` with an additional set of parentheses: + +.. code-block:: cpp + + template + using overload_cast_ = pybind11::detail::overload_cast_impl; + + py::class_(m, "Pet") + .def("set", overload_cast_()(&Pet::set), "Set the pet's age") + .def("set", overload_cast_()(&Pet::set), "Set the pet's name"); + +.. [#cpp14] A compiler which supports the ``-std=c++14`` flag + or Visual Studio 2015 Update 2 and newer. + +.. note:: + + To define multiple overloaded constructors, simply declare one after the + other using the ``.def(py::init<...>())`` syntax. The existing machinery + for specifying keyword and default arguments also works. + +Enumerations and internal types +=============================== + +Let's now suppose that the example class contains internal types like enumerations, e.g.: + +.. code-block:: cpp + + struct Pet { + enum Kind { + Dog = 0, + Cat + }; + + struct Attributes { + float age = 0; + }; + + Pet(const std::string &name, Kind type) : name(name), type(type) { } + + std::string name; + Kind type; + Attributes attr; + }; + +The binding code for this example looks as follows: + +.. code-block:: cpp + + py::class_ pet(m, "Pet"); + + pet.def(py::init()) + .def_readwrite("name", &Pet::name) + .def_readwrite("type", &Pet::type) + .def_readwrite("attr", &Pet::attr); + + py::enum_(pet, "Kind") + .value("Dog", Pet::Kind::Dog) + .value("Cat", Pet::Kind::Cat) + .export_values(); + + py::class_ attributes(pet, "Attributes") + .def(py::init<>()) + .def_readwrite("age", &Pet::Attributes::age); + + +To ensure that the nested types ``Kind`` and ``Attributes`` are created within the scope of ``Pet``, the +``pet`` :class:`class_` instance must be supplied to the :class:`enum_` and :class:`class_` +constructor. The :func:`enum_::export_values` function exports the enum entries +into the parent scope, which should be skipped for newer C++11-style strongly +typed enums. + +.. code-block:: pycon + + >>> p = Pet("Lucy", Pet.Cat) + >>> p.type + Kind.Cat + >>> int(p.type) + 1L + +The entries defined by the enumeration type are exposed in the ``__members__`` property: + +.. code-block:: pycon + + >>> Pet.Kind.__members__ + {'Dog': Kind.Dog, 'Cat': Kind.Cat} + +The ``name`` property returns the name of the enum value as a unicode string. + +.. note:: + + It is also possible to use ``str(enum)``, however these accomplish different + goals. The following shows how these two approaches differ. + + .. code-block:: pycon + + >>> p = Pet("Lucy", Pet.Cat) + >>> pet_type = p.type + >>> pet_type + Pet.Cat + >>> str(pet_type) + 'Pet.Cat' + >>> pet_type.name + 'Cat' + +.. note:: + + When the special tag ``py::arithmetic()`` is specified to the ``enum_`` + constructor, pybind11 creates an enumeration that also supports rudimentary + arithmetic and bit-level operations like comparisons, and, or, xor, negation, + etc. + + .. code-block:: cpp + + py::enum_(pet, "Kind", py::arithmetic()) + ... + + By default, these are omitted to conserve space. diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/cmake/index.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/cmake/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..69128e036ae1fd31677d8bffc5340e961b0eed17 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/cmake/index.rst @@ -0,0 +1,8 @@ +CMake helpers +------------- + +Pybind11 can be used with ``add_subdirectory(extern/pybind11)``, or from an +install with ``find_package(pybind11 CONFIG)``. The interface provided in +either case is functionally identical. + +.. cmake-module:: ../../tools/pybind11Config.cmake.in diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/compiling.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/compiling.rst new file mode 100644 index 0000000000000000000000000000000000000000..6d37f4d66ebb5ec1577498228e41401fc470ad4c --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/compiling.rst @@ -0,0 +1,648 @@ +.. _compiling: + +Build systems +############# + +.. _build-setuptools: + +Building with setuptools +======================== + +For projects on PyPI, building with setuptools is the way to go. Sylvain Corlay +has kindly provided an example project which shows how to set up everything, +including automatic generation of documentation using Sphinx. Please refer to +the [python_example]_ repository. + +.. [python_example] https://github.com/pybind/python_example + +A helper file is provided with pybind11 that can simplify usage with setuptools. + +To use pybind11 inside your ``setup.py``, you have to have some system to +ensure that ``pybind11`` is installed when you build your package. There are +four possible ways to do this, and pybind11 supports all four: You can ask all +users to install pybind11 beforehand (bad), you can use +:ref:`setup_helpers-pep518` (good, but very new and requires Pip 10), +:ref:`setup_helpers-setup_requires` (discouraged by Python packagers now that +PEP 518 is available, but it still works everywhere), or you can +:ref:`setup_helpers-copy-manually` (always works but you have to manually sync +your copy to get updates). + +An example of a ``setup.py`` using pybind11's helpers: + +.. code-block:: python + + from glob import glob + from setuptools import setup + from pybind11.setup_helpers import Pybind11Extension + + ext_modules = [ + Pybind11Extension( + "python_example", + sorted(glob("src/*.cpp")), # Sort source files for reproducibility + ), + ] + + setup(..., ext_modules=ext_modules) + +If you want to do an automatic search for the highest supported C++ standard, +that is supported via a ``build_ext`` command override; it will only affect +``Pybind11Extensions``: + +.. code-block:: python + + from glob import glob + from setuptools import setup + from pybind11.setup_helpers import Pybind11Extension, build_ext + + ext_modules = [ + Pybind11Extension( + "python_example", + sorted(glob("src/*.cpp")), + ), + ] + + setup(..., cmdclass={"build_ext": build_ext}, ext_modules=ext_modules) + +If you have single-file extension modules that are directly stored in the +Python source tree (``foo.cpp`` in the same directory as where a ``foo.py`` +would be located), you can also generate ``Pybind11Extensions`` using +``setup_helpers.intree_extensions``: ``intree_extensions(["path/to/foo.cpp", +...])`` returns a list of ``Pybind11Extensions`` which can be passed to +``ext_modules``, possibly after further customizing their attributes +(``libraries``, ``include_dirs``, etc.). By doing so, a ``foo.*.so`` extension +module will be generated and made available upon installation. + +``intree_extension`` will automatically detect if you are using a ``src``-style +layout (as long as no namespace packages are involved), but you can also +explicitly pass ``package_dir`` to it (as in ``setuptools.setup``). + +Since pybind11 does not require NumPy when building, a light-weight replacement +for NumPy's parallel compilation distutils tool is included. Use it like this: + +.. code-block:: python + + from pybind11.setup_helpers import ParallelCompile + + # Optional multithreaded build + ParallelCompile("NPY_NUM_BUILD_JOBS").install() + + setup(...) + +The argument is the name of an environment variable to control the number of +threads, such as ``NPY_NUM_BUILD_JOBS`` (as used by NumPy), though you can set +something different if you want; ``CMAKE_BUILD_PARALLEL_LEVEL`` is another choice +a user might expect. You can also pass ``default=N`` to set the default number +of threads (0 will take the number of threads available) and ``max=N``, the +maximum number of threads; if you have a large extension you may want set this +to a memory dependent number. + +If you are developing rapidly and have a lot of C++ files, you may want to +avoid rebuilding files that have not changed. For simple cases were you are +using ``pip install -e .`` and do not have local headers, you can skip the +rebuild if an object file is newer than its source (headers are not checked!) +with the following: + +.. code-block:: python + + from pybind11.setup_helpers import ParallelCompile, naive_recompile + + ParallelCompile("NPY_NUM_BUILD_JOBS", needs_recompile=naive_recompile).install() + + +If you have a more complex build, you can implement a smarter function and pass +it to ``needs_recompile``, or you can use [Ccache]_ instead. ``CXX="cache g++" +pip install -e .`` would be the way to use it with GCC, for example. Unlike the +simple solution, this even works even when not compiling in editable mode, but +it does require Ccache to be installed. + +Keep in mind that Pip will not even attempt to rebuild if it thinks it has +already built a copy of your code, which it deduces from the version number. +One way to avoid this is to use [setuptools_scm]_, which will generate a +version number that includes the number of commits since your last tag and a +hash for a dirty directory. Another way to force a rebuild is purge your cache +or use Pip's ``--no-cache-dir`` option. + +.. [Ccache] https://ccache.dev + +.. [setuptools_scm] https://github.com/pypa/setuptools_scm + +.. _setup_helpers-pep518: + +PEP 518 requirements (Pip 10+ required) +--------------------------------------- + +If you use `PEP 518's `_ +``pyproject.toml`` file, you can ensure that ``pybind11`` is available during +the compilation of your project. When this file exists, Pip will make a new +virtual environment, download just the packages listed here in ``requires=``, +and build a wheel (binary Python package). It will then throw away the +environment, and install your wheel. + +Your ``pyproject.toml`` file will likely look something like this: + +.. code-block:: toml + + [build-system] + requires = ["setuptools>=42", "wheel", "pybind11~=2.6.1"] + build-backend = "setuptools.build_meta" + +.. note:: + + The main drawback to this method is that a `PEP 517`_ compliant build tool, + such as Pip 10+, is required for this approach to work; older versions of + Pip completely ignore this file. If you distribute binaries (called wheels + in Python) using something like `cibuildwheel`_, remember that ``setup.py`` + and ``pyproject.toml`` are not even contained in the wheel, so this high + Pip requirement is only for source builds, and will not affect users of + your binary wheels. If you are building SDists and wheels, then + `pypa-build`_ is the recommended official tool. + +.. _PEP 517: https://www.python.org/dev/peps/pep-0517/ +.. _cibuildwheel: https://cibuildwheel.readthedocs.io +.. _pypa-build: https://pypa-build.readthedocs.io/en/latest/ + +.. _setup_helpers-setup_requires: + +Classic ``setup_requires`` +-------------------------- + +If you want to support old versions of Pip with the classic +``setup_requires=["pybind11"]`` keyword argument to setup, which triggers a +two-phase ``setup.py`` run, then you will need to use something like this to +ensure the first pass works (which has not yet installed the ``setup_requires`` +packages, since it can't install something it does not know about): + +.. code-block:: python + + try: + from pybind11.setup_helpers import Pybind11Extension + except ImportError: + from setuptools import Extension as Pybind11Extension + + +It doesn't matter that the Extension class is not the enhanced subclass for the +first pass run; and the second pass will have the ``setup_requires`` +requirements. + +This is obviously more of a hack than the PEP 518 method, but it supports +ancient versions of Pip. + +.. _setup_helpers-copy-manually: + +Copy manually +------------- + +You can also copy ``setup_helpers.py`` directly to your project; it was +designed to be usable standalone, like the old example ``setup.py``. You can +set ``include_pybind11=False`` to skip including the pybind11 package headers, +so you can use it with git submodules and a specific git version. If you use +this, you will need to import from a local file in ``setup.py`` and ensure the +helper file is part of your MANIFEST. + + +Closely related, if you include pybind11 as a subproject, you can run the +``setup_helpers.py`` inplace. If loaded correctly, this should even pick up +the correct include for pybind11, though you can turn it off as shown above if +you want to input it manually. + +Suggested usage if you have pybind11 as a submodule in ``extern/pybind11``: + +.. code-block:: python + + DIR = os.path.abspath(os.path.dirname(__file__)) + + sys.path.append(os.path.join(DIR, "extern", "pybind11")) + from pybind11.setup_helpers import Pybind11Extension # noqa: E402 + + del sys.path[-1] + + +.. versionchanged:: 2.6 + + Added ``setup_helpers`` file. + +Building with cppimport +======================== + +[cppimport]_ is a small Python import hook that determines whether there is a C++ +source file whose name matches the requested module. If there is, the file is +compiled as a Python extension using pybind11 and placed in the same folder as +the C++ source file. Python is then able to find the module and load it. + +.. [cppimport] https://github.com/tbenthompson/cppimport + +.. _cmake: + +Building with CMake +=================== + +For C++ codebases that have an existing CMake-based build system, a Python +extension module can be created with just a few lines of code: + +.. code-block:: cmake + + cmake_minimum_required(VERSION 3.4...3.18) + project(example LANGUAGES CXX) + + add_subdirectory(pybind11) + pybind11_add_module(example example.cpp) + +This assumes that the pybind11 repository is located in a subdirectory named +:file:`pybind11` and that the code is located in a file named :file:`example.cpp`. +The CMake command ``add_subdirectory`` will import the pybind11 project which +provides the ``pybind11_add_module`` function. It will take care of all the +details needed to build a Python extension module on any platform. + +A working sample project, including a way to invoke CMake from :file:`setup.py` for +PyPI integration, can be found in the [cmake_example]_ repository. + +.. [cmake_example] https://github.com/pybind/cmake_example + +.. versionchanged:: 2.6 + CMake 3.4+ is required. + +Further information can be found at :doc:`cmake/index`. + +pybind11_add_module +------------------- + +To ease the creation of Python extension modules, pybind11 provides a CMake +function with the following signature: + +.. code-block:: cmake + + pybind11_add_module( [MODULE | SHARED] [EXCLUDE_FROM_ALL] + [NO_EXTRAS] [THIN_LTO] [OPT_SIZE] source1 [source2 ...]) + +This function behaves very much like CMake's builtin ``add_library`` (in fact, +it's a wrapper function around that command). It will add a library target +called ```` to be built from the listed source files. In addition, it +will take care of all the Python-specific compiler and linker flags as well +as the OS- and Python-version-specific file extension. The produced target +```` can be further manipulated with regular CMake commands. + +``MODULE`` or ``SHARED`` may be given to specify the type of library. If no +type is given, ``MODULE`` is used by default which ensures the creation of a +Python-exclusive module. Specifying ``SHARED`` will create a more traditional +dynamic library which can also be linked from elsewhere. ``EXCLUDE_FROM_ALL`` +removes this target from the default build (see CMake docs for details). + +Since pybind11 is a template library, ``pybind11_add_module`` adds compiler +flags to ensure high quality code generation without bloat arising from long +symbol names and duplication of code in different translation units. It +sets default visibility to *hidden*, which is required for some pybind11 +features and functionality when attempting to load multiple pybind11 modules +compiled under different pybind11 versions. It also adds additional flags +enabling LTO (Link Time Optimization) and strip unneeded symbols. See the +:ref:`FAQ entry ` for a more detailed explanation. These +latter optimizations are never applied in ``Debug`` mode. If ``NO_EXTRAS`` is +given, they will always be disabled, even in ``Release`` mode. However, this +will result in code bloat and is generally not recommended. + +As stated above, LTO is enabled by default. Some newer compilers also support +different flavors of LTO such as `ThinLTO`_. Setting ``THIN_LTO`` will cause +the function to prefer this flavor if available. The function falls back to +regular LTO if ``-flto=thin`` is not available. If +``CMAKE_INTERPROCEDURAL_OPTIMIZATION`` is set (either ``ON`` or ``OFF``), then +that will be respected instead of the built-in flag search. + +.. note:: + + If you want to set the property form on targets or the + ``CMAKE_INTERPROCEDURAL_OPTIMIZATION_`` versions of this, you should + still use ``set(CMAKE_INTERPROCEDURAL_OPTIMIZATION OFF)`` (otherwise a + no-op) to disable pybind11's ipo flags. + +The ``OPT_SIZE`` flag enables size-based optimization equivalent to the +standard ``/Os`` or ``-Os`` compiler flags and the ``MinSizeRel`` build type, +which avoid optimizations that that can substantially increase the size of the +resulting binary. This flag is particularly useful in projects that are split +into performance-critical parts and associated bindings. In this case, we can +compile the project in release mode (and hence, optimize performance globally), +and specify ``OPT_SIZE`` for the binding target, where size might be the main +concern as performance is often less critical here. A ~25% size reduction has +been observed in practice. This flag only changes the optimization behavior at +a per-target level and takes precedence over the global CMake build type +(``Release``, ``RelWithDebInfo``) except for ``Debug`` builds, where +optimizations remain disabled. + +.. _ThinLTO: http://clang.llvm.org/docs/ThinLTO.html + +Configuration variables +----------------------- + +By default, pybind11 will compile modules with the compiler default or the +minimum standard required by pybind11, whichever is higher. You can set the +standard explicitly with +`CMAKE_CXX_STANDARD `_: + +.. code-block:: cmake + + set(CMAKE_CXX_STANDARD 14 CACHE STRING "C++ version selection") # or 11, 14, 17, 20 + set(CMAKE_CXX_STANDARD_REQUIRED ON) # optional, ensure standard is supported + set(CMAKE_CXX_EXTENSIONS OFF) # optional, keep compiler extensions off + +The variables can also be set when calling CMake from the command line using +the ``-D=`` flag. You can also manually set ``CXX_STANDARD`` +on a target or use ``target_compile_features`` on your targets - anything that +CMake supports. + +Classic Python support: The target Python version can be selected by setting +``PYBIND11_PYTHON_VERSION`` or an exact Python installation can be specified +with ``PYTHON_EXECUTABLE``. For example: + +.. code-block:: bash + + cmake -DPYBIND11_PYTHON_VERSION=3.6 .. + + # Another method: + cmake -DPYTHON_EXECUTABLE=/path/to/python .. + + # This often is a good way to get the current Python, works in environments: + cmake -DPYTHON_EXECUTABLE=$(python3 -c "import sys; print(sys.executable)") .. + + +find_package vs. add_subdirectory +--------------------------------- + +For CMake-based projects that don't include the pybind11 repository internally, +an external installation can be detected through ``find_package(pybind11)``. +See the `Config file`_ docstring for details of relevant CMake variables. + +.. code-block:: cmake + + cmake_minimum_required(VERSION 3.4...3.18) + project(example LANGUAGES CXX) + + find_package(pybind11 REQUIRED) + pybind11_add_module(example example.cpp) + +Note that ``find_package(pybind11)`` will only work correctly if pybind11 +has been correctly installed on the system, e. g. after downloading or cloning +the pybind11 repository : + +.. code-block:: bash + + # Classic CMake + cd pybind11 + mkdir build + cd build + cmake .. + make install + + # CMake 3.15+ + cd pybind11 + cmake -S . -B build + cmake --build build -j 2 # Build on 2 cores + cmake --install build + +Once detected, the aforementioned ``pybind11_add_module`` can be employed as +before. The function usage and configuration variables are identical no matter +if pybind11 is added as a subdirectory or found as an installed package. You +can refer to the same [cmake_example]_ repository for a full sample project +-- just swap out ``add_subdirectory`` for ``find_package``. + +.. _Config file: https://github.com/pybind/pybind11/blob/master/tools/pybind11Config.cmake.in + + +.. _find-python-mode: + +FindPython mode +--------------- + +CMake 3.12+ (3.15+ recommended, 3.18.2+ ideal) added a new module called +FindPython that had a highly improved search algorithm and modern targets +and tools. If you use FindPython, pybind11 will detect this and use the +existing targets instead: + +.. code-block:: cmake + + cmake_minimum_required(VERSION 3.15...3.19) + project(example LANGUAGES CXX) + + find_package(Python COMPONENTS Interpreter Development REQUIRED) + find_package(pybind11 CONFIG REQUIRED) + # or add_subdirectory(pybind11) + + pybind11_add_module(example example.cpp) + +You can also use the targets (as listed below) with FindPython. If you define +``PYBIND11_FINDPYTHON``, pybind11 will perform the FindPython step for you +(mostly useful when building pybind11's own tests, or as a way to change search +algorithms from the CMake invocation, with ``-DPYBIND11_FINDPYTHON=ON``. + +.. warning:: + + If you use FindPython2 and FindPython3 to dual-target Python, use the + individual targets listed below, and avoid targets that directly include + Python parts. + +There are `many ways to hint or force a discovery of a specific Python +installation `_), +setting ``Python_ROOT_DIR`` may be the most common one (though with +virtualenv/venv support, and Conda support, this tends to find the correct +Python version more often than the old system did). + +.. warning:: + + When the Python libraries (i.e. ``libpythonXX.a`` and ``libpythonXX.so`` + on Unix) are not available, as is the case on a manylinux image, the + ``Development`` component will not be resolved by ``FindPython``. When not + using the embedding functionality, CMake 3.18+ allows you to specify + ``Development.Module`` instead of ``Development`` to resolve this issue. + +.. versionadded:: 2.6 + +Advanced: interface library targets +----------------------------------- + +Pybind11 supports modern CMake usage patterns with a set of interface targets, +available in all modes. The targets provided are: + + ``pybind11::headers`` + Just the pybind11 headers and minimum compile requirements + + ``pybind11::python2_no_register`` + Quiets the warning/error when mixing C++14 or higher and Python 2 + + ``pybind11::pybind11`` + Python headers + ``pybind11::headers`` + ``pybind11::python2_no_register`` (Python 2 only) + + ``pybind11::python_link_helper`` + Just the "linking" part of pybind11:module + + ``pybind11::module`` + Everything for extension modules - ``pybind11::pybind11`` + ``Python::Module`` (FindPython CMake 3.15+) or ``pybind11::python_link_helper`` + + ``pybind11::embed`` + Everything for embedding the Python interpreter - ``pybind11::pybind11`` + ``Python::Python`` (FindPython) or Python libs + + ``pybind11::lto`` / ``pybind11::thin_lto`` + An alternative to `INTERPROCEDURAL_OPTIMIZATION` for adding link-time optimization. + + ``pybind11::windows_extras`` + ``/bigobj`` and ``/mp`` for MSVC. + + ``pybind11::opt_size`` + ``/Os`` for MSVC, ``-Os`` for other compilers. Does nothing for debug builds. + +Two helper functions are also provided: + + ``pybind11_strip(target)`` + Strips a target (uses ``CMAKE_STRIP`` after the target is built) + + ``pybind11_extension(target)`` + Sets the correct extension (with SOABI) for a target. + +You can use these targets to build complex applications. For example, the +``add_python_module`` function is identical to: + +.. code-block:: cmake + + cmake_minimum_required(VERSION 3.4) + project(example LANGUAGES CXX) + + find_package(pybind11 REQUIRED) # or add_subdirectory(pybind11) + + add_library(example MODULE main.cpp) + + target_link_libraries(example PRIVATE pybind11::module pybind11::lto pybind11::windows_extras) + + pybind11_extension(example) + pybind11_strip(example) + + set_target_properties(example PROPERTIES CXX_VISIBILITY_PRESET "hidden" + CUDA_VISIBILITY_PRESET "hidden") + +Instead of setting properties, you can set ``CMAKE_*`` variables to initialize these correctly. + +.. warning:: + + Since pybind11 is a metatemplate library, it is crucial that certain + compiler flags are provided to ensure high quality code generation. In + contrast to the ``pybind11_add_module()`` command, the CMake interface + provides a *composable* set of targets to ensure that you retain flexibility. + It can be especially important to provide or set these properties; the + :ref:`FAQ ` contains an explanation on why these are needed. + +.. versionadded:: 2.6 + +.. _nopython-mode: + +Advanced: NOPYTHON mode +----------------------- + +If you want complete control, you can set ``PYBIND11_NOPYTHON`` to completely +disable Python integration (this also happens if you run ``FindPython2`` and +``FindPython3`` without running ``FindPython``). This gives you complete +freedom to integrate into an existing system (like `Scikit-Build's +`_ ``PythonExtensions``). +``pybind11_add_module`` and ``pybind11_extension`` will be unavailable, and the +targets will be missing any Python specific behavior. + +.. versionadded:: 2.6 + +Embedding the Python interpreter +-------------------------------- + +In addition to extension modules, pybind11 also supports embedding Python into +a C++ executable or library. In CMake, simply link with the ``pybind11::embed`` +target. It provides everything needed to get the interpreter running. The Python +headers and libraries are attached to the target. Unlike ``pybind11::module``, +there is no need to manually set any additional properties here. For more +information about usage in C++, see :doc:`/advanced/embedding`. + +.. code-block:: cmake + + cmake_minimum_required(VERSION 3.4...3.18) + project(example LANGUAGES CXX) + + find_package(pybind11 REQUIRED) # or add_subdirectory(pybind11) + + add_executable(example main.cpp) + target_link_libraries(example PRIVATE pybind11::embed) + +.. _building_manually: + +Building manually +================= + +pybind11 is a header-only library, hence it is not necessary to link against +any special libraries and there are no intermediate (magic) translation steps. + +On Linux, you can compile an example such as the one given in +:ref:`simple_example` using the following command: + +.. code-block:: bash + + $ c++ -O3 -Wall -shared -std=c++11 -fPIC $(python3 -m pybind11 --includes) example.cpp -o example$(python3-config --extension-suffix) + +The flags given here assume that you're using Python 3. For Python 2, just +change the executable appropriately (to ``python`` or ``python2``). + +The ``python3 -m pybind11 --includes`` command fetches the include paths for +both pybind11 and Python headers. This assumes that pybind11 has been installed +using ``pip`` or ``conda``. If it hasn't, you can also manually specify +``-I /include`` together with the Python includes path +``python3-config --includes``. + +Note that Python 2.7 modules don't use a special suffix, so you should simply +use ``example.so`` instead of ``example$(python3-config --extension-suffix)``. +Besides, the ``--extension-suffix`` option may or may not be available, depending +on the distribution; in the latter case, the module extension can be manually +set to ``.so``. + +On macOS: the build command is almost the same but it also requires passing +the ``-undefined dynamic_lookup`` flag so as to ignore missing symbols when +building the module: + +.. code-block:: bash + + $ c++ -O3 -Wall -shared -std=c++11 -undefined dynamic_lookup $(python3 -m pybind11 --includes) example.cpp -o example$(python3-config --extension-suffix) + +In general, it is advisable to include several additional build parameters +that can considerably reduce the size of the created binary. Refer to section +:ref:`cmake` for a detailed example of a suitable cross-platform CMake-based +build system that works on all platforms including Windows. + +.. note:: + + On Linux and macOS, it's better to (intentionally) not link against + ``libpython``. The symbols will be resolved when the extension library + is loaded into a Python binary. This is preferable because you might + have several different installations of a given Python version (e.g. the + system-provided Python, and one that ships with a piece of commercial + software). In this way, the plugin will work with both versions, instead + of possibly importing a second Python library into a process that already + contains one (which will lead to a segfault). + + +Building with Bazel +=================== + +You can build with the Bazel build system using the `pybind11_bazel +`_ repository. + +Generating binding code automatically +===================================== + +The ``Binder`` project is a tool for automatic generation of pybind11 binding +code by introspecting existing C++ codebases using LLVM/Clang. See the +[binder]_ documentation for details. + +.. [binder] http://cppbinder.readthedocs.io/en/latest/about.html + +[AutoWIG]_ is a Python library that wraps automatically compiled libraries into +high-level languages. It parses C++ code using LLVM/Clang technologies and +generates the wrappers using the Mako templating engine. The approach is automatic, +extensible, and applies to very complex C++ libraries, composed of thousands of +classes or incorporating modern meta-programming constructs. + +.. [AutoWIG] https://github.com/StatisKit/AutoWIG + +[robotpy-build]_ is a is a pure python, cross platform build tool that aims to +simplify creation of python wheels for pybind11 projects, and provide +cross-project dependency management. Additionally, it is able to autogenerate +customizable pybind11-based wrappers by parsing C++ header files. + +.. [robotpy-build] https://robotpy-build.readthedocs.io diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/conf.py b/third-party/DPVO/DPRetrieval/pybind11/docs/conf.py new file mode 100644 index 0000000000000000000000000000000000000000..d8ca1e1a08361aadfd170e3375cb1c461efddf0a --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/conf.py @@ -0,0 +1,381 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +# +# pybind11 documentation build configuration file, created by +# sphinx-quickstart on Sun Oct 11 19:23:48 2015. +# +# This file is execfile()d with the current directory set to its +# containing dir. +# +# Note that not all possible configuration values are present in this +# autogenerated file. +# +# All configuration values have a default; values that are commented out +# serve to show the default. + +import os +import re +import subprocess +import sys +from pathlib import Path + +DIR = Path(__file__).parent.resolve() + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# sys.path.insert(0, os.path.abspath('.')) + +# -- General configuration ------------------------------------------------ + +# If your documentation needs a minimal Sphinx version, state it here. +# needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + "breathe", + "sphinxcontrib.rsvgconverter", + "sphinxcontrib.moderncmakedomain", +] + +breathe_projects = {"pybind11": ".build/doxygenxml/"} +breathe_default_project = "pybind11" +breathe_domain_by_extension = {"h": "cpp"} + +# Add any paths that contain templates here, relative to this directory. +templates_path = [".templates"] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# source_suffix = ['.rst', '.md'] +source_suffix = ".rst" + +# The encoding of source files. +# source_encoding = 'utf-8-sig' + +# The master toctree document. +master_doc = "index" + +# General information about the project. +project = "pybind11" +copyright = "2017, Wenzel Jakob" +author = "Wenzel Jakob" + +# The version info for the project you're documenting, acts as replacement for +# |version| and |release|, also used in various other places throughout the +# built documents. + +# Read the listed version +with open("../pybind11/_version.py") as f: + code = compile(f.read(), "../pybind11/_version.py", "exec") +loc = {} +exec(code, loc) + +# The full version, including alpha/beta/rc tags. +version = loc["__version__"] + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# There are two options for replacing |today|: either, you set today to some +# non-false value, then it is used: +# today = '' +# Else, today_fmt is used as the format for a strftime call. +# today_fmt = '%B %d, %Y' + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +exclude_patterns = [".build", "release.rst"] + +# The reST default role (used for this markup: `text`) to use for all +# documents. +default_role = "any" + +# If true, '()' will be appended to :func: etc. cross-reference text. +# add_function_parentheses = True + +# If true, the current module name will be prepended to all description +# unit titles (such as .. function::). +# add_module_names = True + +# If true, sectionauthor and moduleauthor directives will be shown in the +# output. They are ignored by default. +# show_authors = False + +# The name of the Pygments (syntax highlighting) style to use. +# pygments_style = 'monokai' + +# A list of ignored prefixes for module index sorting. +# modindex_common_prefix = [] + +# If true, keep warnings as "system message" paragraphs in the built documents. +# keep_warnings = False + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = False + + +# -- Options for HTML output ---------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. + +on_rtd = os.environ.get("READTHEDOCS", None) == "True" + +if not on_rtd: # only import and set the theme if we're building docs locally + import sphinx_rtd_theme + + html_theme = "sphinx_rtd_theme" + html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] + + html_context = {"css_files": ["_static/theme_overrides.css"]} +else: + html_context = { + "css_files": [ + "//media.readthedocs.org/css/sphinx_rtd_theme.css", + "//media.readthedocs.org/css/readthedocs-doc-embed.css", + "_static/theme_overrides.css", + ] + } + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# html_theme_options = {} + +# Add any paths that contain custom themes here, relative to this directory. +# html_theme_path = [] + +# The name for this set of Sphinx documents. If None, it defaults to +# " v documentation". +# html_title = None + +# A shorter title for the navigation bar. Default is the same as html_title. +# html_short_title = None + +# The name of an image file (relative to this directory) to place at the top +# of the sidebar. +# html_logo = None + +# The name of an image file (within the static path) to use as favicon of the +# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 +# pixels large. +# html_favicon = None + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ["_static"] + +# Add any extra paths that contain custom files (such as robots.txt or +# .htaccess) here, relative to this directory. These files are copied +# directly to the root of the documentation. +# html_extra_path = [] + +# If not '', a 'Last updated on:' timestamp is inserted at every page bottom, +# using the given strftime format. +# html_last_updated_fmt = '%b %d, %Y' + +# If true, SmartyPants will be used to convert quotes and dashes to +# typographically correct entities. +# html_use_smartypants = True + +# Custom sidebar templates, maps document names to template names. +# html_sidebars = {} + +# Additional templates that should be rendered to pages, maps page names to +# template names. +# html_additional_pages = {} + +# If false, no module index is generated. +# html_domain_indices = True + +# If false, no index is generated. +# html_use_index = True + +# If true, the index is split into individual pages for each letter. +# html_split_index = False + +# If true, links to the reST sources are added to the pages. +# html_show_sourcelink = True + +# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. +# html_show_sphinx = True + +# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. +# html_show_copyright = True + +# If true, an OpenSearch description file will be output, and all pages will +# contain a tag referring to it. The value of this option must be the +# base URL from which the finished HTML is served. +# html_use_opensearch = '' + +# This is the file name suffix for HTML files (e.g. ".xhtml"). +# html_file_suffix = None + +# Language to be used for generating the HTML full-text search index. +# Sphinx supports the following languages: +# 'da', 'de', 'en', 'es', 'fi', 'fr', 'h', 'it', 'ja' +# 'nl', 'no', 'pt', 'ro', 'r', 'sv', 'tr' +# html_search_language = 'en' + +# A dictionary with options for the search language support, empty by default. +# Now only 'ja' uses this config value +# html_search_options = {'type': 'default'} + +# The name of a javascript file (relative to the configuration directory) that +# implements a search results scorer. If empty, the default will be used. +# html_search_scorer = 'scorer.js' + +# Output file base name for HTML help builder. +htmlhelp_basename = "pybind11doc" + +# -- Options for LaTeX output --------------------------------------------- + +latex_engine = "pdflatex" + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # 'papersize': 'letterpaper', + # + # The font size ('10pt', '11pt' or '12pt'). + # 'pointsize': '10pt', + # + # Additional stuff for the LaTeX preamble. + # remove blank pages (between the title page and the TOC, etc.) + "classoptions": ",openany,oneside", + "preamble": r""" +\usepackage{fontawesome} +\usepackage{textgreek} +\DeclareUnicodeCharacter{00A0}{} +\DeclareUnicodeCharacter{2194}{\faArrowsH} +\DeclareUnicodeCharacter{1F382}{\faBirthdayCake} +\DeclareUnicodeCharacter{1F355}{\faAdjust} +\DeclareUnicodeCharacter{0301}{'} +\DeclareUnicodeCharacter{03C0}{\textpi} + +""", + # Latex figure (float) alignment + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, "pybind11.tex", "pybind11 Documentation", "Wenzel Jakob", "manual"), +] + +# The name of an image file (relative to this directory) to place at the top of +# the title page. +# latex_logo = 'pybind11-logo.png' + +# For "manual" documents, if this is true, then toplevel headings are parts, +# not chapters. +# latex_use_parts = False + +# If true, show page references after internal links. +# latex_show_pagerefs = False + +# If true, show URL addresses after external links. +# latex_show_urls = False + +# Documents to append as an appendix to all manuals. +# latex_appendices = [] + +# If false, no module index is generated. +# latex_domain_indices = True + + +# -- Options for manual page output --------------------------------------- + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [(master_doc, "pybind11", "pybind11 Documentation", [author], 1)] + +# If true, show URL addresses after external links. +# man_show_urls = False + + +# -- Options for Texinfo output ------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + ( + master_doc, + "pybind11", + "pybind11 Documentation", + author, + "pybind11", + "One line description of project.", + "Miscellaneous", + ), +] + +# Documents to append as an appendix to all manuals. +# texinfo_appendices = [] + +# If false, no module index is generated. +# texinfo_domain_indices = True + +# How to display URL addresses: 'footnote', 'no', or 'inline'. +# texinfo_show_urls = 'footnote' + +# If true, do not generate a @detailmenu in the "Top" node's menu. +# texinfo_no_detailmenu = False + +primary_domain = "cpp" +highlight_language = "cpp" + + +def generate_doxygen_xml(app): + build_dir = os.path.join(app.confdir, ".build") + if not os.path.exists(build_dir): + os.mkdir(build_dir) + + try: + subprocess.call(["doxygen", "--version"]) + retcode = subprocess.call(["doxygen"], cwd=app.confdir) + if retcode < 0: + sys.stderr.write("doxygen error code: {}\n".format(-retcode)) + except OSError as e: + sys.stderr.write("doxygen execution failed: {}\n".format(e)) + + +def prepare(app): + with open(DIR.parent / "README.rst") as f: + contents = f.read() + + if app.builder.name == "latex": + # Remove badges and stuff from start + contents = contents[contents.find(r".. start") :] + + # Filter out section titles for index.rst for LaTeX + contents = re.sub(r"^(.*)\n[-~]{3,}$", r"**\1**", contents, flags=re.MULTILINE) + + with open(DIR / "readme.rst", "w") as f: + f.write(contents) + + +def clean_up(app, exception): + (DIR / "readme.rst").unlink() + + +def setup(app): + + # Add hook for building doxygen xml when needed + app.connect("builder-inited", generate_doxygen_xml) + + # Copy the readme in + app.connect("builder-inited", prepare) + + # Clean up the generated readme + app.connect("build-finished", clean_up) diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/faq.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/faq.rst new file mode 100644 index 0000000000000000000000000000000000000000..abe7f67c27ce1eaf17ebc472e01733f5f5b2dbc3 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/faq.rst @@ -0,0 +1,343 @@ +Frequently asked questions +########################## + +"ImportError: dynamic module does not define init function" +=========================================================== + +1. Make sure that the name specified in PYBIND11_MODULE is identical to the +filename of the extension library (without suffixes such as ``.so``). + +2. If the above did not fix the issue, you are likely using an incompatible +version of Python (for instance, the extension library was compiled against +Python 2, while the interpreter is running on top of some version of Python +3, or vice versa). + +"Symbol not found: ``__Py_ZeroStruct`` / ``_PyInstanceMethod_Type``" +======================================================================== + +See the first answer. + +"SystemError: dynamic module not initialized properly" +====================================================== + +See the first answer. + +The Python interpreter immediately crashes when importing my module +=================================================================== + +See the first answer. + +.. _faq_reference_arguments: + +Limitations involving reference arguments +========================================= + +In C++, it's fairly common to pass arguments using mutable references or +mutable pointers, which allows both read and write access to the value +supplied by the caller. This is sometimes done for efficiency reasons, or to +realize functions that have multiple return values. Here are two very basic +examples: + +.. code-block:: cpp + + void increment(int &i) { i++; } + void increment_ptr(int *i) { (*i)++; } + +In Python, all arguments are passed by reference, so there is no general +issue in binding such code from Python. + +However, certain basic Python types (like ``str``, ``int``, ``bool``, +``float``, etc.) are **immutable**. This means that the following attempt +to port the function to Python doesn't have the same effect on the value +provided by the caller -- in fact, it does nothing at all. + +.. code-block:: python + + def increment(i): + i += 1 # nope.. + +pybind11 is also affected by such language-level conventions, which means that +binding ``increment`` or ``increment_ptr`` will also create Python functions +that don't modify their arguments. + +Although inconvenient, one workaround is to encapsulate the immutable types in +a custom type that does allow modifications. + +An other alternative involves binding a small wrapper lambda function that +returns a tuple with all output arguments (see the remainder of the +documentation for examples on binding lambda functions). An example: + +.. code-block:: cpp + + int foo(int &i) { i++; return 123; } + +and the binding code + +.. code-block:: cpp + + m.def("foo", [](int i) { int rv = foo(i); return std::make_tuple(rv, i); }); + + +How can I reduce the build time? +================================ + +It's good practice to split binding code over multiple files, as in the +following example: + +:file:`example.cpp`: + +.. code-block:: cpp + + void init_ex1(py::module_ &); + void init_ex2(py::module_ &); + /* ... */ + + PYBIND11_MODULE(example, m) { + init_ex1(m); + init_ex2(m); + /* ... */ + } + +:file:`ex1.cpp`: + +.. code-block:: cpp + + void init_ex1(py::module_ &m) { + m.def("add", [](int a, int b) { return a + b; }); + } + +:file:`ex2.cpp`: + +.. code-block:: cpp + + void init_ex2(py::module_ &m) { + m.def("sub", [](int a, int b) { return a - b; }); + } + +:command:`python`: + +.. code-block:: pycon + + >>> import example + >>> example.add(1, 2) + 3 + >>> example.sub(1, 1) + 0 + +As shown above, the various ``init_ex`` functions should be contained in +separate files that can be compiled independently from one another, and then +linked together into the same final shared object. Following this approach +will: + +1. reduce memory requirements per compilation unit. + +2. enable parallel builds (if desired). + +3. allow for faster incremental builds. For instance, when a single class + definition is changed, only a subset of the binding code will generally need + to be recompiled. + +"recursive template instantiation exceeded maximum depth of 256" +================================================================ + +If you receive an error about excessive recursive template evaluation, try +specifying a larger value, e.g. ``-ftemplate-depth=1024`` on GCC/Clang. The +culprit is generally the generation of function signatures at compile time +using C++14 template metaprogramming. + +.. _`faq:hidden_visibility`: + +"‘SomeClass’ declared with greater visibility than the type of its field ‘SomeClass::member’ [-Wattributes]" +============================================================================================================ + +This error typically indicates that you are compiling without the required +``-fvisibility`` flag. pybind11 code internally forces hidden visibility on +all internal code, but if non-hidden (and thus *exported*) code attempts to +include a pybind type (for example, ``py::object`` or ``py::list``) you can run +into this warning. + +To avoid it, make sure you are specifying ``-fvisibility=hidden`` when +compiling pybind code. + +As to why ``-fvisibility=hidden`` is necessary, because pybind modules could +have been compiled under different versions of pybind itself, it is also +important that the symbols defined in one module do not clash with the +potentially-incompatible symbols defined in another. While Python extension +modules are usually loaded with localized symbols (under POSIX systems +typically using ``dlopen`` with the ``RTLD_LOCAL`` flag), this Python default +can be changed, but even if it isn't it is not always enough to guarantee +complete independence of the symbols involved when not using +``-fvisibility=hidden``. + +Additionally, ``-fvisibility=hidden`` can deliver considerably binary size +savings. (See the following section for more details.) + + +.. _`faq:symhidden`: + +How can I create smaller binaries? +================================== + +To do its job, pybind11 extensively relies on a programming technique known as +*template metaprogramming*, which is a way of performing computation at compile +time using type information. Template metaprogramming usually instantiates code +involving significant numbers of deeply nested types that are either completely +removed or reduced to just a few instructions during the compiler's optimization +phase. However, due to the nested nature of these types, the resulting symbol +names in the compiled extension library can be extremely long. For instance, +the included test suite contains the following symbol: + +.. only:: html + + .. code-block:: none + + _​_​Z​N​8​p​y​b​i​n​d​1​1​1​2​c​p​p​_​f​u​n​c​t​i​o​n​C​1​I​v​8​E​x​a​m​p​l​e​2​J​R​N​S​t​3​_​_​1​6​v​e​c​t​o​r​I​N​S​3​_​1​2​b​a​s​i​c​_​s​t​r​i​n​g​I​w​N​S​3​_​1​1​c​h​a​r​_​t​r​a​i​t​s​I​w​E​E​N​S​3​_​9​a​l​l​o​c​a​t​o​r​I​w​E​E​E​E​N​S​8​_​I​S​A​_​E​E​E​E​E​J​N​S​_​4​n​a​m​e​E​N​S​_​7​s​i​b​l​i​n​g​E​N​S​_​9​i​s​_​m​e​t​h​o​d​E​A​2​8​_​c​E​E​E​M​T​0​_​F​T​_​D​p​T​1​_​E​D​p​R​K​T​2​_ + +.. only:: not html + + .. code-block:: cpp + + __ZN8pybind1112cpp_functionC1Iv8Example2JRNSt3__16vectorINS3_12basic_stringIwNS3_11char_traitsIwEENS3_9allocatorIwEEEENS8_ISA_EEEEEJNS_4nameENS_7siblingENS_9is_methodEA28_cEEEMT0_FT_DpT1_EDpRKT2_ + +which is the mangled form of the following function type: + +.. code-block:: cpp + + pybind11::cpp_function::cpp_function, std::__1::allocator >, std::__1::allocator, std::__1::allocator > > >&, pybind11::name, pybind11::sibling, pybind11::is_method, char [28]>(void (Example2::*)(std::__1::vector, std::__1::allocator >, std::__1::allocator, std::__1::allocator > > >&), pybind11::name const&, pybind11::sibling const&, pybind11::is_method const&, char const (&) [28]) + +The memory needed to store just the mangled name of this function (196 bytes) +is larger than the actual piece of code (111 bytes) it represents! On the other +hand, it's silly to even give this function a name -- after all, it's just a +tiny cog in a bigger piece of machinery that is not exposed to the outside +world. So we'll generally only want to export symbols for those functions which +are actually called from the outside. + +This can be achieved by specifying the parameter ``-fvisibility=hidden`` to GCC +and Clang, which sets the default symbol visibility to *hidden*, which has a +tremendous impact on the final binary size of the resulting extension library. +(On Visual Studio, symbols are already hidden by default, so nothing needs to +be done there.) + +In addition to decreasing binary size, ``-fvisibility=hidden`` also avoids +potential serious issues when loading multiple modules and is required for +proper pybind operation. See the previous FAQ entry for more details. + +Working with ancient Visual Studio 2008 builds on Windows +========================================================= + +The official Windows distributions of Python are compiled using truly +ancient versions of Visual Studio that lack good C++11 support. Some users +implicitly assume that it would be impossible to load a plugin built with +Visual Studio 2015 into a Python distribution that was compiled using Visual +Studio 2008. However, no such issue exists: it's perfectly legitimate to +interface DLLs that are built with different compilers and/or C libraries. +Common gotchas to watch out for involve not ``free()``-ing memory region +that that were ``malloc()``-ed in another shared library, using data +structures with incompatible ABIs, and so on. pybind11 is very careful not +to make these types of mistakes. + +How can I properly handle Ctrl-C in long-running functions? +=========================================================== + +Ctrl-C is received by the Python interpreter, and holds it until the GIL +is released, so a long-running function won't be interrupted. + +To interrupt from inside your function, you can use the ``PyErr_CheckSignals()`` +function, that will tell if a signal has been raised on the Python side. This +function merely checks a flag, so its impact is negligible. When a signal has +been received, you must either explicitly interrupt execution by throwing +``py::error_already_set`` (which will propagate the existing +``KeyboardInterrupt``), or clear the error (which you usually will not want): + +.. code-block:: cpp + + PYBIND11_MODULE(example, m) + { + m.def("long running_func", []() + { + for (;;) { + if (PyErr_CheckSignals() != 0) + throw py::error_already_set(); + // Long running iteration + } + }); + } + +CMake doesn't detect the right Python version +============================================= + +The CMake-based build system will try to automatically detect the installed +version of Python and link against that. When this fails, or when there are +multiple versions of Python and it finds the wrong one, delete +``CMakeCache.txt`` and then add ``-DPYTHON_EXECUTABLE=$(which python)`` to your +CMake configure line. (Replace ``$(which python)`` with a path to python if +your prefer.) + +You can alternatively try ``-DPYBIND11_FINDPYTHON=ON``, which will activate the +new CMake FindPython support instead of pybind11's custom search. Requires +CMake 3.12+, and 3.15+ or 3.18.2+ are even better. You can set this in your +``CMakeLists.txt`` before adding or finding pybind11, as well. + +Inconsistent detection of Python version in CMake and pybind11 +============================================================== + +The functions ``find_package(PythonInterp)`` and ``find_package(PythonLibs)`` +provided by CMake for Python version detection are modified by pybind11 due to +unreliability and limitations that make them unsuitable for pybind11's needs. +Instead pybind11 provides its own, more reliable Python detection CMake code. +Conflicts can arise, however, when using pybind11 in a project that *also* uses +the CMake Python detection in a system with several Python versions installed. + +This difference may cause inconsistencies and errors if *both* mechanisms are +used in the same project. Consider the following CMake code executed in a +system with Python 2.7 and 3.x installed: + +.. code-block:: cmake + + find_package(PythonInterp) + find_package(PythonLibs) + find_package(pybind11) + +It will detect Python 2.7 and pybind11 will pick it as well. + +In contrast this code: + +.. code-block:: cmake + + find_package(pybind11) + find_package(PythonInterp) + find_package(PythonLibs) + +will detect Python 3.x for pybind11 and may crash on +``find_package(PythonLibs)`` afterwards. + +There are three possible solutions: + +1. Avoid using ``find_package(PythonInterp)`` and ``find_package(PythonLibs)`` + from CMake and rely on pybind11 in detecting Python version. If this is not + possible, the CMake machinery should be called *before* including pybind11. +2. Set ``PYBIND11_FINDPYTHON`` to ``True`` or use ``find_package(Python + COMPONENTS Interpreter Development)`` on modern CMake (3.12+, 3.15+ better, + 3.18.2+ best). Pybind11 in these cases uses the new CMake FindPython instead + of the old, deprecated search tools, and these modules are much better at + finding the correct Python. +3. Set ``PYBIND11_NOPYTHON`` to ``TRUE``. Pybind11 will not search for Python. + However, you will have to use the target-based system, and do more setup + yourself, because it does not know about or include things that depend on + Python, like ``pybind11_add_module``. This might be ideal for integrating + into an existing system, like scikit-build's Python helpers. + +How to cite this project? +========================= + +We suggest the following BibTeX template to cite pybind11 in scientific +discourse: + +.. code-block:: bash + + @misc{pybind11, + author = {Wenzel Jakob and Jason Rhinelander and Dean Moldovan}, + year = {2017}, + note = {https://github.com/pybind/pybind11}, + title = {pybind11 -- Seamless operability between C++11 and Python} + } diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/index.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..dbb2ad6b67f177dd7ef96e8d7fa7ef18e44bd213 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/index.rst @@ -0,0 +1,48 @@ +.. only:: latex + + Intro + ===== + +.. include:: readme.rst + +.. only:: not latex + + Contents: + +.. toctree:: + :maxdepth: 1 + + changelog + upgrade + +.. toctree:: + :caption: The Basics + :maxdepth: 2 + + installing + basics + classes + compiling + +.. toctree:: + :caption: Advanced Topics + :maxdepth: 2 + + advanced/functions + advanced/classes + advanced/exceptions + advanced/smart_ptrs + advanced/cast/index + advanced/pycpp/index + advanced/embedding + advanced/misc + +.. toctree:: + :caption: Extra Information + :maxdepth: 1 + + faq + benchmark + limitations + reference + cmake/index diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/installing.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/installing.rst new file mode 100644 index 0000000000000000000000000000000000000000..0fb4602ccfebfca833c29b94bef26a17f4bf1569 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/installing.rst @@ -0,0 +1,105 @@ +.. _installing: + +Installing the library +###################### + +There are several ways to get the pybind11 source, which lives at +`pybind/pybind11 on GitHub `_. The pybind11 +developers recommend one of the first three ways listed here, submodule, PyPI, +or conda-forge, for obtaining pybind11. + +.. _include_as_a_submodule: + +Include as a submodule +====================== + +When you are working on a project in Git, you can use the pybind11 repository +as a submodule. From your git repository, use: + +.. code-block:: bash + + git submodule add -b stable ../../pybind/pybind11 extern/pybind11 + git submodule update --init + +This assumes you are placing your dependencies in ``extern/``, and that you are +using GitHub; if you are not using GitHub, use the full https or ssh URL +instead of the relative URL ``../../pybind/pybind11`` above. Some other servers +also require the ``.git`` extension (GitHub does not). + +From here, you can now include ``extern/pybind11/include``, or you can use +the various integration tools (see :ref:`compiling`) pybind11 provides directly +from the local folder. + +Include with PyPI +================= + +You can download the sources and CMake files as a Python package from PyPI +using Pip. Just use: + +.. code-block:: bash + + pip install pybind11 + +This will provide pybind11 in a standard Python package format. If you want +pybind11 available directly in your environment root, you can use: + +.. code-block:: bash + + pip install "pybind11[global]" + +This is not recommended if you are installing with your system Python, as it +will add files to ``/usr/local/include/pybind11`` and +``/usr/local/share/cmake/pybind11``, so unless that is what you want, it is +recommended only for use in virtual environments or your ``pyproject.toml`` +file (see :ref:`compiling`). + +Include with conda-forge +======================== + +You can use pybind11 with conda packaging via `conda-forge +`_: + +.. code-block:: bash + + conda install -c conda-forge pybind11 + + +Include with vcpkg +================== +You can download and install pybind11 using the Microsoft `vcpkg +`_ dependency manager: + +.. code-block:: bash + + git clone https://github.com/Microsoft/vcpkg.git + cd vcpkg + ./bootstrap-vcpkg.sh + ./vcpkg integrate install + vcpkg install pybind11 + +The pybind11 port in vcpkg is kept up to date by Microsoft team members and +community contributors. If the version is out of date, please `create an issue +or pull request `_ on the vcpkg +repository. + +Global install with brew +======================== + +The brew package manager (Homebrew on macOS, or Linuxbrew on Linux) has a +`pybind11 package +`_. +To install: + +.. code-block:: bash + + brew install pybind11 + +.. We should list Conan, and possibly a few other C++ package managers (hunter, +.. perhaps). Conan has a very clean CMake integration that would be good to show. + +Other options +============= + +Other locations you can find pybind11 are `listed here +`_; these are maintained +by various packagers and the community. diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/limitations.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/limitations.rst new file mode 100644 index 0000000000000000000000000000000000000000..645ea06001c010c7e96c7454e1d517861f211e55 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/limitations.rst @@ -0,0 +1,72 @@ +Limitations +########### + +Design choices +^^^^^^^^^^^^^^ + +pybind11 strives to be a general solution to binding generation, but it also has +certain limitations: + +- pybind11 casts away ``const``-ness in function arguments and return values. + This is in line with the Python language, which has no concept of ``const`` + values. This means that some additional care is needed to avoid bugs that + would be caught by the type checker in a traditional C++ program. + +- The NumPy interface ``pybind11::array`` greatly simplifies accessing + numerical data from C++ (and vice versa), but it's not a full-blown array + class like ``Eigen::Array`` or ``boost.multi_array``. ``Eigen`` objects are + directly supported, however, with ``pybind11/eigen.h``. + +Large but useful features could be implemented in pybind11 but would lead to a +significant increase in complexity. Pybind11 strives to be simple and compact. +Users who require large new features are encouraged to write an extension to +pybind11; see `pybind11_json `_ for an +example. + + +Known bugs +^^^^^^^^^^ + +These are issues that hopefully will one day be fixed, but currently are +unsolved. If you know how to help with one of these issues, contributions +are welcome! + +- Intel 20.2 is currently having an issue with the test suite. + `#2573 `_ + +- Debug mode Python does not support 1-5 tests in the test suite currently. + `#2422 `_ + +- PyPy3 7.3.1 and 7.3.2 have issues with several tests on 32-bit Windows. + +Known limitations +^^^^^^^^^^^^^^^^^ + +These are issues that are probably solvable, but have not been fixed yet. A +clean, well written patch would likely be accepted to solve them. + +- Type casters are not kept alive recursively. + `#2527 `_ + One consequence is that containers of ``char *`` are currently not supported. + `#2245 `_ + +- The ``cpptest`` does not run on Windows with Python 3.8 or newer, due to DLL + loader changes. User code that is correctly installed should not be affected. + `#2560 `_ + +Python 3.9.0 warning +^^^^^^^^^^^^^^^^^^^^ + +Combining older versions of pybind11 (< 2.6.0) with Python on exactly 3.9.0 +will trigger undefined behavior that typically manifests as crashes during +interpreter shutdown (but could also destroy your data. **You have been +warned**). + +This issue was `fixed in Python `_. +As a mitigation for this bug, pybind11 2.6.0 or newer includes a workaround +specifically when Python 3.9.0 is detected at runtime, leaking about 50 bytes +of memory when a callback function is garbage collected. For reference, the +pybind11 test suite has about 2,000 such callbacks, but only 49 are garbage +collected before the end-of-process. Wheels (even if built with Python 3.9.0) +will correctly avoid the leak when run in Python 3.9.1, and this does not +affect other 3.X versions. diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/pybind11-logo.png b/third-party/DPVO/DPRetrieval/pybind11/docs/pybind11-logo.png new file mode 100644 index 0000000000000000000000000000000000000000..4cbad54f797d3ced04d4048f282df5e4336d4af4 Binary files /dev/null and b/third-party/DPVO/DPRetrieval/pybind11/docs/pybind11-logo.png differ diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/pybind11_vs_boost_python1.png b/third-party/DPVO/DPRetrieval/pybind11/docs/pybind11_vs_boost_python1.png new file mode 100644 index 0000000000000000000000000000000000000000..833231f240809884fb6eb4079db528b9b3c0a9ac Binary files /dev/null and b/third-party/DPVO/DPRetrieval/pybind11/docs/pybind11_vs_boost_python1.png differ diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/pybind11_vs_boost_python1.svg b/third-party/DPVO/DPRetrieval/pybind11/docs/pybind11_vs_boost_python1.svg new file mode 100644 index 0000000000000000000000000000000000000000..f5b8c5b594028793eb971569579174f9f597e599 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/pybind11_vs_boost_python1.svg @@ -0,0 +1,427 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/pybind11_vs_boost_python2.png b/third-party/DPVO/DPRetrieval/pybind11/docs/pybind11_vs_boost_python2.png new file mode 100644 index 0000000000000000000000000000000000000000..9f17272c50663957d6ae6d8e23fdd5a15757e71f Binary files /dev/null and b/third-party/DPVO/DPRetrieval/pybind11/docs/pybind11_vs_boost_python2.png differ diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/pybind11_vs_boost_python2.svg b/third-party/DPVO/DPRetrieval/pybind11/docs/pybind11_vs_boost_python2.svg new file mode 100644 index 0000000000000000000000000000000000000000..2bd1214e45c3018513c373096c6f4ebe6f83111c --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/pybind11_vs_boost_python2.svg @@ -0,0 +1,427 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/reference.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/reference.rst new file mode 100644 index 0000000000000000000000000000000000000000..a2803f2d7cdf1cf7f48358e654b1ef0a204e869e --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/reference.rst @@ -0,0 +1,130 @@ +.. _reference: + +.. warning:: + + Please be advised that the reference documentation discussing pybind11 + internals is currently incomplete. Please refer to the previous sections + and the pybind11 header files for the nitty gritty details. + +Reference +######### + +.. _macros: + +Macros +====== + +.. doxygendefine:: PYBIND11_MODULE + +.. _core_types: + +Convenience classes for arbitrary Python types +============================================== + +Common member functions +----------------------- + +.. doxygenclass:: object_api + :members: + +Without reference counting +-------------------------- + +.. doxygenclass:: handle + :members: + +With reference counting +----------------------- + +.. doxygenclass:: object + :members: + +.. doxygenfunction:: reinterpret_borrow + +.. doxygenfunction:: reinterpret_steal + +Convenience classes for specific Python types +============================================= + +.. doxygenclass:: module_ + :members: + +.. doxygengroup:: pytypes + :members: + +Convenience functions converting to Python types +================================================ + +.. doxygenfunction:: make_tuple(Args&&...) + +.. doxygenfunction:: make_iterator(Iterator, Sentinel, Extra &&...) +.. doxygenfunction:: make_iterator(Type &, Extra&&...) + +.. doxygenfunction:: make_key_iterator(Iterator, Sentinel, Extra &&...) +.. doxygenfunction:: make_key_iterator(Type &, Extra&&...) + +.. doxygenfunction:: make_value_iterator(Iterator, Sentinel, Extra &&...) +.. doxygenfunction:: make_value_iterator(Type &, Extra&&...) + +.. _extras: + +Passing extra arguments to ``def`` or ``class_`` +================================================ + +.. doxygengroup:: annotations + :members: + +Embedding the interpreter +========================= + +.. doxygendefine:: PYBIND11_EMBEDDED_MODULE + +.. doxygenfunction:: initialize_interpreter + +.. doxygenfunction:: finalize_interpreter + +.. doxygenclass:: scoped_interpreter + +Redirecting C++ streams +======================= + +.. doxygenclass:: scoped_ostream_redirect + +.. doxygenclass:: scoped_estream_redirect + +.. doxygenfunction:: add_ostream_redirect + +Python built-in functions +========================= + +.. doxygengroup:: python_builtins + :members: + +Inheritance +=========== + +See :doc:`/classes` and :doc:`/advanced/classes` for more detail. + +.. doxygendefine:: PYBIND11_OVERRIDE + +.. doxygendefine:: PYBIND11_OVERRIDE_PURE + +.. doxygendefine:: PYBIND11_OVERRIDE_NAME + +.. doxygendefine:: PYBIND11_OVERRIDE_PURE_NAME + +.. doxygenfunction:: get_override + +Exceptions +========== + +.. doxygenclass:: error_already_set + :members: + +.. doxygenclass:: builtin_exception + :members: + +Literals +======== + +.. doxygennamespace:: literals diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/release.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/release.rst new file mode 100644 index 0000000000000000000000000000000000000000..5d19b9d2616c921f4caea5d0509cf69b2869341f --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/release.rst @@ -0,0 +1,97 @@ +On version numbers +^^^^^^^^^^^^^^^^^^ + +The two version numbers (C++ and Python) must match when combined (checked when +you build the PyPI package), and must be a valid `PEP 440 +`_ version when combined. + +For example: + +.. code-block:: C++ + + #define PYBIND11_VERSION_MAJOR X + #define PYBIND11_VERSION_MINOR Y + #define PYBIND11_VERSION_PATCH Z.dev1 + +For beta, ``PYBIND11_VERSION_PATCH`` should be ``Z.b1``. RC's can be ``Z.rc1``. +Always include the dot (even though PEP 440 allows it to be dropped). For a +final release, this must be a simple integer. There is also a HEX version of +the version just below. + + +To release a new version of pybind11: +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +If you don't have nox, you should either use ``pipx run nox`` instead, or use +``pipx install nox`` or ``brew install nox`` (Unix). + +- Update the version number + - Update ``PYBIND11_VERSION_MAJOR`` etc. in + ``include/pybind11/detail/common.h``. PATCH should be a simple integer. + - Update the version HEX just below, as well. + - Update ``pybind11/_version.py`` (match above) + - Run ``nox -s tests_packaging`` to ensure this was done correctly. + - Ensure that all the information in ``setup.cfg`` is up-to-date, like + supported Python versions. + - Add release date in ``docs/changelog.rst``. + - Check to make sure + `needs-changelog `_ + issues are entered in the changelog (clear the label when done). + - ``git add`` and ``git commit``, ``git push``. **Ensure CI passes**. (If it + fails due to a known flake issue, either ignore or restart CI.) +- Add a release branch if this is a new minor version, or update the existing release branch if it is a patch version + - New branch: ``git checkout -b vX.Y``, ``git push -u origin vX.Y`` + - Update branch: ``git checkout vX.Y``, ``git merge ``, ``git push`` +- Update tags (optional; if you skip this, the GitHub release makes a + non-annotated tag for you) + - ``git tag -a vX.Y.Z -m 'vX.Y.Z release'``. + - ``git push --tags``. +- Update stable + - ``git checkout stable`` + - ``git merge master`` + - ``git push`` +- Make a GitHub release (this shows up in the UI, sends new release + notifications to users watching releases, and also uploads PyPI packages). + (Note: if you do not use an existing tag, this creates a new lightweight tag + for you, so you could skip the above step.) + - GUI method: Under `releases `_ + click "Draft a new release" on the far right, fill in the tag name + (if you didn't tag above, it will be made here), fill in a release name + like "Version X.Y.Z", and copy-and-paste the markdown-formatted (!) changelog + into the description (usually ``cat docs/changelog.rst | pandoc -f rst -t gfm``). + Check "pre-release" if this is a beta/RC. + - CLI method: with ``gh`` installed, run ``gh release create vX.Y.Z -t "Version X.Y.Z"`` + If this is a pre-release, add ``-p``. + +- Get back to work + - Make sure you are on master, not somewhere else: ``git checkout master`` + - Update version macros in ``include/pybind11/detail/common.h`` (set PATCH to + ``0.dev1`` and increment MINOR). + - Update ``_version.py`` to match + - Run ``nox -s tests_packaging`` to ensure this was done correctly. + - Add a spot for in-development updates in ``docs/changelog.rst``. + - ``git add``, ``git commit``, ``git push`` + +If a version branch is updated, remember to set PATCH to ``1.dev1``. + +If you'd like to bump homebrew, run: + +.. code-block:: console + + brew bump-formula-pr --url https://github.com/pybind/pybind11/archive/vX.Y.Z.tar.gz + +Conda-forge should automatically make a PR in a few hours, and automatically +merge it if there are no issues. + + +Manual packaging +^^^^^^^^^^^^^^^^ + +If you need to manually upload releases, you can download the releases from the job artifacts and upload them with twine. You can also make the files locally (not recommended in general, as your local directory is more likely to be "dirty" and SDists love picking up random unrelated/hidden files); this is the procedure: + +.. code-block:: bash + + nox -s build + twine upload dist/* + +This makes SDists and wheels, and the final line uploads them. diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/requirements.txt b/third-party/DPVO/DPRetrieval/pybind11/docs/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..d13e8b482732d7c0a31ebfef783f70ea69480d43 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/requirements.txt @@ -0,0 +1,5 @@ +breathe==4.31.0 +sphinx==3.5.4 +sphinx_rtd_theme==1.0.0 +sphinxcontrib-moderncmakedomain==3.19 +sphinxcontrib-svg2pdfconverter==1.1.1 diff --git a/third-party/DPVO/DPRetrieval/pybind11/docs/upgrade.rst b/third-party/DPVO/DPRetrieval/pybind11/docs/upgrade.rst new file mode 100644 index 0000000000000000000000000000000000000000..5507834925059c88c0cc08a3efb00f1b3e00f9a2 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/docs/upgrade.rst @@ -0,0 +1,552 @@ +Upgrade guide +############# + +This is a companion guide to the :doc:`changelog`. While the changelog briefly +lists all of the new features, improvements and bug fixes, this upgrade guide +focuses only the subset which directly impacts your experience when upgrading +to a new version. But it goes into more detail. This includes things like +deprecated APIs and their replacements, build system changes, general code +modernization and other useful information. + +.. _upgrade-guide-2.9: + +v2.9 +==== + +* Any usage of the recently added ``py::make_simple_namespace`` should be + converted to using ``py::module_::import("types").attr("SimpleNamespace")`` + instead. + +* The use of ``_`` in custom type casters can now be replaced with the more + readable ``const_name`` instead. The old ``_`` shortcut has been retained + unless it is being used as a macro (like for gettext). + + +.. _upgrade-guide-2.7: + +v2.7 +==== + +*Before* v2.7, ``py::str`` can hold ``PyUnicodeObject`` or ``PyBytesObject``, +and ``py::isinstance()`` is ``true`` for both ``py::str`` and +``py::bytes``. Starting with v2.7, ``py::str`` exclusively holds +``PyUnicodeObject`` (`#2409 `_), +and ``py::isinstance()`` is ``true`` only for ``py::str``. To help in +the transition of user code, the ``PYBIND11_STR_LEGACY_PERMISSIVE`` macro +is provided as an escape hatch to go back to the legacy behavior. This macro +will be removed in future releases. Two types of required fixes are expected +to be common: + +* Accidental use of ``py::str`` instead of ``py::bytes``, masked by the legacy + behavior. These are probably very easy to fix, by changing from + ``py::str`` to ``py::bytes``. + +* Reliance on py::isinstance(obj) being ``true`` for + ``py::bytes``. This is likely to be easy to fix in most cases by adding + ``|| py::isinstance(obj)``, but a fix may be more involved, e.g. if + ``py::isinstance`` appears in a template. Such situations will require + careful review and custom fixes. + + +.. _upgrade-guide-2.6: + +v2.6 +==== + +Usage of the ``PYBIND11_OVERLOAD*`` macros and ``get_overload`` function should +be replaced by ``PYBIND11_OVERRIDE*`` and ``get_override``. In the future, the +old macros may be deprecated and removed. + +``py::module`` has been renamed ``py::module_``, but a backward compatible +typedef has been included. This change was to avoid a language change in C++20 +that requires unqualified ``module`` not be placed at the start of a logical +line. Qualified usage is unaffected and the typedef will remain unless the +C++ language rules change again. + +The public constructors of ``py::module_`` have been deprecated. Use +``PYBIND11_MODULE`` or ``module_::create_extension_module`` instead. + +An error is now thrown when ``__init__`` is forgotten on subclasses. This was +incorrect before, but was not checked. Add a call to ``__init__`` if it is +missing. + +A ``py::type_error`` is now thrown when casting to a subclass (like +``py::bytes`` from ``py::object``) if the conversion is not valid. Make a valid +conversion instead. + +The undocumented ``h.get_type()`` method has been deprecated and replaced by +``py::type::of(h)``. + +Enums now have a ``__str__`` method pre-defined; if you want to override it, +the simplest fix is to add the new ``py::prepend()`` tag when defining +``"__str__"``. + +If ``__eq__`` defined but not ``__hash__``, ``__hash__`` is now set to +``None``, as in normal CPython. You should add ``__hash__`` if you intended the +class to be hashable, possibly using the new ``py::hash`` shortcut. + +The constructors for ``py::array`` now always take signed integers for size, +for consistency. This may lead to compiler warnings on some systems. Cast to +``py::ssize_t`` instead of ``std::size_t``. + +The ``tools/clang`` submodule and ``tools/mkdoc.py`` have been moved to a +standalone package, `pybind11-mkdoc`_. If you were using those tools, please +use them via a pip install from the new location. + +The ``pybind11`` package on PyPI no longer fills the wheel "headers" slot - if +you were using the headers from this slot, they are available by requesting the +``global`` extra, that is, ``pip install "pybind11[global]"``. (Most users will +be unaffected, as the ``pybind11/include`` location is reported by ``python -m +pybind11 --includes`` and ``pybind11.get_include()`` is still correct and has +not changed since 2.5). + +.. _pybind11-mkdoc: https://github.com/pybind/pybind11-mkdoc + +CMake support: +-------------- + +The minimum required version of CMake is now 3.4. Several details of the CMake +support have been deprecated; warnings will be shown if you need to change +something. The changes are: + +* ``PYBIND11_CPP_STANDARD=`` is deprecated, please use + ``CMAKE_CXX_STANDARD=`` instead, or any other valid CMake CXX or CUDA + standard selection method, like ``target_compile_features``. + +* If you do not request a standard, pybind11 targets will compile with the + compiler default, but not less than C++11, instead of forcing C++14 always. + If you depend on the old behavior, please use ``set(CMAKE_CXX_STANDARD 14 CACHE STRING "")`` + instead. + +* Direct ``pybind11::module`` usage should always be accompanied by at least + ``set(CMAKE_CXX_VISIBILITY_PRESET hidden)`` or similar - it used to try to + manually force this compiler flag (but not correctly on all compilers or with + CUDA). + +* ``pybind11_add_module``'s ``SYSTEM`` argument is deprecated and does nothing; + linking now behaves like other imported libraries consistently in both + config and submodule mode, and behaves like a ``SYSTEM`` library by + default. + +* If ``PYTHON_EXECUTABLE`` is not set, virtual environments (``venv``, + ``virtualenv``, and ``conda``) are prioritized over the standard search + (similar to the new FindPython mode). + +In addition, the following changes may be of interest: + +* ``CMAKE_INTERPROCEDURAL_OPTIMIZATION`` will be respected by + ``pybind11_add_module`` if set instead of linking to ``pybind11::lto`` or + ``pybind11::thin_lto``. + +* Using ``find_package(Python COMPONENTS Interpreter Development)`` before + pybind11 will cause pybind11 to use the new Python mechanisms instead of its + own custom search, based on a patched version of classic ``FindPythonInterp`` + / ``FindPythonLibs``. In the future, this may become the default. A recent + (3.15+ or 3.18.2+) version of CMake is recommended. + + + +v2.5 +==== + +The Python package now includes the headers as data in the package itself, as +well as in the "headers" wheel slot. ``pybind11 --includes`` and +``pybind11.get_include()`` report the new location, which is always correct +regardless of how pybind11 was installed, making the old ``user=`` argument +meaningless. If you are not using the function to get the location already, you +are encouraged to switch to the package location. + + +v2.2 +==== + +Deprecation of the ``PYBIND11_PLUGIN`` macro +-------------------------------------------- + +``PYBIND11_MODULE`` is now the preferred way to create module entry points. +The old macro emits a compile-time deprecation warning. + +.. code-block:: cpp + + // old + PYBIND11_PLUGIN(example) { + py::module m("example", "documentation string"); + + m.def("add", [](int a, int b) { return a + b; }); + + return m.ptr(); + } + + // new + PYBIND11_MODULE(example, m) { + m.doc() = "documentation string"; // optional + + m.def("add", [](int a, int b) { return a + b; }); + } + + +New API for defining custom constructors and pickling functions +--------------------------------------------------------------- + +The old placement-new custom constructors have been deprecated. The new approach +uses ``py::init()`` and factory functions to greatly improve type safety. + +Placement-new can be called accidentally with an incompatible type (without any +compiler errors or warnings), or it can initialize the same object multiple times +if not careful with the Python-side ``__init__`` calls. The new-style custom +constructors prevent such mistakes. See :ref:`custom_constructors` for details. + +.. code-block:: cpp + + // old -- deprecated (runtime warning shown only in debug mode) + py::class(m, "Foo") + .def("__init__", [](Foo &self, ...) { + new (&self) Foo(...); // uses placement-new + }); + + // new + py::class(m, "Foo") + .def(py::init([](...) { // Note: no `self` argument + return new Foo(...); // return by raw pointer + // or: return std::make_unique(...); // return by holder + // or: return Foo(...); // return by value (move constructor) + })); + +Mirroring the custom constructor changes, ``py::pickle()`` is now the preferred +way to get and set object state. See :ref:`pickling` for details. + +.. code-block:: cpp + + // old -- deprecated (runtime warning shown only in debug mode) + py::class(m, "Foo") + ... + .def("__getstate__", [](const Foo &self) { + return py::make_tuple(self.value1(), self.value2(), ...); + }) + .def("__setstate__", [](Foo &self, py::tuple t) { + new (&self) Foo(t[0].cast(), ...); + }); + + // new + py::class(m, "Foo") + ... + .def(py::pickle( + [](const Foo &self) { // __getstate__ + return py::make_tuple(self.value1(), self.value2(), ...); // unchanged + }, + [](py::tuple t) { // __setstate__, note: no `self` argument + return new Foo(t[0].cast(), ...); + // or: return std::make_unique(...); // return by holder + // or: return Foo(...); // return by value (move constructor) + } + )); + +For both the constructors and pickling, warnings are shown at module +initialization time (on import, not when the functions are called). +They're only visible when compiled in debug mode. Sample warning: + +.. code-block:: none + + pybind11-bound class 'mymodule.Foo' is using an old-style placement-new '__init__' + which has been deprecated. See the upgrade guide in pybind11's docs. + + +Stricter enforcement of hidden symbol visibility for pybind11 modules +--------------------------------------------------------------------- + +pybind11 now tries to actively enforce hidden symbol visibility for modules. +If you're using either one of pybind11's :doc:`CMake or Python build systems +` (the two example repositories) and you haven't been exporting any +symbols, there's nothing to be concerned about. All the changes have been done +transparently in the background. If you were building manually or relied on +specific default visibility, read on. + +Setting default symbol visibility to *hidden* has always been recommended for +pybind11 (see :ref:`faq:symhidden`). On Linux and macOS, hidden symbol +visibility (in conjunction with the ``strip`` utility) yields much smaller +module binaries. `CPython's extension docs`_ also recommend hiding symbols +by default, with the goal of avoiding symbol name clashes between modules. +Starting with v2.2, pybind11 enforces this more strictly: (1) by declaring +all symbols inside the ``pybind11`` namespace as hidden and (2) by including +the ``-fvisibility=hidden`` flag on Linux and macOS (only for extension +modules, not for embedding the interpreter). + +.. _CPython's extension docs: https://docs.python.org/3/extending/extending.html#providing-a-c-api-for-an-extension-module + +The namespace-scope hidden visibility is done automatically in pybind11's +headers and it's generally transparent to users. It ensures that: + +* Modules compiled with different pybind11 versions don't clash with each other. + +* Some new features, like ``py::module_local`` bindings, can work as intended. + +The ``-fvisibility=hidden`` flag applies the same visibility to user bindings +outside of the ``pybind11`` namespace. It's now set automatic by pybind11's +CMake and Python build systems, but this needs to be done manually by users +of other build systems. Adding this flag: + +* Minimizes the chances of symbol conflicts between modules. E.g. if two + unrelated modules were statically linked to different (ABI-incompatible) + versions of the same third-party library, a symbol clash would be likely + (and would end with unpredictable results). + +* Produces smaller binaries on Linux and macOS, as pointed out previously. + +Within pybind11's CMake build system, ``pybind11_add_module`` has always been +setting the ``-fvisibility=hidden`` flag in release mode. From now on, it's +being applied unconditionally, even in debug mode and it can no longer be opted +out of with the ``NO_EXTRAS`` option. The ``pybind11::module`` target now also +adds this flag to its interface. The ``pybind11::embed`` target is unchanged. + +The most significant change here is for the ``pybind11::module`` target. If you +were previously relying on default visibility, i.e. if your Python module was +doubling as a shared library with dependents, you'll need to either export +symbols manually (recommended for cross-platform libraries) or factor out the +shared library (and have the Python module link to it like the other +dependents). As a temporary workaround, you can also restore default visibility +using the CMake code below, but this is not recommended in the long run: + +.. code-block:: cmake + + target_link_libraries(mymodule PRIVATE pybind11::module) + + add_library(restore_default_visibility INTERFACE) + target_compile_options(restore_default_visibility INTERFACE -fvisibility=default) + target_link_libraries(mymodule PRIVATE restore_default_visibility) + + +Local STL container bindings +---------------------------- + +Previous pybind11 versions could only bind types globally -- all pybind11 +modules, even unrelated ones, would have access to the same exported types. +However, this would also result in a conflict if two modules exported the +same C++ type, which is especially problematic for very common types, e.g. +``std::vector``. :ref:`module_local` were added to resolve this (see +that section for a complete usage guide). + +``py::class_`` still defaults to global bindings (because these types are +usually unique across modules), however in order to avoid clashes of opaque +types, ``py::bind_vector`` and ``py::bind_map`` will now bind STL containers +as ``py::module_local`` if their elements are: builtins (``int``, ``float``, +etc.), not bound using ``py::class_``, or bound as ``py::module_local``. For +example, this change allows multiple modules to bind ``std::vector`` +without causing conflicts. See :ref:`stl_bind` for more details. + +When upgrading to this version, if you have multiple modules which depend on +a single global binding of an STL container, note that all modules can still +accept foreign ``py::module_local`` types in the direction of Python-to-C++. +The locality only affects the C++-to-Python direction. If this is needed in +multiple modules, you'll need to either: + +* Add a copy of the same STL binding to all of the modules which need it. + +* Restore the global status of that single binding by marking it + ``py::module_local(false)``. + +The latter is an easy workaround, but in the long run it would be best to +localize all common type bindings in order to avoid conflicts with +third-party modules. + + +Negative strides for Python buffer objects and numpy arrays +----------------------------------------------------------- + +Support for negative strides required changing the integer type from unsigned +to signed in the interfaces of ``py::buffer_info`` and ``py::array``. If you +have compiler warnings enabled, you may notice some new conversion warnings +after upgrading. These can be resolved using ``static_cast``. + + +Deprecation of some ``py::object`` APIs +--------------------------------------- + +To compare ``py::object`` instances by pointer, you should now use +``obj1.is(obj2)`` which is equivalent to ``obj1 is obj2`` in Python. +Previously, pybind11 used ``operator==`` for this (``obj1 == obj2``), but +that could be confusing and is now deprecated (so that it can eventually +be replaced with proper rich object comparison in a future release). + +For classes which inherit from ``py::object``, ``borrowed`` and ``stolen`` +were previously available as protected constructor tags. Now the types +should be used directly instead: ``borrowed_t{}`` and ``stolen_t{}`` +(`#771 `_). + + +Stricter compile-time error checking +------------------------------------ + +Some error checks have been moved from run time to compile time. Notably, +automatic conversion of ``std::shared_ptr`` is not possible when ``T`` is +not directly registered with ``py::class_`` (e.g. ``std::shared_ptr`` +or ``std::shared_ptr>`` are not automatically convertible). +Attempting to bind a function with such arguments now results in a compile-time +error instead of waiting to fail at run time. + +``py::init<...>()`` constructor definitions are also stricter and now prevent +bindings which could cause unexpected behavior: + +.. code-block:: cpp + + struct Example { + Example(int &); + }; + + py::class_(m, "Example") + .def(py::init()); // OK, exact match + // .def(py::init()); // compile-time error, mismatch + +A non-``const`` lvalue reference is not allowed to bind to an rvalue. However, +note that a constructor taking ``const T &`` can still be registered using +``py::init()`` because a ``const`` lvalue reference can bind to an rvalue. + +v2.1 +==== + +Minimum compiler versions are enforced at compile time +------------------------------------------------------ + +The minimums also apply to v2.0 but the check is now explicit and a compile-time +error is raised if the compiler does not meet the requirements: + +* GCC >= 4.8 +* clang >= 3.3 (appleclang >= 5.0) +* MSVC >= 2015u3 +* Intel C++ >= 15.0 + + +The ``py::metaclass`` attribute is not required for static properties +--------------------------------------------------------------------- + +Binding classes with static properties is now possible by default. The +zero-parameter version of ``py::metaclass()`` is deprecated. However, a new +one-parameter ``py::metaclass(python_type)`` version was added for rare +cases when a custom metaclass is needed to override pybind11's default. + +.. code-block:: cpp + + // old -- emits a deprecation warning + py::class_(m, "Foo", py::metaclass()) + .def_property_readonly_static("foo", ...); + + // new -- static properties work without the attribute + py::class_(m, "Foo") + .def_property_readonly_static("foo", ...); + + // new -- advanced feature, override pybind11's default metaclass + py::class_(m, "Bar", py::metaclass(custom_python_type)) + ... + + +v2.0 +==== + +Breaking changes in ``py::class_`` +---------------------------------- + +These changes were necessary to make type definitions in pybind11 +future-proof, to support PyPy via its ``cpyext`` mechanism (`#527 +`_), and to improve efficiency +(`rev. 86d825 `_). + +1. Declarations of types that provide access via the buffer protocol must + now include the ``py::buffer_protocol()`` annotation as an argument to + the ``py::class_`` constructor. + + .. code-block:: cpp + + py::class_("Matrix", py::buffer_protocol()) + .def(py::init<...>()) + .def_buffer(...); + +2. Classes which include static properties (e.g. ``def_readwrite_static()``) + must now include the ``py::metaclass()`` attribute. Note: this requirement + has since been removed in v2.1. If you're upgrading from 1.x, it's + recommended to skip directly to v2.1 or newer. + +3. This version of pybind11 uses a redesigned mechanism for instantiating + trampoline classes that are used to override virtual methods from within + Python. This led to the following user-visible syntax change: + + .. code-block:: cpp + + // old v1.x syntax + py::class_("MyClass") + .alias() + ... + + // new v2.x syntax + py::class_("MyClass") + ... + + Importantly, both the original and the trampoline class are now specified + as arguments to the ``py::class_`` template, and the ``alias<..>()`` call + is gone. The new scheme has zero overhead in cases when Python doesn't + override any functions of the underlying C++ class. + `rev. 86d825 `_. + + The class type must be the first template argument given to ``py::class_`` + while the trampoline can be mixed in arbitrary order with other arguments + (see the following section). + + +Deprecation of the ``py::base()`` attribute +---------------------------------------------- + +``py::base()`` was deprecated in favor of specifying ``T`` as a template +argument to ``py::class_``. This new syntax also supports multiple inheritance. +Note that, while the type being exported must be the first argument in the +``py::class_`` template, the order of the following types (bases, +holder and/or trampoline) is not important. + +.. code-block:: cpp + + // old v1.x + py::class_("Derived", py::base()); + + // new v2.x + py::class_("Derived"); + + // new -- multiple inheritance + py::class_("Derived"); + + // new -- apart from `Derived` the argument order can be arbitrary + py::class_("Derived"); + + +Out-of-the-box support for ``std::shared_ptr`` +---------------------------------------------- + +The relevant type caster is now built in, so it's no longer necessary to +include a declaration of the form: + +.. code-block:: cpp + + PYBIND11_DECLARE_HOLDER_TYPE(T, std::shared_ptr) + +Continuing to do so won’t cause an error or even a deprecation warning, +but it's completely redundant. + + +Deprecation of a few ``py::object`` APIs +---------------------------------------- + +All of the old-style calls emit deprecation warnings. + ++---------------------------------------+---------------------------------------------+ +| Old syntax | New syntax | ++=======================================+=============================================+ +| ``obj.call(args...)`` | ``obj(args...)`` | ++---------------------------------------+---------------------------------------------+ +| ``obj.str()`` | ``py::str(obj)`` | ++---------------------------------------+---------------------------------------------+ +| ``auto l = py::list(obj); l.check()`` | ``py::isinstance(obj)`` | ++---------------------------------------+---------------------------------------------+ +| ``py::object(ptr, true)`` | ``py::reinterpret_borrow(ptr)`` | ++---------------------------------------+---------------------------------------------+ +| ``py::object(ptr, false)`` | ``py::reinterpret_steal(ptr)`` | ++---------------------------------------+---------------------------------------------+ +| ``if (obj.attr("foo"))`` | ``if (py::hasattr(obj, "foo"))`` | ++---------------------------------------+---------------------------------------------+ +| ``if (obj["bar"])`` | ``if (obj.contains("bar"))`` | ++---------------------------------------+---------------------------------------------+ diff --git a/third-party/DPVO/DPRetrieval/pybind11/include/pybind11/attr.h b/third-party/DPVO/DPRetrieval/pybind11/include/pybind11/attr.h new file mode 100644 index 0000000000000000000000000000000000000000..f2fb548cff9d033f4a111d6ab9c7508760b5afce --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/include/pybind11/attr.h @@ -0,0 +1,675 @@ +/* + pybind11/attr.h: Infrastructure for processing custom + type and function attributes + + Copyright (c) 2016 Wenzel Jakob + + All rights reserved. Use of this source code is governed by a + BSD-style license that can be found in the LICENSE file. +*/ + +#pragma once + +#include "cast.h" + +#include + +PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE) + +/// \addtogroup annotations +/// @{ + +/// Annotation for methods +struct is_method { + handle class_; + explicit is_method(const handle &c) : class_(c) {} +}; + +/// Annotation for operators +struct is_operator {}; + +/// Annotation for classes that cannot be subclassed +struct is_final {}; + +/// Annotation for parent scope +struct scope { + handle value; + explicit scope(const handle &s) : value(s) {} +}; + +/// Annotation for documentation +struct doc { + const char *value; + explicit doc(const char *value) : value(value) {} +}; + +/// Annotation for function names +struct name { + const char *value; + explicit name(const char *value) : value(value) {} +}; + +/// Annotation indicating that a function is an overload associated with a given "sibling" +struct sibling { + handle value; + explicit sibling(const handle &value) : value(value.ptr()) {} +}; + +/// Annotation indicating that a class derives from another given type +template +struct base { + + PYBIND11_DEPRECATED( + "base() was deprecated in favor of specifying 'T' as a template argument to class_") + base() {} // NOLINT(modernize-use-equals-default): breaks MSVC 2015 when adding an attribute +}; + +/// Keep patient alive while nurse lives +template +struct keep_alive {}; + +/// Annotation indicating that a class is involved in a multiple inheritance relationship +struct multiple_inheritance {}; + +/// Annotation which enables dynamic attributes, i.e. adds `__dict__` to a class +struct dynamic_attr {}; + +/// Annotation which enables the buffer protocol for a type +struct buffer_protocol {}; + +/// Annotation which requests that a special metaclass is created for a type +struct metaclass { + handle value; + + PYBIND11_DEPRECATED("py::metaclass() is no longer required. It's turned on by default now.") + // NOLINTNEXTLINE(modernize-use-equals-default): breaks MSVC 2015 when adding an attribute + metaclass() {} + + /// Override pybind11's default metaclass + explicit metaclass(handle value) : value(value) {} +}; + +/// Specifies a custom callback with signature `void (PyHeapTypeObject*)` that +/// may be used to customize the Python type. +/// +/// The callback is invoked immediately before `PyType_Ready`. +/// +/// Note: This is an advanced interface, and uses of it may require changes to +/// work with later versions of pybind11. You may wish to consult the +/// implementation of `make_new_python_type` in `detail/classes.h` to understand +/// the context in which the callback will be run. +struct custom_type_setup { + using callback = std::function; + + explicit custom_type_setup(callback value) : value(std::move(value)) {} + + callback value; +}; + +/// Annotation that marks a class as local to the module: +struct module_local { + const bool value; + constexpr explicit module_local(bool v = true) : value(v) {} +}; + +/// Annotation to mark enums as an arithmetic type +struct arithmetic {}; + +/// Mark a function for addition at the beginning of the existing overload chain instead of the end +struct prepend {}; + +/** \rst + A call policy which places one or more guard variables (``Ts...``) around the function call. + + For example, this definition: + + .. code-block:: cpp + + m.def("foo", foo, py::call_guard()); + + is equivalent to the following pseudocode: + + .. code-block:: cpp + + m.def("foo", [](args...) { + T scope_guard; + return foo(args...); // forwarded arguments + }); + \endrst */ +template +struct call_guard; + +template <> +struct call_guard<> { + using type = detail::void_type; +}; + +template +struct call_guard { + static_assert(std::is_default_constructible::value, + "The guard type must be default constructible"); + + using type = T; +}; + +template +struct call_guard { + struct type { + T guard{}; // Compose multiple guard types with left-to-right default-constructor order + typename call_guard::type next{}; + }; +}; + +/// @} annotations + +PYBIND11_NAMESPACE_BEGIN(detail) +/* Forward declarations */ +enum op_id : int; +enum op_type : int; +struct undefined_t; +template +struct op_; +void keep_alive_impl(size_t Nurse, size_t Patient, function_call &call, handle ret); + +/// Internal data structure which holds metadata about a keyword argument +struct argument_record { + const char *name; ///< Argument name + const char *descr; ///< Human-readable version of the argument value + handle value; ///< Associated Python object + bool convert : 1; ///< True if the argument is allowed to convert when loading + bool none : 1; ///< True if None is allowed when loading + + argument_record(const char *name, const char *descr, handle value, bool convert, bool none) + : name(name), descr(descr), value(value), convert(convert), none(none) {} +}; + +/// Internal data structure which holds metadata about a bound function (signature, overloads, +/// etc.) +struct function_record { + function_record() + : is_constructor(false), is_new_style_constructor(false), is_stateless(false), + is_operator(false), is_method(false), has_args(false), has_kwargs(false), + prepend(false) {} + + /// Function name + char *name = nullptr; /* why no C++ strings? They generate heavier code.. */ + + // User-specified documentation string + char *doc = nullptr; + + /// Human-readable version of the function signature + char *signature = nullptr; + + /// List of registered keyword arguments + std::vector args; + + /// Pointer to lambda function which converts arguments and performs the actual call + handle (*impl)(function_call &) = nullptr; + + /// Storage for the wrapped function pointer and captured data, if any + void *data[3] = {}; + + /// Pointer to custom destructor for 'data' (if needed) + void (*free_data)(function_record *ptr) = nullptr; + + /// Return value policy associated with this function + return_value_policy policy = return_value_policy::automatic; + + /// True if name == '__init__' + bool is_constructor : 1; + + /// True if this is a new-style `__init__` defined in `detail/init.h` + bool is_new_style_constructor : 1; + + /// True if this is a stateless function pointer + bool is_stateless : 1; + + /// True if this is an operator (__add__), etc. + bool is_operator : 1; + + /// True if this is a method + bool is_method : 1; + + /// True if the function has a '*args' argument + bool has_args : 1; + + /// True if the function has a '**kwargs' argument + bool has_kwargs : 1; + + /// True if this function is to be inserted at the beginning of the overload resolution chain + bool prepend : 1; + + /// Number of arguments (including py::args and/or py::kwargs, if present) + std::uint16_t nargs; + + /// Number of leading positional arguments, which are terminated by a py::args or py::kwargs + /// argument or by a py::kw_only annotation. + std::uint16_t nargs_pos = 0; + + /// Number of leading arguments (counted in `nargs`) that are positional-only + std::uint16_t nargs_pos_only = 0; + + /// Python method object + PyMethodDef *def = nullptr; + + /// Python handle to the parent scope (a class or a module) + handle scope; + + /// Python handle to the sibling function representing an overload chain + handle sibling; + + /// Pointer to next overload + function_record *next = nullptr; +}; + +/// Special data structure which (temporarily) holds metadata about a bound class +struct type_record { + PYBIND11_NOINLINE type_record() + : multiple_inheritance(false), dynamic_attr(false), buffer_protocol(false), + default_holder(true), module_local(false), is_final(false) {} + + /// Handle to the parent scope + handle scope; + + /// Name of the class + const char *name = nullptr; + + // Pointer to RTTI type_info data structure + const std::type_info *type = nullptr; + + /// How large is the underlying C++ type? + size_t type_size = 0; + + /// What is the alignment of the underlying C++ type? + size_t type_align = 0; + + /// How large is the type's holder? + size_t holder_size = 0; + + /// The global operator new can be overridden with a class-specific variant + void *(*operator_new)(size_t) = nullptr; + + /// Function pointer to class_<..>::init_instance + void (*init_instance)(instance *, const void *) = nullptr; + + /// Function pointer to class_<..>::dealloc + void (*dealloc)(detail::value_and_holder &) = nullptr; + + /// List of base classes of the newly created type + list bases; + + /// Optional docstring + const char *doc = nullptr; + + /// Custom metaclass (optional) + handle metaclass; + + /// Custom type setup. + custom_type_setup::callback custom_type_setup_callback; + + /// Multiple inheritance marker + bool multiple_inheritance : 1; + + /// Does the class manage a __dict__? + bool dynamic_attr : 1; + + /// Does the class implement the buffer protocol? + bool buffer_protocol : 1; + + /// Is the default (unique_ptr) holder type used? + bool default_holder : 1; + + /// Is the class definition local to the module shared object? + bool module_local : 1; + + /// Is the class inheritable from python classes? + bool is_final : 1; + + PYBIND11_NOINLINE void add_base(const std::type_info &base, void *(*caster)(void *) ) { + auto *base_info = detail::get_type_info(base, false); + if (!base_info) { + std::string tname(base.name()); + detail::clean_type_id(tname); + pybind11_fail("generic_type: type \"" + std::string(name) + + "\" referenced unknown base type \"" + tname + "\""); + } + + if (default_holder != base_info->default_holder) { + std::string tname(base.name()); + detail::clean_type_id(tname); + pybind11_fail("generic_type: type \"" + std::string(name) + "\" " + + (default_holder ? "does not have" : "has") + + " a non-default holder type while its base \"" + tname + "\" " + + (base_info->default_holder ? "does not" : "does")); + } + + bases.append((PyObject *) base_info->type); + + if (base_info->type->tp_dictoffset != 0) { + dynamic_attr = true; + } + + if (caster) { + base_info->implicit_casts.emplace_back(type, caster); + } + } +}; + +inline function_call::function_call(const function_record &f, handle p) : func(f), parent(p) { + args.reserve(f.nargs); + args_convert.reserve(f.nargs); +} + +/// Tag for a new-style `__init__` defined in `detail/init.h` +struct is_new_style_constructor {}; + +/** + * Partial template specializations to process custom attributes provided to + * cpp_function_ and class_. These are either used to initialize the respective + * fields in the type_record and function_record data structures or executed at + * runtime to deal with custom call policies (e.g. keep_alive). + */ +template +struct process_attribute; + +template +struct process_attribute_default { + /// Default implementation: do nothing + static void init(const T &, function_record *) {} + static void init(const T &, type_record *) {} + static void precall(function_call &) {} + static void postcall(function_call &, handle) {} +}; + +/// Process an attribute specifying the function's name +template <> +struct process_attribute : process_attribute_default { + static void init(const name &n, function_record *r) { r->name = const_cast(n.value); } +}; + +/// Process an attribute specifying the function's docstring +template <> +struct process_attribute : process_attribute_default { + static void init(const doc &n, function_record *r) { r->doc = const_cast(n.value); } +}; + +/// Process an attribute specifying the function's docstring (provided as a C-style string) +template <> +struct process_attribute : process_attribute_default { + static void init(const char *d, function_record *r) { r->doc = const_cast(d); } + static void init(const char *d, type_record *r) { r->doc = const_cast(d); } +}; +template <> +struct process_attribute : process_attribute {}; + +/// Process an attribute indicating the function's return value policy +template <> +struct process_attribute : process_attribute_default { + static void init(const return_value_policy &p, function_record *r) { r->policy = p; } +}; + +/// Process an attribute which indicates that this is an overloaded function associated with a +/// given sibling +template <> +struct process_attribute : process_attribute_default { + static void init(const sibling &s, function_record *r) { r->sibling = s.value; } +}; + +/// Process an attribute which indicates that this function is a method +template <> +struct process_attribute : process_attribute_default { + static void init(const is_method &s, function_record *r) { + r->is_method = true; + r->scope = s.class_; + } +}; + +/// Process an attribute which indicates the parent scope of a method +template <> +struct process_attribute : process_attribute_default { + static void init(const scope &s, function_record *r) { r->scope = s.value; } +}; + +/// Process an attribute which indicates that this function is an operator +template <> +struct process_attribute : process_attribute_default { + static void init(const is_operator &, function_record *r) { r->is_operator = true; } +}; + +template <> +struct process_attribute + : process_attribute_default { + static void init(const is_new_style_constructor &, function_record *r) { + r->is_new_style_constructor = true; + } +}; + +inline void check_kw_only_arg(const arg &a, function_record *r) { + if (r->args.size() > r->nargs_pos && (!a.name || a.name[0] == '\0')) { + pybind11_fail("arg(): cannot specify an unnamed argument after a kw_only() annotation or " + "args() argument"); + } +} + +inline void append_self_arg_if_needed(function_record *r) { + if (r->is_method && r->args.empty()) { + r->args.emplace_back("self", nullptr, handle(), /*convert=*/true, /*none=*/false); + } +} + +/// Process a keyword argument attribute (*without* a default value) +template <> +struct process_attribute : process_attribute_default { + static void init(const arg &a, function_record *r) { + append_self_arg_if_needed(r); + r->args.emplace_back(a.name, nullptr, handle(), !a.flag_noconvert, a.flag_none); + + check_kw_only_arg(a, r); + } +}; + +/// Process a keyword argument attribute (*with* a default value) +template <> +struct process_attribute : process_attribute_default { + static void init(const arg_v &a, function_record *r) { + if (r->is_method && r->args.empty()) { + r->args.emplace_back( + "self", /*descr=*/nullptr, /*parent=*/handle(), /*convert=*/true, /*none=*/false); + } + + if (!a.value) { +#if !defined(NDEBUG) + std::string descr("'"); + if (a.name) { + descr += std::string(a.name) + ": "; + } + descr += a.type + "'"; + if (r->is_method) { + if (r->name) { + descr += " in method '" + (std::string) str(r->scope) + "." + + (std::string) r->name + "'"; + } else { + descr += " in method of '" + (std::string) str(r->scope) + "'"; + } + } else if (r->name) { + descr += " in function '" + (std::string) r->name + "'"; + } + pybind11_fail("arg(): could not convert default argument " + descr + + " into a Python object (type not registered yet?)"); +#else + pybind11_fail("arg(): could not convert default argument " + "into a Python object (type not registered yet?). " + "Compile in debug mode for more information."); +#endif + } + r->args.emplace_back(a.name, a.descr, a.value.inc_ref(), !a.flag_noconvert, a.flag_none); + + check_kw_only_arg(a, r); + } +}; + +/// Process a keyword-only-arguments-follow pseudo argument +template <> +struct process_attribute : process_attribute_default { + static void init(const kw_only &, function_record *r) { + append_self_arg_if_needed(r); + if (r->has_args && r->nargs_pos != static_cast(r->args.size())) { + pybind11_fail("Mismatched args() and kw_only(): they must occur at the same relative " + "argument location (or omit kw_only() entirely)"); + } + r->nargs_pos = static_cast(r->args.size()); + } +}; + +/// Process a positional-only-argument maker +template <> +struct process_attribute : process_attribute_default { + static void init(const pos_only &, function_record *r) { + append_self_arg_if_needed(r); + r->nargs_pos_only = static_cast(r->args.size()); + if (r->nargs_pos_only > r->nargs_pos) { + pybind11_fail("pos_only(): cannot follow a py::args() argument"); + } + // It also can't follow a kw_only, but a static_assert in pybind11.h checks that + } +}; + +/// Process a parent class attribute. Single inheritance only (class_ itself already guarantees +/// that) +template +struct process_attribute::value>> + : process_attribute_default { + static void init(const handle &h, type_record *r) { r->bases.append(h); } +}; + +/// Process a parent class attribute (deprecated, does not support multiple inheritance) +template +struct process_attribute> : process_attribute_default> { + static void init(const base &, type_record *r) { r->add_base(typeid(T), nullptr); } +}; + +/// Process a multiple inheritance attribute +template <> +struct process_attribute : process_attribute_default { + static void init(const multiple_inheritance &, type_record *r) { + r->multiple_inheritance = true; + } +}; + +template <> +struct process_attribute : process_attribute_default { + static void init(const dynamic_attr &, type_record *r) { r->dynamic_attr = true; } +}; + +template <> +struct process_attribute { + static void init(const custom_type_setup &value, type_record *r) { + r->custom_type_setup_callback = value.value; + } +}; + +template <> +struct process_attribute : process_attribute_default { + static void init(const is_final &, type_record *r) { r->is_final = true; } +}; + +template <> +struct process_attribute : process_attribute_default { + static void init(const buffer_protocol &, type_record *r) { r->buffer_protocol = true; } +}; + +template <> +struct process_attribute : process_attribute_default { + static void init(const metaclass &m, type_record *r) { r->metaclass = m.value; } +}; + +template <> +struct process_attribute : process_attribute_default { + static void init(const module_local &l, type_record *r) { r->module_local = l.value; } +}; + +/// Process a 'prepend' attribute, putting this at the beginning of the overload chain +template <> +struct process_attribute : process_attribute_default { + static void init(const prepend &, function_record *r) { r->prepend = true; } +}; + +/// Process an 'arithmetic' attribute for enums (does nothing here) +template <> +struct process_attribute : process_attribute_default {}; + +template +struct process_attribute> : process_attribute_default> {}; + +/** + * Process a keep_alive call policy -- invokes keep_alive_impl during the + * pre-call handler if both Nurse, Patient != 0 and use the post-call handler + * otherwise + */ +template +struct process_attribute> + : public process_attribute_default> { + template = 0> + static void precall(function_call &call) { + keep_alive_impl(Nurse, Patient, call, handle()); + } + template = 0> + static void postcall(function_call &, handle) {} + template = 0> + static void precall(function_call &) {} + template = 0> + static void postcall(function_call &call, handle ret) { + keep_alive_impl(Nurse, Patient, call, ret); + } +}; + +/// Recursively iterate over variadic template arguments +template +struct process_attributes { + static void init(const Args &...args, function_record *r) { + PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(r); + PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(r); + using expander = int[]; + (void) expander{ + 0, ((void) process_attribute::type>::init(args, r), 0)...}; + } + static void init(const Args &...args, type_record *r) { + PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(r); + PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(r); + using expander = int[]; + (void) expander{0, + (process_attribute::type>::init(args, r), 0)...}; + } + static void precall(function_call &call) { + PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(call); + using expander = int[]; + (void) expander{0, + (process_attribute::type>::precall(call), 0)...}; + } + static void postcall(function_call &call, handle fn_ret) { + PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(call, fn_ret); + PYBIND11_WORKAROUND_INCORRECT_GCC_UNUSED_BUT_SET_PARAMETER(fn_ret); + using expander = int[]; + (void) expander{ + 0, (process_attribute::type>::postcall(call, fn_ret), 0)...}; + } +}; + +template +using is_call_guard = is_instantiation; + +/// Extract the ``type`` from the first `call_guard` in `Extras...` (or `void_type` if none found) +template +using extract_guard_t = typename exactly_one_t, Extra...>::type; + +/// Check the number of named arguments at compile time +template ::value...), + size_t self = constexpr_sum(std::is_same::value...)> +constexpr bool expected_num_args(size_t nargs, bool has_args, bool has_kwargs) { + PYBIND11_WORKAROUND_INCORRECT_MSVC_C4100(nargs, has_args, has_kwargs); + return named == 0 || (self + named + size_t(has_args) + size_t(has_kwargs)) == nargs; +} + +PYBIND11_NAMESPACE_END(detail) +PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE) diff --git a/third-party/DPVO/DPRetrieval/pybind11/include/pybind11/buffer_info.h b/third-party/DPVO/DPRetrieval/pybind11/include/pybind11/buffer_info.h new file mode 100644 index 0000000000000000000000000000000000000000..6a63a802868c8d6686e932aa7be90d1e56a3f4d4 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/include/pybind11/buffer_info.h @@ -0,0 +1,193 @@ +/* + pybind11/buffer_info.h: Python buffer object interface + + Copyright (c) 2016 Wenzel Jakob + + All rights reserved. Use of this source code is governed by a + BSD-style license that can be found in the LICENSE file. +*/ + +#pragma once + +#include "detail/common.h" + +PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE) + +PYBIND11_NAMESPACE_BEGIN(detail) + +// Default, C-style strides +inline std::vector c_strides(const std::vector &shape, ssize_t itemsize) { + auto ndim = shape.size(); + std::vector strides(ndim, itemsize); + if (ndim > 0) { + for (size_t i = ndim - 1; i > 0; --i) { + strides[i - 1] = strides[i] * shape[i]; + } + } + return strides; +} + +// F-style strides; default when constructing an array_t with `ExtraFlags & f_style` +inline std::vector f_strides(const std::vector &shape, ssize_t itemsize) { + auto ndim = shape.size(); + std::vector strides(ndim, itemsize); + for (size_t i = 1; i < ndim; ++i) { + strides[i] = strides[i - 1] * shape[i - 1]; + } + return strides; +} + +PYBIND11_NAMESPACE_END(detail) + +/// Information record describing a Python buffer object +struct buffer_info { + void *ptr = nullptr; // Pointer to the underlying storage + ssize_t itemsize = 0; // Size of individual items in bytes + ssize_t size = 0; // Total number of entries + std::string format; // For homogeneous buffers, this should be set to + // format_descriptor::format() + ssize_t ndim = 0; // Number of dimensions + std::vector shape; // Shape of the tensor (1 entry per dimension) + std::vector strides; // Number of bytes between adjacent entries + // (for each per dimension) + bool readonly = false; // flag to indicate if the underlying storage may be written to + + buffer_info() = default; + + buffer_info(void *ptr, + ssize_t itemsize, + const std::string &format, + ssize_t ndim, + detail::any_container shape_in, + detail::any_container strides_in, + bool readonly = false) + : ptr(ptr), itemsize(itemsize), size(1), format(format), ndim(ndim), + shape(std::move(shape_in)), strides(std::move(strides_in)), readonly(readonly) { + if (ndim != (ssize_t) shape.size() || ndim != (ssize_t) strides.size()) { + pybind11_fail("buffer_info: ndim doesn't match shape and/or strides length"); + } + for (size_t i = 0; i < (size_t) ndim; ++i) { + size *= shape[i]; + } + } + + template + buffer_info(T *ptr, + detail::any_container shape_in, + detail::any_container strides_in, + bool readonly = false) + : buffer_info(private_ctr_tag(), + ptr, + sizeof(T), + format_descriptor::format(), + static_cast(shape_in->size()), + std::move(shape_in), + std::move(strides_in), + readonly) {} + + buffer_info(void *ptr, + ssize_t itemsize, + const std::string &format, + ssize_t size, + bool readonly = false) + : buffer_info(ptr, itemsize, format, 1, {size}, {itemsize}, readonly) {} + + template + buffer_info(T *ptr, ssize_t size, bool readonly = false) + : buffer_info(ptr, sizeof(T), format_descriptor::format(), size, readonly) {} + + template + buffer_info(const T *ptr, ssize_t size, bool readonly = true) + : buffer_info( + const_cast(ptr), sizeof(T), format_descriptor::format(), size, readonly) {} + + explicit buffer_info(Py_buffer *view, bool ownview = true) + : buffer_info( + view->buf, + view->itemsize, + view->format, + view->ndim, + {view->shape, view->shape + view->ndim}, + /* Though buffer::request() requests PyBUF_STRIDES, ctypes objects + * ignore this flag and return a view with NULL strides. + * When strides are NULL, build them manually. */ + view->strides + ? std::vector(view->strides, view->strides + view->ndim) + : detail::c_strides({view->shape, view->shape + view->ndim}, view->itemsize), + (view->readonly != 0)) { + // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer) + this->m_view = view; + // NOLINTNEXTLINE(cppcoreguidelines-prefer-member-initializer) + this->ownview = ownview; + } + + buffer_info(const buffer_info &) = delete; + buffer_info &operator=(const buffer_info &) = delete; + + buffer_info(buffer_info &&other) noexcept { (*this) = std::move(other); } + + buffer_info &operator=(buffer_info &&rhs) noexcept { + ptr = rhs.ptr; + itemsize = rhs.itemsize; + size = rhs.size; + format = std::move(rhs.format); + ndim = rhs.ndim; + shape = std::move(rhs.shape); + strides = std::move(rhs.strides); + std::swap(m_view, rhs.m_view); + std::swap(ownview, rhs.ownview); + readonly = rhs.readonly; + return *this; + } + + ~buffer_info() { + if (m_view && ownview) { + PyBuffer_Release(m_view); + delete m_view; + } + } + + Py_buffer *view() const { return m_view; } + Py_buffer *&view() { return m_view; } + +private: + struct private_ctr_tag {}; + + buffer_info(private_ctr_tag, + void *ptr, + ssize_t itemsize, + const std::string &format, + ssize_t ndim, + detail::any_container &&shape_in, + detail::any_container &&strides_in, + bool readonly) + : buffer_info( + ptr, itemsize, format, ndim, std::move(shape_in), std::move(strides_in), readonly) {} + + Py_buffer *m_view = nullptr; + bool ownview = false; +}; + +PYBIND11_NAMESPACE_BEGIN(detail) + +template +struct compare_buffer_info { + static bool compare(const buffer_info &b) { + return b.format == format_descriptor::format() && b.itemsize == (ssize_t) sizeof(T); + } +}; + +template +struct compare_buffer_info::value>> { + static bool compare(const buffer_info &b) { + return (size_t) b.itemsize == sizeof(T) + && (b.format == format_descriptor::value + || ((sizeof(T) == sizeof(long)) + && b.format == (std::is_unsigned::value ? "L" : "l")) + || ((sizeof(T) == sizeof(size_t)) + && b.format == (std::is_unsigned::value ? "N" : "n"))); + } +}; + +PYBIND11_NAMESPACE_END(detail) +PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE) diff --git a/third-party/DPVO/DPRetrieval/pybind11/include/pybind11/cast.h b/third-party/DPVO/DPRetrieval/pybind11/include/pybind11/cast.h new file mode 100644 index 0000000000000000000000000000000000000000..e047eb2dd8f4afce24fff44aede1c40ec8fcc4c8 --- /dev/null +++ b/third-party/DPVO/DPRetrieval/pybind11/include/pybind11/cast.h @@ -0,0 +1,1663 @@ +/* + pybind11/cast.h: Partial template specializations to cast between + C++ and Python types + + Copyright (c) 2016 Wenzel Jakob + + All rights reserved. Use of this source code is governed by a + BSD-style license that can be found in the LICENSE file. +*/ + +#pragma once + +#include "detail/common.h" +#include "detail/descr.h" +#include "detail/type_caster_base.h" +#include "detail/typeid.h" +#include "pytypes.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE) +PYBIND11_NAMESPACE_BEGIN(detail) + +template +class type_caster : public type_caster_base {}; +template +using make_caster = type_caster>; + +// Shortcut for calling a caster's `cast_op_type` cast operator for casting a type_caster to a T +template +typename make_caster::template cast_op_type cast_op(make_caster &caster) { + return caster.operator typename make_caster::template cast_op_type(); +} +template +typename make_caster::template cast_op_type::type> +cast_op(make_caster &&caster) { + return std::move(caster).operator typename make_caster:: + template cast_op_type::type>(); +} + +template +class type_caster> { +private: + using caster_t = make_caster; + caster_t subcaster; + using reference_t = type &; + using subcaster_cast_op_type = typename caster_t::template cast_op_type; + + static_assert( + std::is_same::type &, subcaster_cast_op_type>::value + || std::is_same::value, + "std::reference_wrapper caster requires T to have a caster with an " + "`operator T &()` or `operator const T &()`"); + +public: + bool load(handle src, bool convert) { return subcaster.load(src, convert); } + static constexpr auto name = caster_t::name; + static handle + cast(const std::reference_wrapper &src, return_value_policy policy, handle parent) { + // It is definitely wrong to take ownership of this pointer, so mask that rvp + if (policy == return_value_policy::take_ownership + || policy == return_value_policy::automatic) { + policy = return_value_policy::automatic_reference; + } + return caster_t::cast(&src.get(), policy, parent); + } + template + using cast_op_type = std::reference_wrapper; + explicit operator std::reference_wrapper() { return cast_op(subcaster); } +}; + +#define PYBIND11_TYPE_CASTER(type, py_name) \ +protected: \ + type value; \ + \ +public: \ + static constexpr auto name = py_name; \ + template >::value, \ + int> = 0> \ + static ::pybind11::handle cast( \ + T_ *src, ::pybind11::return_value_policy policy, ::pybind11::handle parent) { \ + if (!src) \ + return ::pybind11::none().release(); \ + if (policy == ::pybind11::return_value_policy::take_ownership) { \ + auto h = cast(std::move(*src), policy, parent); \ + delete src; \ + return h; \ + } \ + return cast(*src, policy, parent); \ + } \ + operator type *() { return &value; } /* NOLINT(bugprone-macro-parentheses) */ \ + operator type &() { return value; } /* NOLINT(bugprone-macro-parentheses) */ \ + operator type &&() && { return std::move(value); } /* NOLINT(bugprone-macro-parentheses) */ \ + template \ + using cast_op_type = ::pybind11::detail::movable_cast_op_type + +template +using is_std_char_type = any_of, /* std::string */ +#if defined(PYBIND11_HAS_U8STRING) + std::is_same, /* std::u8string */ +#endif + std::is_same, /* std::u16string */ + std::is_same, /* std::u32string */ + std::is_same /* std::wstring */ + >; + +template +struct type_caster::value && !is_std_char_type::value>> { + using _py_type_0 = conditional_t; + using _py_type_1 = conditional_t::value, + _py_type_0, + typename std::make_unsigned<_py_type_0>::type>; + using py_type = conditional_t::value, double, _py_type_1>; + +public: + bool load(handle src, bool convert) { + py_type py_value; + + if (!src) { + return false; + } + +#if !defined(PYPY_VERSION) + auto index_check = [](PyObject *o) { return PyIndex_Check(o); }; +#else + // In PyPy 7.3.3, `PyIndex_Check` is implemented by calling `__index__`, + // while CPython only considers the existence of `nb_index`/`__index__`. + auto index_check = [](PyObject *o) { return hasattr(o, "__index__"); }; +#endif + + if (std::is_floating_point::value) { + if (convert || PyFloat_Check(src.ptr())) { + py_value = (py_type) PyFloat_AsDouble(src.ptr()); + } else { + return false; + } + } else if (PyFloat_Check(src.ptr()) + || (!convert && !PYBIND11_LONG_CHECK(src.ptr()) && !index_check(src.ptr()))) { + return false; + } else { + handle src_or_index = src; + // PyPy: 7.3.7's 3.8 does not implement PyLong_*'s __index__ calls. +#if PY_VERSION_HEX < 0x03080000 || defined(PYPY_VERSION) + object index; + if (!PYBIND11_LONG_CHECK(src.ptr())) { // So: index_check(src.ptr()) + index = reinterpret_steal(PyNumber_Index(src.ptr())); + if (!index) { + PyErr_Clear(); + if (!convert) + return false; + } else { + src_or_index = index; + } + } +#endif + if (std::is_unsigned::value) { + py_value = as_unsigned(src_or_index.ptr()); + } else { // signed integer: + py_value = sizeof(T) <= sizeof(long) + ? (py_type) PyLong_AsLong(src_or_index.ptr()) + : (py_type) PYBIND11_LONG_AS_LONGLONG(src_or_index.ptr()); + } + } + + // Python API reported an error + bool py_err = py_value == (py_type) -1 && PyErr_Occurred(); + + // Check to see if the conversion is valid (integers should match exactly) + // Signed/unsigned checks happen elsewhere + if (py_err + || (std::is_integral::value && sizeof(py_type) != sizeof(T) + && py_value != (py_type) (T) py_value)) { + PyErr_Clear(); + if (py_err && convert && (PyNumber_Check(src.ptr()) != 0)) { + auto tmp = reinterpret_steal(std::is_floating_point::value + ? PyNumber_Float(src.ptr()) + : PyNumber_Long(src.ptr())); + PyErr_Clear(); + return load(tmp, false); + } + return false; + } + + value = (T) py_value; + return true; + } + + template + static typename std::enable_if::value, handle>::type + cast(U src, return_value_policy /* policy */, handle /* parent */) { + return PyFloat_FromDouble((double) src); + } + + template + static typename std::enable_if::value && std::is_signed::value + && (sizeof(U) <= sizeof(long)), + handle>::type + cast(U src, return_value_policy /* policy */, handle /* parent */) { + return PYBIND11_LONG_FROM_SIGNED((long) src); + } + + template + static typename std::enable_if::value && std::is_unsigned::value + && (sizeof(U) <= sizeof(unsigned long)), + handle>::type + cast(U src, return_value_policy /* policy */, handle /* parent */) { + return PYBIND11_LONG_FROM_UNSIGNED((unsigned long) src); + } + + template + static typename std::enable_if::value && std::is_signed::value + && (sizeof(U) > sizeof(long)), + handle>::type + cast(U src, return_value_policy /* policy */, handle /* parent */) { + return PyLong_FromLongLong((long long) src); + } + + template + static typename std::enable_if::value && std::is_unsigned::value + && (sizeof(U) > sizeof(unsigned long)), + handle>::type + cast(U src, return_value_policy /* policy */, handle /* parent */) { + return PyLong_FromUnsignedLongLong((unsigned long long) src); + } + + PYBIND11_TYPE_CASTER(T, const_name::value>("int", "float")); +}; + +template +struct void_caster { +public: + bool load(handle src, bool) { + if (src && src.is_none()) { + return true; + } + return false; + } + static handle cast(T, return_value_policy /* policy */, handle /* parent */) { + return none().inc_ref(); + } + PYBIND11_TYPE_CASTER(T, const_name("None")); +}; + +template <> +class type_caster : public void_caster {}; + +template <> +class type_caster : public type_caster { +public: + using type_caster::cast; + + bool load(handle h, bool) { + if (!h) { + return false; + } + if (h.is_none()) { + value = nullptr; + return true; + } + + /* Check if this is a capsule */ + if (isinstance(h)) { + value = reinterpret_borrow(h); + return true; + } + + /* Check if this is a C++ type */ + const auto &bases = all_type_info((PyTypeObject *) type::handle_of(h).ptr()); + if (bases.size() == 1) { // Only allowing loading from a single-value type + value = values_and_holders(reinterpret_cast(h.ptr())).begin()->value_ptr(); + return true; + } + + /* Fail */ + return false; + } + + static handle cast(const void *ptr, return_value_policy /* policy */, handle /* parent */) { + if (ptr) { + return capsule(ptr).release(); + } + return none().inc_ref(); + } + + template + using cast_op_type = void *&; + explicit operator void *&() { return value; } + static constexpr auto name = const_name("capsule"); + +private: + void *value = nullptr; +}; + +template <> +class type_caster : public void_caster {}; + +template <> +class type_caster { +public: + bool load(handle src, bool convert) { + if (!src) { + return false; + } + if (src.ptr() == Py_True) { + value = true; + return true; + } + if (src.ptr() == Py_False) { + value = false; + return true; + } + if (convert || (std::strcmp("numpy.bool_", Py_TYPE(src.ptr())->tp_name) == 0)) { + // (allow non-implicit conversion for numpy booleans) + + Py_ssize_t res = -1; + if (src.is_none()) { + res = 0; // None is implicitly converted to False + } +#if defined(PYPY_VERSION) + // On PyPy, check that "__bool__" (or "__nonzero__" on Python 2.7) attr exists + else if (hasattr(src, PYBIND11_BOOL_ATTR)) { + res = PyObject_IsTrue(src.ptr()); + } +#else + // Alternate approach for CPython: this does the same as the above, but optimized + // using the CPython API so as to avoid an unneeded attribute lookup. + else if (auto *tp_as_number = src.ptr()->ob_type->tp_as_number) { + if (PYBIND11_NB_BOOL(tp_as_number)) { + res = (*PYBIND11_NB_BOOL(tp_as_number))(src.ptr()); + } + } +#endif + if (res == 0 || res == 1) { + value = (res != 0); + return true; + } + PyErr_Clear(); + } + return false; + } + static handle cast(bool src, return_value_policy /* policy */, handle /* parent */) { + return handle(src ? Py_True : Py_False).inc_ref(); + } + PYBIND11_TYPE_CASTER(bool, const_name("bool")); +}; + +// Helper class for UTF-{8,16,32} C++ stl strings: +template +struct string_caster { + using CharT = typename StringType::value_type; + + // Simplify life by being able to assume standard char sizes (the standard only guarantees + // minimums, but Python requires exact sizes) + static_assert(!std::is_same::value || sizeof(CharT) == 1, + "Unsupported char size != 1"); +#if defined(PYBIND11_HAS_U8STRING) + static_assert(!std::is_same::value || sizeof(CharT) == 1, + "Unsupported char8_t size != 1"); +#endif + static_assert(!std::is_same::value || sizeof(CharT) == 2, + "Unsupported char16_t size != 2"); + static_assert(!std::is_same::value || sizeof(CharT) == 4, + "Unsupported char32_t size != 4"); + // wchar_t can be either 16 bits (Windows) or 32 (everywhere else) + static_assert(!std::is_same::value || sizeof(CharT) == 2 || sizeof(CharT) == 4, + "Unsupported wchar_t size != 2/4"); + static constexpr size_t UTF_N = 8 * sizeof(CharT); + + bool load(handle src, bool) { +#if PY_MAJOR_VERSION < 3 + object temp; +#endif + handle load_src = src; + if (!src) { + return false; + } + if (!PyUnicode_Check(load_src.ptr())) { +#if PY_MAJOR_VERSION >= 3 + return load_bytes(load_src); +#else + if (std::is_same::value) { + return load_bytes(load_src); + } + + // The below is a guaranteed failure in Python 3 when PyUnicode_Check returns false + if (!PYBIND11_BYTES_CHECK(load_src.ptr())) + return false; + + temp = reinterpret_steal(PyUnicode_FromObject(load_src.ptr())); + if (!temp) { + PyErr_Clear(); + return false; + } + load_src = temp; +#endif + } + +#if PY_VERSION_HEX >= 0x03030000 + // On Python >= 3.3, for UTF-8 we avoid the need for a temporary `bytes` + // object by using `PyUnicode_AsUTF8AndSize`. + if (PYBIND11_SILENCE_MSVC_C4127(UTF_N == 8)) { + Py_ssize_t size = -1; + const auto *buffer + = reinterpret_cast(PyUnicode_AsUTF8AndSize(load_src.ptr(), &size)); + if (!buffer) { + PyErr_Clear(); + return false; + } + value = StringType(buffer, static_cast(size)); + return true; + } +#endif + + auto utfNbytes + = reinterpret_steal(PyUnicode_AsEncodedString(load_src.ptr(), + UTF_N == 8 ? "utf-8" + : UTF_N == 16 ? "utf-16" + : "utf-32", + nullptr)); + if (!utfNbytes) { + PyErr_Clear(); + return false; + } + + const auto *buffer + = reinterpret_cast(PYBIND11_BYTES_AS_STRING(utfNbytes.ptr())); + size_t length = (size_t) PYBIND11_BYTES_SIZE(utfNbytes.ptr()) / sizeof(CharT); + // Skip BOM for UTF-16/32 + if (PYBIND11_SILENCE_MSVC_C4127(UTF_N > 8)) { + buffer++; + length--; + } + value = StringType(buffer, length); + + // If we're loading a string_view we need to keep the encoded Python object alive: + if (IsView) { + loader_life_support::add_patient(utfNbytes); + } + + return true; + } + + static handle + cast(const StringType &src, return_value_policy /* policy */, handle /* parent */) { + const char *buffer = reinterpret_cast(src.data()); + auto nbytes = ssize_t(src.size() * sizeof(CharT)); + handle s = decode_utfN(buffer, nbytes); + if (!s) { + throw error_already_set(); + } + return s; + } + + PYBIND11_TYPE_CASTER(StringType, const_name(PYBIND11_STRING_NAME)); + +private: + static handle decode_utfN(const char *buffer, ssize_t nbytes) { +#if !defined(PYPY_VERSION) + return UTF_N == 8 ? PyUnicode_DecodeUTF8(buffer, nbytes, nullptr) + : UTF_N == 16 ? PyUnicode_DecodeUTF16(buffer, nbytes, nullptr, nullptr) + : PyUnicode_DecodeUTF32(buffer, nbytes, nullptr, nullptr); +#else + // PyPy segfaults when on PyUnicode_DecodeUTF16 (and possibly on PyUnicode_DecodeUTF32 as + // well), so bypass the whole thing by just passing the encoding as a string value, which + // works properly: + return PyUnicode_Decode(buffer, + nbytes, + UTF_N == 8 ? "utf-8" + : UTF_N == 16 ? "utf-16" + : "utf-32", + nullptr); +#endif + } + + // When loading into a std::string or char*, accept a bytes object as-is (i.e. + // without any encoding/decoding attempt). For other C++ char sizes this is a no-op. + // which supports loading a unicode from a str, doesn't take this path. + template + bool load_bytes(enable_if_t::value, handle> src) { + if (PYBIND11_BYTES_CHECK(src.ptr())) { + // We were passed a Python 3 raw bytes; accept it into a std::string or char* + // without any encoding attempt. + const char *bytes = PYBIND11_BYTES_AS_STRING(src.ptr()); + if (bytes) { + value = StringType(bytes, (size_t) PYBIND11_BYTES_SIZE(src.ptr())); + return true; + } + } + + return false; + } + + template + bool load_bytes(enable_if_t::value, handle>) { + return false; + } +}; + +template +struct type_caster, + enable_if_t::value>> + : string_caster> {}; + +#ifdef PYBIND11_HAS_STRING_VIEW +template +struct type_caster, + enable_if_t::value>> + : string_caster, true> {}; +#endif + +// Type caster for C-style strings. We basically use a std::string type caster, but also add the +// ability to use None as a nullptr char* (which the string caster doesn't allow). +template +struct type_caster::value>> { + using StringType = std::basic_string; + using StringCaster = type_caster; + StringCaster str_caster; + bool none = false; + CharT one_char = 0; + +public: + bool load(handle src, bool convert) { + if (!src) { + return false; + } + if (src.is_none()) { + // Defer accepting None to other overloads (if we aren't in convert mode): + if (!convert) { + return false; + } + none = true; + return true; + } + return str_caster.load(src, convert); + } + + static handle cast(const CharT *src, return_value_policy policy, handle parent) { + if (src == nullptr) { + return pybind11::none().inc_ref(); + } + return StringCaster::cast(StringType(src), policy, parent); + } + + static handle cast(CharT src, return_value_policy policy, handle parent) { + if (std::is_same::value) { + handle s = PyUnicode_DecodeLatin1((const char *) &src, 1, nullptr); + if (!s) { + throw error_already_set(); + } + return s; + } + return StringCaster::cast(StringType(1, src), policy, parent); + } + + explicit operator CharT *() { + return none ? nullptr : const_cast(static_cast(str_caster).c_str()); + } + explicit operator CharT &() { + if (none) { + throw value_error("Cannot convert None to a character"); + } + + auto &value = static_cast(str_caster); + size_t str_len = value.size(); + if (str_len == 0) { + throw value_error("Cannot convert empty string to a character"); + } + + // If we're in UTF-8 mode, we have two possible failures: one for a unicode character that + // is too high, and one for multiple unicode characters (caught later), so we need to + // figure out how long the first encoded character is in bytes to distinguish between these + // two errors. We also allow want to allow unicode characters U+0080 through U+00FF, as + // those can fit into a single char value. + if (PYBIND11_SILENCE_MSVC_C4127(StringCaster::UTF_N == 8) && str_len > 1 && str_len <= 4) { + auto v0 = static_cast(value[0]); + // low bits only: 0-127 + // 0b110xxxxx - start of 2-byte sequence + // 0b1110xxxx - start of 3-byte sequence + // 0b11110xxx - start of 4-byte sequence + size_t char0_bytes = (v0 & 0x80) == 0 ? 1 + : (v0 & 0xE0) == 0xC0 ? 2 + : (v0 & 0xF0) == 0xE0 ? 3 + : 4; + + if (char0_bytes == str_len) { + // If we have a 128-255 value, we can decode it into a single char: + if (char0_bytes == 2 && (v0 & 0xFC) == 0xC0) { // 0x110000xx 0x10xxxxxx + one_char = static_cast(((v0 & 3) << 6) + + (static_cast(value[1]) & 0x3F)); + return one_char; + } + // Otherwise we have a single character, but it's > U+00FF + throw value_error("Character code point not in range(0x100)"); + } + } + + // UTF-16 is much easier: we can only have a surrogate pair for values above U+FFFF, thus a + // surrogate pair with total length 2 instantly indicates a range error (but not a "your + // string was too long" error). + else if (PYBIND11_SILENCE_MSVC_C4127(StringCaster::UTF_N == 16) && str_len == 2) { + one_char = static_cast(value[0]); + if (one_char >= 0xD800 && one_char < 0xE000) { + throw value_error("Character code point not in range(0x10000)"); + } + } + + if (str_len != 1) { + throw value_error("Expected a character, but multi-character string found"); + } + + one_char = value[0]; + return one_char; + } + + static constexpr auto name = const_name(PYBIND11_STRING_NAME); + template + using cast_op_type = pybind11::detail::cast_op_type<_T>; +}; + +// Base implementation for std::tuple and std::pair +template