From 16a7c3f0bf65e97834ffdf44f6267b71133daade Mon Sep 17 00:00:00 2001 From: Noah <11950016@mail.sustech.edu.cn> Date: Wed, 11 Jun 2025 14:43:19 +0800 Subject: [PATCH] lidargaitv2 open-source --- README.md | 2 + configs/lidargaitv2/README.md | 48 + configs/lidargaitv2/lidargaitv2_freegait.yaml | 111 + .../lidargaitv2/lidargaitv2_sustech1k.yaml | 110 + datasets/FreeGait/FreeGait.json | 5467 +++++++++++++++++ datasets/FreeGait/rearrange_freegait.py | 29 + opengait/data/collate_fn.py | 13 +- opengait/data/transform.py | 117 +- opengait/evaluation/evaluator.py | 43 +- opengait/modeling/models/lidargaitv2.py | 83 + opengait/modeling/models/lidargaitv2_utils.py | 377 ++ 11 files changed, 6396 insertions(+), 4 deletions(-) create mode 100644 configs/lidargaitv2/README.md create mode 100644 configs/lidargaitv2/lidargaitv2_freegait.yaml create mode 100644 configs/lidargaitv2/lidargaitv2_sustech1k.yaml create mode 100644 datasets/FreeGait/FreeGait.json create mode 100644 datasets/FreeGait/rearrange_freegait.py create mode 100644 opengait/modeling/models/lidargaitv2.py create mode 100644 opengait/modeling/models/lidargaitv2_utils.py diff --git a/README.md b/README.md index 9951863..1904234 100644 --- a/README.md +++ b/README.md @@ -13,6 +13,7 @@ The corresponding [paper](https://openaccess.thecvf.com/content/CVPR2023/papers/ The extension [paper](https://arxiv.org/pdf/2405.09138) has been accepted to TPAMI2025. ## What's New +- **[Jun 2025]** [LidarGait++](https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_LidarGait_Learning_Local_Features_and_Size_Awareness_from_LiDAR_Point_CVPR_2025_paper.pdf) has been accepted to CVPR2025🎉 and open-source in [configs/lidargaitv2](./configs/lidargaitv2/README.md). - **[Jun 2025]** The extension paper of [OpenGait](https://arxiv.org/pdf/2405.09138), further strengthened by the advancements of [DeepGaitV2](https://github.com/ShiqiYu/OpenGait/blob/master/opengait/modeling/models/deepgaitv2.py), SkeletonGait, and [SkeletonGait++](opengait/modeling/models/skeletongait%2B%2B.py), has been accepted for publication in TPAMI🎉. We sincerely acknowledge the valuable contributions and continuous support from the OpenGait community. - **[Feb 2025]** The diffusion-based [DenoisingGait](https://arxiv.org/pdf/2505.18582) has been accepted to CVPR2025🎉 Congratulations to [Dongyang](https://scholar.google.com.hk/citations?user=1xA5KxAAAAAJ)! This is his SECOND paper! - **[Feb 2025]** Chao successfully defended his Ph.D. thesis in Oct. 2024🎉🎉🎉 You can access the full text in [*Chao's Thesis in English*](https://www.researchgate.net/publication/388768400_Gait_Representation_Learning_and_Recognition?_sg%5B0%5D=qaGVpS8gKWPyR7olHoFd4bCs40AZdJzaM96P3TSnxrpiP9zCIUTxzeEq8YhQOlE4WemB7iMF2fHvcJFAYHTlJhTIB2J6faVa5s-xcQVj.4112nauMM4MWUNSyUa9eMeF0MEeplptpFOgb5kSgIk3lMcfPK6TdPX1bW1y_bKSdbwXuBf29GloRsVwBdexhug&_tp=eyJjb250ZXh0Ijp7ImZpcnN0UGFnZSI6ImhvbWUiLCJwYWdlIjoicHJvZmlsZSIsInByZXZpb3VzUGFnZSI6InByb2ZpbGUiLCJwb3NpdGlvbiI6InBhZ2VDb250ZW50In19) or [*樊超的学位论文（中文版）*](https://www.researchgate.net/publication/388768605_butaitezhengxuexiyushibiesuanfayanjiu). @@ -39,6 +40,7 @@ Our team's latest checkpoints for projects such as DeepGaitv2, SkeletonGait, Ske - [Mar 2022] Dataset [GREW](https://www.grew-benchmark.org) is supported in [datasets/GREW](./datasets/GREW). --> ## Our Works +- [**CVPR'25**] LidarGait++: Learning Local Features and Size Awareness from LiDAR Point Clouds for 3D Gait Recognition. [*Paper*](https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_LidarGait_Learning_Local_Features_and_Size_Awareness_from_LiDAR_Point_CVPR_2025_paper.pdf) and [*LidarGait++ Code*](configs/lidargaitv2/README.md) - [**TPAMI'25**] OpenGait: A Comprehensive Benchmark Study for Gait Recognition Towards Better Practicality. [*Paper*](https://arxiv.org/pdf/2405.09138). _This extension includes a key update with in-depth insights into emerging trends and challenges of gait recognition in Sec. VII_. - [**CVPR'25**] On Denoising Walking Videos for Gait Recognition. [*Paper*](https://arxiv.org/pdf/2505.18582) and [*DenoisingGait Code* (coming soon)] - [**Chao's Thesis**] Gait Representation Learning and Recognition, [Chinese Original](https://www.researchgate.net/publication/388768605_butaitezhengxuexiyushibiesuanfayanjiu) and [English Translation](https://www.academia.edu/127496287/Gait_Representation_Learning_and_Recognition). diff --git a/configs/lidargaitv2/README.md b/configs/lidargaitv2/README.md new file mode 100644 index 0000000..814eec4 --- /dev/null +++ b/configs/lidargaitv2/README.md @@ -0,0 +1,48 @@ +# LidarGait++: Learning Local Features and Size Awareness from LiDAR Point Clouds for 3D Gait Recognition + +This [paper](https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_LidarGait_Learning_Local_Features_and_Size_Awareness_from_LiDAR_Point_CVPR_2025_paper.pdf) has been accepted by CVPR 2025. + + + +## Prepare dataset +**SUSTech1K**: +- Step 1. Apply for [SUSTech1K](https://lidargait.github.io/). + +**FreeGait** (Optional): + +- Step 1. Download [FreeGait](https://drive.google.com/drive/folders/1I9zOCmqUuBUcOmvO1cgZtUC6uSfmAq7h) first. + +- Then rearrange the folder structure like SUSTech1K/CASIA-B to fit OpenGait framework. + ``` + python datasets/FreeGait/rearrange_freegait.py --input_path yout_freegait_path + ``` + +## Train +To train on SUSTech1K, run +``` +CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./configs/lidargaitv2/lidargaitv2_sustech1k.yaml --phase train +``` +or train on FreeGait, run +``` +CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs ./configs/lidargaitv2/lidargaitv2_freegait.yaml --phase train +``` + +## Citation + +```bibtex +@inproceedings{shen2023lidargait, + title={Lidargait: Benchmarking 3d gait recognition with point clouds}, + author={Shen, Chuanfu and Fan, Chao and Wu, Wei and Wang, Rui and Huang, George Q and Yu, Shiqi}, + booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, + pages={1054--1063}, + year={2023} +} + +@inproceedings{shen2025lidargait++, + title={LidarGait++: Learning Local Features and Size Awareness from LiDAR Point Clouds for 3D Gait Recognition}, + author={Shen, Chuanfu and Wang, Rui and Duan, Lixin and Yu, Shiqi}, + booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference}, + pages={6627--6636}, + year={2025} +} +``` diff --git a/configs/lidargaitv2/lidargaitv2_freegait.yaml b/configs/lidargaitv2/lidargaitv2_freegait.yaml new file mode 100644 index 0000000..f642e1b --- /dev/null +++ b/configs/lidargaitv2/lidargaitv2_freegait.yaml @@ -0,0 +1,111 @@ +data_cfg: + dataset_name: FreeGait + dataset_root: your_path + dataset_partition: ./datasets/FreeGait/FreeGait.json + num_workers: 1 + data_in_use: [false, false, true, false, false] + remove_no_gallery: false # Remove probe if no gallery for it + test_dataset_name: FreeGait + + +evaluator_cfg: + enable_float16: false #true #false #true + restore_ckpt_strict: true + restore_hint: 80000 + save_name: lidargaitv2 + eval_func: evaluate_FreeGait + sampler: + batch_shuffle: false + points_in_use: # For point-based gait recognition using point clouds + pointcloud_index: 0 + points_num: 256 #312 #256 #2048 + batch_size: 8 + frames_num_fixed: 10 # fixed frames number for training + frames_skip_num: 0 + #sample_type: fixed_ordered + sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options: fixed_unordered + frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory + metric: euc # cos + transform: + - type: PointCloudsTransform + xyz_only: true + scale_aware: true + +loss_cfg: + - loss_term_weight: 1.0 + margin: 0.2 + type: TripletLoss + log_prefix: triplet + lazy: False + - loss_term_weight: 0.1 + scale: 31 #16 + type: CrossEntropyLoss + log_prefix: softmax + log_accuracy: true + + +model_cfg: + model: LidarGaitPlusPlus + pool: PPP_HAP + sampling: knn + channel: 32 + npoints: [256, 192, 128] + nsample: 16 + scale_aware: true + normalize_dp: true + SeparateFCs: + in_channels: 256 + out_channels: 256 + parts_num: 31 + SeparateBNNecks: + class_num: 500 + in_channels: 256 + parts_num: 31 + scale: + - 1 + - 2 + - 4 + - 8 + - 16 + + +optimizer_cfg: + lr: 0.1 + momentum: 0.9 + solver: SGD + weight_decay: 0.0005 + +scheduler_cfg: + T_max: 80000 + eta_min: 0.0001 + scheduler: CosineAnnealingLR + + +trainer_cfg: + enable_float16: false #true # half_percesion float for memory reduction and speedup + fix_BN: false + with_test: true + log_iter: 100 + restore_ckpt_strict: true + restore_hint: 0 + save_iter: 5000 + save_name: lidargaitv2 + sync_BN: true + total_iter: 80000 + sampler: + batch_shuffle: true + batch_size: + - 32 # TripletSampler, batch_size[0] indicates Number of Identity + - 4 # batch_size[1] indicates Samples sequqnce for each Identity + frames_num_fixed: 10 # fixed frames number for training + sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered + type: TripletSampler + points_in_use: + pointcloud_index: 0 + points_num: 256 + transform: + - type: PointCloudsTransform + xyz_only: true + scale_aware: true + scale_prob: 0.2 + flip_prob: 0.1 \ No newline at end of file diff --git a/configs/lidargaitv2/lidargaitv2_sustech1k.yaml b/configs/lidargaitv2/lidargaitv2_sustech1k.yaml new file mode 100644 index 0000000..edbd140 --- /dev/null +++ b/configs/lidargaitv2/lidargaitv2_sustech1k.yaml @@ -0,0 +1,110 @@ +data_cfg: + dataset_name: SUSTech1K + dataset_root: your_path # download from https://lidargait.github.io/ if you don't have dataset + dataset_partition: ./datasets/SUSTech1K/SUSTech1K.json + num_workers: 4 + data_in_use: [true, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false, false] + remove_no_gallery: false # Remove probe if no gallery for it + test_dataset_name: SUSTech1K + +evaluator_cfg: + enable_float16: true + restore_ckpt_strict: true + restore_hint: 40000 + save_name: lidargaitv2 + eval_func: evaluate_indoor_dataset + sampler: + batch_shuffle: false + points_in_use: # For point-based gait recognition using point clouds + pointcloud_index: 0 + points_num: 1024 #2048 + batch_size: 4 + frames_num_fixed: 10 # fixed frames number for training + frames_skip_num: 0 + #sample_type: fixed_ordered + sample_type: all_ordered # all indicates whole sequence used to test, while ordered means input sequence by its natural order; Other options: fixed_unordered + frames_all_limit: 720 # limit the number of sampled frames to prevent out of memory + metric: euc # cos + transform: + - type: PointCloudsTransform + xyz_only: true + scale_aware: true + +loss_cfg: + - loss_term_weight: 1.0 + margin: 0.2 + type: TripletLoss + log_prefix: triplet + lazy: False + - loss_term_weight: 0.1 + scale: 31 + type: CrossEntropyLoss + log_prefix: softmax + log_accuracy: true + + +model_cfg: + model: LidarGaitPlusPlus + pool: PPP_HAP + sampling: knn + channel: 16 + npoints: [512, 256, 128] + nsample: 32 + scale_aware: true + normalize_dp: true + SeparateFCs: + in_channels: 256 + out_channels: 256 + parts_num: 31 + SeparateBNNecks: + class_num: 250 + in_channels: 256 + parts_num: 31 + scale: + - 1 + - 2 + - 4 + - 8 + - 16 + + +optimizer_cfg: + lr: 0.1 + momentum: 0.9 + solver: SGD + weight_decay: 0.0005 + +scheduler_cfg: + T_max: 40000 + eta_min: 0.0001 + scheduler: CosineAnnealingLR + + +trainer_cfg: + enable_float16: false #true # half_percesion float for memory reduction and speedup + fix_BN: false + with_test: true + log_iter: 100 + restore_ckpt_strict: true + restore_hint: 0 + save_iter: 5000 + save_name: lidargaitv2 + sync_BN: true + total_iter: 40000 + sampler: + batch_shuffle: true + batch_size: + - 32 # TripletSampler, batch_size[0] indicates Number of Identity + - 4 # batch_size[1] indicates Samples sequqnce for each Identity + frames_num_fixed: 10 # fixed frames number for training + sample_type: fixed_unordered # fixed control input frames number, unordered for controlling order of input tensor; Other options: unfixed_ordered or all_ordered + type: TripletSampler + points_in_use: + pointcloud_index: 0 + points_num: 1024 + transform: + - type: PointCloudsTransform + xyz_only: true + scale_aware: true + scale_prob: 1 + flip_prob: 0.15 \ No newline at end of file diff --git a/datasets/FreeGait/FreeGait.json b/datasets/FreeGait/FreeGait.json new file mode 100644 index 0000000..903692a --- /dev/null +++ b/datasets/FreeGait/FreeGait.json @@ -0,0 +1,5467 @@ +{ + "TRAIN_SET": [ + "1231", + "1204", + "1233", + "1229", + "1209", + "1168", + "1247", + "1178", + "1206", + "1174", + "1235", + "1225", + "1215", + "1220", + "1243", + "1240", + "1151", + "1237", + "1189", + "1199", + "1246", + "1194", + "1153", + "1252", + "1205", + "1150", + 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"1062-devid_1-seq_00_new", + "1064-devid_3-seq_00_new", + "1065-devid_1-seq_00_new", + "1067-devid_2-seq_01_new", + "1067-devid_1-seq_06_new", + "1067-devid_1-seq_00_new", + "1067-devid_2-seq_00_new", + "1067-devid_1-seq_01_new", + "1067-devid_1-seq_03_new", + "1067-devid_1-seq_04_new", + "1067-devid_1-seq_02_new", + "1069-devid_3-seq_01_new", + "1069-devid_3-seq_00_new", + "1069-devid_1-seq_01_new", + "1069-devid_2-seq_00_new", + "1069-devid_1-seq_02_new", + "1072-devid_1-seq_04_new", + "1072-devid_1-seq_01_new", + "1072-devid_3-seq_00_new", + "1072-devid_1-seq_03_new", + "1072-devid_1-seq_00_new", + "1072-devid_2-seq_00_new", + "1073-devid_1-seq_01_new", + "1073-devid_3-seq_00_new", + "1073-devid_3-seq_02_new", + "1073-devid_3-seq_01_new", + "1077-devid_2-seq_00_new", + "1077-devid_2-seq_02_new", + "1079-devid_3-seq_00_new", + "1079-devid_1-seq_00_new", + "1079-devid_3-seq_01_new", + "1079-devid_1-seq_02_new", + "1080-devid_3-seq_00_new", + "1080-devid_1-seq_00_new", + "1080-devid_3-seq_02_new", + "1080-devid_1-seq_01_new", + "1081-devid_1-seq_03_new", + "1081-devid_2-seq_10_new", + "1081-devid_2-seq_04_new", + "1081-devid_2-seq_03_new", + "1081-devid_2-seq_09_new", + "1081-devid_2-seq_11_new", + "1081-devid_1-seq_00_new", + "1081-devid_2-seq_05_new", + "1081-devid_1-seq_07_new", + "1081-devid_2-seq_00_new", + "1081-devid_1-seq_01_new", + "1081-devid_1-seq_02_new", + "1081-devid_1-seq_06_new", + "1081-devid_1-seq_08_new", + "1081-devid_2-seq_06_new", + "1081-devid_1-seq_09_new", + "1081-devid_2-seq_08_new", + "1081-devid_2-seq_01_new", + "1081-devid_1-seq_10_new", + "1081-devid_2-seq_02_new", + "1081-devid_2-seq_07_new", + "1081-devid_1-seq_05_new", + "1082-devid_2-seq_04_new", + "1082-devid_1-seq_00_new", + "1082-devid_1-seq_03_new", + "1082-devid_1-seq_02_new", + "1082-devid_2-seq_03_new", + "1082-devid_2-seq_00_new", + "1082-devid_1-seq_01_new", + "1082-devid_2-seq_01_new", + "1086-devid_3-seq_02_new", + "1086-devid_3-seq_00_new", + "1086-devid_3-seq_03_new", + "1087-devid_1-seq_00_new", + "1087-devid_1-seq_02_new", + "1087-devid_2-seq_01_new", + "1087-devid_2-seq_00_new", + "1087-devid_1-seq_03_new", + "1089-devid_2-seq_03_new", + "1089-devid_2-seq_04_new", + "1089-devid_1-seq_01_new", + "1089-devid_2-seq_00_new", + "1089-devid_2-seq_01_new", + "1089-devid_1-seq_02_new", + "1089-devid_2-seq_05_new", + "1089-devid_1-seq_00_new", + "1091-devid_1-seq_10_new", + "1091-devid_1-seq_01_new", + "1091-devid_1-seq_02_new", + "1091-devid_1-seq_04_new", + "1091-devid_1-seq_08_new", + "1091-devid_1-seq_07_new", + "1091-devid_1-seq_03_new", + "1091-devid_1-seq_09_new", + "1091-devid_1-seq_05_new", + "1091-devid_1-seq_00_new", + "1092-devid_3-seq_00_new", + "1092-devid_2-seq_00_new", + "1092-devid_2-seq_01_new", + "1092-devid_3-seq_01_new", + "1092-devid_1-seq_00_new", + "1092-devid_3-seq_02_new", + "1093-devid_1-seq_01_new", + "1093-devid_2-seq_01_new", + "1093-devid_2-seq_00_new", + "1094-devid_1-seq_01_new", + "1094-devid_1-seq_00_new", + "1094-devid_3-seq_00_new", + "1094-devid_3-seq_01_new", + "1095-devid_2-seq_00_new", + "1095-devid_2-seq_03_new", + "1095-devid_2-seq_01_new", + "1095-devid_3-seq_00_new", + "1095-devid_3-seq_01_new", + "1096-devid_1-seq_02_new", + "1096-devid_1-seq_00_new", + "1096-devid_1-seq_01_new", + "1097-devid_2-seq_00_new", + "1097-devid_1-seq_02_new", + "1097-devid_2-seq_01_new", + "1097-devid_1-seq_01_new", + "1097-devid_1-seq_00_new", + "1099-devid_2-seq_01_new", + "1099-devid_2-seq_02_new", + "1099-devid_3-seq_00_new", + "1099-devid_3-seq_01_new", + "1104-devid_3-seq_01_new", + "1104-devid_3-seq_02_new", + "1106-devid_1-seq_00_new", + "1106-devid_2-seq_00_new", + "1106-devid_1-seq_01_new", + "1106-devid_1-seq_03_new", + "1106-devid_2-seq_01_new", + "1107-devid_2-seq_01_new", + "1107-devid_3-seq_00_new", + "1107-devid_2-seq_00_new", + "1108-devid_2-seq_00_new", + "1108-devid_3-seq_02_new", + "1108-devid_3-seq_01_new", + "1108-devid_2-seq_01_new", + "1109-devid_2-seq_00_new", + "1109-devid_2-seq_03_new", + "1109-devid_3-seq_02_new", + "1109-devid_2-seq_01_new", + "1109-devid_3-seq_01_new", + "1109-devid_3-seq_00_new", + "1109-devid_2-seq_02_new", + "1110-devid_2-seq_01_new", + "1110-devid_2-seq_00_new", + "1110-devid_1-seq_00_new", + "1110-devid_1-seq_02_new", + "1110-devid_1-seq_01_new", + "1112-devid_2-seq_00_new", + "1112-devid_2-seq_01_new", + "1112-devid_2-seq_02_new", + "1115-devid_2-seq_01_new", + "1115-devid_3-seq_03_new", + "1115-devid_3-seq_01_new", + "1115-devid_3-seq_02_new", + "1115-devid_2-seq_00_new", + "1115-devid_3-seq_04_new", + "1116-devid_1-seq_00_new", + "1116-devid_1-seq_01_new", + "1116-devid_1-seq_02_new", + "1117-devid_1-seq_01_new", + "1117-devid_1-seq_00_new", + "1117-devid_1-seq_03_new", + "1118-devid_1-seq_00_new", + "1118-devid_1-seq_02_new", + "1119-devid_2-seq_07_new", + "1119-devid_2-seq_08_new", + "1119-devid_2-seq_02_new", + "1119-devid_2-seq_01_new", + "1119-devid_1-seq_04_new", + "1119-devid_1-seq_00_new", + "1119-devid_1-seq_02_new", + "1119-devid_2-seq_06_new", + "1119-devid_1-seq_05_new", + "1119-devid_2-seq_05_new", + "1119-devid_2-seq_04_new", + "1119-devid_1-seq_01_new", + "1119-devid_2-seq_03_new", + "1119-devid_2-seq_00_new", + "1121-devid_3-seq_01_new", + "1121-devid_3-seq_02_new", + "1126-devid_3-seq_00_new", + "1126-devid_3-seq_01_new", + "1127-devid_2-seq_04_new", + "1127-devid_2-seq_00_new", + "1127-devid_1-seq_01_new", + "1127-devid_1-seq_00_new", + "1127-devid_2-seq_03_new", + "1127-devid_2-seq_01_new", + "1127-devid_1-seq_02_new", + "1128-devid_2-seq_02_new", + "1128-devid_2-seq_04_new", + "1128-devid_1-seq_00_new", + "1128-devid_1-seq_03_new", + "1128-devid_1-seq_01_new", + "1128-devid_2-seq_01_new", + "1128-devid_2-seq_03_new", + "1128-devid_1-seq_02_new", + "1131-devid_2-seq_01_new", + "1132-devid_1-seq_01_new", + "1132-devid_1-seq_00_new", + "1132-devid_2-seq_03_new", + "1132-devid_2-seq_01_new", + "1132-devid_3-seq_01_new", + "1132-devid_3-seq_00_new", + "1132-devid_2-seq_02_new", + "1133-devid_1-seq_00_new", + "1133-devid_1-seq_04_new", + "1133-devid_1-seq_06_new", + "1133-devid_1-seq_01_new", + "1133-devid_1-seq_02_new", + "1133-devid_1-seq_08_new", + "1133-devid_1-seq_05_new", + "1133-devid_1-seq_03_new", + "1134-devid_3-seq_02_new", + "1134-devid_1-seq_02_new", + "1134-devid_2-seq_01_new", + "1134-devid_1-seq_00_new", + "1134-devid_3-seq_03_new", + "1134-devid_3-seq_01_new", + "1134-devid_1-seq_04_new", + "1134-devid_3-seq_04_new", + "1134-devid_1-seq_05_new", + "1134-devid_1-seq_01_new", + "1134-devid_1-seq_03_new", + "1134-devid_3-seq_00_new", + "1140-devid_1-seq_04_new", + "1140-devid_1-seq_00_new", + "1140-devid_1-seq_02_new", + "1140-devid_1-seq_05_new", + "1140-devid_1-seq_01_new", + "1140-devid_1-seq_03_new", + "1141-devid_2-seq_01_new", + "1154-devid0-seq_00", + "1154-devid0-seq_01", + "1154-devid0-seq_02", + "1154-devid0-seq_03", + "1156-devid0-seq_00", + "1156-devid0-seq_01", + "1156-devid0-seq_02", + "1156-devid0-seq_03", + "1160-devid0-seq_00", + "1160-devid0-seq_01", + "1160-devid0-seq_02", + "1160-devid0-seq_03", + "1162-devid0-seq_00", + "1162-devid0-seq_01", + "1162-devid0-seq_02", + "1162-devid0-seq_03", + "1163-devid0-seq_00", + "1163-devid0-seq_01", + "1163-devid0-seq_02", + "1163-devid0-seq_03", + "1164-devid0-seq_00", + "1164-devid0-seq_01", + "1164-devid0-seq_02", + "1164-devid0-seq_03", + "1166-devid0-seq_00", + "1166-devid0-seq_01", + "1166-devid0-seq_02", + "1166-devid0-seq_03", + "1167-devid0-seq_00", + "1167-devid0-seq_01", + "1167-devid0-seq_02", + "1167-devid0-seq_03", + "1171-devid0-seq_00", + "1171-devid0-seq_01", + "1171-devid0-seq_02", + "1171-devid0-seq_03", + "1172-devid0-seq_00", + "1172-devid0-seq_01", + "1172-devid0-seq_02", + "1172-devid0-seq_03", + "1173-devid0-seq_00", + "1173-devid0-seq_01", + "1173-devid0-seq_02", + "1173-devid0-seq_03", + "1175-devid0-seq_00", + "1175-devid0-seq_01", + "1175-devid0-seq_02", + "1175-devid0-seq_03", + "1179-devid0-seq_00", + "1179-devid0-seq_01", + "1179-devid0-seq_02", + "1179-devid0-seq_03", + "1181-devid0-seq_00", + "1181-devid0-seq_01", + "1181-devid0-seq_02", + "1181-devid0-seq_03", + "1182-devid0-seq_00", + "1182-devid0-seq_01", + "1182-devid0-seq_02", + "1182-devid0-seq_03", + "1183-devid0-seq_00", + "1183-devid0-seq_01", + "1183-devid0-seq_02", + "1183-devid0-seq_03", + "1184-devid0-seq_00", + "1184-devid0-seq_01", + "1184-devid0-seq_02", + "1184-devid0-seq_03", + "1185-devid0-seq_00", + "1185-devid0-seq_01", + "1185-devid0-seq_02", + "1185-devid0-seq_03", + "1186-devid0-seq_00", + "1186-devid0-seq_01", + "1186-devid0-seq_02", + "1186-devid0-seq_03", + "1187-devid0-seq_00", + "1187-devid0-seq_01", + "1187-devid0-seq_02", + "1187-devid0-seq_03", + "1188-devid0-seq_00", + "1188-devid0-seq_01", + "1188-devid0-seq_02", + "1188-devid0-seq_03", + "1190-devid0-seq_00", + "1190-devid0-seq_01", + "1190-devid0-seq_02", + "1190-devid0-seq_03", + "1192-devid0-seq_00", + "1192-devid0-seq_01", + "1192-devid0-seq_02", + "1192-devid0-seq_03", + "1193-devid0-seq_00", + "1193-devid0-seq_01", + "1193-devid0-seq_02", + "1193-devid0-seq_03", + "1198-devid0-seq_00", + "1198-devid0-seq_01", + "1198-devid0-seq_02", + "1198-devid0-seq_03", + "1200-devid0-seq_00", + "1200-devid0-seq_01", + "1200-devid0-seq_02", + "1200-devid0-seq_03", + "1201-devid0-seq_00", + "1201-devid0-seq_01", + "1201-devid0-seq_02", + "1201-devid0-seq_03", + "1202-devid0-seq_00", + "1202-devid0-seq_01", + "1202-devid0-seq_02", + "1202-devid0-seq_03", + "1203-devid0-seq_00", + "1203-devid0-seq_01", + "1203-devid0-seq_02", + "1203-devid0-seq_03", + "1207-devid0-seq_00", + "1207-devid0-seq_01", + "1207-devid0-seq_02", + "1207-devid0-seq_03", + "1210-devid0-seq_00", + "1210-devid0-seq_01", + "1210-devid0-seq_02", + "1210-devid0-seq_03", + "1211-devid0-seq_00", + "1211-devid0-seq_01", + "1211-devid0-seq_02", + "1211-devid0-seq_03", + "1213-devid0-seq_00", + "1213-devid0-seq_01", + "1213-devid0-seq_02", + "1213-devid0-seq_03", + "1214-devid0-seq_00", + "1214-devid0-seq_01", + "1214-devid0-seq_02", + "1214-devid0-seq_03", + "1216-devid0-seq_00", + "1216-devid0-seq_01", + "1216-devid0-seq_02", + "1216-devid0-seq_03", + "1217-devid0-seq_00", + "1217-devid0-seq_01", + "1217-devid0-seq_02", + "1217-devid0-seq_03", + "1221-devid0-seq_00", + "1221-devid0-seq_01", + "1221-devid0-seq_02", + "1221-devid0-seq_03", + "1223-devid0-seq_00", + "1223-devid0-seq_01", + "1223-devid0-seq_02", + "1223-devid0-seq_03", + "1224-devid0-seq_00", + "1224-devid0-seq_01", + "1224-devid0-seq_02", + "1224-devid0-seq_03", + "1226-devid0-seq_00", + "1226-devid0-seq_01", + "1226-devid0-seq_02", + "1226-devid0-seq_03", + "1227-devid0-seq_00", + "1227-devid0-seq_01", + "1227-devid0-seq_02", + "1227-devid0-seq_03", + "1228-devid0-seq_00", + "1228-devid0-seq_01", + "1228-devid0-seq_02", + "1228-devid0-seq_03", + "1230-devid0-seq_00", + "1230-devid0-seq_01", + "1230-devid0-seq_02", + "1230-devid0-seq_03", + "1232-devid0-seq_00", + "1232-devid0-seq_01", + "1232-devid0-seq_02", + "1232-devid0-seq_03", + "1238-devid0-seq_00", + "1238-devid0-seq_01", + "1238-devid0-seq_02", + "1238-devid0-seq_03", + "1239-devid0-seq_00", + "1239-devid0-seq_01", + "1239-devid0-seq_02", + "1239-devid0-seq_03", + "1241-devid0-seq_00", + "1241-devid0-seq_01", + "1241-devid0-seq_02", + "1241-devid0-seq_03", + "1242-devid0-seq_00", + "1242-devid0-seq_01", + "1242-devid0-seq_02", + "1242-devid0-seq_03", + "1244-devid0-seq_00", + "1244-devid0-seq_01", + "1244-devid0-seq_02", + "1244-devid0-seq_03", + "1248-devid0-seq_00", + "1248-devid0-seq_01", + "1248-devid0-seq_02", + "1248-devid0-seq_03", + "1251-devid0-seq_00", + "1251-devid0-seq_01", + "1251-devid0-seq_02", + "1251-devid0-seq_03" + ] +} \ No newline at end of file diff --git a/datasets/FreeGait/rearrange_freegait.py b/datasets/FreeGait/rearrange_freegait.py new file mode 100644 index 0000000..199c7d0 --- /dev/null +++ b/datasets/FreeGait/rearrange_freegait.py @@ -0,0 +1,29 @@ +import argparse +import os +import shutil +from pathlib import Path +from tqdm import tqdm + + + +def rearrange(input_path: Path) -> None: + for id in tqdm(os.listdir(input_path)): + for device in os.listdir(os.path.join(input_path,id)): + for seq in os.listdir(os.path.join(input_path,id,device)): + for pkl in ['image', 'lidar', 'range_pkl', 'smpl', 'kp3d']: + pkl_dir = os.path.join(input_path, id, device, seq, pkl, pkl+'.pkl') + target_dir = os.path.join(input_path, id, device, seq, pkl+'.pkl') + shutil.move(pkl_dir, target_dir) + os.rmdir(os.path.join(input_path, id, device, seq, pkl)) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='FreeGait rearrange tool') + parser.add_argument('-i', '--input_path', required=True, type=str, + help='Root path of raw dataset.') + + args = parser.parse_args() + + input_path = Path(args.input_path).resolve() + rearrange(input_path) + print('Done!') diff --git a/opengait/data/collate_fn.py b/opengait/data/collate_fn.py index 8917bf5..ae3ac4b 100644 --- a/opengait/data/collate_fn.py +++ b/opengait/data/collate_fn.py @@ -33,6 +33,8 @@ class CollateFn(object): if self.sampler == 'all' and 'frames_all_limit' in sample_config: self.frames_all_limit = sample_config['frames_all_limit'] + self.points_in_use = sample_config.get('points_in_use') + def __call__(self, batch): batch_size = len(batch) # currently, the functionality of feature_num is not fully supported yet, it refers to 1 now. We are supposed to make our framework support multiple source of input data, such as silhouette, or skeleton. @@ -88,7 +90,14 @@ class CollateFn(object): for i in range(feature_num): for j in indices[:self.frames_all_limit] if self.frames_all_limit > -1 and len(indices) > self.frames_all_limit else indices: - sampled_fras[i].append(seqs[i][j]) + point_cloud_index = self.points_in_use.get('pointcloud_index') + if self.points_in_use is not None and point_cloud_index is not None and i == point_cloud_index: + points_num = self.points_in_use.get('points_num') + sample_points = (random.choices(range(len(seqs[i][j])), k=points_num) + if points_num is not None else list(range(len(seqs[i][j])))) + sampled_fras[i].append(np.asarray([seqs[i][j][p] for p in sample_points])) + else: + sampled_fras[i].append(seqs[i][j]) return sampled_fras # f: feature_num @@ -112,4 +121,4 @@ class CollateFn(object): batch[-1] = np.asarray(seqL_batch) batch[0] = fras_batch - return batch + return batch \ No newline at end of file diff --git a/opengait/data/transform.py b/opengait/data/transform.py index cf6d882..9e4d403 100644 --- a/opengait/data/transform.py +++ b/opengait/data/transform.py @@ -228,6 +228,121 @@ def get_transform(trf_cfg=None): return transform raise "Error type for -Transform-Cfg-" +# **************** For LidarGait++ **************** +# Shen, et al: LidarGait++: Learning Local Features and Size Awareness from LiDAR Point Clouds for 3D Gait Recognition, CVPR2025 + +def normalize_point_cloud(batch_data): + """Normalize the batch data using coordinates of the block centered at origin. + + Input: + batch_data: BxNxC array + Output: + BxNxC array + """ + centroids = np.mean(batch_data, axis=1, keepdims=True) # shape: (B, 1, C) + centered = batch_data - centroids + scales = np.max(np.linalg.norm(centered, axis=2), axis=1, keepdims=True) # shape: (B, 1) + scales = scales.reshape(batch_data.shape[0], 1, 1) # (B, 1, 1) for broadcasting + return centered / scales + + +def dropout_point_cloud(batch_data, max_dropout_ratio=0.875, prob=0.2): + """Randomly drop points in each point cloud. + + Input: + batch_data: BxNx3 array + Output: + BxNx3 array, with dropped points replaced by the first point in each cloud. + """ + if np.random.rand() >= prob: + return batch_data + B, N, C = batch_data.shape + # 为每个点云生成一个 dropout_ratio（范围 0 ~ max_dropout_ratio） + dropout_ratio = np.random.rand(B, 1) * max_dropout_ratio # shape: (B, 1) + random_matrix = np.random.rand(B, N) + drop_mask = random_matrix <= dropout_ratio # shape: (B, N) + # 构造每个点云第一个点重复 N 次的数组，用于替换被 dropout 的点 + first_points = np.repeat(batch_data[:, :1, :], N, axis=1) + return np.where(drop_mask[..., None], first_points, batch_data) + +def shift_point_cloud(batch_data, shift_range=0.1, prob=0.2): + """ Randomly shift point cloud. Shift is per point cloud. + Input: + BxNx3 array, original batch of point clouds + Return: + BxNx3 array, shifted batch of point clouds + """ + if np.random.rand() >= prob: + return batch_data + B, N, C = batch_data.shape + shifts = np.random.uniform(-shift_range, shift_range, (B, N,3)) + batch_data += shifts + return batch_data + + +def scale_point_cloud(batch_data, scale_low=0.8, scale_high=1.25, prob=0.2): + """ Randomly scale the point cloud. Scale is per point cloud. + Input: + BxNx3 array, original batch of point clouds + Return: + BxNx3 array, scaled batch of point clouds + """ + if np.random.rand() >= prob: + return batch_data + B, N, C = batch_data.shape + scales = np.random.uniform(scale_low, scale_high, B) + for batch_index in range(B): + batch_data[batch_index,:,:] *= scales[batch_index] + return batch_data + +def jitter_point_cloud(batch_data, std=0.01, clip=0.05, prob=0.2): + if np.random.rand() >= prob: + return batch_data + B, N, C = batch_data.shape + jittered_data = np.random.normal(loc=0.0, scale=std, size=(B, N, C)) + jittered_data = np.clip(jittered_data, -clip, clip) + batch_data += jittered_data + return batch_data + +def flip_point_cloud_y(batch_data, prob=0.25): + if np.random.rand() >= prob: + return batch_data + batch_data[:, :, 1] = -batch_data[:, :, 1] + return batch_data + + +def getxyz(batch_data,col = 2,to_ground=False): + B,N,C = batch_data.shape + last_col = batch_data[:, :, col] + result = last_col.reshape((B, N, 1)) + if to_ground: + result -= result.min(axis=1,keepdims=True) + return result + +class PointCloudsTransform(): + def __init__(self, xyz_only=True, scale_aware=False, drop_prob=0, shift_prob=0, jit_prob=0,scale_prob=0, flip_prob=0): + self.scale_aware = scale_aware + self.xyz_only = xyz_only + + self.flip_prob, self.shift_prob, self.jit_prob, self.scale_prob, self.drop_prob = flip_prob, shift_prob, jit_prob, scale_prob, drop_prob + def __call__(self, points): + if self.xyz_only: + points = points[:,:,:3] + + heights = getxyz(points, col = 2, to_ground=True) + points = normalize_point_cloud(points) + + points = flip_point_cloud_y(points, prob=self.flip_prob) + points = shift_point_cloud(points, prob=self.shift_prob) + points = jitter_point_cloud(points, prob=self.jit_prob) + points = scale_point_cloud(points, prob=self.scale_prob) + points = dropout_point_cloud(points, prob=self.drop_prob) + + if self.scale_aware: + points = np.concatenate([points,heights],axis=-1) + return points + + # **************** For GaitSSB **************** # Fan, et al: Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark, T-PAMI2023 @@ -587,4 +702,4 @@ class MSGGTransform(): def __call__(self, x): result=x[...,self.mask,:].copy() - return result + return result \ No newline at end of file diff --git a/opengait/evaluation/evaluator.py b/opengait/evaluation/evaluator.py index 3f4f6f2..cf93374 100644 --- a/opengait/evaluation/evaluator.py +++ b/opengait/evaluation/evaluator.py @@ -456,4 +456,45 @@ def evaluate_scoliosis(data, dataset, metric='euc'): print(f"{cls} Specificity: {TNR[i] * 100:.2f}%") print(f"Accuracy: {accuracy * 100:.2f}%") - return result_dict \ No newline at end of file + return result_dict + +def evaluate_FreeGait(data, dataset, metric='euc'): + msg_mgr = get_msg_mgr() + + features, labels, cams, time_seqs = data['embeddings'], data['labels'], data['types'], data['views'] + import json + probe_sets = json.load( + open('./datasets/FreeGait/FreeGait.json', 'rb'))['PROBE_SET'] + + probe_mask = [] + for id, ty, sq in zip(labels, cams, time_seqs): + if '-'.join([id, ty, sq]) in probe_sets: + probe_mask.append(True) + else: + probe_mask.append(False) + probe_mask = np.array(probe_mask) + + # probe_features = features[:probe_num] + probe_features = features[probe_mask] + # gallery_features = features[probe_num:] + gallery_features = features[~probe_mask] + # probe_lbls = np.asarray(labels[:probe_num]) + # gallery_lbls = np.asarray(labels[probe_num:]) + probe_lbls = np.asarray(labels)[probe_mask] + gallery_lbls = np.asarray(labels)[~probe_mask] + + results = {} + msg_mgr.log_info(f"The test metric you choose is {metric}.") + dist = cuda_dist(probe_features, gallery_features, metric).cpu().numpy() + cmc, all_AP, all_INP = evaluate_rank(dist, probe_lbls, gallery_lbls) + + mAP = np.mean(all_AP) + mINP = np.mean(all_INP) + for r in [1, 5, 10]: + results['scalar/test_accuracy/Rank-{}'.format(r)] = cmc[r - 1] * 100 + results['scalar/test_accuracy/mAP'] = mAP * 100 + results['scalar/test_accuracy/mINP'] = mINP * 100 + + # print_csv_format(dataset_name, results) + msg_mgr.log_info(results) + return results diff --git a/opengait/modeling/models/lidargaitv2.py b/opengait/modeling/models/lidargaitv2.py new file mode 100644 index 0000000..a15a605 --- /dev/null +++ b/opengait/modeling/models/lidargaitv2.py @@ -0,0 +1,83 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + +from .lidargaitv2_utils import PointNetSetAbstraction, PPPooling, PPPooling_UDP,NetVLAD + +from ..base_model import BaseModel +from ..modules import SeparateFCs, SeparateBNNecks + + +class LidarGaitPlusPlus(BaseModel): + def build_network(self, model_cfg): + C = model_cfg['channel'] + C_out = model_cfg['SeparateFCs']['in_channels'] + scale_aware = model_cfg['scale_aware'] + normalize_dp = model_cfg['normalize_dp'] + sampling = model_cfg['sampling'] + + npoints = model_cfg.get('npoints', [512, 256, 128]) + nsample = model_cfg.get('nsample', 32) + in_channel = 4 if scale_aware else 3 + + self.sa1 = PointNetSetAbstraction(npoint=npoints[0], radius=0.1, nsample=nsample, in_channel=in_channel, mlp=[2*C, 2*C, 4*C], group_all=False, sampling=sampling, scale_aware=scale_aware, normalize_dp=normalize_dp) + self.sa2 = PointNetSetAbstraction(npoint=npoints[1], radius=0.2, nsample=nsample, in_channel=4*C + in_channel, mlp=[4*C, 4*C, 8*C], group_all=False, sampling=sampling, scale_aware=scale_aware, normalize_dp=normalize_dp) + self.sa3 = PointNetSetAbstraction(npoint=npoints[2], radius=0.4, nsample=nsample, in_channel=8*C + in_channel, mlp=[8*C, 8*C, 16*C], group_all=False, sampling=sampling, scale_aware=scale_aware, normalize_dp=normalize_dp) + self.sa4 = PointNetSetAbstraction(npoint=None, radius=None, nsample=None, in_channel=16*C + in_channel, mlp=[16*C, 16*C, C_out], group_all=True, sampling=sampling, scale_aware=scale_aware, normalize_dp=normalize_dp) + + if model_cfg['pool'] == 'VLAD': + self.pool = NetVLAD(num_clusters=16, dim=C_out, alpha=1.0) + elif model_cfg['pool'] == 'GMaxP': + self.pool = PPPooling_UDP([1]) + elif model_cfg['pool'] == 'PPP_UDP': + self.pool = PPPooling_UDP(model_cfg['scale']) + elif model_cfg['pool'] == 'PPP_UAP': + self.pool = PPPooling(scale_aware=False, bin_num=model_cfg['scale']) + elif model_cfg['pool'] == 'PPP_HAP': + self.pool = PPPooling(scale_aware=True, bin_num=model_cfg['scale']) + + + + self.BNNecks = SeparateBNNecks(**model_cfg['SeparateBNNecks']) + self.FCs = SeparateFCs(**model_cfg['SeparateFCs']) + + + def forward(self, inputs): + ipts, labs, _, views, seqL = inputs + + xyz = ipts[0] + B, T, N, C = xyz.shape + xyz = rearrange(xyz, 'B T N C -> (B T) C N') + + + l1_xyz, l1_points = self.sa1(xyz, None) + l1_points = torch.max(l1_points, dim=-2)[0] + + l2_xyz, l2_points = self.sa2(l1_xyz, l1_points) + l2_points = torch.max(l2_points, dim=-2)[0] + + l3_xyz, l3_points = self.sa3(l2_xyz, l2_points) + l3_points = torch.max(l3_points, dim=-2)[0] + + l4_xyz, l4_points = self.sa4(l3_xyz, l3_points) + + x = self.pool(l4_points, l3_xyz) + + + x = rearrange(x, '(B T) feat p -> B T feat p', B=B) + feat = x.max(1)[0]# x.mean(1) # x.max(1)[0] + embed = self.FCs(feat) # [n, c, p] + embed_2, logits = self.BNNecks(embed) # [n, c, p] + retval = { + 'training_feat': { + 'triplet': {'embeddings': embed, 'labels': labs}, + 'softmax': {'logits': logits, 'labels': labs} + }, + 'visual_summary': { + }, + 'inference_feat': { + 'embeddings': embed, + } + } + return retval \ No newline at end of file diff --git a/opengait/modeling/models/lidargaitv2_utils.py b/opengait/modeling/models/lidargaitv2_utils.py new file mode 100644 index 0000000..148d5f2 --- /dev/null +++ b/opengait/modeling/models/lidargaitv2_utils.py @@ -0,0 +1,377 @@ +import torch.nn as nn +import torch.nn.functional as F +import torch +import torch.nn as nn +import torch.utils.data +import torch.nn.functional as F +from einops import rearrange +from torch.autograd import Variable +import numpy as np + + +def square_distance(src, dst): + """ + Calculate Euclid distance between each two points. + src^T * dst = xn * xm + yn * ym + zn * zm； + sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn; + sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm; + dist = (xn - xm)^2 + (yn - ym)^2 + (zn - zm)^2 + = sum(src**2,dim=-1)+sum(dst**2,dim=-1)-2*src^T*dst + Input: + src: source points, [B, N, C] + dst: target points, [B, M, C] + Output: + dist: per-point square distance, [B, N, M] + """ + B, N, _ = src.shape + _, M, _ = dst.shape + dist = -2 * torch.matmul(src, dst.permute(0, 2, 1)) + dist += torch.sum(src ** 2, -1).view(B, N, 1) + dist += torch.sum(dst ** 2, -1).view(B, 1, M) + return dist + + +def index_points(points, idx): + """ + Input: + points: input points data, [B, N, C] + idx: sample index data, [B, S] + Return: + new_points:, indexed points data, [B, S, C] + """ + device = points.device + B = points.shape[0] + view_shape = list(idx.shape) + view_shape[1:] = [1] * (len(view_shape) - 1) + repeat_shape = list(idx.shape) + repeat_shape[0] = 1 + batch_indices = torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape) + new_points = points[batch_indices, idx, :] + return new_points + + +def farthest_point_sample(xyz, npoint): + """ + Input: + xyz: pointcloud data, [B, N, 3] + npoint: number of samples + Return: + centroids: sampled pointcloud index, [B, npoint] + """ + device = xyz.device + B, N, C = xyz.shape + centroids = torch.zeros(B, npoint, dtype=torch.long).to(device) + distance = torch.ones(B, N).to(device) * 1e10 + farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device) + batch_indices = torch.arange(B, dtype=torch.long).to(device) + for i in range(npoint): + centroids[:, i] = farthest + centroid = xyz[batch_indices, farthest, :].view(B, 1, C) + dist = torch.sum((xyz - centroid) ** 2, -1) + mask = dist < distance + distance[mask] = dist[mask] + farthest = torch.max(distance, -1)[1] + return centroids + + +def ball_query(radius, nsample, xyz, new_xyz): + """ + Input: + radius: local region radius + nsample: max sample number in local region + xyz: all points, [B, N, 3] + new_xyz: query points, [B, S, 3] + Return: + group_idx: grouped points index, [B, S, nsample] + """ + device = xyz.device + B, N, C = xyz.shape + _, S, _ = new_xyz.shape + group_idx = torch.arange(N, dtype=torch.long).to(device).view(1, 1, N).repeat([B, S, 1]) + xyz = xyz[:,:,:3] + new_xyz = new_xyz[:,:,:3] + sqrdists = square_distance(new_xyz, xyz) + group_idx[sqrdists > radius ** 2] = N + group_idx = group_idx.sort(dim=-1)[0][:, :, :nsample] + group_first = group_idx[:, :, 0].view(B, S, 1).repeat([1, 1, nsample]) + mask = group_idx == N + group_idx[mask] = group_first[mask] + return group_idx + + +def knn_query(k, xyz, new_xyz): + """ + Input: + k: number of nearest neighbors to query + xyz: all points, [B, N, 3] + new_xyz: query points, [B, S, 3] + Return: + group_idx: indices of k-nearest neighbors, [B, S, k] + """ + B, N, C = xyz.shape + _, S, _ = new_xyz.shape + xyz = xyz[:,:,:3] + new_xyz = new_xyz[:,:,:3] + dists = square_distance(new_xyz, xyz) + #scaling_factor = torch.Tensor([1, 1, 0.6]).to(new_xyz.device) + #dists = torch.sum(torch.square(new_xyz.unsqueeze(2) - xyz.unsqueeze(1)) / scaling_factor, dim=-1) + group_idx = dists.sort(dim=-1)[1][:, :, :k] + return group_idx + + +def sample_and_group(npoint, radius, nsample, xyz, points, returnfps=False, sampling='ball',scale_aware=False, normalize_dp=False): + """ + Input: + npoint: + radius: + nsample: + xyz: input points position data, [B, N, 3] + points: input points data, [B, N, D] + Return: + new_xyz: sampled points position data, [B, npoint, nsample, 3] + new_points: sampled points data, [B, npoint, nsample, 3+D] + """ + B, N, C = xyz.shape + S = npoint + fps_idx = farthest_point_sample(xyz[:,:,:3], npoint) # [B, npoint, C] + new_xyz = index_points(xyz, fps_idx) + + if sampling == 'ball': + idx = ball_query(radius, nsample, xyz, new_xyz) + elif sampling == 'knn': + idx = knn_query(nsample, xyz, new_xyz) + else: + raise ValueError("Unsupported sampling type. Use 'ball' or 'knn'.") + + grouped_xyz = index_points(xyz, idx) # [B, npoint, nsample, C] + grouped_xyz_norm = grouped_xyz - new_xyz.view(B, S, 1, C) + if normalize_dp: # and sampling!='knn': + grouped_xyz_norm /= radius + grouped_xyz_norm = grouped_xyz_norm if scale_aware else grouped_xyz_norm[:,:,:,:3] + + + if points is not None: + grouped_points = index_points(points, idx) + new_points = torch.cat([grouped_xyz_norm, grouped_points], dim=-1) # [B, npoint, nsample, C+D] + else: + new_points = grouped_xyz_norm + if returnfps: + return new_xyz, new_points, grouped_xyz, fps_idx + else: + return new_xyz, new_points + + +def sample_and_group_all(xyz, points, scale_aware=False): + """ + Input: + xyz: input points position data, [B, N, 3] + points: input points data, [B, N, D] + Return: + new_xyz: sampled points position data, [B, 1, 3] + new_points: sampled points data, [B, 1, N, 3+D] + """ + device = xyz.device + B, N, C = xyz.shape + new_xyz = torch.zeros(B, 1, C).to(device) + grouped_xyz = xyz.view(B, 1, N, C) + grouped_xyz = grouped_xyz if scale_aware else grouped_xyz[:,:,:,:3] + if points is not None: + new_points = torch.cat([grouped_xyz, points.view(B, 1, N, -1)], dim=-1) + else: + new_points = grouped_xyz + return new_xyz, new_points + + +class PointNetSetAbstraction(nn.Module): + def __init__(self, npoint, radius, nsample, in_channel, mlp, group_all, sampling='ball', scale_aware=False,normalize_dp=False): + super(PointNetSetAbstraction, self).__init__() + self.npoint = npoint + self.radius = radius + self.nsample = nsample + self.mlp_convs = nn.ModuleList() + self.mlp_bns = nn.ModuleList() + last_channel = in_channel + for out_channel in mlp: + self.mlp_convs.append(nn.Conv2d(last_channel, out_channel, 1)) + self.mlp_bns.append(nn.BatchNorm2d(out_channel)) + last_channel = out_channel + self.group_all = group_all + self.scale_aware = scale_aware + self.normalize_dp = normalize_dp + self.sampling = sampling + def forward(self, xyz, points): + """ + Input: + xyz: input points position data, [B, C, N] + points: input points data, [B, D, N] + Return: + new_xyz: sampled points position data, [B, C, S] + new_points_concat: sample points feature data, [B, D', S] + """ + xyz = xyz.permute(0, 2, 1) + if points is not None: + points = points.permute(0, 2, 1) + + if self.group_all: + new_xyz, new_points = sample_and_group_all(xyz, points, scale_aware=self.scale_aware) + else: + new_xyz, new_points = sample_and_group(self.npoint, self.radius, self.nsample, xyz, points, sampling=self.sampling, scale_aware=self.scale_aware,normalize_dp=self.normalize_dp) + # new_xyz: sampled points position data, [B, ], C] + # new_points: sampled points data, [B, npoint, nsample, C+D] + new_points = new_points.permute(0, 3, 2, 1) # [B, C+D, nsample,npoint] + for i, conv in enumerate(self.mlp_convs): + bn = self.mlp_bns[i] + new_points = F.relu(bn(conv(new_points))) + + new_points = new_points + new_xyz = new_xyz.permute(0, 2, 1) + return new_xyz, new_points + +class PPPooling_UDP(): + """ + Hierarchically Clustered Point Pooling + """ + + def __init__(self, bin_num=None): + if bin_num is None: + bin_num = [16, 8, 4, 2, 1] + self.bin_num = bin_num + + def __call__(self, x, xyz): + """ + x : [n, c, h, w] + xyz: [n, 3, p] + ret: [n, c, p] + """ + #print(xyz.shape) + #x = rearrange(x, 'b n c -> b c n 1') + n, c = x.size()[:2] + _, idx = xyz[:, 2, :].sort() + x = x.gather(2, idx.unsqueeze(1).unsqueeze(-1).expand_as(x)) + features = [] + for b in self.bin_num: + z = x.view(n, c, b, -1) + z = z.mean(-1) + z.max(-1)[0] + features.append(z) + return torch.cat(features, -1) + + +class PPPooling(): + def __init__(self, scale_aware=False, bin_num=None): + # 默认设置多个分辨率的分bin数量 + self.bin_num = bin_num if bin_num is not None else [16, 8, 4, 2, 1] + self.scale_aware = scale_aware + + def __call__(self, point_clouds, points): + # 调整维度：输入 point_clouds: B x C x N x 1 转换为 B x N x C， + # points: B x C x N 转换为 B x N x C + point_clouds = rearrange(point_clouds, 'B C N 1 -> B N C') + points = rearrange(points, 'B C N -> B N C') + B, N, C = point_clouds.shape + + if self.scale_aware: # PPPooling_HAP + z = points[:, :, 3] # shape: (B, N) + # 固定的 z 范围（例如：0 到 2） + z_min, z_max = 0.0, 2.0 + else: + # PPPooling_UAP + # 使用 points 的第 3 个通道作为 z 坐标，归一化到 [0, 1] + z = points[:, :, 2] # shape: (B, N) + z_min = z.min(dim=1, keepdim=True)[0][0].item() + z_max = z.max(dim=1, keepdim=True)[0][0].item() + z_range = z_max - z_min + 1e-6 + z = (z - z_min) / z_range # shape: (B, N) + z_min, z_max = 0.0, 1.0 + + all_pooled = [] + for M in self.bin_num: + # 由于 z 已归一化，直接构造均匀分布的 bin 边界 + edges = torch.linspace(z_min, z_max, steps=M + 1, device=point_clouds.device) + # 利用 bucketize 将每个点分配到 [0, M-1] 内的 bin（不需要额外处理首尾） + # 注意：这里使用 edges[1:-1] 作为分界，保证边界值归到正确 bin + bin_idx = torch.bucketize(z.contiguous(), edges[1:-1], right=False) # shape: (B, N) + + # 为每个 bin计算 max 和 mean 池化值，利用 scatter_reduce 与 scatter_add 操作： + # 构造初始 tensor，形状均为 (B, M, C) + pooled_max = torch.full((B, M, C), float('-inf'), device=point_clouds.device, dtype=point_clouds.dtype) + pooled_sum = torch.zeros((B, M, C), device=point_clouds.device, dtype=point_clouds.dtype) + counts = torch.zeros((B, M, 1), device=point_clouds.device, dtype=point_clouds.dtype) + + # 将 bin_idx 扩展到与 point_clouds 对应的维度 (B, N, C) + bin_idx_exp = bin_idx.unsqueeze(-1).expand(-1, -1, C) + # max 池化：scatter_reduce 计算每个 bin 内的最大值 + pooled_max = pooled_max.scatter_reduce(1, bin_idx_exp, point_clouds, reduce='amax', include_self=True) + # sum 池化：scatter_add 计算每个 bin 内的和 + pooled_sum = pooled_sum.scatter_add(1, bin_idx_exp, point_clouds) + # 计算每个 bin 的计数 + counts = counts.scatter_add(1, bin_idx.unsqueeze(-1), torch.ones((B, N, 1), device=point_clouds.device)) + # 计算 mean 池化 + pooled_mean = pooled_sum / counts.clamp(min=1) + # 这里采用 max 与 mean 的和作为最终池化结果（也可以用 concat） + pooled = pooled_max + pooled_mean + # 将没有点（max为 -inf）的 bin 置 0 + pooled[pooled == float('-inf')] = 0 + + all_pooled.append(pooled) + + # 将各分辨率下的池化结果在 bin 维度上拼接，并调整为 B x C x M_total + output = torch.cat(all_pooled, dim=1) + output = rearrange(output, 'B M C -> B C M') + return output + + +class NetVLAD(nn.Module): + """NetVLAD layer implementation""" + + def __init__(self, num_clusters=64, dim=128, alpha=100.0, + normalize_input=True): + """ + Args: + num_clusters : int + The number of clusters + dim : int + Dimension of descriptors + alpha : float + Parameter of initialization. Larger value is harder assignment. + normalize_input : bool + If true, descriptor-wise L2 normalization is applied to input. + """ + super(NetVLAD, self).__init__() + self.num_clusters = num_clusters + self.dim = dim + self.alpha = alpha + self.normalize_input = normalize_input + self.conv = nn.Conv2d(dim, num_clusters, kernel_size=(1, 1), bias=True) + self.centroids = nn.Parameter(torch.rand(num_clusters, dim)) + self._init_params() + + def _init_params(self): + self.conv.weight = nn.Parameter( + (2.0 * self.alpha * self.centroids).unsqueeze(-1).unsqueeze(-1) + ) + self.conv.bias = nn.Parameter( + - self.alpha * self.centroids.norm(dim=1) + ) + + def forward(self, x, xyz): + N, C = x.shape[:2] + + if self.normalize_input: + x = F.normalize(x, p=2, dim=1) # across descriptor dim + + # soft-assignment + soft_assign = self.conv(x).view(N, self.num_clusters, -1) + soft_assign = F.softmax(soft_assign, dim=1) + + x_flatten = x.view(N, C, -1) + + # calculate residuals to each clusters + residual = x_flatten.expand(self.num_clusters, -1, -1, -1).permute(1, 0, 2, 3) - \ + self.centroids.expand(x_flatten.size(-1), -1, -1).permute(1, 2, 0).unsqueeze(0) + residual *= soft_assign.unsqueeze(2) + vlad = residual.sum(dim=-1) + + vlad = F.normalize(vlad, p=2, dim=2) # intra-normalization + vlad = vlad.view(x.size(0), -1) # flatten b num c -> b num c + vlad = F.normalize(vlad, p=2, dim=1) # L2 normalize + return vlad.unsqueeze(-1)