IDPT: Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models

This repository provides the official implementation of Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models at ICCV 2023.

📨 News

[2023.12.10] 🔥 Our paper Point-FEMAE (github) has been accepted by AAAI 2024! 🎉🎉 Many thanks to all the collaborators and anonymous reviewers! 🤓
[2023.07.18] 🔥 Release the code and instructions. 🤓
[2023.07.14] 🔥 Our paper IDPT has been accepted by ICCV 2023! 🎉🎉 Many thanks to all the collaborators and anonymous reviewers! 🥰

1. Introduction

We first explore prompt tuning for pre-trained point cloud models and propose a novel Instance-aware Dynamic Prompt Tuning (IDPT) strategy to enhance the model robustness against distributional diversity (caused by various noises) in real-world point clouds. IDPT generally utilizes a lightweight prompt generation module to perceive the semantic prior features and generate instance-aware prompt tokens for the pre-trained point cloud model. Compared with the common (static) prompt tuning strategies like Visual Prompt Tuning (VPT), IDPT shows notable improvement in downstream adaptation. IDPT is also competitive with full fine-tuning while requiring only ~7% of the trainable parameters.

In the following, we will guide you how to use this repository step by step. 🤗

2. Preparation

git clone git@github.com:zyh16143998882/ICCV23-IDPT.git
cd ICCV23-IDPT/

2.1 Requirements

gcc >= 4.9
cuda >= 9.0
python >= 3.7
pytorch >= 1.7.0 < 1.11.0
anaconda
torchvision

conda create -y -n idpt python=3.7
conda activate idpt
pip install torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt

# Chamfer Distance & emd
cd ./extensions/chamfer_dist
python setup.py install --user
cd ./extensions/emd
python setup.py install --user

# PointNet++
pip install "git+https://github.com/erikwijmans/Pointnet2_PyTorch.git#egg=pointnet2_ops&subdirectory=pointnet2_ops_lib"

# GPU kNN
pip install --upgrade https://github.com/unlimblue/KNN_CUDA/releases/download/0.2/KNN_CUDA-0.2-py3-none-any.whl
pip install torch-scatter

2.2 Download the point cloud datasets and organize them properly

Before running the code, we need to make sure that everything needed is ready. First, the working directory is expected to be organized as below:

click to expand 👈

ICCV23-IDPT/
├── cfgs/
├── data/
│   ├── ModelNet/ # ModelNet40
│   │   └── modelnet40_normal_resampled/
│   │       ├── modelnet40_shape_names.txt
│   │       ├── modelnet40_train.txt
│   │       ├── modelnet40_test.txt
│   │       ├── modelnet40_train_8192pts_fps.dat
│   │       └── modelnet40_test_8192pts_fps.dat
│   ├── ModelNetFewshot/ # ModelNet Few-shot
│   │   ├── 5way10shot/
│   │   │   ├── 0.pkl
│   │   │   ├── ...
│   │   │   └── 9.pkl
│   │   ├── 5way20shot/
│   │   │   ├── ...
│   │   │   ...
│   │   ├── 10way10shot/
│   │   │   ├── ...
│   │   │   ...
│   │   └── 10way20shot/
│   │       ├── ...
│   │       ...
│   ├── ScanObjectNN/ # ScanObjectNN
│   │   ├── main_split/
│   │   │   ├── training_objectdataset_augmentedrot_scale75.h5
│   │   │   ├── test_objectdataset_augmentedrot_scale75.h5
│   │   │   ├── training_objectdataset.h5
│   │   │   └── test_objectdataset.h5
│   │   └── main_split_nobg/
│   │       ├── training_objectdataset.h5
│   │       └── test_objectdataset.h5
│   ├── ShapeNet55-34/ # ShapeNet55/34
│   │   ├── shapenet_pc/
│   │   │   ├── 02691156-1a04e3eab45ca15dd86060f189eb133.npy
│   │   │   ├── 02691156-1a6ad7a24bb89733f412783097373bdc.npy
│   │   │   ├── ...
│   │   │   ...
│   │   └── ShapeNet-55/
│   │       ├── train.txt
│   │       └── test.txt
│   └── shapenetcore_partanno_segmentation_benchmark_v0_normal/ # ShapeNetPart
│       ├── 02691156/
│       │   ├── 1a04e3eab45ca15dd86060f189eb133.txt
│       │   ├── ...
│       │   ...
│       │── ...
│       │── train_test_split/
│       └── synsetoffset2category.txt
├── datasets/
├── ...
...

Here we have also collected the download links of required datasets for you:

ShapeNet55/34 (for pre-training): [link].
ScanObjectNN: [link].
ModelNet40: [link 1] (pre-processed) or [link 2] (raw).
ModelNet Few-shot: [link].
ShapeNetPart: [link].

3. Pre-train a point cloud model (e.g. Point-MAE)

To pre-train Point-MAE on ShapeNet training set, you can run the following command:

# CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/pretrain.yaml --exp_name <output_file_name>
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/pretrain.yaml --exp_name pretrain_pointmae

If you want to try other models or change pre-training configuration, e.g., mask ratios, just create a new configuration file and pass its path to --config.

For a quick start, we also have provided the pre-trained checkpoint of Point-MAE [link].

4. Tune pre-trained point cloud models on downstream tasks

We take VPT and IDPT as two showcases of prompt tuning for pre-trained point cloud models. Executable commands of different downstream tasks are listed below.

4.1 Object Classification

4.1.1 ModelNet40

VPT-Deep (click to expand 👈)

# CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/finetune_modelnet_vpt.yaml --finetune_model --exp_name <output_file_name> --ckpts <path/to/pre-trained/model>
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/finetune_modelnet_vpt.yaml --ckpts ./checkpoint/pretrain/mae/ckpt-last.pth --finetune_model --exp_name modelnet_vpt

# further enable voting mechanism
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/finetune_modelnet.yaml --test --vote --exp_name modelnet_vpt_vote --ckpts ./experiments/finetune_modelnet/cfgs/modelnet_vpt/ckpt-best.pth

IDPT (click to expand 👈)

# CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/finetune_modelnet_idpt.yaml --finetune_model --exp_name <output_file_name> --ckpts <path/to/pre-trained/model>
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/finetune_modelnet_idpt.yaml --ckpts ./checkpoint/pretrain/mae/ckpt-last.pth --finetune_model --exp_name modelnet_idpt

# further enable voting mechanism
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/finetune_modelnet.yaml --test --vote --exp_name modelnet_idpt_vote --ckpts ./experiments/finetune_modelnet/cfgs/modelnet_idpt/ckpt-best.pth

4.1.2 ScanObjectNN (OBJ-BG)

VPT-Deep (click to expand 👈)

# CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/finetune_scan_objbg_vpt.yaml --finetune_model --exp_name <output_file_name> --ckpts <path/to/pre-trained/model>
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/finetune_scan_objbg_vpt.yaml --ckpts ./checkpoint/pretrain/mae/ckpt-last.pth --finetune_model --exp_name bg_vpt

IDPT (click to expand 👈)

# CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/finetune_scan_objbg_idpt.yaml --finetune_model --exp_name <output_file_name> --ckpts <path/to/pre-trained/model>
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/finetune_scan_objbg_idpt.yaml --ckpts ./checkpoint/pretrain/mae/ckpt-last.pth --finetune_model --exp_name bg_idpt

4.1.3 ScanObjectNN (OBJ-ONLY)

VPT-Deep (click to expand 👈)

# CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/finetune_scan_objonly_vpt.yaml --finetune_model --exp_name <output_file_name> --ckpts <path/to/pre-trained/model>
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/finetune_scan_objonly_vpt.yaml --ckpts ./checkpoint/pretrain/mae/ckpt-last.pth --finetune_model --exp_name only_vpt

IDPT (click to expand 👈)

# CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/finetune_scan_objonly_idpt.yaml --finetune_model --exp_name <output_file_name> --ckpts <path/to/pre-trained/model>
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/finetune_scan_objonly_idpt.yaml --ckpts ./checkpoint/pretrain/mae/ckpt-last.pth --finetune_model --exp_name only_idpt

4.1.4 ScanObjectNN (PB-T50-RS)

VPT-Deep (click to expand 👈)

# CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/finetune_scan_hardest_vpt.yaml --finetune_model --exp_name <output_file_name> --ckpts <path/to/pre-trained/model>
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/finetune_scan_hardest_vpt.yaml --ckpts ./checkpoint/pretrain/mae/ckpt-last.pth --finetune_model --exp_name hard_vpt

IDPT (click to expand 👈)

# CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/finetune_scan_hardest_idpt.yaml --finetune_model --exp_name <output_file_name> --ckpts <path/to/pre-trained/model>
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/finetune_scan_hardest_idpt.yaml --ckpts ./checkpoint/pretrain/mae/ckpt-last.pth --finetune_model --exp_name hard_idpt

4.2 Few-shot Learning on ModelNet Few-shot

VPT-Deep (click to expand 👈)

# CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/fewshot_vpt.yaml --finetune_model --ckpts <path/to/pre-trained/model> --exp_name <output_file_name> --way <5 or 10> --shot <10 or 20> --fold <0-9>
for WAY in 5 10
do
  for SHOT in 10 20
  do
    for FOLD in $(seq 0 9)
    do
	  CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/fewshot_vpt.yaml --finetune_model --ckpts ./checkpoint/pretrain/mae/ckpt-last.pth --exp_name fewshot_vpt --way ${WAY} --shot ${SHOT} --fold ${FOLD}
    done
  done
done

IDPT (click to expand 👈)

# CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/fewshot_idpt.yaml --finetune_model --ckpts <path/to/pre-trained/model> --exp_name <output_file_name> --way <5 or 10> --shot <10 or 20> --fold <0-9>
for WAY in 5 10
do
  for SHOT in 10 20
  do
    for FOLD in $(seq 0 9)
    do
	  CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/fewshot_idpt.yaml --finetune_model --ckpts ./checkpoint/pretrain/mae/ckpt-last.pth --exp_name fewshot_idpt --way ${WAY} --shot ${SHOT} --fold ${FOLD}
    done
  done
done

4.3 Part Segmentation on ShapeNet-Part

IDPT (click to expand 👈)

cd segmentation

# python main.py --model prompt_pt3 --optimizer_part only_new --ckpts <path/to/pre-trained/model> --root path/to/data --learning_rate 0.0002 --epoch 300
CUDA_VISIBLE_DEVICES=0 python main.py --model prompt_pt3 --optimizer_part only_new --ckpts ../checkpoint/pretrain/mae/ckpt-last.pth --root ../data/shapenetcore_partanno_segmentation_benchmark_v0_normal/ --log_dir seg_idpt --learning_rate 0.0002 --epoch 300

5. Validate with checkpoints

For reproducibility, logs and checkpoints of tuned models via IDPT can be found in the table below.

Task	Dataset	Trainable Parameters	log	Acc.	Checkpoints Download
Pre-training	ShapeNet	1.7M	-	N.A.	Point-MAE
Classification	ScanObjectNN	1.7M	finetune_scan_objbg.log	93.63%	OBJ-BG
Classification	ScanObjectNN	1.7M	finetune_scan_objonly.log	93.12%	OBJ-ONLY
Classification	ScanObjectNN	1.7M	finetune_scan_hardest.log	88.51%	PB-T50-RS
Classification	ModelNet40	1.7M	finetune_modelnet.log	93.3%	ModelNet-1k
Classification	ModelNet40 (vote)	-	finetune_modelnet_vote.log	94.4%	-

Task	Dataset	log	5w10s (%)	5w20s (%)	10w10s (%)	10w20s (%)
Few-shot learning	ModelNet40	fewshot_logs	97.3 ± 2.1	97.9 ± 1.1	92.8 ± 4.1	95.4 ± 2.9

The evaluation commands with checkpoints should be in the following format:

CUDA_VISIBLE_DEVICES=<GPU> python main.py --test --config <yaml_file_name> --exp_name <output_file_name> --ckpts <path/to/ckpt>

For example, click to expand 👈

# object classification on ScanObjectNN (PB-T50-RS)
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/finetune_scan_hardest_idpt.yaml --ckpts ./checkpoint/hardest/ckpt-best.pth --test --exp_name hard_test

# object classification on ScanObjectNN (OBJ-BG)
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/finetune_scan_objbg_idpt.yaml --ckpts ./checkpoint/bg/ckpt-best.pth --test --exp_name bg_test

# object classification on ScanObjectNN (OBJ-ONLY)
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/finetune_scan_objonly_idpt.yaml --ckpts ./checkpoint/only/ckpt-best.pth --test --exp_name only_test

# object classification on ModelNet40 (w/o voting)
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/finetune_modelnet_idpt.yaml --ckpts ./checkpoint/modelnet40/ckpt-best.pth --test --exp_name model_test

# object classification on ModelNet40 (w/ voting)
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/finetune_modelnet_idpt.yaml --test --vote --exp_name modelnet_idpt_vote --ckpts ./checkpoint/modelnet40/ckpt-best.pth

# few-show learning on ModelNet40
python parse_test_res.py ./experiments/all/fewshot --multi-exp --few-shot

6. t-SNE visualization

We used t-SNE to visualize the results obtained by IDPT on the ModelNet40 and ScanObjectNN test sets. The execution commands are as follows:

CUDA_VISIBLE_DEVICES=<GPU> python main.py --tsne --config <yaml_file_name> --exp_name <output_file_name> --ckpts <path/to/ckpt>

For example, click to expand 👈

# t-SNE on ScanObjectNN (PB-T50-RS)
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/tsne/finetune_scan_hardest_idpt_tsne.yaml --ckpts ./checkpoint/hardest/ckpt-best.pth --tsne --exp_name hard_test_tsne

# object classification on ScanObjectNN (OBJ-BG)
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/tsne/finetune_scan_objbg_idpt_tsne.yaml --ckpts ./checkpoint/bg/ckpt-best.pth --tsne --exp_name bg_test_tsne

# object classification on ScanObjectNN (OBJ-ONLY)
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/tsne/finetune_scan_objonly_idpt_tsne.yaml --ckpts ./checkpoint/only/ckpt-best.pth --tsne --exp_name only_test_tsne

# object classification on ModelNet40 
CUDA_VISIBLE_DEVICES=0 python main.py --config cfgs/tsne/finetune_modelnet_idpt_tsne.yaml --ckpts ./checkpoint/modelnet40/ckpt-best.pth --tsne --exp_name model_test_tsne

You can also modify the code yourself to implement a customized t-SNE visualization. 🤗

7. Bibliography

If you find this code useful or use the toolkit in your work, please consider citing:

@inproceedings{zha2023_IDPT,
  title={Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models},
  author={Zha, Yaohua and Wang, Jinpeng and Dai, Tao and Chen, Bin and Wang, Zhi and Xia, Shu-Tao},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2023}
}

8. Acknowledgements

Our codes are built upon Point-MAE, Point-BERT, ACT, DGCNN and VPT. Thanks for their efforts.

9. Contact

If you have any question, you can raise an issue or email Yaohua Zha (chayh21@mails.tsinghua.edu.cn) and Jinpeng Wang (wjp20@mails.tsinghua.edu.cn). We will reply you soon.

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Repository files navigation

IDPT: Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models

📨 News

1. Introduction

2. Preparation

2.1 Requirements

2.2 Download the point cloud datasets and organize them properly

3. Pre-train a point cloud model (e.g. Point-MAE)

4. Tune pre-trained point cloud models on downstream tasks

4.1 Object Classification

4.1.1 ModelNet40

4.1.2 ScanObjectNN (OBJ-BG)

4.1.3 ScanObjectNN (OBJ-ONLY)

4.1.4 ScanObjectNN (PB-T50-RS)

4.2 Few-shot Learning on ModelNet Few-shot

4.3 Part Segmentation on ShapeNet-Part

5. Validate with checkpoints

6. t-SNE visualization

7. Bibliography

8. Acknowledgements

9. Contact

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Name		Name	Last commit message	Last commit date
Latest commit History 14 Commits
cfgs		cfgs
datasets		datasets
extensions		extensions
figure		figure
models		models
segmentation		segmentation
tools		tools
utils		utils
README.md		README.md
main.py		main.py
parse_test_res.py		parse_test_res.py
requirements.txt		requirements.txt

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IDPT: Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models

📨 News

1. Introduction

2. Preparation

2.1 Requirements

2.2 Download the point cloud datasets and organize them properly

3. Pre-train a point cloud model (e.g. Point-MAE)

4. Tune pre-trained point cloud models on downstream tasks

4.1 Object Classification

4.1.1 ModelNet40

4.1.2 ScanObjectNN (OBJ-BG)

4.1.3 ScanObjectNN (OBJ-ONLY)

4.1.4 ScanObjectNN (PB-T50-RS)

4.2 Few-shot Learning on ModelNet Few-shot

4.3 Part Segmentation on ShapeNet-Part

5. Validate with checkpoints

6. t-SNE visualization

7. Bibliography

8. Acknowledgements

9. Contact

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