PDPP

[CVPR 2023 Hightlight] PDPP: Projected Diffusion for Procedure Planning in Instructional Videos

This repository gives the official PyTorch implementation of PDPP:Projected Diffusion for Procedure Planning in Instructional Videos (CVPR 2023)

News

• We have updated our paper with the following changes:
  • We correct a mistake in the classification result of MLP for $CrossTask_{Base}$ when T=3, which should be around 83. The evaluation metrics results for $CrossTask_{Base}$ when T=3 thus dropped slightly, but still outperform all previous methods.
  • We notice the initial random noise for sampling can influence the result, especially for NIV. Thus we update our results to the mean values of multiple sampling results with different initial random noises. We use the DDIM sampling process to get all results.
  • The batch size value in our "Impact of batch size on mIoU" section of supplement is the sum of 8 GPUs in our old version paper. We rewrite it as the batch size value for a single GPU to avoid misunderstanding.

Setup

---

In a conda env with cuda available, run:

pip install -r requirements.txt

Data Preparation

---

CrossTask

1. Download datasets&features

cd {root}/dataset/crosstask
bash download.sh

2. move your datasplit files and action one-hot coding file to {root}/dataset/crosstask/crosstask_release/

mv *.json crosstask_release
mv actions_one_hot.npy crosstask_release

COIN

1. Download datasets&features

cd {root}/dataset/coin
bash download.sh

NIV

1. Download datasets&features

cd {root}/dataset/NIV
bash download.sh

Train

---

1. Train MLPs for task category prediction(By default,8 GPUs are used for training), you can modify the dataset, train steps, horizon(prediction length), json files savepath etc. in args.py.

python train_mlp.py --multiprocessing-distributed --num_thread_reader=8 --cudnn_benchmark=1 --pin_memory --checkpoint_dir=whl --resume --batch_size=256 --batch_size_val=256 --evaluate

​ Dimensions for different datasets are listed below:

Dataset	observation_dim	action_dim	class_dim
CrossTask	1536(how) 9600(base)	105	18
COIN	1536	778	180
NIV	1536	48	5

​ The trained MLPs will be saved in {root}/save_max_mlp and json files for training and testing data will be generated. Then run temp.py to generate json files with predicted task class for testing:

​ Modify the checkpoint path(L86) and json file path(L111) in temp.py and run:

CUDA_VISIBLE_DEVICES=0 python temp.py --multiprocessing-distributed --num_thread_reader=1 --cudnn_benchmark=1 --pin_memory --checkpoint_dir=whl --resume --batch_size=32 --batch_size_val=32 --evaluate

2. Train PDPP: Modify the 'json_path_val' in args.py as the output file of temp.py and run:

python main_distributed.py --multiprocessing-distributed --num_thread_reader=8 --cudnn_benchmark=1 --pin_memory --checkpoint_dir=whl --resume --batch_size=256 --batch_size_val=256 --evaluate

​ Training settings for different datasets are listed below:

Dataset	n_diffusion_steps	n_train_steps	epochs	learning-rate
CrossTask$_{Base}$	200	200	60	8e-4
CrossTask$_{How}$	200	200	120	5e-4
COIN	200	200	800	1e-5
NIV	50	50	130	3e-4

​ Learning-rate schedule can be adjusted in helpers.py. Schedule details can be found in the supplement. The trained models will be saved in {root}/save_max.

​ To train the $Deterministic$ and $Noise$ baselines, you need to modify temporal.py to remove 'time_mlp' modules and modify diffusion.py to change the initial noise, 'training' functions and p_sample_loop process.

Inference

---

Checkpoints

Note: Numbers may vary from runs to runs for PDPP and $Noise$ baseline, due to probalistic sampling.

For Metrics

​ Modify the checkpoint path(L244) as the evaluated model in inference.py and run:

python inference.py --multiprocessing-distributed --num_thread_reader=8 --cudnn_benchmark=1 --pin_memory --checkpoint_dir=whl --resume --batch_size=256 --batch_size_val=256 --evaluate > output.txt

​ Results of given checkpoints:

	SR	mAcc	MIoU
Crosstask_T=3_diffusion	37.20	64.67	66.57
COIN_T=3_diffusion	21.33	45.62	51.82
NIV_T=3_diffusion	30.20	48.45	57.28

For probabilistic modeling

​ To evaluate the $Deterministic$ and $Noise$ baselines, you need to modify temporal.py to remove 'time_mlp' modules and modify diffusion.py to change the initial noise and p_sample_loop process. For $Deterministic$ baseline, num_sampling(L26) in uncertain.py should be 1.

​ Modify the checkpoint path(L309) as the evaluated model in uncertain.py and run:

CUDA_VISIBLE_DEVICES=0 python uncertain.py --multiprocessing-distributed --num_thread_reader=1 --cudnn_benchmark=1 --pin_memory --checkpoint_dir=whl --resume --batch_size=32 --batch_size_val=32 --evaluate > output.txt

​ Results of given checkpoints:

	NLL	KL-Div	ModePrec	ModeRec
Crosstask_T=6_diffusion	4.06	2.76	25.61	22.68
Crosstask_T=6_noise	4.79	3.49	24.51	11.04
Crosstask_T=6_zero	5.12	3.82	25.24	6.75

Citation

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If this project helps you in your research or project, please cite our paper:

@inproceedings{wang2023pdppprojected,
      title={PDPP:Projected Diffusion for Procedure Planning in Instructional Videos}, 
      author={Hanlin Wang and Yilu Wu and Sheng Guo and Limin Wang},
      booktitle={{CVPR}},
      year={2023}
}

Acknowledgements

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We would like to thank He Zhao for his help in extracting the s3d features and providing the evaluation code of probabilistic modeling in P3IV. The diffusion model implementation is based on diffuser and improved-diffusion. We also reference and use some code from PlaTe. Very sincere thanks to the contributors to these excellent codebases.