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https://github.com/wgcban/apt

PyTorch Implementation of Attention Prompt Tuning: Parameter-Efficient Adaptation of Pre-Trained Models for Action Recognition
https://github.com/wgcban/apt

adapter-tuning computer-vision hmdb51 linear-probing prompt-tuning something-something tuning-parameters ucf-101 video-understanding

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PyTorch Implementation of Attention Prompt Tuning: Parameter-Efficient Adaptation of Pre-Trained Models for Action Recognition

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# APT: Attention Prompt Tuning

> A Parameter-Efficient Adaptation of Pre-Trained Models for Action Recognition ...



> [Wele Gedara Chaminda Bandara](https://github.com/wgcban), [Vishal M Patel](https://engineering.jhu.edu/vpatel36/team/vishalpatel/)
Johns Hopkins University



> Accepted at [FG'24](https://fg2024.ieee-biometrics.org)



> [Paper (on ArXiv)](https://arxiv.org/abs/2403.06978)



## Overview of Proposed Method





    





    Comparison of our Attention Prompt Tuning (APT) for videos action classification with other existing tuning methods:  linear probing, adapter tuning, visual prompt tuning (VPT), and full fine-tuning.






    





    Attention Prompt Tuning (APT) injects learnable prompts directly into the MHA unlike VPT.




## Getting Started



### Step 1: Conda Environment



Setup the virtual conda environment using the `environment.yml`:

```

conda env create -f environment.yml

```



Then activate the conda environment:

```

conda activate apt

```



### Step 2: Download the VideoMAE Pre-trained Models:



We use [VideoMAE](https://github.com/MCG-NJU/VideoMAE) pretrianed on [Kinetics-400](https://github.com/cvdfoundation/kinetics-dataset) dataset for our experiments.



The pre-trained models for ViT-Small and ViT-Base backbones can be downloaded from below links:



|  Method  | Extra Data | Backbone | Epoch | \#Frame |                          Pre-train                           |

| :------: | :--------: | :------: | :---: | :-----: | :----------------------------------------------------------: |

| VideoMAE |  ***no***  |  ViT-S   |  1600  | 16x5x3  | [checkpoint](https://drive.google.com/file/d/1nU-H1u3eJ-VuyCveU7v-WIOcAVxs5Hww/view?usp=sharing) |

| VideoMAE |  ***no***  |  ViT-B   | 1600  | 16x5x3  | [checkpoint](https://drive.google.com/file/d/1tEhLyskjb755TJ65ptsrafUG2llSwQE1/view?usp=sharing) |



If you need other pre-trained models please refer [MODEL_ZOO.md](https://github.com/wgcban/apt/blob/main/MODEL_ZOO.md).



### Step 3: Download the datasets



We conduct experiments on three action recognition datasets: 1) UCF101 2) HMDB51 3) Something-Something-V2. 



Please refer [DATASETS.md](https://github.com/wgcban/apt/blob/main/DATASET.md) for access to those links and pre-processing steps.



### Step 4: Attention Prompt Tuning



We provide example scripts to run the attention prompt tuning on UCF101, HMDB51, and SSv2 datasets in `scripts/` folder.



Inside `scripts/` you can find two folders which corresponds to APT finetuning with ViT-Small and ViT-Base architectures. 



To fine-tune with APT you just need to execute `finetune.sh` file -- which will launch the job with distributed training by



For example, to fine-tune ViT-Base on SSv2 with APT, you may run:

```

sh scripts/ssv2/vit_base/finetune.sh

```



The `finetune.sh` looks like this:



```bash

# APT on SSv2

OUTPUT_DIR='experiments/APT/SSV2/ssv2_videomae_pretrain_base_patch16_224_frame_16x2_tube_mask_ratio_0.9_e2400/adam_mome9e-1_wd1e-5_lr5se-2_pl2_ps0_pe11_drop10'

DATA_PATH='datasets/ss2/list_ssv2/'

MODEL_PATH='experiments/pretrain/ssv2_videomae_pretrain_base_patch16_224_frame_16x2_tube_mask_ratio_0.9_e2400/checkpoint.pth'



NCCL_P2P_DISABLE=1 OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=0,1,3,4,5,6,7,8 python -m torch.distributed.launch --nproc_per_node=8 \

    run_class_apt.py \

    --model vit_base_patch16_224 \

    --transfer_type prompt \

    --prompt_start 0 \

    --prompt_end 11 \

    --prompt_num_tokens 2 \

    --prompt_dropout 0.1 \

    --data_set SSV2 \

    --nb_classes 174 \

    --data_path ${DATA_PATH} \

    --finetune ${MODEL_PATH} \

    --log_dir ${OUTPUT_DIR} \

    --output_dir ${OUTPUT_DIR} \

    --batch_size 8 \

    --batch_size_val 8 \

    --num_sample 2 \

    --input_size 224 \

    --short_side_size 224 \

    --save_ckpt_freq 10 \

    --num_frames 16 \

    --opt adamw \

    --lr 0.05 \

    --weight_decay 0.00001 \

    --epochs 100 \

    --warmup_epochs 10 \

    --test_num_segment 2 \

    --test_num_crop 3 \

    --dist_eval \

    --pin_mem \

    --enable_deepspeed \

    --prompt_reparam \

    --is_aa \

    --aa rand-m4-n2-mstd0.2-inc1



```



Here,



- `OUTPUT_DIR`: place where you wants to save the results (i.e., logs and checkpoints)

- `DATA_PATH`: path to where the dataset is stored

- `MODEL_PATH`: path to the downloaded videomae pre-trained model

- specifiy thich gpus (gpu ids) you wants to use for finetuning in `CUDA_VISIBLE_DEVICES=`...

- `nproc_per_node` is the number of gpus using for fine-tuning

- `model` is the vit-base (vit_base_patch16_224) or vit-small (vit_small_patch16_224)

- `transfer_type` specifies which finetuning method to use. 'random' means random initialization, 'end2end' means full end-to-end fine tuning, 'prompt' means APT (ours), 'linear' means linear probing

- `prompt_start` refers to starting trasnformer block where you add attention prompts. 0 means you start adding learninable prompts from 1st transformer block in vit

- `prompt_end` refers to ending trasformer block where you stop adding attention prompts. vit-base / vit-small has 12 transformer blocks. hence 11 here means you add prompts until last trasnformer block

- `data_set` specifies the dataset

- * all the other parameters are hyperparamters related to apt fine-tuning. 



## ✏️ Citation



If you think this project is helpful, please feel free to leave a star and cite our paper:



```bibtex

@misc{bandara2024attention,

      title={Attention Prompt Tuning: Parameter-efficient Adaptation of Pre-trained Models for Spatiotemporal Modeling}, 

      author={Wele Gedara Chaminda Bandara and Vishal M. Patel},

      year={2024},

      eprint={2403.06978},

      archivePrefix={arXiv},

      primaryClass={cs.CV}

}

```



## ✏️ Disclaimer



This repocitory is built on top of VideoMAE: https://github.com/MCG-NJU/VideoMAE codebase and we approcite the authors of VideoMAE for making their codebase publically available.