Awesome
CF-ViT: A General Coarse-to-Fine Method for Vision Transformer (AAAI 2023 Oral)
This is Pytorch implementation of our paper "CF-ViT: A General Coarse-to-Fine Method for Vision Transformer".
Pre-trained Models
Backbone | # of Coarse Seage | Accuracy(threshold=1) | Checkpoints Links | Logs Links |
---|---|---|---|---|
DeiT-S | 7x7 | 80.8 | Google Drive | Google Drive |
DeiT-S | 9x9 | 81.9 | Google Drive | Google Drive |
LV-ViT-S | 7x7 | 83.6 | Google Drive | Google Drive |
LV-ViT-S | 9x9 | 84.4 | Google Drive | Google Drive |
- What are contained in the checkpoints:
**.pth
├── model: state dictionaries of the model
├── flop: a list containing the GFLOPs corresponding to exiting at each stage
├── anytime_classification: Top-1 accuracy of each stage
├── budgeted_batch_classification: results of budgeted batch classification (a two-item list, [0] and [1] correspond to the two coordinates of a curve)
Requirements
- python 3.9.7
- pytorch 1.10.1
- torchvision 0.11.2
- apex
Data Preparation
- The ImageNet dataset should be prepared as follows:
ImageNet
├── train
│ ├── folder 1 (class 1)
│ ├── folder 2 (class 2)
│ ├── ...
├── val
│ ├── folder 1 (class 1)
│ ├── folder 2 (class 2)
│ ├── ...
Evaluate Pre-trained Models
- Get accuracy of each stage
CUDA_VISIBLE_DEVICES=0 python dynamic_inference.py --eval-mode 0 --data_url PATH_TO_IMAGENET --batch_size 64 --model {cf_deit_small, cf_lvvit_small} --checkpoint_path PATH_TO_CHECKPOINT --coarse-stage-size {7,9}
- Infer the model on the validation set with various threshold([0.01:1:0.01])
CUDA_VISIBLE_DEVICES=0 python dynamic_inference.py --eval-mode 1 --data_url PATH_TO_IMAGENET --batch_size 64 --model {cf_deit_small, cf_lvvit_small} --checkpoint_path PATH_TO_CHECKPOINT --coarse-stage-size {7,9}
- Infer the model on the validation set with one threshold and meature the throughput
CUDA_VISIBLE_DEVICES=0 python dynamic_inference.py --eval-mode 2 --data_url PATH_TO_IMAGENET --batch_size 1024 --model {cf_deit_small, cf_lvvit_small} --checkpoint_path PATH_TO_CHECKPOINT --coarse-stage-size {7,9} --threshold THRESHOLD
- Read the evaluation results saved in pre-trained models
CUDA_VISIBLE_DEVICES=0 python dynamic_inference.py --eval-mode 3 --data_url PATH_TO_IMAGENET --batch_size 64 --model {cf_deit_small, cf_lvvit_small} --checkpoint_path PATH_TO_CHECKPOINT --coarse-stage-size {7,9}
Train
- Train CF-ViT(DeiT-S) on ImageNet
python -m torch.distributed.launch --nproc_per_node=4 main_deit.py --model cf_deit_small --batch-size 256 --data-path PATH_TO_IMAGENET --coarse-stage-size {7,9} --dist-eval --output PATH_TO_LOG
- Train CF-ViT(LV-ViT-S) on ImageNet
python -m torch.distributed.launch --nproc_per_node=4 main_lvvit.py PATH_TO_IMAGENET --model cf_lvvit_small -b 256 --apex-amp --drop-path 0.1 --token-label --token-label-data PATH_TO_TOKENLABEL --model-ema --eval-metric top1_f --coarse-stage-size {7,9} --output PATH_TO_LOG
Visualization
- Visualization of images correctly classified at coarse stage and fine stage.
python visualize.py --model cf_deit_small --resume PATH_TO_CHECKPOINT --output_dir PATH_TP_SAVE --data-path PATH_TO_IMAGENET --batch-size 64
- Other drawing code can be found in draw_picture.ipynb
Citation
@inproceedings{CFViT,
title={CF-ViT: A General Coarse-to-Fine Method for Vision Transformer},
author={Mengzhao Chen and Mingbao Lin and Ke Li and Yunhang Shen and Yongjian Wu and Fei Chao and Rongrong Ji},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={37}
}
Acknowledgment
Our code of LV-ViT is from here. Our code of DeiT is from here. The visualization code is modified from Evo-ViT. The dynamic inference with early-exit code is modified from DVT. Thanks to these authors.