PaCa-ViT (CVPR'23)
_{Official PyTorch Implementation}

This repo contains the PyTorch version of model definitions (Tiny, Small, Base), training code and pre-trained weights for ImageNet-1k classification, MS-COCO object detection and instance segmentation, and MIT-ADE20k image semantic segmenation for our PaCa-ViT paper. It is refactored using PyTorch 2.0 and the latest timm, mmdetection 3.x and mmsegmentation 1.x. The trained checkpoints are converted accordingly. We thank the teams of those open-sourced packages.

PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Transformers
Ryan Grainger, Thomas Paniagua, Xi Song, Naresh Cuntoor, Mun Wai Lee and Tianfu Wu

NC State University, BlueHalo, X. Song is an independent researcher.

Vision Transformers (ViTs) are built on the assumption of treating image patches as ``visual tokens" and learn patch-to-patch attention. The patch embedding based tokenizer has a semantic gap with respect to its counterpart, the textual tokenizer. The patch-to-patch attention suffers from the quadratic complexity issue, and also makes it non-trivial to explain learned ViTs. To address these issues in ViT, this paper proposes to learn Patch-to-Cluster attention (PaCa) in ViTs.

Queries in our PaCa-ViT starts with patches, while keys and values are directly based on clustering (with a predefined small number of clusters). The clusters are learned end-to-end, leading to better tokenizers and inducing joint clustering-for-attention and attention-for-clustering for better and interpretable models. The quadratic complexity is relaxed to linear complexity. The proposed PaCa module is used in designing efficient and interpretable ViT backbones and semantic segmentation head networks.

We study four aspects of the PaCa module:

Where to compute the cluster assignments? Consider the stage-wise pyramidical architecture of assembling ViT blocks, a stage consists of a number of blocks. We test two settings: block-wise by computing the cluster assignment for each block, or stage-wise by computing it only in the first block in a stage and then sharing it with the remaining blocks. Both give comparable performance. The latter is more efficient when the model becomes deeper.

How to compute the cluster assignment? We also test two settings: using 2D convolution or Multi-Layer Perceptron (MLP) based implementation. Both have similar performance. The latter is more generic and sheds light on exploiting PaCa for more general Token-to-Cluster attention (ToCa) in a domain agnostic way.

How to leverage an external clustering teacher? We investigate a method of exploiting a lightweight convolution neural network in learning the cluster assignments that are shared by all blocks in a stage. It gives some interesting observations, and potentially pave a way for distilling large foundation models.

What if the number of clusters is known? We further extend the PaCa module in designing an effective head sub-network for dense prediction tasks such as image semantic segmentation where the number of clusters $M$ is available based on the ground-truth number of classes and the learned cluster assignment $\mathcal{C}_{N, M}$ has direct supervision. The PaCa segmentation head significantly improves the performance with reduced model complexity.

Results and Trained Models

(please refer to the paper for details of the settings)

• [] To leverage the efficient implementation of multi-head self-attention via the xformers package, the head dimensions should be divisible by $32$, for which we can change the number of heads in the current archtectural settings accordingly. We have observed significantly faster training in our on-going experiments.
• [] We did not test the simpler linear projection based clustering module (i.e., Linear+Softmax) in our paper (in addition the convolution-based and mlp-based clustering modules), which we have preliminarily observed is also effective using the blockwise clustering setting.

ImageNet-1K (224x224) trained weights

name	acc@1	#params	FLOPs	model
PaCa-Tiny (conv)	80.9	12.2M	3.2G	model
PaCa-Small (conv)	83.08	22.0M	5.5G	model
PaCa-Small (mlp)	83.13	22.6M	5.9G	model
PaCa-Small (teacher)	83.17	21.1M	5.4G	model
PaCa-Base (conv)	83.96	46.9M	9.5G	model
PaCa-Base (teacher)	84.22	46.7M	9.7G	model

We conduct some ablation studies on the number of clusters using the PaCa-Small architecture and convolution-based implementation of clustering.

where	#Clusters, $M$	acc@1	#params	FLOPs	model
stage-wise	[2,2,2,0]	82.28	22.0M	4.7G	model
stage-wise	[49,49,49,0]	82.95	22.0M	5.0G	model
stage-wise	[49,64,81,100]	82.98	22.2M	5.3G	model
stage-wise	[100,81,64,49]	82.87	22.2M	5.3G	model
stage-wise	[100,100,100,100]	83.05	22.3M	5.6G	model
block-wise	[100,100,100,100]	82.93	24.1M	6.1G	model

MS-COCO trained Mask R-CNN weights

name	#params	FLOPs	$AP^b$	$AP_{50}^b$	$AP_{75}^b$	$AP^m$	$AP_{50}^m$	$AP_{75}^m$	model
PaCa-Tiny (conv)	32.0	252	43.3	66.0	47.5	39.6	62.9	42.4	model
PaCa-Small (conv)	41.8	296	46.4	68.7	50.9	41.8	65.5	45.0	model
PaCa-Small (mlp)	42.4	303	46.6	69.0	51.3	41.9	65.7	45.0	model
PaCa-Small (teacher)	40.9	292	45.8	68.0	50.3	41.4	64.9	44.5	model
PaCa-Base (conv)	66.6	373	48.0	69.7	52.1	42.9	66.6	45.6	model
PaCa-Base (teacher)	61.4	372	48.3	70.5	52.6	43.3	67.2	46.6	model

For the models in the ablation studies,

where	#Clusters, $M$	#params	FLOPs	$AP^b$	$AP_{50}^b$	$AP_{75}^b$	$AP^m$	$AP_{50}^m$	$AP_{75}^m$	model
stage-wise	[2,2,2,0]	41.6	283	45.5	68.4	49.9	41.1	64.9	43.9	model
stage-wise	[49,49,49,0]	41.8	289	46.2	68.6	50.6	41.6	65.4	44.3	model
stage-wise	[49,64,81,100]	42.0	289	46.1	68.7	50.4	41.5	65.3	44.3	model
stage-wise	[100,81,64,49]	42.0	291	46.1	68.4	50.3	41.7	65.4	44.7	model
stage-wise	[100,100,100,100]	42.0	294	46.4	68.8	51.0	41.8	65.6	44.6	model
block-wise	[100,100,100,100]	44.0	304	46.5	68.7	51.0	41.8	65.6	45.0	model

MIT-ADE2Ok trained weights

name	head	#params	FLOPs	mIOU (single)	model
PaCa-Tiny (conv)	UperNet	41.6	229.9	44.49	model
PaCa-Small (conv)	UperNet	51.4	242.7	47.6	model
PaCa-Base (conv)	UperNet	77.2	264.1	49.67	model
PaCa-Tiny (conv)	PaCa	13.3	34.4	45.65	model
PaCa-Small (conv)	PaCa	23.2	47.2	48.3	model
PaCa-Small (mlp)	PaCa	24.0	50.0	48.2	model
PaCa-Small (teacher)	PaCa	22.2	46.4	46.2	model
PaCa-Base (conv)	PaCa	48.0	68.5	50.39	model
PaCa-Base (teacher)	PaCa	48.8	68.7	48.4	model

For the models in the ablation studies,

where	#Clusters, $M$	head	#params	FLOPs	mIOU (single)	model
stage-wise	[2,2,2,0]	PaCa	23.2	47.2	47.7	model
stage-wise	[49,49,49,0]	PaCa	23.4	47.3	47.8	model
stage-wise	[49,64,81,100]	PaCa	23.2	47.2	48.1	model
stage-wise	[100,81,64,49]	PaCa	23.7	47.3	47.6	model
stage-wise	[100,100,100,100]	PaCa	23.4	47.3	48.3	model
block-wise	[100,100,100,100]	PaCa	25.8	50.9	48.0	model

Installation

We provide self-contained installation scripts and environment configurations.

git clone https://github.com/iVMCL/PaCaViT.git 
cd PaCaViT 
ln -s ./models ./classification/
ln -s ./models ./detection/
ln -s ./models ./segmentation/
chmod +x ./*.sh
./install.sh pacavit 

Training

We provide scripts for training models using a single-node GPU server (e.g., 8 NVIDIA GPUs).

ImageNet-1k Classification

We borrow from the timm package.

Data preparation. Download the ImageNet dataset to YOUR_IMNET_PATH and unzip it. Move validation images to labeled subfolders using this script.

cd $YOUR_IMNET_PATH/Data/CLS-LOC/val
chmod +x ./valprep.sh
./valprep.sh

Add the symbolic link to the ImageNet dataset,

cd PaCaViT
mkdir datasets
ln -s $YOUR_IMNET_PATH/Data/CLS-LOC ./datasets/IMNET

Use the provided self-contained training script and model configurations to train a model,

train_timm.sh \
Config_file \
Model_name \
Dataset_name \
Img_size \
Remove_old_if_exist_0_or_1 \
Resume_or_not_if_exist \
Exp_name \
Tag \
Gpus \
Nb_gpus \
Workers \
Port \
[others]

e.g., train a PaCa-Tiny model pacavit_tiny_p2cconv_100_0,

cd PaCaViT/classification 
./train_timm.sh configs/imagenet_vit_adamw.yml pacavit_tiny_p2cconv_100_0 IMNET 224 1 1 cvpr23 try1 0,1,2,3,4,5,6,7 8 8 23900

The training results will be saved in PaCaViT/work_dirs/classification/cvpr23/pacavit_tiny_p2cconv_100_0_try1/ before the training is completed. After completed, they will be auto-moved to PaCaViT/work_dirs/classification/cvpr23/TrainingFinished/pacavit_tiny_p2cconv_100_0_try1/

MS-COCO Object Detection and Instance Segmentation

We borrow from the mmdetection 3.x package.

Data preparation: Download COCO 2017 datasets to YOUR_COCO_PATH.

Add the symbolic link to the MS-COCO dataset,

cd PaCaViT
ln -s $YOUR_COCO_PATH ./datasets/coco

Use the provided training script and select one of the configuration files.

cd PaCaViT/detection
chmod +x ./*.sh

train_mmdet.sh \
Relative_config_filename \
Remove_old_if_exist_0_or_1 \
Exp_name \
Tag \
gpus \
nb_gpus \
port \
[others]

e.g., train a Mask R-CNN with the PaCa-Tiny backbone pacavit_tiny_p2cconv_100_0_downstream,

cd PaCaViT/detection 
./train_mmdet.sh configs/paca_vit/mask_rcnn_1x/mask_rcnn_pacavit_tiny_p2cconv_100_0_mstrain_480_800_1x_coco.py 1 cvpr23 try1 0,1,2,3,4,5,6,7 8 23900

The training results will be saved in PaCaViT/work_dirs/detection/cvpr23/mask_rcnn_pacavit_tiny_p2cconv_100_0_mstrain_480_800_1x_coco_try1/

MIT-ADE20k Image Semantic Segmentation

We borrow from the mmsegmentation 1.x package.

Data preparation: Download MIT ADE2Ok 2016 dataset to YOUR_ADE_PATH.

Add the symbolic link to the MIT-ADE dataset,

cd PaCaViT
ln -s $YOUR_ADE_PATH ./datasets/ADEChallengeData2016

Use the provided training script and select one of the configuration files.

cd PaCaViT/segmentation
chmod +x ./*.sh

train_mmseg.sh \
Relative_config_filename \
Remove_old_if_exist_0_or_1 \
Exp_name \
Tag \
gpus \
nb_gpus \
port \
[others]

e.g., train the segmentor with the PaCa segmentation head and the PaCa-Tiny backbone pacavit_tiny_p2cconv_100_0_downstream,

cd PaCaViT/segmentation 
./train_mmseg.sh configs/paca_vit/paca_head/pacahead_pacavit_tiny_p2cconv_100_0_512x512_160k_ade20k.py 1 cvpr23 try1 0,1,2,3,4,5,6,7 8 23900

The training results will be saved in PaCaViT/work_dirs/segmentation/cvpr23/pacahead_pacavit_tiny_p2cconv_100_0_512x512_160k_ade20k_try1/

Evaluation

Accuracy

Please refer to the provided self-contained evaluation scripts for ImageNet-1k evaluation, MS-COCO evaluation and MIT-ADE20k evaluation.

Model Parameters and FLOPs

Please refer to the provided scripts for benchmarking a feature backbone, benchmarking a detector and benchmarking a segmentator.

License

This project is released under the MIT license. Please see the LICENSE file for more information.

Citation

If you find this repository helpful, please consider citing:

@inproceedings{Grainger2023PaCaViT,
  title={PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Transformers},
  author={Ryan Grainger and Thomas Paniagua and Xi Song and Naresh Cuntoor and Mun Wai Lee and Tianfu Wu},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2023}
}