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<div align="center"> <h1>VMamba </h1> <h3>VMamba: Visual State Space Model</h3>Yue Liu<sup>1</sup>,Yunjie Tian<sup>1</sup>,Yuzhong Zhao<sup>1</sup>, Hongtian Yu<sup>1</sup>, Lingxi Xie<sup>2</sup>, Yaowei Wang<sup>3</sup>, Qixiang Ye<sup>1</sup>, Yunfan Liu<sup>1</sup>
<sup>1</sup> University of Chinese Academy of Sciences, <sup>2</sup> HUAWEI Inc., <sup>3</sup> PengCheng Lab.
Paper: (arXiv 2401.10166)
</div>:white_check_mark: Updates
Sep. 25th, 2024
: Update: VMamba is accepted by NeurIPS2024 (spotlight)!June. 14th, 2024
: Update: we clean the code to be easier to read; we add support formamba2
.May. 26th, 2024
: Update: we release the updated weights of VMambav2, together with the new arxiv paper.May. 7th, 2024
: Update: Important! usingtorch.backends.cudnn.enabled=True
in downstream tasks may be quite slow. If you found vmamba quite slow in your machine, disable it in vmamba.py, else, ignore this.- ...
for details see detailed_updates.md
Abstract
Designing computationally efficient network architectures persists as an ongoing necessity in computer vision. In this paper, we transplant Mamba, a state-space language model, into VMamba, a vision backbone that works in linear time complexity. At the core of VMamba lies a stack of Visual State-Space (VSS) blocks with the 2D Selective Scan (SS2D) module. By traversing along four scanning routes, SS2D helps bridge the gap between the ordered nature of 1D selective scan and the non-sequential structure of 2D vision data, which facilitates the gathering of contextual information from various sources and perspectives. Based on the VSS blocks, we develop a family of VMamba architectures and accelerate them through a succession of architectural and implementation enhancements. Extensive experiments showcase VMamba’s promising performance across diverse visual perception tasks, highlighting its advantages in input scaling efficiency compared to existing benchmark models.
Overview
- VMamba serves as a general-purpose backbone for computer vision.
- 2D-Selective-Scan of VMamba
- VMamba has global effective receptive field
- VMamba resembles Transformer-Based Methods in Activation Map
Main Results
<!-- copied from assets/performance.md --> <!-- :book: --> <!-- ***The checkpoints of some of the models listed below will be released in weeks!*** -->:book: For details see performance.md.
Classification on ImageNet-1K
name | pretrain | resolution | acc@1 | #params | FLOPs | TP. | Train TP. | configs/logs/ckpts |
---|---|---|---|---|---|---|---|---|
Swin-T | ImageNet-1K | 224x224 | 81.2 | 28M | 4.5G | 1244 | 987 | -- |
Swin-S | ImageNet-1K | 224x224 | 83.2 | 50M | 8.7G | 718 | 642 | -- |
Swin-B | ImageNet-1K | 224x224 | 83.5 | 88M | 15.4G | 458 | 496 | -- |
VMamba-S[s2l15 ] | ImageNet-1K | 224x224 | 83.6 | 50M | 8.7G | 877 | 314 | config/log/ckpt |
VMamba-B[s2l15 ] | ImageNet-1K | 224x224 | 83.9 | 89M | 15.4G | 646 | 247 | config/log/ckpt |
VMamba-T[s1l8 ] | ImageNet-1K | 224x224 | 82.6 | 30M | 4.9G | 1686 | 571 | config/log/ckpt |
- Models in this subsection is trained from scratch with random or manual initialization. The hyper-parameters are inherited from Swin, except for
drop_path_rate
andEMA
. All models are trained with EMA except for theVanilla-VMamba-T
. TP.(Throughput)
andTrain TP. (Train Throughput)
are assessed on an A100 GPU paired with an AMD EPYC 7542 CPU, with batch size 128.Train TP.
is tested with mix-resolution, excluding the time consumption of optimizers.FLOPs
andparameters
are now gathered withhead
(In previous versions, they were counted without head, so the numbers raise a little bit).- we calculate
FLOPs
with the algorithm @albertgu provides, which will be bigger than previous calculation (which is based on theselective_scan_ref
function, and ignores the hardware-aware algorithm).
Object Detection on COCO
Backbone | #params | FLOPs | Detector | bboxAP | bboxAP50 | bboxAP75 | segmAP | segmAP50 | segmAP75 | configs/logs/ckpts |
---|---|---|---|---|---|---|---|---|---|---|
Swin-T | 48M | 267G | MaskRCNN@1x | 42.7 | 65.2 | 46.8 | 39.3 | 62.2 | 42.2 | -- |
Swin-S | 69M | 354G | MaskRCNN@1x | 44.8 | 66.6 | 48.9 | 40.9 | 63.4 | 44.2 | -- |
Swin-B | 107M | 496G | MaskRCNN@1x | 46.9 | -- | -- | 42.3 | -- | -- | -- |
VMamba-S[s2l15 ] | 70M | 384G | MaskRCNN@1x | 48.7 | 70.0 | 53.4 | 43.7 | 67.3 | 47.0 | config/log/ckpt |
VMamba-B[s2l15 ] | 108M | 485G | MaskRCNN@1x | 49.2 | 71.4 | 54.0 | 44.1 | 68.3 | 47.7 | config/log/ckpt |
VMamba-B[s2l15 ] | 108M | 485G | MaskRCNN@1x[bs8 ] | 49.2 | 70.9 | 53.9 | 43.9 | 67.7 | 47.6 | config/log/ckpt |
VMamba-T[s1l8 ] | 50M | 271G | MaskRCNN@1x | 47.3 | 69.3 | 52.0 | 42.7 | 66.4 | 45.9 | config/log/ckpt |
:---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
Swin-T | 48M | 267G | MaskRCNN@3x | 46.0 | 68.1 | 50.3 | 41.6 | 65.1 | 44.9 | -- |
Swin-S | 69M | 354G | MaskRCNN@3x | 48.2 | 69.8 | 52.8 | 43.2 | 67.0 | 46.1 | -- |
VMamba-S[s2l15 ] | 70M | 384G | MaskRCNN@3x | 49.9 | 70.9 | 54.7 | 44.20 | 68.2 | 47.7 | config/log/ckpt |
VMamba-T[s1l8 ] | 50M | 271G | MaskRCNN@3x | 48.8 | 70.4 | 53.50 | 43.7 | 67.4 | 47.0 | config/log/ckpt |
- Models in this subsection is initialized from the models trained in
classfication
. - we now calculate FLOPs with the algrithm @albertgu provides, which will be bigger than previous calculation (which is based on the
selective_scan_ref
function, and ignores the hardware-aware algrithm).
Semantic Segmentation on ADE20K
Backbone | Input | #params | FLOPs | Segmentor | mIoU(SS) | mIoU(MS) | configs/logs/logs(ms)/ckpts |
---|---|---|---|---|---|---|---|
Swin-T | 512x512 | 60M | 945G | UperNet@160k | 44.4 | 45.8 | -- |
Swin-S | 512x512 | 81M | 1039G | UperNet@160k | 47.6 | 49.5 | -- |
Swin-B | 512x512 | 121M | 1188G | UperNet@160k | 48.1 | 49.7 | -- |
VMamba-S[s2l15 ] | 512x512 | 82M | 1028G | UperNet@160k | 50.6 | 51.2 | config/log/log(ms)/ckpt |
VMamba-B[s2l15 ] | 512x512 | 122M | 1170G | UperNet@160k | 51.0 | 51.6 | config/log/log(ms)/ckpt |
VMamba-T[s1l8 ] | 512x512 | 62M | 949G | UperNet@160k | 47.9 | 48.8 | config/log/log(ms)/ckpt |
- Models in this subsection is initialized from the models trained in
classfication
. - we now calculate FLOPs with the algrithm @albertgu provides, which will be bigger than previous calculation (which is based on the
selective_scan_ref
function, and ignores the hardware-aware algrithm).
Getting Started
Installation
Step 1: Clone the VMamba repository:
To get started, first clone the VMamba repository and navigate to the project directory:
git clone https://github.com/MzeroMiko/VMamba.git
cd VMamba
Step 2: Environment Setup:
VMamba recommends setting up a conda environment and installing dependencies via pip. Use the following commands to set up your environment: Also, We recommend using the pytorch>=2.0, cuda>=11.8. But lower version of pytorch and CUDA are also supported.
Create and activate a new conda environment
conda create -n vmamba
conda activate vmamba
Install Dependencies
pip install -r requirements.txt
cd kernels/selective_scan && pip install .
<!-- cd kernels/cross_scan && pip install . -->
Check Selective Scan (optional)
-
If you want to check the modules compared with
mamba_ssm
, installmamba_ssm
first! -
If you want to check if the implementation of
selective scan
of ours is the same withmamba_ssm
,selective_scan/test_selective_scan.py
is here for you. Change toMODE = "mamba_ssm_sscore"
inselective_scan/test_selective_scan.py
, and runpytest selective_scan/test_selective_scan.py
. -
If you want to check if the implementation of
selective scan
of ours is the same with reference code (selective_scan_ref
), change toMODE = "sscore"
inselective_scan/test_selective_scan.py
, and runpytest selective_scan/test_selective_scan.py
. -
MODE = "mamba_ssm"
stands for checking whether the results ofmamba_ssm
is close toselective_scan_ref
, and"sstest"
is preserved for development. -
If you find
mamba_ssm
(selective_scan_cuda
) orselective_scan
(selctive_scan_cuda_core
) is not close enough toselective_scan_ref
, and the test failed, do not worry. Check ifmamba_ssm
andselective_scan
are close enough instead. -
If you are interested in selective scan, you can check mamba, mamba-mini, mamba.py mamba-minimal for more information.
Dependencies for Detection
and Segmentation
(optional)
pip install mmengine==0.10.1 mmcv==2.1.0 opencv-python-headless ftfy regex
pip install mmdet==3.3.0 mmsegmentation==1.2.2 mmpretrain==1.2.0
Model Training and Inference
Classification
To train VMamba models for classification on ImageNet, use the following commands for different configurations:
python -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=8 --master_addr="127.0.0.1" --master_port=29501 main.py --cfg </path/to/config> --batch-size 128 --data-path </path/of/dataset> --output /tmp
If you only want to test the performance (together with params and flops):
python -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=1 --master_addr="127.0.0.1" --master_port=29501 main.py --cfg </path/to/config> --batch-size 128 --data-path </path/of/dataset> --output /tmp --pretrained </path/of/checkpoint>
please refer to modelcard for more details.
Detection and Segmentation
To evaluate with mmdetection
or mmsegmentation
:
bash ./tools/dist_test.sh </path/to/config> </path/to/checkpoint> 1
use --tta
to get the mIoU(ms)
in segmentation
To train with mmdetection
or mmsegmentation
:
bash ./tools/dist_train.sh </path/to/config> 8
For more information about detection and segmentation tasks, please refer to the manual of mmdetection
and mmsegmentation
. Remember to use the appropriate backbone configurations in the configs
directory.
Analysis Tools
VMamba includes tools for visualizing mamba "attention" and effective receptive field, analysing throughput and train-throughput. Use the following commands to perform analysis:
# Visualize Mamba "Attention"
CUDA_VISIBLE_DEVICES=0 python analyze/attnmap.py
# Analyze the effective receptive field
CUDA_VISIBLE_DEVICES=0 python analyze/erf.py
# Analyze the throughput and train throughput
CUDA_VISIBLE_DEVICES=0 python analyze/tp.py
We also included other analysing tools that we may use in this project. Thanks to all who have contributes to these tools.
Star History
Citation
@article{liu2024vmamba,
title={VMamba: Visual State Space Model},
author={Liu, Yue and Tian, Yunjie and Zhao, Yuzhong and Yu, Hongtian and Xie, Lingxi and Wang, Yaowei and Ye, Qixiang and Liu, Yunfan},
journal={arXiv preprint arXiv:2401.10166},
year={2024}
}
Acknowledgment
This project is based on Mamba (paper, code), Swin-Transformer (paper, code), ConvNeXt (paper, code), OpenMMLab,
and the analyze/get_erf.py
is adopted from replknet, thanks for their excellent works.
- We release Fast-iTPN recently, which reports the best performance on ImageNet-1K at Tiny/Small/Base level models as far as we know. (Tiny-24M-86.5%, Small-40M-87.8%, Base-85M-88.75%)