VMamba

VMamba: Visual State Space Model

Getting Started

Installation

step1: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

step2:Environment Setup:

VMamba recommends setting up a conda environment and installing dependencies via pip. Use the following commands to set up your environment:

Create and activate a new conda environment

conda create -n vmamba
conda activate vmamba

Install Dependencies.

pip install -r requirements.txt
# Install selective_scan and its dependencies
cd selective_scan && pip install . && pytest

Optional Dependencies for Model Detection and Segmentation:

pip install mmengine==0.10.1 mmcv==2.1.0 opencv-python-headless ftfy
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:

# For VMamba Tiny
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 configs/vssm/vssm_tiny_224.yaml --batch-size 128 --data-path /dataset/ImageNet2012 --output /tmp

# For VMamba Small
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 configs/vssm/vssm_small_224.yaml --batch-size 128 --data-path /dataset/ImageNet2012 --output /tmp

# For VMamba Base
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 configs/vssm/vssm_base_224.yaml --batch-size 128 --data-path /dataset/ImageNet2012 --output /tmp

Detection and Segmentation:

For detection and segmentation tasks, follow similar steps using the appropriate config files from the configs/vssm directory. Adjust the --cfg, --data-path, and --output parameters according to your dataset and desired output location.

Analysis Tools

VMamba includes tools for analyzing the effective receptive field, FLOPs, loss, and scaling behavior of the models. Use the following commands to perform analysis:

# Analyze the effective receptive field
CUDA_VISIBLE_DEVICES=0 python analyze/get_erf.py > analyze/show/erf/get_erf.log 2>&1

# Analyze FLOPs
CUDA_VISIBLE_DEVICES=0 python analyze/get_flops.py > analyze/show/flops/flops.log 2>&1

# Analyze loss
CUDA_VISIBLE_DEVICES=0 python analyze/get_loss.py

# Further analysis on scaling behavior
python analyze/scaleup_show.py

landskape-ai / VMamba