<code># 1. 创建自己的虚拟环境

conda create -n VMamba python=3.10.13

conda activate VMamba

# 2. cuda-11.8

conda install cudatoolkit==11.8 -c nvidia

# 3. torch torchvision torchaudio 与 官网命令一致

pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org/whl/cu118

# 4. nvcc

conda install -c "nvidia/label/cuda-11.8.0" cuda-nvcc

# 5. packaging

conda install packaging

# 6.openlab环境

pip install mmcv==2.1.0 -f https://download.openmmlab.com/mmcv/dist/cu118/torch2.1/index.html

pip install mmengine==0.10.1

pip install mmdet==3.3.0 mmsegmentation==1.2.2 mmpretrain==1.2.0

# 7. causal-conv1d== & mamba-ssm==1.1.1

pip install causal-conv1d==1.1.1

pip install mamba-ssm==1.1.2 #1.1.1实测会报错

causal-conv1d和mamba-ssm上述方法安装失败，可以见链接，采用whl安装。

关于NameError: name 'selective_scan_cuda_core问题，是跟cd kernels/selective_scan && pip install .这个安装不成功有关，（分析原因应该是cuda版本不对）

NameError: name 'selective_scan_cuda_core' is not defined. Did you mean: 'selective_scan_flop_jit'?

这边直接上我的环境信息吧，

certifi==2022.12.7

charset-normalizer==2.1.1

einops==0.8.0

filelock==3.13.1

fsspec==2024.2.0

fvcore==0.1.5.post20221221

huggingface-hub==0.23.0

idna==3.4

iopath==0.1.10

Jinja2==3.1.3

MarkupSafe==2.1.5

mpmath==1.3.0

networkx==3.2.1

ninja==1.11.1.1

numpy==1.26.3

packaging @ file:///home/conda/feedstock_root/build_artifacts/packaging_1637239678211/work

pillow==10.2.0

portalocker==2.8.2

pyparsing @ file:///home/conda/feedstock_root/build_artifacts/pyparsing_1652235407899/work

PyYAML==6.0.1

requests==2.28.1

safetensors==0.4.3

selective-scan @ file:///home/rycc/cc/VMamba/kernels/selective_scan

sympy==1.12

tabulate==0.9.0

termcolor==2.4.0

timm==0.9.16

torch==2.1.1+cu118

torchaudio==2.1.1+cu118

torchvision==0.16.1+cu118

tqdm==4.66.4

triton==2.1.0

typing_extensions==4.9.0

urllib3==1.26.13

yacs==0.1.8

测试代码，在classification/models/vmamba.py最后添加，并运行

device = torch.device("cuda:0")

hidden_dim = 3 # 假设 VSSBlock 的隐藏维度为 64

network = VSSM(hidden_dim).to('cuda:0')

# 随机生成一张输入图片作为示例

input_image = torch.randn(1, 3, 224, 224) # 假设输入图片大小为 224x224，通道数为 3（RGB图像）

input_image = input_image.to(device)

# 前向传播

output = network(input_image)

# 输出结果的大小

print("Output shape:", output.shape)

输出结果，表示成功配置

训练命令 （目前数据集还未下载，后续更新）

<code>python -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=2 --master_addr="127.0.0.1" --master_port=29501 main.py --cfg /home/rycc/cc/VMamba/classification/configs/vssm/vmambav0_base_224.yamlcode>