https://github.com/StriveZs/MSA-Conv
TiC: Exploring Vision Transformer in Convolution
https://github.com/StriveZs/MSA-Conv
Last synced: 7 months ago
JSON representation
TiC: Exploring Vision Transformer in Convolution
- Host: GitHub
- URL: https://github.com/StriveZs/MSA-Conv
- Owner: StriveZs
- Created: 2023-10-06T09:23:15.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-24T04:56:57.000Z (over 1 year ago)
- Last Synced: 2023-11-29T03:46:54.147Z (over 1 year ago)
- Language: C++
- Size: 31.3 KB
- Stars: 10
- Watchers: 2
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-Segment-Anything - [code
README
# MSA-Conv CUDA Version
pytorch implementation "TiC: Exploring Vision Transformer in Convolution", Self-Attention Meets Conv!
[Paper Link](https://arxiv.org/pdf/2310.04134.pdf)
Installation Guide for MSA-Conv
## Environment Setup
1. Docker container
We are utilizing CUDA Version 10.1 consequently, we have installed the cuda-pytorch:10.1-1.5 image to fulfill our machine's environmental requisites. It is worth noting that the installation of other CUDA versions can also be accomplished seamlessly and accurately.
```
docker run -it --user root --gpus all --ipc=host --shm-size 8G -e NVIDIA_VISIBLE_DEVICES=xxx --name="xxx" -p xxx:22 -v xxx:/file nablascom/cuda-pytorch:10.1-1.5 /bin/bash
```
2. Minconda
We utilize Anaconda for Python environment management. Our environment is based on Python 3.7, but higher or lower versions are also compatible.
```
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
conda create -y --name pytorch_use python=3.7
```
3. Pytorch
Torch==1.5.0 is not a stringent requirement; users can install the version that aligns with their needs.
```
pip install torch==1.5.0+cu101 torchvision==0.6.0+cu101 -i https://pypi.tuna.tsinghua.edu.cn/simple/ -f https://download.pytorch.org/whl/torch_stable.html
```
4. pybind11
Pybind11 is a mandatory prerequisite for installing the MSA-Conv into the Python package
```
pip install "pybind11[global]" -i https://pypi.tuna.tsinghua.edu.cn/simple/
```
## MSA-Conv Setup
Users may need to modify certain file paths in setup.py to align with their specific environment.
1. cmd installation
```
cd /../msa_conv_cuda/
python setup.py develop
```
## Usage
The validation file employs our MSA-Conv to assess and validate the accuracy of gradients obtained through backward propagation