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IncepFormer: Efficient Inception Transformer with Spatial Selection Decoder for Semantic Segmentation
<!--  --> <div align="center"> <img src="./images/IncepFormer.png"> </div> <p align="center"> </p> <!-- ### [Project page](https://github.com/shendu0321/IncepFormer) | [Paper](http://arxiv.org/abs/2212.03035) IncepFormer: Efficient Inception Transformer with Pyramid Pooling for Semantic Segmentation.<br> Lihua Fu, Haoyue Tian, Xiangping Bryce Zhai, Pan Gao, Xiaojiang Peng This repository contains the official Pytorch implementation of training & evaluation code and the pretrained models for [IncepFormer](http://arxiv.org/abs/2212.03035). -->We use MMSegmentation v0.29.0 as the codebase.
Installation
For install and data preparation, please refer to the guidelines in MMSegmentation v0.29.0.
Other requirements:
pip install timm==0.4.12
An example (works for me): CUDA 11.0 and pytorch 1.7.0
pip install torchvision==0.8.0
pip install timm==0.4.12
pip install mmcv-full==1.5.3
pip install opencv-python==4.6.0.66
cd IncepFormer && pip install -e .
Training
Download weights
(
google drive
)
pretrained on ImageNet-1K, and put them in a folder pretrained/.
Example: train IncepFormer-T on ADE20K:
# Single-gpu training
python tools/train.py local_configs/incepformer/Tiny/tiny_ade_512×512_160k.py
# Multi-gpu training
./tools/dist_train.sh local_configs/incepformer/Tiny/tiny_ade_512×512_160k.py <GPU_NUM>
Evaluation
Example: evaluate IncepFormer-T on ADE20K:
# Single-gpu testing
python tools/test.py local_configs/incepformer/Tiny/tiny_ade_512×512_160k.py /path/to/checkpoint_file
# Multi-gpu testing
./tools/dist_test.sh local_configs/incepformer/Tiny/tiny_ade_512×512_160k.py /path/to/checkpoint_file <GPU_NUM>
# Multi-gpu, multi-scale testing
tools/dist_test.sh local_configs/incepformer/Tiny/tiny_ade_512×512_160k.py /path/to/checkpoint_file <GPU_NUM> --aug-test