IncepFormer: Efficient Inception Transformer with Pyramid Pooling for Semantic Segmentation

Project page | Paper

IncepFormer: Efficient Inception Transformer with Pyramid Pooling for Semantic Segmentation.
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.

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

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