Awesome

SSformer

SSformer is A Lightweight Transformer for Semantic Segmentation.

SSformer structure:

We use MMSegmentation v0.24.1 as the codebase.

Installation

For install , please refer to the guidelines in MMSegmentation v0.24.1.

An example (works for me): CUDA 11.3 and pytorch 1.10.0

A from-scratch setup script

Linux

Here is a full script for setting up SSformer with conda and link the dataset path (supposing that your dataset path is $DATA_ROOT).

conda create -n SSformer python=3.10 -y
conda activate SSformer
conda install pytorch=1.11.0 torchvision cudatoolkit=11.3 -c pytorch
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html
git clone https://github.com/shiwt03/SSformer.git
cd SSformer
pip install -e .  # or "python setup.py develop"
mkdir data
ln -s $DATA_ROOT data

Windows(Experimental)

Here is a full script for setting up SSformer with conda and link the dataset path (supposing that your dataset path is %DATA_ROOT%. Notice: It must be an absolute path).

conda create -n SSformer python=3.10 -y
conda activate SSformer
conda install pytorch=1.11.0 torchvision cudatoolkit=11.3 -c pytorch
set PATH=full\path\to\your\cpp\compiler;%PATH%
pip install mmcv
git clone https://github.com/shiwt03/SSformer.git
cd SSformer
pip install -e .  # or "python setup.py develop"
mklink /D data %DATA_ROOT%

Dataset Preparation

For dataset preparation, please refer to the guidelines in this link.

It is recommended to symlink the dataset root to SSformer/data. If your folder structure is different, you may need to change the corresponding paths in config files.

The fold structure is recommended to be:

SSformer
├── data
│   ├── cityscapes
│   │   ├── leftImg8bit
│   │   │   ├── train
│   │   │   ├── val
│   │   ├── gtFine
│   │   │   ├── train
│   │   │   ├── val
│   ├── ade
│   │   ├── ADEChallengeData2016
│   │   │   ├── annotations
│   │   │   │   ├── training
│   │   │   │   ├── validation
│   │   │   ├── images
│   │   │   │   ├── training
│   │   │   │   ├── validation

Cityscapes

The data could be found here after registration.

By convention, **labelTrainIds.png are used for cityscapes training. MMsegmentation provided a script based on cityscapesscripts to generate **labelTrainIds.png.

# --nproc means 8 process for conversion, which could be omitted as well.
python tools/convert_datasets/cityscapes.py data/cityscapes --nproc 8

Part of SSformer's segmentation results on Cityscapes:

ADE20K

The training and validation set of ADE20K could be download from this link. You may also download test set from here.

Part of SSformer's segmentation results on ADE20K:

Evaluation

Download trained weights.

ADE20K

Example: evaluate SSformer on ADE20K:

# Single-gpu testing
python tools/test.py configs/SSformer/SSformer_swin_512x512_160k_ade20k.py /path/to/checkpoint_file --show

Cityscapes

Example: evaluate SSformer on Cityscapes:

# Single-gpu testing
python tools/test.py configs/SSformer/SSformer_swin_1024x1024_160k_Cityscapes.py /path/to/checkpoint_file --show

Training

Download weights pretrained on ImageNet-22K, and put them in a folder pretrained/.

Example: train SSFormer on ADE20K:

# Single-gpu training
python tools/train.py configs/SSformer/SSformer_swin_512x512_160k_ade20k.py

Visualize

Here is a demo script to test a single image. More details refer to MMSegmentation's Doc.

python demo/image_demo.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${DEVICE_NAME}] [--palette-thr ${PALETTE}]

Example: visualize SSformer on CityScapes:

python demo/image_demo.py demo/demo.png configs/SSformer/SSformer_swin_1024x1024_80k_Cityscapes.py \
/path/to/checkpoint_file --device cuda:0 --palette cityscapes

License

Please check the LICENSE file.

Acknowledgment

Thanks to previous open-sourced repo: mmsegmentation Swin-Transformer SegFormer