更改

• 增加Cityscapes数据集的脚本文件datasets/cityscapesScripts, 和KITTI数据集的脚本文件datasets/KITTI_devkit_semantics. 用于处理数据集;
• 在KITTI数据集脚本中增加将原label图像转为用于本网络训练的19个种类的label图像的脚本datasets/KITTI_devkit_semantics/devkit/helpers/createTrainIdImgs.py;
• 增加分割的像素集置信度输出;
• 增加对KITTI数据集的读取类, 二次训练文件train/main (kitti).py;
• 在app/run_KITTI_offline.py中对分割结果进行处理, 并将信息保存为yaml文件;
• 训练的权重文件

在实际环境分割结果为(左侧是在KITTI训练后的, 中间为原图, 右侧是在CityScapes训练后的):

增加的像素级置信度图:

原LEDNet代码

LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation

Table of Contents:

• Introduction
• Project Structure
• Installation
• Datasets
• Train
• Resuming training
• Test
• Results
• Reference
• Tips

Introduction

This project contains the code (Note: The code is test in the environment with python=3.6, cuda=9.0, PyTorch-0.4.1, also support Pytorch-0.4.1+) for: LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation by Yu Wang.

The extensive computational burden limits the usage of CNNs in mobile devices for dense estimation tasks, a.k.a semantic segmentation. In this paper, we present a lightweight network to address this problem, namely LEDNet, which employs an asymmetric encoder-decoder architecture for the task of real-time semantic segmentation.More specifically, the encoder adopts a ResNet as backbone network, where two new operations, channel split and shuffle, are utilized in each residual block to greatly reduce computation cost while maintaining higher segmentation accuracy. On the other hand, an attention pyramid network (APN) is employed in the decoder to further lighten the entire network complexity. Our model has less than 1M parameters, and is able to run at over 71 FPS on a single GTX 1080Ti GPU card. The comprehensive experiments demonstrate that our approach achieves state-of-the-art results in terms of speed and accuracy trade-off on Cityscapes dataset. and becomes an effective method for real-time semantic segmentation tasks.

Project-Structure

├── datasets  # contains all datasets for the project
|  └── cityscapes #  cityscapes dataset
|  |  └── gtCoarse #  Coarse cityscapes annotation
|  |  └── gtFine #  Fine cityscapes annotation
|  |  └── leftImg8bit #  cityscapes training image
|  └── cityscapesscripts #  cityscapes dataset label convert scripts！
├── utils
|  └── dataset.py # dataloader for cityscapes dataset
|  └── iouEval.py # for test 'iou mean' and 'iou per class'
|  └── transform.py # data preprocessing
|  └── visualize.py # Visualize with visdom 
|  └── loss.py # loss function 
├── checkpoint
|  └── xxx.pth # pretrained models encoder form ImageNet
├── save
|  └── xxx.pth # trained models form scratch 
├── imagenet-pretrain
|  └── lednet_imagenet.py # 
|  └── main.py # 
├── train
|  └── lednet.py  # model definition for semantic segmentation
|  └── main.py # train model scripts
├── test
|  |  └── dataset.py 
|  |  └── lednet.py # model definition
|  |  └── lednet_no_bn.py # Remove the BN layer in model definition
|  |  └── eval_cityscapes_color.py # Test the results to generate RGB images
|  |  └── eval_cityscapes_server.py # generate result uploaded official server
|  |  └── eval_forward_time.py # Test model inference time
|  |  └── eval_iou.py 
|  |  └── iouEval.py 
|  |  └── transform.py 

Installation

• Python 3.6.x. Recommended using Anaconda3
• Set up python environment

pip3 install -r requirements.txt

• Env: PyTorch_0.4.1; cuda_9.0; cudnn_7.1; python_3.6,

• Clone this repository.

git clone https://github.com/xiaoyufenfei/LEDNet.git
cd LEDNet-master

• Install Visdom.
• Install torchsummary
• Download the dataset by following the Datasets below.
• Note: For training, we currently support cityscapes , aim to add Camvid and VOC and ADE20K dataset

Datasets

• You can download cityscapes from here. Note: please download leftImg8bit_trainvaltest.zip(11GB) and gtFine_trainvaltest(241MB) and gtCoarse(1.3GB).
• You can download CityscapesScripts, and convert the dataset to 19 categories. It should have this basic structure.

├── leftImg8bit
│   ├── train
│   ├──  val
│   └── test
├── gtFine
│   ├── train
│   ├──  val
│   └── test
├── gtCoarse
│   ├── train
│   ├── train_extra
│   └── val

Training-LEDNet

• For help on the optional arguments you can run: python main.py -h

• By default, we assume you have downloaded the cityscapes dataset in the ./data/cityscapes dir.

• To train LEDNet using the train/main.py script the parameters listed in main.py as a flag or manually change them.

python main.py --savedir logs --model lednet --datadir path/root_directory/  --num-epochs xx --batch-size xx ...

Resuming-training-if-decoder-part-broken

• for help on the optional arguments you can run: python main.py -h

python main.py --savedir logs --name lednet --datadir path/root_directory/  --num-epochs xx --batch-size xx --decoder --state "../save/logs/model_best_enc.pth.tar"...

Testing

• the trained models of training process can be found at here. This may not be the best one, you can train one from scratch by yourself or Fine-tuning the training decoder with model encoder pre-trained on ImageNet, For instance

more details refer ./test/README.md

Results

• Please refer to our article for more details.

Method	Dataset	Fine	Coarse	IoU_cla	IoU_cat	FPS
LEDNet	cityscapes	yes	yes	70.6​%	87.1​%​	70​+​

qualitative segmentation result examples:

Citation

If you find this code useful for your research, please use the following BibTeX entry.

 @article{wang2019lednet,
  title={LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation},
  author={Wang, Yu and Zhou, Quan and Liu, Jia and Xiong，Jian and Gao, Guangwei and Wu, Xiaofu, and Latecki Jan Longin},
  journal={arXiv preprint arXiv:1905.02423},
  year={2019}
}

Tips

• Limited by GPU resources, the project results need to be further improved...
• It is recommended to pre-train Encoder on ImageNet and then Fine-turning Decoder part. The result will be better.

Reference

1. Deep residual learning for image recognition
2. Enet: A deep neural network architecture for real-time semantic segmentation
3. Erfnet: Efficient residual factorized convnet for real-time semantic segmentation
4. Shufflenet: An extremely efficient convolutional neural network for mobile devices