SemSegPaddle

SemSegPaddle: A Paddle-based Framework for Deep Learning in Semantic Segmentation

This is a Paddle implementation of semantic segmentation models on multiple datasets, including Cityscapes, Pascal Context, and ADE20K.

Updates

• [2020/01/08] We release PSPNet-ResNet101 and GloRe-ResNet101 models on Pascal Context and Cityscapes datasets.

Highlights

Synchronized Batch Normlization is important for segmenation.

• The implementation is easy to use as it is pure-python, no any C++ extra extension libs.

• Paddle provides sync_batch_norm.

Support models

We split our models into backbone and decoder network, where backbone network are transfered from classification networks.

Backbone:

• ResNet
• ResNeXt
• HRNet
• EfficientNet

Decoder:

• PSPNet: Pyramid Scene Parsing Network
• DeepLabv3: Rethinking Atrous Convolution for Semantic Image Segmentation
• GloRe: Graph-Based Global Reasoning Networks
• GINet: GINet: Graph Interaction Netowrk for Scene Parsing

Peformance

• Performance of Cityscapes validation set.

Method	Backbone	lr	BatchSize	epoch	mean IoU (Single-scale)	Trained weights
PSPNet	resnet101	0.01	8	80	78.1	pspnet_resnet_cityscapes_epoch_80.pdparams
GloRe	resnet101	0.01	8	80	78.4	pspnet_resnet_pascalcontext_epoch_80.pdparams

• Performance of Pascal-context validation set.

Method	Backbone	lr	BatchSize	epoch	mean IoU (Single-scale)	Trained weights
PSPNet	resnet101	0.005	16	80	48.9	glore_resnet_cityscapes_epoch_80.pdparams
GloRe	resnet101	0.005	16	80	48.4	glore_resnet_pascalcontext_epoch_80.pdparams

Environment

This repo is developed under the following configurations:

• Hardware: 4 GPUs for training, 1 GPU for testing
• Software: Centos 6.10, CUDA>=9.2 Python>=3.6, Paddle>=1.6

Quick start: training and testing models

1. Preparing data

Download the Cityscapes dataset. It should have this basic structure:

  cityscapes/
  ├── cityscapes_list
  │   ├── test.lst
  │   ├── train.lst
  │   ├── train+.lst
  │   ├── train++.lst
  │   ├── trainval.lst
  │   └── val.lst
  ├── gtFine
  │   ├── test
  │   ├── train
  │   └── val
  ├── leftImg8bit
  │   ├── test
  │   ├── train
  │   └── val
  ├── license.txt
  └── README

Download Pascal-Context dataset. It should have this basic structure:

  pascalContext/
  ├── GroundTruth_trainval_mat
  ├── GroundTruth_trainval_png
  ├── JPEGImages
  ├── pascal_context_train.txt
  ├── pascal_context_val.txt
  ├── README.md
  └── VOCdevkit

Then, create symlinks for the Cityscapes and Pascal-Context datasets

cd SemSegPaddle/data
ln -s $cityscapes ./
ln -s $pascalContext ./

2. Download pretrained weights

Downlaod pretrained resnet-101 weights file, and put it into the directory: ./pretrained_model

Then, run the following command:

  tar -zxvf  ./repretrained/resnet101_v2.tgz -C pretrained_model 

3. Training

select confiure file for training according to the DECODER_NAME, BACKBONE_NAME and DATASET_NAME.

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m paddle.distributed.launch train.py  --use_gpu --use_mpio \
                                  --cfg ./configs/pspnet_res101_cityscapes.yaml 

4. Testing

select confiure file for testing according to the DECODER_NAME, BACKBONE_NAME and DATASET_NAME.

Single-scale testing:

CUDA_VISIBLE_DEVICES=0 python  eval.py --use_gpu \
                                       --use_mpio \
                                       --cfg ./configs/pspnet_res101_cityscapes.yaml 

Multi-scale testing:

CUDA_VISIBLE_DEVICES=0 python  eval.py --use_gpu \
                                       --use_mpio \
                                       --multi_scales \
                                       --cfg ./configs/pspnet_res101_cityscapes.yaml 

Contact

If you have any questions regarding the repo, please create an issue.