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（不同模型的实现代码目录： awesome-semantic-segmentation-pytorch/core/models/）

Semantic Segmentation on PyTorch

python-image pytorch-image lic-image

This project aims at providing a concise, easy-to-use, modifiable reference implementation for semantic segmentation models using PyTorch.

Installation

# semantic-segmentation-pytorch dependencies
pip install ninja tqdm

# follow PyTorch installation in https://pytorch.org/get-started/locally/
conda install pytorch torchvision -c pytorch

# install PyTorch Segmentation
git clone https://github.com/Tramac/awesome-semantic-segmentation-pytorch.git

# the following will install the lib with symbolic links, so that you can modify
# the files if you want and won't need to re-build it
cd awesome-semantic-segmentation-pytorch/core/nn
python setup.py build develop

Usage

Train

  • Single GPU training
# for example, train fcn32_vgg16_pascal_voc:
python train.py --model fcn32s --backbone vgg16 --dataset pascal_voc --lr 0.0001 --epochs 50
  • Multi-GPU training
# for example, train fcn32_vgg16_pascal_voc with 4 GPUs:
export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS train.py --model fcn32s --backbone vgg16 --dataset pascal_voc --lr 0.0001 --epochs 50

Evaluation

  • Single GPU evaluating
# for example, evaluate fcn32_vgg16_pascal_voc
python eval.py --model fcn32s --backbone vgg16 --dataset pascal_voc
  • Multi-GPU evaluating
# for example, evaluate fcn32_vgg16_pascal_voc with 4 GPUs:
export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS eval.py --model fcn32s --backbone vgg16 --dataset pascal_voc

Demo

cd ./scripts
python demo.py --model fcn32s_vgg16_voc --input-pic ./datasets/test.jpg

.{SEG_ROOT}
├── scripts
│   ├── demo.py
│   ├── eval.py
│   └── train.py

Support

Model

DETAILS for model & backbone.

.{SEG_ROOT}
├── core
│   ├── models
│   │   ├── bisenet.py
│   │   ├── danet.py
│   │   ├── deeplabv3.py
│   │   ├── deeplabv3+.py
│   │   ├── denseaspp.py
│   │   ├── dunet.py
│   │   ├── encnet.py
│   │   ├── fcn.py
│   │   ├── pspnet.py
│   │   ├── icnet.py
│   │   ├── enet.py
│   │   ├── ocnet.py
│   │   ├── ccnet.py
│   │   ├── psanet.py
│   │   ├── cgnet.py
│   │   ├── espnet.py
│   │   ├── lednet.py
│   │   ├── dfanet.py
│   │   ├── ......

Dataset

You can run script to download dataset, such as:

cd ./core/data/downloader
python ade20k.py --download-dir ../datasets/ade
Dataset training set validation set testing set
VOC2012 1464 1449 ✘
VOCAug 11355 2857 ✘
ADK20K 20210 2000 ✘
Cityscapes 2975 500 ✘
COCO
SBU-shadow 4085 638 ✘
LIP(Look into Person) 30462 10000 10000 
.{SEG_ROOT}
├── core
│   ├── data
│   │   ├── dataloader
│   │   │   ├── ade.py
│   │   │   ├── cityscapes.py
│   │   │   ├── mscoco.py
│   │   │   ├── pascal_aug.py
│   │   │   ├── pascal_voc.py
│   │   │   ├── sbu_shadow.py
│   │   └── downloader
│   │       ├── ade20k.py
│   │       ├── cityscapes.py
│   │       ├── mscoco.py
│   │       ├── pascal_voc.py
│   │       └── sbu_shadow.py

Result

  • PASCAL VOC 2012
Methods Backbone TrainSet EvalSet crops_size epochs JPU Mean IoU pixAcc
FCN32s vgg16 train val 480 60 ✘ 47.50 85.39
FCN16s vgg16 train val 480 60 ✘ 49.16 85.98
FCN8s vgg16 train val 480 60 ✘ 48.87 85.02
FCN32s resnet50 train val 480 50 ✘ 54.60 88.57
PSPNet resnet50 train val 480 60 ✘ 63.44 89.78
DeepLabv3 resnet50 train val 480 60 ✘ 60.15 88.36

Note: lr=1e-4, batch_size=4, epochs=80.

Overfitting Test

See TEST for details.

.{SEG_ROOT}
├── tests
│   └── test_model.py

To Do

  • add train script
  • remove syncbn
  • train & evaluate
  • test distributed training
  • fix syncbn (Why SyncBN?)
  • add distributed (How DIST?)

References