yuanli12139/pytorch-semantic-segmentation

segmentation repo using pytorch

Pytorch-Semantic-Segmentation

Reference

• ERFNet
• PiWise

Network

• fcn
• segnet
• erfnet
• pspnet
• unet

Environment

• pytorch 0.2.0
• torchvision 0.2.0
• python 3.5.2
• cython

Download

Recommand you use virtualenv.

virtualenv -p python3 YourVirtualEnv --no-site-packages

git clone https://github.com/mapleneverfade/pytorch-semantic-segmentation.git

Install CSUPPORT (Options)

To speed up calculating IoU:

cd ./utils/cityscape/

python setup.py install

Train

If gpu is available:

CUDA_VISIBLE_DEVICES=0 python3 train.py --datadir ./data/ --savedir ./save_models/ --model segnet
else:

python3 train.py --cuda False --datadir ./data/ --savedir ./save_models/ --model segnet

There are some example pictures in ./data, so you could just run the command to test whether it work.

More Training Options

--model        model to use ['segnet fcn8 fcn16 fcn32 erfnet pspnet unet'] [default=segnet]·
--datadir      where you store [train/val].In my case, './data' should have subfolders './data/train/' and './data/val/' where store image.txt' and 'label.txt'. [default='./data/']
--savedir      path to savedir [default='./save_models/']
--lr           learning rate [default=5e-4]·
--num-epochs   epochs [default=150]·
--num-classes  number of labels, pascalvoc is 21, cityscape is 20. change it when you training your own dataset.[default=21] 

Test

CUDA_VISIBLE_DEVICES=0 python3 test.py --datadir ./data/test --model segnet --model-dir ./save_models/segnet_50.pth --save-dir ./results/
"--model-dir" is the path to your trained model

More Testing Options

--model        [default=segnet]
--model-dir    path to your trained model. For example './save_models/segnet/segnet_epoch_5.pth'
--datadir      [default='./data/test/']
--num-classes  number of labels, pascalvoc is 21, cityscape is 20. change it when you training your own dataset.[default=21]
--size         reshape size [default=(672,480)]
--savedir      [default='./results/']

Options

split_train_val.py mainly to split original [image.txt,label.txt] into './train/[image.txt,label.txt]' and './val/[image.txt,label.txt]'

Options for split:

--savedir        [default='./data/']
--imagepath      path to your own image.txt
--labelpath      path to your own label.txt
--random-state   random seed [default=10000]
--train-size     ratio of train set [default=0.7]
--val-size       ratio of val set   [default=0.3]

For example, if your original folder like this :

./data
      image.txt
      label.txt

After run 'python3 split_train_val.py --savedir ./data --imagepath ./data/image.txt --labelpath ./data/label.txt', you will see this:

./data
      ./train
             image.txt
             label.txt
      ./val   
             image.txt
             label.txt

Detail

1. More train and test options see ./options
2. datadir include image.txt and label.txt, and the default datasets is for pascalvoc
3. If you want train your own data. Remember to modify your data labels in ./utils/cityscapes/helpers/labels,and NUM_CLASSES in options.
4. You can change the way the model load data in ./dataloader/ to fit your format of dataset.
5. test.py would calculate the mIoU and save the result of segmented picture in --savedir.

ToDo

1. More Network
2. Clean up the code.