Reinforced Active Learning for Image Segmentation (RALIS)

Code for the paper Reinforced Active Learning for Image Segmentation

Additional Notes by C Baumgartner for use in Tue ML Cloud

Additions to base code:

• add number of slurm job files to scripts
• add a template for running an interactive training to scripts
• make code_path an additional argument to prevent hard coded img_paths
• remove dataset from all hardcoded path variables in dataset classes
• Remove foor loop over seeds in scripts by a slurm array (for version without slurm arrays checkout commit ee71c2e325165eef32768f62e5e79d81a237a7e2)

Todo's to make code run

• Change paths in utils/parser.py
• Change paths in scripts/slurm_* and scripts/interative_slurm_call_debug.sh to match system

Run code on Tue ML Coud Slurm

To run ralis training with 5 random seeds execute

sbatch devel/ralis/scripts/slurm_train_ralis.sh

This works on my system.

Use the other scripts/slurm_* scripts to run other stuff. For some reason baselines didn't run on my system, without throwing an error. Jobs just got hung. Perhaps, not enough memory given in SBATCH settings?

To run stuff depending on the pre-trained models, don't forget to download them from the Google Drive link below and copy into your checkpoints folder (e.g. ckpt_seg)

Dependencies

• python 3.6.5
• numpy 1.14.5
• scipy 1.1.0
• Pytorch 0.4.0

Scripts

The folder 'scripts' contains the different bash scripts that could be used to train the same models used in the paper, for both Camvid and Cityscapes datasets.

• launch_supervised.sh: To train the pretrained segmentation models.
• launch_baseline.sh: To train the baselines 'random', 'entropy' and 'bald'.
• launch_train_ralis.sh: To train the 'ralis' model.
• launch_test_ralis.sh: To test the 'ralis' model.

Datasets

Camvid: https://github.com/alexgkendall/SegNet-Tutorial/tree/master/CamVid

Cityscapes: https://www.cityscapes-dataset.com/

Trained models

To download the trained RALIS models for Camvid and Cityscapes (as well as the pretrained segmentation model on GTA and D_T subsets): https://drive.google.com/file/d/13C4e0bWw6SEjTAD7JdAfLGVz7p7Veeb9/view?usp=sharing

Citation

If you use this code, please cite:

@inproceedings{
Casanova2020Reinforced,
title={Reinforced active learning for image segmentation},
author={Arantxa Casanova and Pedro O. Pinheiro and Negar Rostamzadeh and Christopher J. Pal},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=SkgC6TNFvr}
}