LANMNG/RoadDA

Acknowledgement

This repo is heavily borrowed from IntraDA. We sincerely thank the authors for providing such a great framework.

Pre-requsites

• Python 3.7
• Pytorch >= 0.4.1
• CUDA 9.0 or higher

Installation

0. Clone the repo:

$ git clone https://github.com/LANMNG/RoadDA.git
$ cd RoadDA

1. Install OpenCV if you don't already have it:

$ conda install -c menpo opencv

if it doesn't work, please try to use conda pip

$ which pip    # should be $HOME/anaconda3/bin/pip, be sure to use conda pip
$ pip install opencv-python 

2. Install ADVENT submodule and the dependices using pip: if you use

$ pip install -e <root_dir/ADVENT>

With this, you can edit the ADVENT code on the fly and import function and classes of ADVENT in other project as well.

Datasets

The format of the road data and the construction are suggested to be the same as the GTA5 and Cityscapes in IntraDA.

Training

Our training environment is based on pytorch 0.4.1 and CUDA 9.0. To reach to the comparable performance you may need to train a few times. By default, logs and snapshots are stored in <root_dir>/experiments with this structure:

<root_dir>/ADVENT/experiments/logs
<root_dir>/ADVENT/experiments/snapshots

Step 1. Conduct inter-domain adaptation by training ADVENT:

$ cd <root_dir>/ADVENT/advent/scripts
$ python train.py --cfg ./config/advent.yml 
$ python train.py --cfg ./config/advent.yml --tensorboard % using tensorboard

After inter-domain training, it is needed to get best IoU iteration by runing:

$ cd <root_dir>/ADVENT/advent/scripts
$ python test.py --cfg ./config/advent.yml

The best IoU iteration BEST_ID will be a parameter to step 2.

Step 2. Entropy-based ranking to split training set of target data into easy split and hard split:

$ cd <root_dir>/entropy_rank
$ python entropy.py --best_iter BEST_ID --normalize False --lambda1 0.7 

You will see the pseudo labels generated in color_masks, the easy split file names in easy_split.txt, and the hard split file names in hard_split.txt.

Step 3. Conduct intra-domain adaptation by runing:

$ cd <root_dir>/intrada
$ python train.py --cfg ./intrada.yml
$ python train.py --cfg ./intrada.yml --tensorboard % using tensorboard

After intra-domain training, it is needed to get best IoU iteration by runing:

$ cd <root_dir>/intrada
$ python test.py --cfg ./intrada.yml

Step 4. Conduct the self-training stage by repeating the step 2 and step 3 for several times.

Testing

After the self-training stage, we get the performance by running:

$ cd <root_dir>/intrada
$ python test.py --cfg ./intrada.yml