Skip to content
Code for Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach
Python
Branch: master
Clone or download
lianqing
Latest commit 4e1b0f2 Sep 11, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
cfgs update readme Sep 8, 2019
dataset update dataset and network Sep 7, 2019
lib update modules Sep 7, 2019
models update dataset and network Sep 7, 2019
.gitignore
README.md Update README.md Sep 10, 2019
segtransforms.py update model Sep 8, 2019
test_adabn.py update evaluation Sep 11, 2019
train_adabn.py update model Sep 8, 2019
train_pycda_local.py update evaluation Sep 11, 2019
train_source_only.py update model Sep 8, 2019

README.md

PyCDA

Code for Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach.

Enviorment

The code is developed under the following configuration.

Hardware:

4-8 GPUs(With at least 11G GPU memories), which is set for the correspoinding batch size.

Software:

Python(3.6) and Pytorch(0.4.1) is necessary before running the scripts. To install the required pythonn packages(expect Pytorch), run

pip install -r requirements.txt

Datasets

To train and validate the network, this repo use the GTAV or SYNTHIA as the source domain dataset and user Cityscapes as the target domain dataset.

To monitor the convergence of the network, we split 500 images out of Cityscapes training dataset as our validation set and test on Cityscapes valdiation set. You can check it in the ./dataset/cityscapes_list/directory.

To train on your own enviorment, please download the dataset and modify the dataset path in the corresponding cfgs docunment. Downloaded pretrained model

Training

Source only

sh run.sh train_source_only.py cfgs/source_only_exp001.yaml

Adabn

sh run.sh train_adabn.py cfgs/adabn_exp001.yaml

PyCDA

sh run.sh train_pycda_local.py cfgs/pycda_local_exp001.yaml

Convert batchnorm statistics

sh run.sh test_adabn.py $your_script

Performance

License

This project is licensed under the MIT License - see the LICENSE.md file for details

You can’t perform that action at this time.