We study the problem of domain adaptation between GTA and cityscapes datasets.
This was done as part of the group project for ECE 285 (ML for Image Processing) course at UCSD.
Contributors: Manjot Singh Bilkhu, Anurag Paul, Tushar Dobhal, Harshul Gupta and Sreekrishna Ramaswamy.
- Install python3 and pytorch.
- install other requirements using
pip install -r requirements.txt
Download the dataset by running
bash ./get_dataset.sh
This will place the dataset in a foder named 'dataset' in the root directory.
Download pretrained models using
bash ./get_pretrained_models.sh
This will create a folder named models and will download pre-trained models which will be used by the test and demo notebooks.
cycle_gan.py : Contains the class which implements the CycleGAN architecture.
data_loader.py : Contains the DataLoader class which has utility functions to load and see our dataset. Also has functions to save and display images.
demo.ipynb : Ipython notebook to run a demo with our pre-trained models and display results.
dual_gans.py : Contains the class which implements DualGAN architecture.
logger.py : Contains utility functions to display and format logs.
networks.py : Contains implementations of all building blocks used by our GAN's. Has implementations of different Generator and Discriminator architectures.
params.yaml : Contains hypermarameters used by loss functions and optimizers.
semantics_test.ipynb : Ipython notebook to test the semantic segmentation model using which we compare the results of our models.
test_cycle_gan.py : Test CycleGAN model using pre-trained models.
test_dual_gans.py : Test DualGAN model using pre-trained models.
train.ipynb : Ipython notebook to replicate the training.
train_cycle_gan.py : train our CycleGAN model.
train_dual_gans.py : train our DualGAN model.
train_test.p : Pickle file containing the train-test indexes of images.
utils.py : Utility functions used across different files.
- Sample Output
