Welcome to this in-depth guide on implementing the One-Sided Unsupervised Domain Mapping using DistanceGAN in PyTorch. This implementation draws inspiration from both the architectures of DiscoGAN and CycleGAN. In this blog, we’ll walk you through the prerequisites, data preparation, model training, and testing.

Prerequisites

• Python 2.7
• PyTorch 0.1.12
• Numpy
• SciPy
• Pandas
• Progressbar
• OpenCV
• visdom
• dominate

Setting Up the Distance Model Based on DiscoGAN Architecture

Downloading the Dataset

To get started, download the dataset by executing the following command:

python datasets/download.py $DATASET_NAME

Use .datasets/combine_A_and_B.py to create a dataset of handbags to shoes. Additionally, download other datasets separately such as:

• CelebA dataset
• Car dataset
• HeadFace dataset

Extract these datasets into the .datasets folder.

Training Examples

Here are some training commands for different tasks:

python .discogan_arch/distance_gan_model.py --task_name=celebA --style_A=Male

• Male to Female: python .discogan_arch/distance_gan_model.py --task_name=celebA --style_A=Male
• Blond to Black Hair: python .discogan_arch/distance_gan_model.py --task_name=celebA --style_A=Blond_Hair --style_B=Black_Hair --constraint=Male --constraint_type=-1
• With to Without Eyeglasses: python .discogan_arch/distance_gan_model.py --task_name=celebA --style_A=Eyeglasses --constraint=Male --constraint_type=1
• Edges to Shoes: python .discogan_arch/distance_gan_model.py --task_name=edges2shoes --num_layers=3
• Car to HeadFace: python .discogan_arch/distance_gan_angle_pairing_model.py --task_name=car2face

This is just a glimpse of how to set up training. The result of the training could be mesmerizing, resembling the transformation from one concept to another—like a painter who can turn a canvas of skies into a beautiful sunset.

Distance Model Based on CycleGAN Architecture

Horse to Zebra Transformation

First, download the dataset for horse to zebra transformation:

python datasets/download.py horse2zebra

Then, to begin training, run:

python train.py --dataroot .datasets/horse2zebra --name horse2zebra_distancegan --model distance_gan

After training, you can test your model with the following command:

python test.py --dataroot .datasets/horse2zebra --name horse2zebra_distancegan --model distance_gan --phase test

Viewing Results

Your results will be saved in . To keep track of loss results and plots, you can use visdom. Start the server by running:

python -m visdom.server

Then navigate to http://localhost:8097 to see your results.

Troubleshooting Ideas

If you encounter issues during implementation, consider the following:

• Ensure all required libraries are installed and compatible versions are being used.
• Verify dataset paths and file formats to make sure they are correctly referenced.
• Check the command syntax for running scripts and adjust options as needed.
• If you experience low performance or unexpected results, try adjusting the model parameters or re-training.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.