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DaGAN Example
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This example demonstrates a GAN-like vision model.
The nnscaler trainer assumes there is only one end-to-end module that needs to be parallelized.
However, GAN-like models always have both a generator and a discriminator.
Here, you will learn how to run your code without the nnscaler trainer, and how to parallelize, synchronize, and update modules during training.

In this example, both ``GeneratorFullModel`` and ``DiscriminatorFullModel`` contain the same keypoint detector, generator, and discriminator modules.
A module cannot be parallelized multiple times, so keypoint detector, generator, and discriminator must be parallelized separately.
Separate synchronization and updates are also needed during training.

.. code-block:: bash

    # clone nnScaler & CVPR2022-DaGAN repositories
    git clone --recursive https://github.com/microsoft/nnscaler
    cd nnscaler/examples/vision/dagan
    git clone https://github.com/harlanhong/CVPR2022-DaGAN.git

    # Install dependent packages
    mv CVPR2022-DaGAN CVPR2022_DaGAN
    pip install --ignore-installed -r CVPR2022_DaGAN/requirements.txt
    export PYTHONPATH=$PYTHONPATH:CVPR2022_DaGAN

    # Run
    CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc-per-node=4 --master_port=12348 run.py \
        --config vox-adv-256.yaml --name DaGAN --batchsize 8 --kp_num 15 --generator DepthAwareGenerator