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name: domain-and-restoration description: "Operate Keras-GAN CCGAN, ContextEncoder, PixelDA, and SRGAN workflows for inpainting, domain adaptation, and super-resolution." disable-model-invocation: true metadata: disco-role: operating license: MIT

Domain Adaptation and Restoration Workflows

Use this sub-skill when the task involves Keras-GAN's specialized image restoration or domain-adaptation examples:

• context-conditional MNIST inpainting with CCGAN
• CIFAR-10 cat/dog context encoding with ContextEncoder
• MNIST to MNIST-M pixel-level domain adaptation with PixelDA
• CelebA-style 4x super-resolution with SRGAN

Do not use this sub-skill for CycleGAN, DiscoGAN, or Pix2Pix image translation, and do not use it for baseline MNIST/Wasserstein generators. Route those requests to the sibling image-translation or mnist-generators sub-skills instead.

Fast routing

1. Identify the workflow and data risk:
   • CCGAN: MNIST, grayscale 32x32, 10x10 random zero mask, can request a Keras MNIST download if the dataset cache is absent.
   • ContextEncoder: CIFAR-10 cats/dogs, RGB 32x32, 8x8 random zero mask, can request a Keras CIFAR-10 download if the dataset cache is absent.
   • PixelDA: MNIST domain A and MNIST-M domain B, RGB 32x32 cached arrays, may try to fetch or rebuild MNIST-M artifacts if cache files are incomplete.
   • SRGAN: image directory named img_align_celeba, derived 64x64 low-resolution and 256x256 high-resolution pairs, constructor can request VGG19 ImageNet weights.
2. Before training or adapting code, read the reference that matches the issue:
   • Model/API reference for class names, method signatures, shapes, losses, and outputs.
   • Workflow playbook for safe dry runs, short-run setup, and adaptation steps.
   • Data formats for expected dataset/cache layouts and normalization.
   • Troubleshooting for legacy Keras/TensorFlow, SciPy image APIs, PixelDA cache/network, and SRGAN VGG19 failures.
3. Use the bundled safe checker instead of importing Keras or running full training when validating local files:

python sub-skills/domain-and-restoration/scripts/check_restoration_layout.py --help
python sub-skills/domain-and-restoration/scripts/check_restoration_layout.py --workflow srgan --data-root ./datasets --dataset-name img_align_celeba --min-images 8
python sub-skills/domain-and-restoration/scripts/check_restoration_layout.py --workflow pixelda --data-root ./datasets --json
python sub-skills/domain-and-restoration/scripts/check_restoration_layout.py --workflow ccgan --check-output-dirs --output-root .

The checker only uses the Python standard library. It never downloads data, imports Keras, mutates datasets, or launches training.

Operating constraints

• Treat this repository as a stale educational script collection, not as an installable Python package.
• Use a legacy-compatible runtime for actual model construction or training: the scripts were verified around Python 3.7-era, TensorFlow 1.15.x, Keras 2.2.x, keras-contrib 2.0.x, NumPy 1.18.x, SciPy 1.2.x, Matplotlib, Pillow, and scikit-image.
• Avoid network access unless the user explicitly permits dataset or VGG19 weight downloads.
• Avoid full default training loops: the upstream defaults are tens of thousands of epochs.
• Create or validate output directories before short runs; several scripts save figures or model JSON/weights into relative images/ and saved_model/ paths.
• For SRGAN dry runs, do not instantiate SRGAN() unless VGG19 ImageNet weights are already cached or the user accepts the download risk.

Expected deliverables for common tasks

• Dataset-readiness diagnosis: run scripts/check_restoration_layout.py, report missing files/directories, and cite the relevant data format section.
• Adaptation plan: identify the model class, expected input/label shapes, data-loader behavior, output paths, and the smallest safe smoke test from workflows.
• Troubleshooting answer: map the error to the closest symptom in troubleshooting, then give a bounded fix that does not require reopening the original repository.