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name: data-and-configuration description: "Validate MUNIT dataset layouts, YAML configs, demo-data assumptions, and data-preparation plans before training or inference." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Data and Configuration

Use this sub-skill when the task is about MUNIT YAML files, folder/list dataset layouts, demo data, path repair, preprocessing scripts, or data-loader failures. The goal is to catch layout and config mistakes before a long CUDA training or checkpointed inference run.

Responsibilities

• Explain the two supported dataset modes: folder mode with data_root and list mode with paired data_folder_* plus data_list_* keys.
• Validate the required training, model, optimizer, logging, resize/crop, and trainer-specific config keys.
• Diagnose image discovery, list-root mismatches, missing domain splits, invalid image extensions, display_size mistakes, and crop/resize issues.
• Distill the repo demo shell scripts into safe data-preparation guidance without running downloads, ImageMagick conversion, or training.
• Provide safe bundled validators that do not import MUNIT, allocate CUDA, download datasets, or mutate user data.

Start Here

1. Read references/configuration.md when editing or reviewing a YAML file.

2. Read references/data-formats.md when the user asks how to arrange folders or list files.

3. Read references/dataset-preparation.md before adapting edges2shoes, edges2handbags, Yosemite, or another unpaired image-to-image dataset.

4. Run the config validator from this sub-skill directory, or from any directory with an explicit path:

   python scripts/validate_munit_config.py --config /path/to/config.yaml --repo-root /path/to/user/munit-checkout
   

5. Inspect a dataset layout before a training run:

   python scripts/inspect_munit_dataset.py --config /path/to/config.yaml --repo-root /path/to/user/munit-checkout
   

Route Elsewhere

• Runtime installation, PyTorch/CUDA compatibility, and dependency imports: ../environment-and-setup/.
• Launching or resuming training after the data/config checks pass: ../training/.
• Single-image or batch checkpointed translation commands: ../inference-and-evaluation/.
• Network/trainer class modifications or PyTorch porting: ../model-internals/.

Safety Gates

• Do not run demo shell scripts blindly. They delete and recreate dataset folders, download public archives, call external convert, and launch a full training command.
• Do not treat a successful YAML parse as sufficient. Check whether the chosen mode's paths and domain splits exist for the user's checkout.
• Do not change a full dataset in place while debugging. Build a tiny copy or validate paths first.
• Do not rely on the original generation checkout's sample images. Future runs should use the user's own dataset and config paths.

Reference Map

• references/configuration.md - required YAML keys, trainer-specific fields, resize/crop rules, and config editing patterns.
• references/data-formats.md - folder mode, list mode, image extensions, and loader behavior.
• references/dataset-preparation.md - safe adaptation of the repo's dataset download/crop scripts.
• references/troubleshooting.md - symptoms, causes, and fixes for config/data-loader failures.
• scripts/validate_munit_config.py - static config validator with path checks.
• scripts/inspect_munit_dataset.py - image-count and list-file inspection helper.