---

name: data-and-configs description: "Author and statically validate pytorch-semseg YAML configs, choose dataset loaders, and troubleshoot dataset layouts and split/path errors before training or validation." disable-model-invocation: true metadata: disco-role: operating license: MIT

data-and-configs

Use this sub-skill when a task is about preparing a pytorch-semseg YAML config, selecting a dataset loader, checking dataset paths/splits, or diagnosing config/data errors before training or validation.

Do not run training, validation, or single-image inference from this sub-skill.

Quick workflow

1. Identify the target dataset key and split names from references/configuration.md.

2. Check that the dataset directory layout matches references/data-formats.md.

3. Draft or update the YAML config with model, data, and training sections.

4. Run the bundled static checker before handing the config to a training or validation workflow:

   python scripts/validate_config.py --config CONFIG.yml --print-summary
   python scripts/validate_config.py --config CONFIG.yml --strict-paths --print-summary
   

   The checker uses yaml.safe_load, does not import pytorch-semseg, does not instantiate loaders, and does not read image or mask data.

Scope and routing

• Config schema, dataset registry keys, split names, data layouts, img_rows/img_cols, augmentations, loss/optimizer/scheduler key checks, and legacy key drift: stay here.
• Model constructor internals, architecture-specific parameters, registry smoke tests, or pretrained-weight side effects: route to model-zoo-and-apis.
• Building or running train.py/validate.py commands, checkpoint resume behavior during execution, and metric interpretation: route to training-and-evaluation.
• test.py single-image command construction, image checkpoint naming, output mask writing, and DenseCRF: route to single-image-inference.

Runtime files

• references/configuration.md: config schema, registry tables, safe YAML snippets, and legacy drift warnings.
• references/data-formats.md: expected dataset root layouts and split conventions by loader.
• references/troubleshooting.md: common symptoms, causes, and fixes for config/data failures.
• scripts/validate_config.py: static YAML validator with optional path-layout checks.

Safety notes

• Treat source example configs as evidence, not as drop-in templates: replace machine-specific paths, set resume: null when starting fresh, and validate registry keys.
• Keep img_rows and img_cols numeric unless the selected loader explicitly supports the string pair same.
• Prefer yaml.safe_load in any adapted script; the original entry points used legacy yaml.load(fp) style that fails on modern PyYAML unless a loader is supplied.