---

name: data-preparation description: "Prepare and validate LR/HR/SR dataset layouts for image-directory and LMDB workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

data-preparation

Use this sub-skill for dataset roots, resize layouts, and safe fixture checks.

Route here when

• A user asks how to prepare SR triplets for datatype: img or datatype: lmdb.
• A config uses dataroot, l_resolution, r_resolution, datatype, mode, or data_len.
• A dataset root is missing lr_<L>, hr_<R>, or sr_<L>_<R>.
• An LMDB root fails to load because keys or length are missing.
• You need a deterministic tiny dataset for smoke testing.

Start with

1. references/data-layouts.md for the directory and LMDB contracts.
2. scripts/validate_dataset_layout.py to check an existing img root.
3. scripts/prepare_tiny_dataset.py to create a small RGB fixture.

What this sub-skill covers

• LR/HR/SR triplet directory layout.
• LMDB key layout and length metadata.
• Dataset config fields that control loading.
• Common data-shape, naming, and mode mismatches.

What it does not cover

• Training, validation, inference, or metric workflows.
• Network downloads or large-scale preprocessing jobs.
• Model architecture or checkpoint selection.

Usage notes

• mode: LRHR expects LR, HR, and SR data.
• mode: HR reads HR and SR only, but the preparation layout still includes all triplets.
• Keep directory contents image-only and aligned by relative path.
• The bundled validator is for img layouts; LMDB checks are described in the references.

Bundled files

• references/data-layouts.md
• references/troubleshooting.md
• scripts/validate_dataset_layout.py
• scripts/prepare_tiny_dataset.py