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Datos e investigación · SKILL.md

setup-and-data

setup-and-data

Install and validate pix2pixHD prerequisites, dataset layout, CLI/config basics, and small data-loader smoke checks.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentación de la Skill

GitHub

name: setup-and-data description: "Install and validate pix2pixHD prerequisites, dataset layout, CLI/config basics, and small data-loader smoke checks." metadata: disco-role: operating disable-model-invocation: true license: NOASSERTION

Setup and Data

Use this sub-skill for repository setup, environment prerequisites, Cityscapes-style dataset layout checks, option parsing basics, and tiny data-loader smoke checks.

Read first

Use when

  • you need to confirm the Python stack is ready for pix2pixHD data inspection
  • you need to validate a Cityscapes-style dataroot before training or inference
  • you need to smoke TrainOptions, TestOptions, AlignedDataset, tensor2label, or tensor2im
  • you need to check legacy resize/crop compatibility before choosing a non-default preprocessing mode

Do not use when

  • you are debugging training losses, checkpoint saving, or learning-rate schedules
  • you are generating inference HTML, exporting ONNX, or running TensorRT
  • you are clustering features or preparing feature caches for instance-conditioned workflows
  • you are performing maintainer-only repo operations

Primary scripts

What this sub-skill covers

  • README prerequisites and the minimal Python stack for data-only inspection
  • options/base_options.py, options/train_options.py, and options/test_options.py defaults that affect data loading
  • data/aligned_dataset.py, data/base_dataset.py, and data/image_folder.py
  • util/util.py tensor/image conversion helpers and Cityscapes colormap smoke checks
  • bundled Cityscapes sample fixture layout under datasets/cityscapes/
  • parser smoke checks and one-sample dataset load checks
  • the legacy torchvision transforms.Scale caveat for resize_and_crop

Cross-links

  • Training: read this first for dataset validation and smoke-ready option defaults.
  • Inference: read this for dataroot and label/instance folder rules before test-time input prep.
  • Instance features: read this for *_feat folder layout; this sub-skill only names the loader rule.

Validation sequence

  1. Run scripts/check_cityscapes_layout.py --repo-root <repo-root>.
  2. Run scripts/check_data_smoke.py --repo-root <repo-root>.
  3. If you must evaluate resize_and_crop, rerun the smoke helper with --probe-legacy-resize and read the compatibility note.

Limits

  • This sub-skill stays CPU-safe for setup and data inspection.
  • TestOptions smoke must use save=False; the class does not define continue_train.
  • The bundled fixture is a smoke target only; do not add download logic here.
  • Keep load_features and feature cache layout questions in the instance-features sub-skill.
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