---

name: data-providers-and-launchers description: "Work with tf_unet data providers, toy generators, paired image files, and launcher-style dataset workflows." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

data-providers-and-launchers

Use this sub-skill when the task is about BaseDataProvider, SimpleDataProvider, ImageDataProvider, GrayScaleDataProvider, RgbDataProvider, toy data generation, TIFF mask pairing, or launcher-style dataset preparation.

If the question is really about the model graph, loss, optimizer, or checkpointing, switch to ../training-and-inference/SKILL.md.

Start here

• Read references/data-formats.md for shapes, label conventions, and file layouts.
• Read references/workflows.md for the toy, paired-image, and launcher-style workflows.
• Run scripts/smoke_data_providers.py when you want to validate provider shapes on synthetic fixtures.
• If the environment itself is failing, run the root smoke helper ../../scripts/check_tf_unet_env.py first.

Core decisions

• BaseDataProvider normalizes the absolute value of the data and then scales it into a 0-1 range.
• ImageDataProvider expects matching data and mask file names with the configured suffixes.
• The toy generators are easiest to use for tiny inspections when you keep border small enough for the chosen image size.
• GrayScaleDataProvider and RgbDataProvider are the safest way to create synthetic fixtures for quick smoke checks.
• External HDF5 workflows are format patterns, not bundled data; document them, but do not assume the files are present.

What to mention in answers

• SimpleDataProvider is for already-prepared arrays; make sure the label shape matches the class layout you want.
• ImageDataProvider infers channel and class counts from the first matched image pair.
• create_image_and_label(...) and the toy data providers can be used to generate quick segmentation fixtures.
• The dataset-specific launcher patterns in this package are useful references even when the original data is unavailable.

Bundled references

• references/data-formats.md — read for array shapes, mask/file suffixes, and launcher data layouts.
• references/workflows.md — read for NumPy, TIFF, and toy-generator recipes.
• references/troubleshooting.md — read when you hit file layout, shape, size, or dependency issues.

Bundled script

• scripts/smoke_data_providers.py — run to validate synthetic NumPy, toy-generator, and paired-image fixtures.

Common safety notes

• Keep the toy generator sizes large enough for the chosen border and cnt settings.
• Do not point future agents at the original checkout's dataset files as if they were bundled runtime assets.
• If the task is about a launcher workflow that needs external data, document the required file layout first and only then decide whether a synthetic smoke is possible.