---

name: datasets-io-utils description: "Use this sub-skill when constructing torchvision datasets, validating dataset roots and downloads, wrapping datasets for transforms v2, decoding or encoding images with torchvision.io, or using torchvision.utils visualization helpers." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

TorchVision Datasets, IO, and Utilities

Use this sub-skill for data ingress and small visual checks in TorchVision workflows: built-in datasets, custom VisionDataset / ImageFolder / DatasetFolder code, safe data roots, wrap_dataset_for_transforms_v2, image decode/encode APIs, and torchvision.utils drawing helpers.

Route by task

• Dataset construction or root layout: use references/datasets-and-data-roots.md for built-in family selection, ImageFolder/DatasetFolder conventions, FakeData, and no-network smoke fixtures.
• Transforms v2 dataset wrapping: start here for wrap_dataset_for_transforms_v2 dataset support and target-shape expectations, then route transform pipeline internals to ../transforms-and-tv-tensors/.
• Image IO and visualization: use references/io-and-visualization.md for decode_image, read_file, encoders/writers, make_grid, draw_bounding_boxes, draw_segmentation_masks, draw_keypoints, and flow_to_image.
• Failures and warnings: use references/troubleshooting.md for download races, root layout mistakes, image extension or codec failures, dtype/channel mistakes, video/TorchCodec migration, and visualization label/color errors.
• Model inference or weight preprocessing: route to ../models-and-weights/; this sub-skill only covers loading/decoding/visualizing data around those workflows.

Quick safe check

Run the bundled script when an agent needs to verify local TorchVision dataset, image IO, and utility surfaces without network access:

python sub-skills/datasets-io-utils/scripts/check_dataset_io.py

The script creates a temporary two-class ImageFolder fixture, uses FakeData, attempts torchvision.io PNG round-trip if the image extension is available, and exercises make_grid plus draw_bounding_boxes.

Hard cases this sub-skill supports

• Build a tiny ImageFolder fixture, validate class discovery/order, attach a transform, and verify tensors without downloading real data.
• Diagnose image decode failures by separating path/layout errors, missing image extension support, optional AVIF/HEIC decoder requirements, and PIL fallback options.

Boundaries

• Do not put augmentation policy details here; link to ../transforms-and-tv-tensors/ for v2 pipelines, TVTensor metadata, bounding-box transform semantics, and dtype/range conversion details.
• Do not cover model output interpretation beyond visualization input requirements; link to ../models-and-weights/ or ../ops-and-detection/ as appropriate.
• Treat legacy C++ IO examples as reference-only gaps; prefer public Python torchvision.io and TorchCodec migration guidance.