---

name: pipeline-composition description: "Build, edit, validate, seed, and debug Albumentations augmentation pipelines with Compose and composition combinators." disable-model-invocation: true metadata: disco-role: operating license: MIT

Albumentations Pipeline Composition

Use this sub-skill when an agent needs to assemble or modify an Albumentations pipeline, reason about pipeline-level validation, or debug pipeline randomness and target routing.

Route Here For

• Building A.Compose / A.ReplayCompose pipelines around existing transforms.
• Choosing composition blocks: OneOf, SomeOf, RandomOrder, Sequential, OneOrOther, and SelectiveChannelTransform.
• Enabling strict, is_check_shapes, additional_targets, mask_interpolation, seed, or save_applied_params on a pipeline.
• Editing a pipeline with +, left-+, or - while preserving compose configuration.
• Debugging invalid keys, shape mismatches, bad operator operands, seed expectations, label-field routing, or mask interpolation surprises.

Route Elsewhere

• Transform family choice and transform parameter recipes: ../transform-catalog/.
• Bbox, keypoint, label, volume, and 3D target formats: ../targets-and-formats/.
• Replay dictionaries, save/load, JSON/YAML, and reproducibility artifacts: ../serialization-and-reproducibility/.
• Tensor conversion and dataloader placement: ../framework-integration/.

References And Helpers

• references/composition-api.md: API signatures, composition semantics, validation options, seed behavior, target routing, operator edits, and worked examples.
• references/troubleshooting.md: Symptom-driven fixes for invalid keys, mismatched shapes, single-transform warnings, missing labels, seed confusion, operator failures, and mask interpolation issues.
• scripts/check_pipeline_contract.py: A tiny self-contained checker that exercises strict keys, shape checks, additional depth targets, mask interpolation propagation, operator metadata preservation, and seed independence from global NumPy seeding.

Default Composition Pattern

Prefer an explicit, strict top-level Compose while developing, then relax only the checks that are intentionally incompatible with the data:

import albumentations as A

pipeline = A.Compose(
    [
        A.Resize(256, 256, p=1),
        A.OneOf([A.HorizontalFlip(p=1), A.VerticalFlip(p=1)], p=0.5),
    ],
    additional_targets={"depth": "mask"},
    is_check_shapes=True,
    strict=True,
    mask_interpolation=0,
    seed=137,
    save_applied_params=True,
)
result = pipeline(image=image, mask=mask, depth=depth)

Keep Compose calls keyword-only: use pipeline(image=image, mask=mask), not pipeline(image).