9. How to use data augmentation¶

This page focuses on training-time augmentation plans and how to inspect available ops. If you are looking for feature engineering and caching that happens before training, see Preprocess how-to.

9.1 Problem statement¶

You want to apply deterministic, training-time augmentations to inputs for SSL methods (for example, weak/strong views). ^[1][2] Keeping augmentations separate makes it easier to swap them without touching cached preprocessing. ^[7][8]

9.2 When to use¶

Use this when your method expects stochastic augmentations (FixMatch-style, strong/weak pipelines). ^[1]

9.3 Steps¶

1) Inspect available ops for your modality. ^[3][4]

2) Define an AugmentationPlan (list of ops + params). ^[2]

3) Build and apply a pipeline with a deterministic context. ^[1][5]

9.4 Copy-paste example¶

Use the CLI when you want to inspect ops quickly (modssc augmentation in src/modssc/cli/augmentation.py), and use Python when you want to build pipelines in code (API in src/modssc/data_augmentation/api.py). ^[4][1]

CLI:

modssc augmentation list --modality vision
modssc augmentation info vision.random_horizontal_flip --as-json

Python:

import numpy as np
from modssc.data_augmentation import AugmentationContext, AugmentationPlan, StepConfig, build_pipeline

plan = AugmentationPlan(
    steps=(
        StepConfig(op_id="vision.random_horizontal_flip", params={"p": 0.5}),
        StepConfig(op_id="vision.cutout", params={"frac": 0.25, "fill": 0.0}),
    ),
    modality="vision",
)
pipeline = build_pipeline(plan)

x = np.zeros((32, 32, 3), dtype=np.float32)
ctx = AugmentationContext(seed=0, sample_id=0, epoch=0)
aug_x = pipeline(x, ctx=ctx)
print(aug_x.shape)

Ops and plan mechanics are defined in src/modssc/data_augmentation/ops/ and src/modssc/data_augmentation/api.py. ^[6][1]

9.5 Pitfalls¶

9.6 Related links¶

• Configuration reference
• Inductive tutorial
• Catalogs and registries