Variopinta

Variopinta is an experimental CPU image-augmentation compiler. Pipelines are configured in Python and executed by optimized Rust kernels, with whole-pipeline planning for buffer reuse, kernel selection, and fewer Python/native crossings.

Variopinta is pre-alpha. The public API may change between 0.y.0 releases; patch releases preserve documented signatures and data contracts unless a correctness or security fix requires otherwise.

Install

Variopinta supports CPython 3.10–3.13 on supported 64-bit Linux and Apple Silicon macOS systems.

python -m pip install variopinta

PyTorch is optional and needed only for ReturnTensor outputs. Platform details and source-build instructions are in Getting started.

Quick start

import numpy as np
import variopinta as vp

pipeline = vp.Pipeline(
    [
        vp.RandomCrop(256, 256),
        vp.Resize(224, 224),
        vp.HorizontalFlip(p=0.5),
        vp.Normalize(),
    ],
    seed=42,
).compile()

image = np.zeros((320, 320, 3), dtype=np.uint8)
output = pipeline(image, key=0)

print(output.shape, output.dtype)  # (224, 224, 3) float32

Pipeline is the semantic reference executor. .compile() selects the optimized execution plan while keeping the same call signature and keyed result. Use pipeline.explain() to inspect the operations, buffers, copies, fusion, dtypes, layouts, and target routes in that plan.

Documentation

Current scope includes images and semantic masks with NumPy, encoded-buffer, and local-path inputs. It does not include boxes, keypoints, GPU execution, native batches, or Python callbacks inside a pipeline.

Project information

Variopinta is licensed under the Apache License 2.0. Native-wheel attributions are in THIRD_PARTY_NOTICES.

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