[Hendrik-code]

What

Optional strength ranges for the five elementwise TAs, and lower bounds plus a kernel size for the unsharp mask. Every default is the old transform.

transform	new keys	map, with the range set
RandomLog1pGPU	scale_range	log(1 + k·x)
RandomSqrtGPU	exponent_range	x**g
RandomSinGPU	frequency_range	sin(2π·f·x + φ), φ ~ U(0, 2π)
RandomExpGPU	rate_range	exp(k·x)
RandomSigmoidGPU	slope_range, center_quantile_range	sigmoid(k·(x − c)), c = the sample's q-quantile
RandomUnsharpMaskGPU	sigma_min, amount_min, kernel_size	σ ~ U(sigma_min, sigma), amount ~ U(amount_min, unsharp_amount), kernel_size: null → 2⌈3σ⌉+1

Draws are per sample, or once per batch with same_on_batch.

Why

Each elementwise TA min-max scales a sample to [0, 1], applies a fixed function and, with retain_stats, restores mean and std. Only the function's curvature on [0, 1] survives. The table gives the share of output variance a linear map of the input cannot explain (1 − r²).

	Sigmoid	Sin	Log1p	Exp	Sqrt
uniform input (analytic)	0.1%	0.5%	0.8%	1.6%	4.0%
SpineGAN MR/CT patches (measured)	0.04%	0.2%	0.5%	1.0%	1.9-3.0%

A gamma of 0.7 or 1.5 is already 1.2-2.0%. At p: 0.1 each (the paper config), 41% of samples get at least one of the five and are effectively unchanged by it.

The unsharp mask measured 0.01-0.9% with a gradient-magnitude correlation of 0.996 to its input. It draws σ from U(0, σ) onto a fixed 3-wide kernel, which truncates σ above about 0.5, and draws its amount from zero up.

• Sigmoid centre: a quantile rather than a fixed point, because the min-max scaling is set by the extremes. On water-weighted Dixon the bulk of the tissue sits in the bottom fifth of [0, 1].
• Sin: non-monotonic from about a quarter cycle up, so it can swap the order of tissue intensities.

Behaviour

• With no range set, each leaf falls through to its fixed function and draws nothing. Tested: identical output and RNG stream to the generic _RandomFunctionBaseGPU(func=...).
• Unsharp defaults reduce to the old rand * x exactly (* (x − 0.0) + 0.0).
• all_augmentations.json regenerated.

Tests

unit_tests/test_function_strength.py:

• defaults match the fixed functions and the generic base's RNG stream;
• a strong range is more nonlinear than the fixed function;
• draws are per sample unless same_on_batch;
• the sigmoid centre lands on the drawn quantile;
• sin folds above half a cycle;
• bad ranges are refused;
• unsharp kernel sizing, the σ lower bound, and "wider sharpens more".

Full suite: 486 passed, 13 skipped. mypy smauglab/ clean, pre-commit passes.

🤖 Generated with Claude Code