Augmentations#

Augmentations are random, train-time data augmentations in konfai/data/augmentation.py (base class DataAugmentation). Unlike transforms, they are sampled per case and applied only during training:

Dataset:
  augmentations:
    DataAugmentation_0:
      data_augmentations:
        Flip:                     # bare name → konfai.data.augmentation.Flip
          f_prob: [0, 0.5, 0.5]   # per-axis flip probability
        prob: 1
      nb: 1                       # number of augmented copies per sample

Note

Lifecycle. Parameters are drawn once per case index and cached, so every patch of a case shares the same draw within an epoch (mandatory for patch consistency). reset_state re-samples each epoch. A subclass implements _state_init (sample params) and _compute (apply); _inverse supports test-time augmentation reassembly.

Important

Only Mask and Permute may change spatial shape (verified against the code). Everything else preserves geometry.

Spatial (Euler transforms)#

Reversible affine warps via grid_sample (nearest-neighbour for label tensors).

Name

Purpose

Key args (defaults)

Shape

Inv

Translate

Random translation (voxels).

t_min=-10, t_max=10, is_int=False

no

yes

Rotate

Random rotation (degrees).

a_min=0, a_max=360, is_quarter=False

no

yes

Scale

Random log2-normal isotropic scale.

s_std=0.2

no

yes

Flip

Per-axis random flip; optional vector-field channel negation.

f_prob=[0.33,0.33,0.33], vector_field=False

no

yes (self-inverse)

Elastix

Random BSpline elastic warp (SimpleITK).

grid_spacing=16, max_displacement=16

no

no

Permute

Random spatial-axis permutation (3-D only).

prob_permute=[0.5,0.5]

yes

yes

Mask

Randomly place a mask volume; outside → value (SimpleITK).

mask (required), value (required)

yes

no

Intensity (colour transforms)#

Apply a per-index colour affine to RGB(3-ch) or L(1-ch) tensors. Their inverse is a no-op (colour changes don’t move voxels).

Name

Purpose

Key args (defaults)

Brightness

Additive brightness.

b_std (required)

Contrast

Multiplicative contrast (log2-normal).

c_std (required)

LumaFlip

Random luma (value) inversion.

—

HUE

Random hue rotation.

hue_max (required)

Saturation

Random saturation scale.

s_std (required)

Other#

Name

Purpose

Key args (defaults)

Notes

Noise

Diffusion-style forward noising (zero-terminal-SNR β schedule).

n_std (required), noise_step=1000

Its prob is the max noise timestep, not an apply probability; it always applies.

CutOUT

Random cutout box filled with value.

c_prob, cutout_size, value (all required)

Gating uses the base probability.

Note

Elastix and Mask require SimpleITK. The vector_field flag on Flip should only be enabled for single-channel or genuine vector-field groups.

Next steps#