Implementation of RandAugment in 3D
Project description
RandAugment-3D
Implementation of RandAugment in 3D
Original paper: arXiv:1909.13719
Original implementations (in tensorflow):
- From official paper: https://github.com/tensorflow/tpu/blob/298d1fa98638f302ab9df34d9d26bbded7220e8b/models/official/efficientnet/autoaugment.py
- More recent: https://github.com/tensorflow/models/blob/1c79ece9f43340e9bc9571e06a4bf9bd8db8d97a/official/vision/beta/ops/augment.py
Most functions used here are implemented by MONAI.
Installation
pip install RandAugment3d
How to use
Args:
- n: number of augmentations to apply
- magnitude: magnitude of augmentations to apply, on a scale where 10 is full level (this scale is used to be as similar as possible to the original implementation). Values > 10 result in more distortion, values < 10 in less.
- excluded_operations: list of names of the excluded operations. Valid names are listed above.
operations names: ['identity', 'rotate_x', 'rotate_y', 'rotate_z', 'translate', 'scale', 'shear_x', 'shear_y', 'shear_z', 'shiftIntensity', 'equalize', 'solarize', 'histogramShift', 'sharpen', 'adjustContrast']
augment = RandAugment3D(n=2, magnitude=10)
Geometric Augmentations only
augment = RandAugment3D(n=2, magnitude=10, excluded_operations=['shiftIntensity',
'equalize',
'solarize',
'histogramShift',
'sharpen',
'adjustContrast'])
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