Differentiable Data Augmentation Library
This library is a core of Faster AutoAugment and its descendants. This library is research oriented, and its AIP may change in the near future.
Requirements and Installation
Requirements
Python>=3.8
PyTorch>=1.5.0
torchvision>=0.6
kornia>=0.2
Installation
pip install -U git+https://github.com/moskomule/dda
APIs
dda.functional
Basic operations that can be differentiable w.r.t. the magnitude parameter mag. When mag=0, no augmentation is applied, and when mag=1 (and mag=-1 if it exists), the severest augmentation is applied. As introduced in Faster AutoAugment, some operations use straight-through estimator to be differentiated w.r.t. their magnitude parameters.
def operation(img: torch.Tensor,
mag: Optional[torch.Tensor]) -> torch.Tensor:
...dda.pil contains the similar APIs using PIL (not differentiable).
dda.operations
class Operation(nn.Module):
def __init__(self,
initial_magnitude: Optional[float] = None,
initial_probability: float = 0.5,
magnitude_range: Optional[Tuple[float, float]] = None,
probability_range: Optional[Tuple[float, float]] = None,
temperature: float = 0.1,
flip_magnitude: bool = False,
magnitude_scale: float = 1,
debug: bool = False):
...If magnitude_range=None, probability_range=None, then magnitude, probability is not Parameter but Buffer, respectively.
magnitude moves in magnitude_scale * magnitude_range.
For example, dda.operations.Rotation has magnitude_range=[0, 1] and magnitude_scale=30 so that magnitude is between 0 to 30 degrees.
To differentiate w.r.t. the probability parameter, RelaxedBernoulli is used.
Examples
Citation
dda (except RandAugment) is developed as a core library of the following research projects.
If you use dda in your academic research, please cite hataya2020a.
@inproceesings{hataya2020a,
title={{Faster AutoAugment: Learning Augmentation Strategies using Backpropagation}},
author={Ryuichiro Hataya and Jan Zdenek and Kazuki Yoshizoe and Hideki Nakayama},
year={2020},
booktitle={ECCV}
}
...