differentiablerandaugment 0.1.2
Differentiable RandAugment
Optimize RandAugment with differentiable operations
Table of Contents
Introduction
Installation
Dependencies
Getting Started
Support Operations
License
Introduction
Differentiable RandAugment is a differentiable version of RandAugment. The original paper proposed to find optimal parameters by using grid search. Instead, this library supports differentiable operations to calculate gradient of the magnitude parameter and optimize it. See getting started.
Installation
To install the latest version from PyPI:
$ pip install -U differentiable_randaugment
Or you can install from source by cloning the repository and running:
$ git clone https://github.com/affjljoo3581/Differentiable-RandAugment.git
$ cd Differentiable-RandAugment
$ python setup.py install
Dependencies
opencv_python
torch>=1.7
albumentations
numpy
Getting Started
First, create RandAugmentModule with your desired number of operations. This module is a differentiable and torch.Tensor calculable version of RandAugment policy. Using this module, you can train the policy as one of the neural-network model. Note that randomly selected num_ops operations will be applied to the images.
from differentiable_randaugment import RandAugmentModule
augmentor = RandAugmentModule(num_ops=2)
Now you need to perform the module to the images. Usually augmentations are applied in Dataset. That is, the operations use np.ndarray images. However, it cannot calculate the gradients for image and magnitude parameter (because the entire optimization procedure is based on torch.Tensors). To resolve this, you should apply this module to torch.Tensor images rather than np.ndarray.
for inputs, labels in train_dataloader:
inputs = inputs.cuda()
logits = model(augmentor(inputs))
...
Of course, other augmentations should be removed from preprocessing:
transform = Compose([
Resize(...),
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
ToTensorV2(),
])
And lastly, create an optimizer with this module parameters. We recommend to use different learning rate for the model and the augmentor:
param_groups = [
{"params": augmentor.parameters(), "lr": 10 * learning_rate},
{"params": model.parameters(), "lr": learning_rate},
]
optimizer = optim.Adam(param_groups)
Now the RandAugment policy will be trained with your prediction model.
After training RandAugmentModule, get the trained optimal magnitude value by calling augmentor.get_magnitude() and use the magnitude as follows:
from differentiable_randaugment import RandAugment
transform = Compose([
Resize(...),
RandAugment(num_ops=..., magnitude=...),
Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
ToTensorV2(),
])
dataset = Dataset(..., transform=transform)
While RandAugment is an extension of albumentations, you can combine other augmentations in albumentations with this class.
Support Operations
Differentiable RandAugment supports 14 operations described in the original paper. The below table shows the detailed differential specification of each operation.
Input Image
Magnitude
Identity
✔
ShearX
✔
✔
ShearY
✔
✔
TranslateX
✔
✔
TranslateY
✔
✔
Rotate
✔
✔
Cutout
✔
✔
AutoContrast
✔
Equalize
✔
Solarize
✔
SolarizeAdd
✔
✔
Posterize
✔
Contrast
✔
✔
Color
✔
✔
Brightness
✔
✔
Sharpness
✔
✔
License
Differentiable RandAugment is Apache-2.0 Licensed.
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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