differentiable-randaugment 0.1.2

Optimize RandAugment with differentiable operations

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.