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differentiable-randaugment-0.1.2
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مشاهده بیشترتوضیحات
Optimize RandAugment with differentiable operations
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | differentiable-randaugment-0.1.2 |
| نام | differentiable-randaugment |
| نسخه کتابخانه | 0.1.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | affjljoo3581 |
| ایمیل نویسنده | affjljoo3581@gmail.com |
| آدرس صفحه اصلی | https://github.com/affjljoo3581/Differentiable-RandAugment |
| آدرس اینترنتی | https://pypi.org/project/differentiable-randaugment/ |
| مجوز | Apache-2.0 |
# Differentiable RandAugment
**Optimize RandAugment with differentiable operations**
[](https://codecov.io/gh/affjljoo3581/Differentiable-RandAugment)
[](https://www.codefactor.io/repository/github/affjljoo3581/differentiable-randaugment)
## Table of Contents
- [Introduction](#introduction)
- [Installation](#installation)
- [Dependencies](#dependencies)
- [Getting Started](#getting-started)
- [Support Operations](#support-operations)
- [License](#license)
## Introduction
**Differentiable RandAugment** is a differentiable version of [RandAugment](https://arxiv.org/abs/1909.13719). The original paper proposed to find optimal parameters by using [**grid search**](https://en.wikipedia.org/wiki/Hyperparameter_optimization#Grid_search). Instead, this library supports differentiable operations to calculate gradient of the magnitude parameter and optimize it. See [getting started](#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.
```python
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.Tensor`s). To resolve this, you should apply this module to `torch.Tensor` images rather than `np.ndarray`.
```python
for inputs, labels in train_dataloader:
inputs = inputs.cuda()
logits = model(augmentor(inputs))
...
```
Of course, other augmentations should be removed from preprocessing:
```python
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:
```python
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:
```python
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](/LICENSE).
نیازمندی
| مقدار | نام |
|---|---|
| - | opencv-python |
| >=1.7 | torch |
| - | albumentations |
| - | numpy |
| - | pytest |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl differentiable-randaugment-0.1.2:
pip install differentiable-randaugment-0.1.2.whl
نصب پکیج tar.gz differentiable-randaugment-0.1.2:
pip install differentiable-randaugment-0.1.2.tar.gz