semvec/pypi-packages huggingface dataset:
FDL-pytorch 1.0
Frequency Distribution Loss (FDL) for misalignment data
License: MIT
Keywords: pytorch,loss,image transformation,misalignment

# Frequency Distribution Loss (FDL) for misaligned data

**The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024**

[Zhangkai Ni](https://eezkni.github.io/), [Juncheng Wu](https://scholar.google.com/citations?user=RSn2gykAAAAJ&hl=en&oi=sra), [Zian Wang](https://scholar.google.com/citations?user=Mi6YUBoAAAAJ&hl=en&oi=sra), [Wenhan Yang](https://flyywh.github.io/), [Hanli Wang](https://mic.tongji.edu.cn/51/91/c9778a86417/page.htm), [Lin Ma](https://forestlinma.com/)

This repository provides the official PyTorch implementation for the paper “Misalignment-Robust Frequency Distribution Loss for Image Transformation”, CVPR-2024. [Paper](https://arxiv.org/abs/2402.18192)

## About FDL

A novel Frequency Distribution Loss (FDL) for image transformation models trained with misaligned data, opening up new avenues for addressing the broad issue of misalignment in image transformation tasks.

## Quick Start

### Installation:

`pip install fdl-pytorch`

### Requirements:

- Python>=3.6
- Pytorch>=1.0

### Usage:

```python
from FDL_pytorch import FDL_loss
fdl_loss = FDL_loss()
# X: (N,C,H,W) 
# Y: (N,C,H,W) 
loss_value = fdl_loss(X, Y)
loss_value.backward()
```

## Citation

If you find our work useful, please cite it as

```
@article{ni2024misalignment,
  title={Misalignment-Robust Frequency Distribution Loss for Image Transformation},
  author={Ni, Zhangkai and Wu, Juncheng and Wang, Zian and Yang, Wenhan and Wang, Hanli and Ma, Lin},
  journal={arXiv preprint arXiv:2402.18192},
  year={2024}
}
```

## Contact

Thanks for your attention! If you have any suggestion or question, feel free to leave a message here or contact Dr. Zhangkai Ni (eezkni@gmail.com).


## License

[MIT License](https://opensource.org/licenses/MIT)
automatingdatabase 0.0.5
A python package for connecting with database.
License: MIT
Requires Python: >=3.7

# requirements_dev.txt we use for the testing
It makes it easier to install and manage dependencies for development and testing, separate from the dependencies required for production.

# difference between requirements_dev.txt and requirements.txt

requirements.txt is used to specify the dependencies required to run the production code of a Python project, while requirements_dev.txt is used to specify the dependencies required for development and testing purposes.

# tox.ini
We use if for the testing in the python package testing against different version of the python 

## how tox works tox enviornment creation
1. Install depedencies and packages 
2. Run commands
3. Its a combination of the (virtualenvwrapper and makefile)
4. It creates a .tox


# pyproject.toml
it is being used for configuration the python project it is a alternative of the setup.cfg file. its containts configuration related to the build system
such as the build tool used package name version author license and dependencies

# setup.cfg
In summary, setup.cfg is used by setuptools to configure the packaging and installation of a Python projec

# Testing python application
*types of testing*
1. Automated testing 
2. Manual testing

*Mode of testing*
1. Unit testing
2. Integration tests

*Testing frameworks*

1. pytest
2. unittest
3. robotframework
4. selenium
5. behave
6. doctest

# check with the code style formatting and syntax(coding standard)

1. pylint
2. flake8(it is best because it containt 3 library pylint pycodestyle mccabe)
3. pycodestyle