semvec/pypi-packages huggingface dataset:
tf-focal-frequency-loss 0.1.2
Focal Frequency Loss for Image Reconstruction and Synthesis - Tensorflow Implementation
Requires Python: >=3.6

# Focal Frequency Loss - Tensorflow Implementation

![teaser](https://raw.githubusercontent.com/EndlessSora/focal-frequency-loss/master/resources/teaser.jpg)

This repository provides the Tensorflow implementation for the following paper:

**Focal Frequency Loss for Image Reconstruction and Synthesis** by Liming Jiang, Bo Dai, Wayne Wu and Chen Change Loy 
in ICCV 2021.

[**Project Page**](https://www.mmlab-ntu.com/project/ffl/index.html) | [**Paper**](https://arxiv.org/abs/2012.12821) | [**Poster**](https://liming-jiang.com/projects/FFL/resources/poster.pdf) | [**Slides**](https://liming-jiang.com/projects/FFL/resources/slides.pdf) | [**YouTube Demo**](https://www.youtube.com/watch?v=RNTnDtKvcpc) | [**Official PyTorch Implementation**](https://github.com/EndlessSora/focal-frequency-loss)

> **Abstract:** *Image reconstruction and synthesis have witnessed remarkable progress thanks to the development of generative models. Nonetheless, gaps could still exist between the real and generated images, especially in the frequency domain. In this study, we show that narrowing gaps in the frequency domain can ameliorate image reconstruction and synthesis quality further. We propose a novel focal frequency loss, which allows a model to adaptively focus on frequency components that are hard to synthesize by down-weighting the easy ones. This objective function is complementary to existing spatial losses, offering great impedance against the loss of important frequency information due to the inherent bias of neural networks. We demonstrate the versatility and effectiveness of focal frequency loss to improve popular models, such as VAE, pix2pix, and SPADE, in both perceptual quality and quantitative performance. We further show its potential on StyleGAN2.*


## Quick Start

Run `pip install tf-focal-frequency-loss` for installation. Then, the following code is all you need.

```python
import tensorflow as tf
from tf_focal_frequency_loss import FocalFrequencyLoss as FFL
ffl = FFL(loss_weight=1.0, alpha=1.0)  # initialize tf.keras.layers.Layer class

fake = tf.random.normal((4, 3, 64, 64))  # replace it with the predicted tensor of shape (N, C, H, W)
real = tf.random.normal((4, 3, 64, 64))  # replace it with the target tensor of shape (N, C, H, W)

loss = ffl(fake, real)  # calculate focal frequency loss
```


## License

All rights reserved. The code is released under the [MIT License](https://github.com/ZohebAbai/tf-focal-frequency-loss/blob/main/LICENSE).
plainquotes 0.0.1
This package adds functionality to the auto plain nested quotes in a string

# Auto plain nested quotes

The purpose of this package is to provide a simple way to get a correct scaped nested quoted string from an unescaped one. The input string uses a combination of single and double quotes. The output one is always using one type of quotes but scaping it wherever needed.

## Getting started

Installation via pip:

```shell
pip install plainquotes
```

Using it:

```python
from plainquotes import plain_quotes

print(plain_quotes("""'This is a "simpler" quoted string'"""))
```

Output:
```shell
'This is a \'simpler\' quoted string'
```
tf_focal_frequency_loss 0.1.1
Focal Frequency Loss for Image Reconstruction and Synthesis - Tensorflow Implementation
Requires Python: >=3.6

# Focal Frequency Loss - Tensorflow Implementation

![teaser](https://raw.githubusercontent.com/EndlessSora/focal-frequency-loss/master/resources/teaser.jpg)

This repository provides the Tensorflow implementation for the following paper:

**Focal Frequency Loss for Image Reconstruction and Synthesis** by Liming Jiang, Bo Dai, Wayne Wu and Chen Change Loy 
in ICCV 2021.

[**Project Page**](https://www.mmlab-ntu.com/project/ffl/index.html) | [**Paper**](https://arxiv.org/abs/2012.12821) | [**Poster**](https://liming-jiang.com/projects/FFL/resources/poster.pdf) | [**Slides**](https://liming-jiang.com/projects/FFL/resources/slides.pdf) | [**YouTube Demo**](https://www.youtube.com/watch?v=RNTnDtKvcpc) | [**Official PyTorch Implementation**](https://github.com/EndlessSora/focal-frequency-loss)

> **Abstract:** *Image reconstruction and synthesis have witnessed remarkable progress thanks to the development of generative models. Nonetheless, gaps could still exist between the real and generated images, especially in the frequency domain. In this study, we show that narrowing gaps in the frequency domain can ameliorate image reconstruction and synthesis quality further. We propose a novel focal frequency loss, which allows a model to adaptively focus on frequency components that are hard to synthesize by down-weighting the easy ones. This objective function is complementary to existing spatial losses, offering great impedance against the loss of important frequency information due to the inherent bias of neural networks. We demonstrate the versatility and effectiveness of focal frequency loss to improve popular models, such as VAE, pix2pix, and SPADE, in both perceptual quality and quantitative performance. We further show its potential on StyleGAN2.*


## Quick Start

Run `pip install tf_focal_frequency_loss` for installation. Then, the following code is all you need.

```python
import tensorflow as tf
from tf_focal_frequency_loss import FocalFrequencyLoss as FFL
ffl = FFL(loss_weight=1.0, alpha=1.0)  # initialize tf.keras.layers.Layer class

fake = tf.random.normal((4, 3, 64, 64))  # replace it with the predicted tensor of shape (N, C, H, W)
real = tf.random.normal((4, 3, 64, 64))  # replace it with the target tensor of shape (N, C, H, W)

loss = ffl(fake, real)  # calculate focal frequency loss
```


## License

All rights reserved. The code is released under the [MIT License](https://github.com/ZohebAbai/tf-focal-frequency-loss/blob/main/LICENSE).
apivatsim 1.1
easy fetch data from vatsim
Requires Python: >=3.6

<h1 align="center">API VATSIM</h1>
<b>API VATSIM</b> is unofficial API wrapper for <a href="https://vatsim.net/">VATSIM</a> to simply fetch information from network. Made by uw935.

<br>
<h2>Installation and requirements</h2>
For install you must have: <br>
<b>Internet</b><br>
<b>Python:</b> 3.7 and higher.<br><br>

Installing: <code>pip install apivatsim</code>
<h2>Initialization and usage</h2>
<b>Initialization</b><br>
<code>from apivatsim import vatsimApi</code><br>
<code>api = vatsimApi()</code><br><br>

<b>Getting user information by VATSIM CID:</b><br>
<code>info_cid = api.get_cid_info('cid')</code><br><br>
<b>Getting currently flight information by VATSIM CID or callsign:</b><br>
<code>flight = api.get_flight_info('CID or callsign')</code><br><br>
<b>Getting airport information by icao:</b><br>
<code>airport = api.get_airport_info('icao')</code><br><br>
<b>Getting random flight information:</b><br>
<code>airport = api.get_random_flight()</code><br><br>
<b>Getting random airport information:</b><br>
<code>airport = api.get_random_airport()</code><br><br>

Output in JSON format.
  
<h2>Contact</h2>
To contact, use my <a href="https://t.me/uw935">telegram</a>. Made by uw935.
<br><br>
GitHub: https://github.com/uw935/apivatsim<br>
<a href="https://map.vatsim.net/">simaware.map</a>, <a href="https://vatsim.net/">vatsim</a><br>
<i>Written in python. Russia. With love.</i>