semvec/pypi-packages huggingface dataset:
qtgrid 1.0.0
Make working with QGridLayout easier
Requires Python: >=3.7.*, <4
Keywords: PyQt5 PyQt6 PySide6 QGridLayout

# qtgrid

[1]:  https://www.riverbankcomputing.com/software/pyqt/     "PyQt"
[2]:  https://www.qt.io/qt-for-python                       "PySide6"
[3]:  https://devlog42.github.io/qtgrid/reference/          "Reference Manual"
[4]:  https://devlog42.github.io/qtgrid/tutorial/           "qtgrid Tutorial"
[5]:  https://devlog42.github.io/qtgrid/api/                "API Documentation"
[6]:  https://github.com/devlog42/repo-skeleton/issues      "Issues Page"
[7]:  https://github.com/devlog42/qtgrid/blob/main/.github/CONTRIBUTING.md    "Contributing"
[8]:  https://github.com/devlog42/qtgrid/blob/main/.github/CODE_OF_CONDUCT.md "Code of Conduct"
[9]:  https://devlog42.github.io/qtgrid                     "Documentation Homepage"
[10]: https://github.com/devlog42/qtgrid/blob/main/LICENSE  "License"

This python **qtgrid** package is for [PyQt][1], or [PySide][2] developers. It helps to build *QGridLayout's* dynamically and with visual help during implementation time. Note that **qtgrid** works in the same way for PyQt6, PyQt5, and PySide6.

Please visit the [documentation homepage][9] to find an example usage, a [tutorial][4], a [reference manual][3], and for the brave the [API Documentation][5].

## Install

Use pip or pipenv

- pip install qtgrid
- pipenv install qtgrid

Get the current development source from GitHub

- git clone https://github.com/devlog42/qtgrid

When importing **qtgrid**, for an installation of PyQt6, PyQt5, or PySide6 is tested in that order.
If none of these are found, a corresponding error message is issued.

## Contribution

Every contribution that advances this project is very welcome.

If you want to report a bug or ask about a new feature, please visit the dedicated [issues][6] page. There you'll find suitable templates for your request, including one that is esspecially intended for mistakes in the documentations.

However, if you want to get involved in development, please check out the [Contribution][7] page first.

When you write posts, it goes without saying that you use a friendly language. Of course there is also a separate page on the topic called [Code of Conduct][8].

## License

The [License][10] of this package comes in terms of *GNU LGPLv3*.
Free-NBA-stream-76ers-vs-hawks-playoffs-2021 2.0.8
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Requires Python: >=3.6, <4
Keywords: sample,setuptools,development

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tevel-segmentation 0.0.3
A package for fast segmentation algorithm based on the architecture of BiSeNet-v2
Requires Python: >=3.6

# Tevel Urban Semantic Segmentation 

Our usage of [BiSeNetV2](https://arxiv.org/abs/1808.00897) for semantic segmentation in an urban images case

## platform
My platform is like this: 
* ubuntu 20.04
* nvidia GTX 1080 Ti
* cuda 11.2
* miniconda python 3.8
* pytorch 1.7.1
* torchvision 0.8.2

## get start
With a pretrained weight, you can run inference on a single image like this: 
```
$ python src/models/inference.py --model bisenetv2 --weight-path models/path/to/your/weights.pth --img-path ./example.png
```
This would run inference on the image and save the result image to `data/inference_results`

## prepare dataset

If you want to train on your own dataset, you should generate annotation files first with the format like this: 
```
munster_000002_000019_leftImg8bit.png,munster_000002_000019_gtFine_labelIds.png
frankfurt_000001_079206_leftImg8bit.png,frankfurt_000001_079206_gtFine_labelIds.png
...
```
Each line is a pair of training sample and ground truth image path, which are separated by a single comma `,`.   
Then you need to change the field of `im_root` and `train/val_im_anns` in the configuration files.

## train
In order to train the model, you can run command like this: 
```
$ export CUDA_VISIBLE_DEVICES=0,1

# if you want to train with apex
$ python -m torch.distributed.launch --nproc_per_node=2 tools/train.py --model bisenetv2 # or bisenetv1

# if you want to train with pytorch fp16 feature from torch 1.6
$ python -m torch.distributed.launch --nproc_per_node=2 tools/train_amp.py --model bisenetv2 # or bisenetv1
```

Note that though `bisenetv2` has fewer flops, it requires much more training iterations. The the training time of `bisenetv1` is shorter.

## finetune from trained model
You can also load the trained model weights and finetune from it:
```
$ export CUDA_VISIBLE_DEVICES=0,1
$ python -m torch.distributed.launch --nproc_per_node=2 tools/train.py --finetune-from ./res/model_final.pth --model bisenetv2 # or bisenetv1

# same with pytorch fp16 feature
$ python -m torch.distributed.launch --nproc_per_node=2 tools/train_amp.py --finetune-from ./res/model_final.pth --model bisenetv2 # or bisenetv1
```

## eval pretrained models
You can also evaluate a trained model like this: 
```
$ python tools/evaluate.py --model bisenetv1 --weight-path /path/to/your/weight.pth
```