semvec/pypi-packages huggingface dataset:
pwseqdist 0.6
Efficiently computes distances between protein sequences
License: MIT

# pwseqdist

[![Build Status](https://travis-ci.com/kmayerb/pwseqdist.svg?branch=master)](https://travis-ci.com/kmayerb/pwseqdist)
[![PyPI version](https://badge.fury.io/py/pwseqdist.svg)](https://badge.fury.io/py/pwseqdist)
[![Coverage Status](https://coveralls.io/repos/github/agartland/pwseqdist/badge.svg?branch=master)](https://coveralls.io/github/agartland/pwseqdist?branch=master)

A small package that efficiently computes distances between protein sequences.
Can accommodate similarity matrices, sequences of different lengths and custom
metrics.

## Install

```
pip install pwseqdist
```

## Example

```python
import pwseqdist as pw
import multiprocessing
from scipy.spatial.distance import squareform

peptides = ['CACADLGAYPDKLIF','CACDALLAYTDKLIF',
            'CACDAVGDTLDKLIF','CACDDVTEVEGDKLIF',
            'CACDFISPSNWGIQSGRNTDKLIF','CACDPVLGDTRLTDKLIF']

dvec = pw.apply_pairwise_sq(seqs = peptides, 
			    metric = pw.metrics.nw_hamming_metric, 
			    ncpus  = multiprocessing.cpu_count() )

dmat = squareform(dvec).astype(int)
dmat
```

```
array([[ 0,  4,  6,  7, 15,  8],
       [ 4,  0,  5,  7, 14,  7],
       [ 6,  5,  0,  6, 14,  4],
       [ 7,  7,  6,  0, 14,  8],
       [15, 14, 14, 14,  0, 11],
       [ 8,  7,  4,  8, 11,  0]])
```
check-orientation 0.0.5
Check if image was rotated by 90, 180, 270 degrees.
License: MIT

# Check orientation

![](https://habrastorage.org/webt/hb/ru/ii/hbruiiuortx05lxfuokzw1skheu.jpeg)

Models to check if image was rotated by 0, 90, 180, 270 degrees.

## Installation
`pip install -U check_orientation`

### Example inference

Colab notebook with the example: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1HIGM_b8rH20N414ROZB0HN9w5p4ko2Bd?usp=sharing)

## Training

### Define the config.
Example at [check_orientation/configs](check_orientation/configs)

### Define the environmental variable `TRAIN_IMAGE_PATH` that points to the folder with train dataset.
Example:
```bash
export TRAIN_IMAGE_PATH=<path to the tranining folder>
```

### Define the environmental variable `VAL_IMAGE_PATH` that points to the folder with validation dataset.
Example:
```bash
export VAL_IMAGE_PATH=<path to the validation folder>
```

### Training
```
python -m check_orientation.train -c <path to config>
```

### Inference

```bash
python -m torch.distributed.launch --nproc_per_node=<num_gpu> check_orientation/inference.py \
                                   -i <path to images> \
                                   -c <path to config> \
                                   -w <path to weights> \
                                   -o <output-path> \
                                   --fp16
```

### Pre-trained models
Models were pre-trained on the [OpenImages dataset](https://storage.googleapis.com/openimages/web/index.html).

| Models        | Validation accuracy | Config file  | Weights |
| ------------- |:--------------------:| :------------:| :------: |
| swsl_resnext50_32x4d|0.9128| [Link](check_orientation/configs/2020-11-16.yaml)| [Link](https://github.com/ternaus/check_orientation/releases/download/v0.0.3/2020-11-16_resnext50_32x4d.zip)|