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Toolset for model training and creation of pipelines

Project description

Glow Library

Collection of tools for easier prototyping with deep learning extensions (PyTorch framework)

Overview

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Installation

For basic installation use:

pip install glow
Specific versions with additional requirements
pip install glow[nn]  # For cv/neural network extras
pip install glow[io]  # For I/O extras
pip install glow[all]  # For all
Glow is compatible with: Python 3.9+, PyTorch 1.9+. Tested on ArchLinux, Ubuntu 18.04/20.04, Windows 10.

Structure

  • glow.* - Core parts, available out the box
  • glow.cv.* - Tools for computer vision tasks
  • glow.io.* - I/O wrappers to access data in convenient formats
  • glow.transforms - Some custom-made augmentations for data
  • glow.nn - Neural nets and building blocks for them
  • glow.metrics - Metric to use while training your neural network

Core features

  • glow.mapped - convenient tool to parallelize computations
  • glow.memoize - use if you want to reduce number of calls for any function

IO features

glow.io.Slide - ndarray-like reader for multiscale images (svs, tiff, etc...)

from glow.io import Slide

slide = Slide.open('test.svs')
shape: tuple[int, ...] = slide.shape
scales: tuple[int, ...] = slide.scales
image: np.ndarray = slide[:2048, :2048]  # Get numpy.ndarray

glow.io.Sound - playable sound wrapper

from datetime import timedelta

import numpy as np
from glow.io import Sound

array: np.ndarray
sound = Sound(array, rate=44100)  # Wrap np.ndarray
sound = Sound.load('test.flac')  # Load sound into memory from file

# Get properties
rate: int = sound.rate
duration: timedelta = sound.duration
dtype: np.dtype = sound.dtype

 # Plays sound through default device, supports Ctrl-C for interruption
sound.play()

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