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
Structure
glow.*
- Core parts, available out the boxglow.cv.*
- Tools for computer vision tasksglow.io.*
- I/O wrappers to access data in convenient formatsglow.transforms
- Some custom-made augmentations for dataglow.nn
- Neural nets and building blocks for themglow.metrics
- Metric to use while training your neural network
Core features
glow.mapped
- convenient tool to parallelize computationsglow.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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