torchhk 0.86.4

Custom utils for PyTorch

pytorch-custom-utils

This is a lightweight repository to help PyTorch users.

Usage

:clipboard: Dependencies

• torch 1.4.0
• torchvision 0.5.0
• python 3.6
• matplotlib 2.2.2
• numpy 1.14.3
• seaborn 0.9.0
• sklearn
• plotly

:hammer: Installation

• pip install torchhk or
• git clone https://github.com/Harry24k/pytorch-custom-utils

from torchhk import *

:rocket: Demos

• RecordManager (code, markdown): RecordManager will help you to keep tracking training records.

• Datasets (code, markdown): Dataset will help you to use torch datasets including split and label-filtering.

Supported datasets

# CIFAR10
datasets = Datasets("CIFAR10", root='./data')

# CIFAR100
datasets = Datasets("CIFAR100", root='./data')

# STL10
datasets = Datasets("STL10", root='./data')

# MNIST
datasets = Datasets("MNIST", root='./data')

# FashionMNIST
datasets = Datasets("FashionMNIST", root='./data')

# SVHN
datasets = Datasets("SVHN", root='./data')

# MNISTM
datasets = Datasets("MNISTM", root='./data')

# ImageNet
datasets = Datasets("ImageNet", root='./data')

# USPS
datasets = Datasets("USPS", root='./data')

# TinyImageNet
datasets = Datasets("TinyImageNet", root='./data')

# CIFAR with Unsupervised
datasets = Datasets("CIFAR10U", root='./data')
datasets = Datasets("CIFAR100U", root='./data')

# Corrupted CIFAR (Only test data will be corrupted)
# CORRUPTIONS = [
#    'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur',
#    'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog',
#    'brightness', 'contrast', 'elastic_transform', 'pixelate',
#    'jpeg_compression'
#]
datasets = Datasets("CIFAR10", root='./data',corruption='gaussian_noise')

• Vis (code, markdown): Vis will help you to visualize torch tensors.

• Transform (code): Transform will help you to change specific layers.

Contribution

Contribution is always welcome! Use pull requests :blush: