pytorch-custom-utils
This is a lightweight repository to help PyTorch users.
Usage
📋 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
🔨 Installation
-
pip install torchhkor git clone https://github.com/Harry24k/pytorch-custom-utils
from torchhk import *
🚀 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
