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A neural network toolkit.

Project description

# pytorch_modules

## Introduction

A neural network toolkit built on pytorch/opencv/numpy that includes neural network layers, modules, loss functions, optimizers, data loaders, data augmentation, etc.

## Features

  • Advanced neural network modules, such as EfficientNet, ResNet, SENet, Xception, DenseNet, FocalLoss, AdaboundW
  • Ultra-efficient dataloader that allows you to take full advantage of GPU
  • High performance and multifunctional data augmentation(See [woodsgao/image_augments](https://github.com/woodsgao/image_augments))

## Installation

sudo pip3 install pytorch_modules

## Usage

### pytorch_modules.nn

This module contains a variety of neural network layers, modules and loss functions.

import torch from pytorch_modules.nn import ResBlock

# NCHW tensor inputs = torch.ones([8, 8, 224, 224]) block = ResBlock(8, 16) outputs = block(inputs)

### pytorch_modules.augments

See [woodsgao/image_augments](https://github.com/woodsgao/image_augments) for more details.

### pytorch_modules.backbones

This module includes a series of modified backbone networks, such as EfficientNet, ResNet, SENet, Xception, DenseNet.

import torch from pytorch_modules.backbones import ResNet

# NCHW tensor inputs = torch.ones([8, 8, 224, 224]) model = ResNet(32) outputs = model(inputs)

### pytorch_modules.datasets

This module includes a series of dataset classes integrated from pytorch_modules.datasets.BasicDataset which is integrated from torch.utils.data.Dataset . The loading method of pytorch_modules.datasets.BasicDataset is modified to cache data with LMDB to speed up data loading. This allows your gpu to be fully used for model training without spending a lot of time on data loading and data augmentation. Please see the corresponding repository for detailed usage.

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