# 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/loss functions/optimizers
Ultra-efficient trainer and dataloader that allows you to take full advantage of GPU
## Installation
sudo pip3 install pytorch_modules
or
sudo python3 setup.py install
## Usage
### pytorch_modules.utils
Includes a variety of utils for pytorch model training. See [woodsgao/pytorch_segmentation](https://github.com/woodsgao/pytorch_segmentation) as a tutorial.
### 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.backbones
This module includes a series of modified backbone networks.
import torch from pytorch_modules.backbones import ResNet
# NCHW tensor inputs = torch.ones([8, 8, 224, 224]) model = ResNet(32) outputs = model.stages[0](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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Metadata
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Release files for pytorch-modules 0.3.5
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