https://github.com/0h-n0/torchex

Pytorch Extension Library.
https://github.com/0h-n0/torchex

deep-learning deep-neural-networks machine-learning ml neural-network pytorch

Last synced: 2 months ago
JSON representation

Pytorch Extension Library.

• Host: GitHub
• URL: https://github.com/0h-n0/torchex
• Owner: 0h-n0
• License: mit
• Created: 2018-11-08T09:24:58.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2023-04-16T06:55:21.000Z (over 1 year ago)
• Last Synced: 2024-10-22T19:50:13.249Z (2 months ago)
• Topics: deep-learning, deep-neural-networks, machine-learning, ml, neural-network, pytorch
• Language: Python
• Homepage: https://torchex.readthedocs.io/en/latest/?badge=latest
• Size: 111 KB
• Stars: 6
• Watchers: 2
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
[![PYTHON version](https://img.shields.io/badge/python-3.6,3.7,3.8-blue.svg)](https://github.com/0h-n0/torchex)

[![PyPI version](https://img.shields.io/pypi/v/torchex.svg)](https://badge.fury.io/py/torchex)

[![Downloads](https://img.shields.io/pypi/dm/torchex.svg)](https://pypi.org/project/torchex/)

# (WIP) `torchex library`

`torchex` library provides advanced Neural Network Layers. You can easily use them like using original pytorch.

## Installation

```

$ pip install torchex

```

## Requirements

* Pytorch >= 1.0

## Documentation

* https://torchex.readthedocs.io/en/latest/index.html

## How to use

### Lazy Style Model Definition

```python

import torch

import torchex.nn as exnn

net = exnn.Linear(10)

# You don't need to give the size of input for this module.

# This network is equivalent to `nn.Linear(100, 10)`.

x = troch.randn(10, 100)

y = net(x)

```

### torchex.nn list

* `torchex.nn.Pass`

* `torchex.nn.Flatten`

* `torchex.nn.Linear`

  * Lazy style

* `torchex.nn.Conv1d`

  * Lazy style

* `torchex.nn.Conv2d`

  * Lazy style

* `torchex.nn.Conv3d`

  * Lazy style

* `torchex.nn.Conv2dLocal`

* `torchex.nn.GlobalAvgPool1d`

* `torchex.nn.GlobalAvgPool2d`

* `torchex.nn.GlobalMaxPool1d`

* `torchex.nn.GlobalMaxPool2d`

* `torchex.nn.MaxAvgPool2d`

* `torch.nn.Crop2d`

* `torch.nn.Crop3d`

* `torch.nn.MLPConv2d`

* `torch.nn.UpsampleConvLayer`

* `torch.nn.CordConv2d`

* `torch.nn.DFT1d`

* `torch.nn.DFT2d`

* `torch.nn.PeriodicPad2d`

* `torch.nn.PeriodicPad3d`

* `torch.nn.Highway`

* `torch.nn.Inception`

* `torch.nn.InceptionBN`

* `torch.nn.IndRNNCell`

* `torch.nn.IndRNNTanhCell`

* `torch.nn.IndRNNReLuCell`

* `torch.nn.IndRNN`

* `torch.nn.GraphLinear`

* `torch.nn.GraphConv`

* `torch.nn.SparseMM`

* `torch.nn.GraphBatchNrom`

### torchex.data.transforms

* `torchex.data.transforms.PadRandomSift`

* `torchex.data.transforms.RandomResize`

### torchex.data.attribute

for visualization

* `torchex.attribute.IntegratedGradients`