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pytorch-seq2seq

(★ 1,515)

An open source framework for seq2seq models in PyTorch.

파일 탐색기

최종 버전 다운로드 (.zip)

- - dataset.html
  - evaluator.html
  - genindex.html
  - index.html
  - loss.html
  - models.html
  - objects.inv
  - optim.html
  - py-modindex.html
  - search.html
  - searchindex.js
  - trainer.html
  - util.html
- - conf.py
  - dataset.rst
  - evaluator.rst
  - index.rst
  - loss.rst
  - models.rst
  - optim.rst
  - trainer.rst
  - util.rst
- .nojekyll
- Makefile
- requirements.txt
- sample.py
- generate_toy_data.py
- integration_test.py
- integration_test.sh
- toy.sh
- - __init__.py
  - fields.py
- - __init__.py
  - evaluator.py
  - predictor.py
- - __init__.py
  - loss.py
- - __init__.py
  - attention.py
  - baseRNN.py
  - DecoderRNN.py
  - EncoderRNN.py
  - seq2seq.py
  - TopKDecoder.py
- - __init__.py
  - optim.py
- - __init__.py
  - supervised_trainer.py
- - __init__.py
  - checkpoint.py
- __init__.py
- - eng-fra-dev.txt
  - eng-fra.txt
  - src_vocab.txt
  - tgt_vocab.txt
- __init__.py
- test_checkpoint.py
- test_decoder_rnn.py
- test_encoder_rnn.py
- test_evaluator.py
- test_fields.py
- test_loss_loss.py
- test_optim_optim.py
- test_predictor.py
- test_seq2seq.py
- test_supervised_trainer.py
- test_topkdecoder.py
.gitignore
.travis.yml
LICENSE
README.md
requirements.txt
setup.py
Vagrantfile

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/IBM/pytorch-seq2seq

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd pytorch-seq2seq

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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# pytorch-seq2seq

[![Build Status](https://travis-ci.org/IBM/pytorch-seq2seq.svg?branch=master)](https://travis-ci.org/IBM/pytorch-seq2seq)
[![Join the chat at https://gitter.im/pytorch-seq2seq/Lobby](https://badges.gitter.im/pytorch-seq2seq/Lobby.svg)](https://gitter.im/pytorch-seq2seq/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

**[Documentation](https://ibm.github.io/pytorch-seq2seq/public/index.html)**

This is a framework for sequence-to-sequence (seq2seq) models implemented in [PyTorch](http://pytorch.org).  The framework has modularized and extensible components for seq2seq models, training and inference, checkpoints, etc.  This is an alpha release. We appreciate any kind of feedback or contribution.

# What's New in 0.1.6

* Compatible with PyTorch 0.4
* Added support for pre-trained word embedding

# Roadmap
Seq2seq is a fast evolving field with new techniques and architectures being published frequently.  The goal of this library is facilitating the development of such techniques and applications.  While constantly improving the quality of code and documentation, we will focus on the following items:

* Evaluation with benchmarks such as WMT machine translation, COCO image captioning, conversational models, etc;
* Provide more flexible model options, improving the usability of the library;
* Adding latest architectures such as the CNN based model proposed by [Convolutional Sequence to Sequence Learning](https://arxiv.org/abs/1705.03122) and the transformer model proposed by [Attention Is All You Need](https://arxiv.org/abs/1706.03762);
* Support features in the new versions of PyTorch.

# Installation
This package requires Python 2.7 or 3.6. We recommend creating a new virtual environment for this project (using virtualenv or conda).

### Prerequisites

* Numpy: `pip install numpy` (Refer [here](https://github.com/numpy/numpy) for problem installing Numpy).
* PyTorch: Refer to [PyTorch website](http://pytorch.org/) to install the version w.r.t. your environment.

### Install from source
Currently we only support installation from source code using setuptools.  Checkout the source code and run the following commands:

pip install -r requirements.txt
    python setup.py install

If you already had a version of PyTorch installed on your system, please verify that the active torch package is at least version 0.1.11.

# Get Started
### Prepare toy dataset

# Run script to generate the reverse toy dataset
    # The generated data is stored in data/toy_reverse by default
	scripts/toy.sh

### Train and play
	TRAIN_PATH=data/toy_reverse/train/data.txt
	DEV_PATH=data/toy_reverse/dev/data.txt
	# Start training
    python examples/sample.py --train_path $TRAIN_PATH --dev_path $DEV_PATH

It will take about 3 minutes to train on CPU and less than 1 minute with a Tesla K80.  Once training is complete, you will be prompted to enter a new sequence to translate and the model will print out its prediction (use ctrl-C to terminate).  Try the example below!

Input:  1 3 5 7 9
	Expected output: 9 7 5 3 1 EOS

### Checkpoints
Checkpoints are organized by experiments and timestamps as shown in the following file structure

experiment_dir
	+-- input_vocab
	+-- output_vocab
	+-- checkpoints
	|  +-- YYYY_mm_dd_HH_MM_SS
	   |  +-- decoder
	   |  +-- encoder
	   |  +-- model_checkpoint

The sample script by default saves checkpoints in the `experiment` folder of the root directory.  Look at the usages of the sample code for more options, including resuming and loading from checkpoints.

# Benchmarks

* WMT Machine Translation (Coming soon)

# Troubleshoots and Contributing
If you have any questions, bug reports, and feature requests, please [open an issue](https://github.com/IBM/pytorch-seq2seq/issues/new) on Github.  For live discussions, please go to our [Gitter lobby](https://gitter.im/pytorch-seq2seq/Lobby).

We appreciate any kind of feedback or contribution.  Feel free to proceed with small issues like bug fixes, documentation improvement.  For major contributions and new features, please discuss with the collaborators in corresponding issues.

### Development Cycle
We are using 4-week release cycles, where during each cycle changes will be pushed to the `develop` branch and finally merge to the `master` branch at the end of each cycle.

### Development Environment
We setup the development environment using [Vagrant](https://www.vagrantup.com/).  Run `vagrant up` with our 'Vagrantfile' to get started.

The following tools are needed and installed in the development environment by default:
* Git
* Python
* Python packages: nose, mock, coverage, flake8

### Test
The quality and the maintainability of the project is ensured by comprehensive tests.  We encourage writing unit tests and integration tests when contributing new codes.

Locally please run `nosetests` in the package root directory to run unit tests.  We use TravisCI to require that a pull request has to pass all unit tests to be eligible to merge.  See [travis configuration](https://github.com/IBM/pytorch-seq2seq/blob/master/.travis.yml) for more information.

### Code Style
We follow [PEP8](https://www.python.org/dev/peps/pep-0008/) for code style.  Especially the style of docstrings is important to generate documentation.

* *Local*: Run the following commands in the package root directory
```
# Python syntax errors or undefined names
flake8 . --count --select=E901,E999,F821,F822,F823 --show-source --statistics
# Style checks
flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
```
* *Github*: We use [Codacy](https://www.codacy.com) to check styles on pull requests and branches.