Official repository for the paper "GN-Transformer: Fusing AST and Source Code information in Graph Networks".

Overview

GN-Transformer AST

This is the official repository for the paper "GN-Transformer: Fusing AST and Source Code information in Graph Networks".

Data Preparing

Preprocess the dataset by yourself

The code we used to preprocess the Java and Python datasets are under in ./preprocess, please read README.md in /Java and /Python respectively to see how to preprocess the corpus.

The original corpus we used are from here:

Java corpus: https://github.com/xing-hu/TL-CodeSum

Python corpus: https://github.com/EdinburghNLP/code-docstring-corpus

Directly use our preprocessed dataset

You can directly download our preprocessed dataset:

Java: https://drive.google.com/file/d/1hVJaA2JA377Iz3bstHLIGaffUh_ogVnG/view?usp=sharing

Python: https://drive.google.com/file/d/1lQhczrERskISdBcWeS6VWLwCMpBAh-YF/view?usp=sharing

Or you can run the data_prepare.sh in ./data to prepare the dataset.

Training

Enter the script folders and run the gntransformer.sh, the training and testing will start.

#GPU: gpu device ids

#NAME: name of the model

Java:

cd ./scripts/java

bash gntransformer.sh #GPU #NAME

Python:

cd ./scripts/python

bash gntransformer.sh #GPU #NAME

Examples:

bash gntransformer.sh 0 some_name # one gpu

bash gntransformer.sh 0,1 some_name # two gpus

...

Trained models

You can download our trained models here:

Java: https://drive.google.com/file/d/1vnIuGLBNGU_AHDwL7yZIkoaByWiLKYxb/view?usp=sharing

Python: https://drive.google.com/file/d/1tk3Wc4YpSo_oLKCi6h3Kitvsux3vWFUO/view?usp=sharing

Or directly run download_models.sh in ./models to download the trained models.

Owner
Cheng Jun-Yan
Cheng Jun-Yan
A visualisation tool for Deep Reinforcement Learning

DRLVIS - Visualising Deep Reinforcement Learning Created by Marios Sirtmatsis with the support of Alex Bäuerle. DRLVis is an application used for visu

Marios Sirtmatsis 1 Nov 04, 2021
Source code of "Hold me tight! Influence of discriminative features on deep network boundaries"

Hold me tight! Influence of discriminative features on deep network boundaries This is the source code to reproduce the experiments of the NeurIPS 202

EPFL LTS4 19 Dec 10, 2021
Hardware accelerated, batchable and differentiable optimizers in JAX.

JAXopt Installation | Examples | References Hardware accelerated (GPU/TPU), batchable and differentiable optimizers in JAX. Installation JAXopt can be

Google 621 Jan 08, 2023
TriMap: Large-scale Dimensionality Reduction Using Triplets

TriMap TriMap is a dimensionality reduction method that uses triplet constraints to form a low-dimensional embedding of a set of points. The triplet c

Ehsan Amid 235 Dec 24, 2022
U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection

The code for our newly accepted paper in Pattern Recognition 2020: "U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."

Xuebin Qin 6.5k Jan 09, 2023
Official implementation of ACMMM'20 paper 'Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework'

Self-supervised Video Representation Learning Using Inter-intra Contrastive Framework Official code for paper, Self-supervised Video Representation Le

Li Tao 103 Dec 21, 2022
[ACMMM 2021 Oral] Enhanced Invertible Encoding for Learned Image Compression

InvCompress Official Pytorch Implementation for "Enhanced Invertible Encoding for Learned Image Compression", ACMMM 2021 (Oral) Figure: Our framework

96 Nov 30, 2022
naked is a Python tool which allows you to strip a model and only keep what matters for making predictions.

naked is a Python tool which allows you to strip a model and only keep what matters for making predictions. The result is a pure Python function with no third-party dependencies that you can simply c

Max Halford 24 Dec 20, 2022
PyTorch implementations of algorithms for density estimation

pytorch-flows A PyTorch implementations of Masked Autoregressive Flow and some other invertible transformations from Glow: Generative Flow with Invert

Ilya Kostrikov 546 Dec 05, 2022
E2C implementation in PyTorch

Embed to Control implementation in PyTorch Paper can be found here: https://arxiv.org/abs/1506.07365 You will need a patched version of OpenAI Gym in

Yicheng Luo 42 Dec 12, 2022
Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extens

TensorLayer Community 7.1k Dec 29, 2022
Doosan robotic arm, simulation, control, visualization in Gazebo and ROS2 for Reinforcement Learning.

Robotic Arm Simulation in ROS2 and Gazebo General Overview This repository includes: First, how to simulate a 6DoF Robotic Arm from scratch using GAZE

David Valencia 12 Jan 02, 2023
A modified version of DeepMind's Alphafold2 to divide CPU part (MSA and template searching) and GPU part (prediction model)

ParallelFold Author: Bozitao Zhong This is a modified version of DeepMind's Alphafold2 to divide CPU part (MSA and template searching) and GPU part (p

Bozitao Zhong 77 Dec 22, 2022
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields.

This repository contains the code release for Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields. This implementation is written in JAX, and is a fork of Google's JaxNeRF

Google 625 Dec 30, 2022
the code used for the preprint Embedding-based Instance Segmentation of Microscopy Images.

EmbedSeg Introduction This repository hosts the version of the code used for the preprint Embedding-based Instance Segmentation of Microscopy Images.

JugLab 88 Dec 25, 2022
Bi-level feature alignment for versatile image translation and manipulation (Under submission of TPAMI)

Bi-level feature alignment for versatile image translation and manipulation (Under submission of TPAMI) Preparation Clone the Synchronized-BatchNorm-P

Fangneng Zhan 12 Aug 10, 2022
PyTorch Implementation for AAAI'21 "Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection"

UMS for Multi-turn Response Selection Implements the model described in the following paper Do Response Selection Models Really Know What's Next? Utte

Taesun Whang 47 Nov 22, 2022
Official implementation for "Image Quality Assessment using Contrastive Learning"

Image Quality Assessment using Contrastive Learning Pavan C. Madhusudana, Neil Birkbeck, Yilin Wang, Balu Adsumilli and Alan C. Bovik This is the offi

Pavan Chennagiri 67 Dec 30, 2022
Simple transformer model for CIFAR10

CIFAR-Transformer Simple transformer model for CIFAR10. Reference: https://www.tensorflow.org/text/tutorials/transformer https://github.com/huggingfac

9 Nov 07, 2022
Official implementation of the paper Momentum Capsule Networks (MoCapsNet)

Momentum Capsule Network Official implementation of the paper Momentum Capsule Networks (MoCapsNet). Abstract Capsule networks are a class of neural n

8 Oct 20, 2022