IOT: Instance-wise Layer Reordering for Transformer Structures

Related tags

Deep LearningIOT
Overview

Introduction

This repository contains the code for Instance-wise Ordered Transformer (IOT), which is introduced in the ICLR2021 paper IOT: Instance-wise Layer Reordering for Transformer Structures.

If you find this work helpful in your research, please cite as:

@inproceedings{
zhu2021iot,
title={{\{}IOT{\}}: Instance-wise Layer Reordering for Transformer Structures},
author={Jinhua Zhu and Lijun Wu and Yingce Xia and Shufang Xie and Tao Qin and Wengang Zhou and Houqiang Li and Tie-Yan Liu},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=ipUPfYxWZvM}
}

Requirements and Installation

  • PyTorch version == 1.0.0
  • Python version >= 3.5

To install IOT:

git clone https://github.com/instance-wise-ordered-transformer/IOT
cd IOT
pip install --editable .

Getting Started

Take IWSLT14 De-En translation as an example.

Data Preprocessing

cd examples/translation/
bash prepare-iwslt14.sh
cd ../..

TEXT=examples/translation/iwslt14.tokenized.de-en
python preprocess.py --source-lang de --target-lang en \
    --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \
    --destdir data-bin/iwslt14.tokenized.de-en --joined-dictionary

Training

Encoder order is set to be the default one without reordering (ENCODER_MAX_ORDER=1), since the paper finds that both reordering encoder and decoder is not good as reordering decoder only.

#!/bin/bash
export CUDA_VISIBLE_DEVICES=${1:-0}
nvidia-smi

ENCODER_MAX_ORDER=1
DECODER_MAX_ORDER=3
DECODER_ORDER="0 3 5"
DIVERSITY=0.1
GS_MAX=20
GS_MIN=2
GS_R=0
GS_UF=5000
KL=0.01
CLAMPVAL=0.05

DECODER_ORDER_NAME=`echo $DECODER_ORDER | sed 's/ //g'`
SAVE_DIR=checkpoints/dec_${DECODER_MAX_ORDER}_order_${DECODER_ORDER_NAME}_div_${DIVERSITY}_gsmax_${GS_MAX}_gsmin_${GS_MIN}_gsr_${GS_R}_gsuf_${GS_UF}_kl_${KL}_clampval_${CLAMPVAL}
mkdir -p ${SAVE_DIR}

python -u train.py data-bin/iwslt14.tokenized.de-en -a transformer_iwslt_de_en \
--optimizer adam --lr 0.0005 -s de -t en --label-smoothing 0.1 --dropout 0.3 --max-tokens 4000 \
--min-lr 1e-09 --lr-scheduler inverse_sqrt --weight-decay 0.0001 --criterion label_smoothed_cross_entropy \
--max-update 100000 --warmup-updates 4000 --warmup-init-lr 1e-07 --adam-betas '(0.9,0.98)' \
--save-dir $SAVE_DIR --share-all-embeddings  --gs-clamp --decoder-orders $DECODER_ORDER  \
--encoder-max-order $ENCODER_MAX_ORDER  --decoder-max-order $DECODER_MAX_ORDER  --diversity $DIVERSITY \
--gumbel-softmax-max $GS_MAX  --gumbel-softmax-min $GS_MIN --gumbel-softmax-tau-r $GS_R  --gumbel-softmax-update-freq $GS_UF \
--kl $KL --clamp-value $CLAMPVAL | tee -a ${SAVE_DIR}/train.log

Evaluation

#!/bin/bash
set -x
set -e

pip install -e . --user
export CUDA_VISIBLE_DEVICES=${1:-0}
nvidia-smi

ENCODER_MAX_ORDER=1
DECODER_MAX_ORDER=3
DECODER_ORDER="0 3 5"
DIVERSITY=0.1
GS_MAX=20
GS_MIN=2
GS_R=0
GS_UF=5000
KL=0.01
CLAMPVAL=0.05

DECODER_ORDER_NAME=`echo $DECODER_ORDER | sed 's/ //g'`
SAVE_DIR=checkpoints/dec_${DECODER_MAX_ORDER}_order_${DECODER_ORDER_NAME}_div_${DIVERSITY}_gsmax_${GS_MAX}_gsmin_${GS_MIN}_gsr_${GS_R}_gsuf_${GS_UF}_kl_${KL}_clampval_${CLAMPVAL}

python generate.py data-bin/iwslt14.tokenized.de-en \
  --path $SAVE_DIR/checkpint_best.pt \
  --batch-size 128 --beam 5 --remove-bpe --quiet --num-ckts $DECODER_MAX_ORDER 
Age Progression/Regression by Conditional Adversarial Autoencoder

Age Progression/Regression by Conditional Adversarial Autoencoder (CAAE) TensorFlow implementation of the algorithm in the paper Age Progression/Regre

Zhifei Zhang 603 Dec 22, 2022
SAPIEN Manipulation Skill Benchmark

ManiSkill Benchmark SAPIEN Manipulation Skill Benchmark (abbreviated as ManiSkill, pronounced as "Many Skill") is a large-scale learning-from-demonstr

Hao Su's Lab, UCSD 107 Jan 08, 2023
PolyTrack: Tracking with Bounding Polygons

PolyTrack: Tracking with Bounding Polygons Abstract In this paper, we present a novel method called PolyTrack for fast multi-object tracking and segme

Gaspar Faure 13 Sep 15, 2022
One implementation of the paper "DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing".

Introduction One implementation of the paper "DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing". Users

seq-to-mind 18 Dec 11, 2022
Pytorch library for fast transformer implementations

Transformers are very successful models that achieve state of the art performance in many natural language tasks

Idiap Research Institute 1.3k Dec 30, 2022
Simple keras FCN Encoder/Decoder model for MS-COCO (food subset) segmentation

FCN_MSCOCO_Food_Segmentation Simple keras FCN Encoder/Decoder model for MS-COCO (food subset) segmentation Input data: [http://mscoco.org/dataset/#ove

Alexander Kalinovsky 11 Jan 08, 2019
modelvshuman is a Python library to benchmark the gap between human and machine vision

modelvshuman is a Python library to benchmark the gap between human and machine vision. Using this library, both PyTorch and TensorFlow models can be evaluated on 17 out-of-distribution datasets with

Bethge Lab 244 Jan 03, 2023
(CVPR2021) DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation

DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation CVPR2021(oral) [arxiv] Requirements python3.7 pytorch==

W-zx-Y 85 Dec 07, 2022
This is the official pytorch implementation of the BoxEL for the description logic EL++

BoxEL: Box EL++ Embedding This is the official pytorch implementation of the BoxEL for the description logic EL++. BoxEL++ is a geometric approach bas

1 Nov 03, 2022
[CVPR'20] TTSR: Learning Texture Transformer Network for Image Super-Resolution

TTSR Official PyTorch implementation of the paper Learning Texture Transformer Network for Image Super-Resolution accepted in CVPR 2020. Contents Intr

Multimedia Research 689 Dec 28, 2022
Music Source Separation; Train & Eval & Inference piplines and pretrained models we used for 2021 ISMIR MDX Challenge.

Introduction 1. Usage (For MSS) 1.1 Prepare running environment 1.2 Use pretrained model 1.3 Train new MSS models from scratch 1.3.1 How to train 1.3.

Leo 100 Dec 25, 2022
Hand-distance-measurement-game - Hand Distance Measurement Game

Hand Distance Measurement Game This is program is made to calculate the distance

Priyansh 2 Jan 12, 2022
“Robust Lightweight Facial Expression Recognition Network with Label Distribution Training”, AAAI 2021.

EfficientFace Zengqun Zhao, Qingshan Liu, Feng Zhou. "Robust Lightweight Facial Expression Recognition Network with Label Distribution Training". AAAI

Zengqun Zhao 119 Jan 08, 2023
Face recognize and crop them

Face Recognize Cropping Module Source 아이디어 Face Alignment with OpenCV and Python Requirement 필요 라이브러리 imutil dlib python-opence (cv2) Usage 사용 방법 open

Cho Moon Gi 1 Feb 15, 2022
A pytorch-version implementation codes of paper: "BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation"

BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation A pytorch-version implementation

11 Oct 08, 2022
Wordplay, an artificial Intelligence based crossword puzzle solver.

Wordplay, AI based crossword puzzle solver A crossword is a word puzzle that usually takes the form of a square or a rectangular grid of white- and bl

Vaibhaw 4 Nov 16, 2022
A Python training and inference implementation of Yolov5 helmet detection in Jetson Xavier nx and Jetson nano

yolov5-helmet-detection-python A Python implementation of Yolov5 to detect head or helmet in the wild in Jetson Xavier nx and Jetson nano. In Jetson X

12 Dec 05, 2022
Weight initialization schemes for PyTorch nn.Modules

nninit Weight initialization schemes for PyTorch nn.Modules. This is a port of the popular nninit for Torch7 by @kaixhin. ##Update This repo has been

Alykhan Tejani 69 Jan 26, 2021
Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search

CLIP-GLaSS Repository for the paper Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search An in-browser demo is

Federico Galatolo 172 Dec 22, 2022
Continuous Query Decomposition for Complex Query Answering in Incomplete Knowledge Graphs

Continuous Query Decomposition This repository contains the official implementation for our ICLR 2021 (Oral) paper, Complex Query Answering with Neura

UCL Natural Language Processing 71 Dec 29, 2022