Compact Bidirectional Transformer for Image Captioning

Related tags

Deep LearningCBTrans
Overview

Compact Bidirectional Transformer for Image Captioning

Requirements

  • Python 3.8
  • Pytorch 1.6
  • lmdb
  • h5py
  • tensorboardX

Prepare Data

  1. Please use git clone --recurse-submodules to clone this repository and remember to follow initialization steps in coco-caption/README.md.
  2. Download the preprocessd dataset from this link and extract it to data/.
  3. Please download the converted VinVL feature from this link and place them under data/mscoco_VinVL/. You can also optionally follow this instruction to prepare the fixed or adaptive bottom-up features extracted by Anderson and place them under data/mscoco/ or data/mscoco_adaptive/.
  4. Download part checkpoints from here and extract them to save/.

Offline Evaluation

To reproduce the results of single CBTIC model on Karpathy test split, just run

python  eval.py  --model  save/nsc-transformer-cb-VinVL-feat/model-best.pth   --infos_path  save/nsc-transformer-cb-VinVL-feat/infos_nsc-transformer-cb-VinVL-feat-best.pkl      --beam_size   2   --id  nsc-transformer-cb-VinVL-feat   --split test

To reproduce the results of ensemble of CBTIC models on Karpathy test split, just run

python eval_ensemble.py   --ids   nsc-transformer-cb-VinVL-feat  nsc-transformer-cb-VinVL-feat-seed1   nsc-transformer-cb-VinVL-feat-seed2  nsc-transformer-cb-VinVL-feat-seed3 --weights  1 1 1 1  --beam_size  2   --split  test

Online Evaluation

Please first run

python eval_ensemble.py   --split  test  --language_eval 0  --ids   nsc-transformer-cb-VinVL-feat  nsc-transformer-cb-VinVL-feat-seed1   nsc-transformer-cb-VinVL-feat-seed2  nsc-transformer-cb-VinVL-feat-seed3 --weights  1 1 1 1  --input_json  data/cocotest.json  --input_fc_dir data/mscoco_VinVL/cocobu_test2014/cocobu_fc --input_att_dir  data/mscoco_VinVL/cocobu_test2014/cocobu_att   --input_label_h5    data/cocotalk_bw_label.h5    --language_eval 0        --batch_size  128   --beam_size   2   --id   captions_test2014_cbtic_results 

and then follow the instruction to upload results.

Training

  1. In the first training stage, such as using VinVL feature, run
python  train.py   --noamopt --noamopt_warmup 20000   --seq_per_img 5 --batch_size 10 --beam_size 1 --learning_rate 5e-4 --num_layers 6 --input_encoding_size 512 --rnn_size 2048 --learning_rate_decay_start 0  --scheduled_sampling_start 0  --save_checkpoint_every 3000 --language_eval 1 --val_images_use 5000 --max_epochs 15     --checkpoint_path   save/transformer-cb-VinVL-feat   --id   transformer-cb-VinVL-feat   --caption_model  cbt     --input_fc_dir   data/mscoco_VinVL/cocobu_fc   --input_att_dir   data/mscoco_VinVL/cocobu_att    --input_box_dir    data/mscoco_VinVL/cocobu_box    
  1. Then in the second training stage, you need two GPUs with 12G memory each, please copy the above pretrained model first
cd save
./copy_model.sh  transformer-cb-VinVL-feat    nsc-transformer-cb-VinVL-feat
cd ..

and then run

python  train.py    --seq_per_img 5 --batch_size 10 --beam_size 1 --learning_rate 1e-5 --num_layers 6 --input_encoding_size 512 --rnn_size 2048  --save_checkpoint_every 3000 --language_eval 1 --val_images_use 5000 --self_critical_after 14  --max_epochs    30  --start_from   save/nsc-transformer-cb-VinVL-feat     --checkpoint_path   save/nsc-transformer-cb-VinVL-feat   --id  nsc-transformer-cb-VinVL-feat   --caption_model  cbt    --input_fc_dir   data/mscoco_VinVL/cocobu_fc   --input_att_dir   data/mscoco_VinVL/cocobu_att    --input_box_dir    data/mscoco_VinVL/cocobu_box 

Note

  1. Even if fixing all random seed, we find that the results of the two runs are still slightly different when using DataParallel on two GPUs. However, the results can be reproduced exactly when using one GPU.
  2. If you are interested in the ablation studies, you can use the git reflog to list all commits and use git reset --hard commit_id to change to corresponding commit.

Citation

@misc{zhou2022compact,
      title={Compact Bidirectional Transformer for Image Captioning}, 
      author={Yuanen Zhou and Zhenzhen Hu and Daqing Liu and Huixia Ben and Meng Wang},
      year={2022},
      eprint={2201.01984},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Acknowledgements

This repository is built upon self-critical.pytorch. Thanks for the released code.

Owner
YE Zhou
YE Zhou
Repository for the Bias Benchmark for QA dataset.

BBQ Repository for the Bias Benchmark for QA dataset. Authors: Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Tho

ML² AT CILVR 18 Nov 18, 2022
Multi-Horizon-Forecasting-for-Limit-Order-Books

Multi-Horizon-Forecasting-for-Limit-Order-Books This jupyter notebook is used to demonstrate our work, Multi-Horizon Forecasting for Limit Order Books

Zihao Zhang 116 Dec 23, 2022
:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

bulbea "Deep Learning based Python Library for Stock Market Prediction and Modelling." Table of Contents Installation Usage Documentation Dependencies

Achilles Rasquinha 1.8k Jan 05, 2023
TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform

TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform

2.6k Jan 04, 2023
How to Become More Salient? Surfacing Representation Biases of the Saliency Prediction Model

How to Become More Salient? Surfacing Representation Biases of the Saliency Prediction Model

Bogdan Kulynych 49 Nov 05, 2022
functorch is a prototype of JAX-like composable function transforms for PyTorch.

functorch is a prototype of JAX-like composable function transforms for PyTorch.

Facebook Research 1.2k Jan 09, 2023
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
This is a repository with the code for the ACL 2019 paper

The Story of Heads This is the official repo for the following papers: (ACL 2019) Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy

231 Nov 15, 2022
BoxInst: High-Performance Instance Segmentation with Box Annotations

Introduction This repository is the code that needs to be submitted for OpenMMLab Algorithm Ecological Challenge, the paper is BoxInst: High-Performan

88 Dec 21, 2022
Hyperparameters tuning and features selection are two common steps in every machine learning pipeline.

shap-hypetune A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models. Overview Hyperparameters t

Marco Cerliani 422 Jan 08, 2023
Fast and accurate optimisation for registration with little learningconvexadam

convexAdam Learn2Reg 2021 Submission Fast and accurate optimisation for registration with little learning Excellent results on Learn2Reg 2021 challeng

17 Dec 06, 2022
Sharpness-Aware Minimization for Efficiently Improving Generalization

Sharpness-Aware-Minimization-TensorFlow This repository provides a minimal implementation of sharpness-aware minimization (SAM) (Sharpness-Aware Minim

Sayak Paul 54 Dec 08, 2022
Text-to-Music Retrieval using Pre-defined/Data-driven Emotion Embeddings

Text2Music Emotion Embedding Text-to-Music Retrieval using Pre-defined/Data-driven Emotion Embeddings Reference Emotion Embedding Spaces for Matching

Minz Won 50 Dec 05, 2022
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization

This project is now archived. It's been fun working on it, but it's time for me to move on. Thank you for all the support and feedback over the last c

Max Pumperla 2.1k Jan 03, 2023
This repository contains the scripts for downloading and validating scripts for the documents

HC4: HLTCOE CLIR Common-Crawl Collection This repository contains the scripts for downloading and validating scripts for the documents. Document ids,

JHU Human Language Technology Center of Excellence 6 Jun 07, 2022
hipCaffe: the HIP port of Caffe

Caffe Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Cent

ROCm Software Platform 126 Dec 05, 2022
Revisiting Global Statistics Aggregation for Improving Image Restoration

Revisiting Global Statistics Aggregation for Improving Image Restoration Xiaojie Chu, Liangyu Chen, Chengpeng Chen, Xin Lu Paper: https://arxiv.org/pd

MEGVII Research 128 Dec 24, 2022
Deep metric learning methods implemented in Chainer

Deep Metric Learning Implementation of several methods for deep metric learning in Chainer v4.2.0. Proxy-NCA: No Fuss Distance Metric Learning using P

ronekko 156 Nov 28, 2022
Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation

Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation Prerequisites This repo is built upon a local copy of transfo

Jixuan Wang 10 Sep 28, 2022
Image Deblurring using Generative Adversarial Networks

DeblurGAN arXiv Paper Version Pytorch implementation of the paper DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks. Our netwo

Orest Kupyn 2.2k Jan 01, 2023