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
GitHub Repository
AAAI 2022: Stationary diffusion state neural estimation

Stationary Diffusion State Neural Estimation Although many graph-based clustering methods attempt to model the stationary diffusion state in their obj

绽琨 33 Nov 24, 2022
Distance-Ratio-Based Formulation for Metric Learning

Distance-Ratio-Based Formulation for Metric Learning Environment Python3 Pytorch (http://pytorch.org/) (version 1.6.0+cu101) json tqdm Preparing datas

Hyeongji Kim 1 Dec 07, 2022
RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation (CIKM'17)

RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation This is the implementation of RATE: Overcoming Noise and Spar

Yu Zhang 5 Feb 10, 2022
ICML 21 - Voice2Series: Reprogramming Acoustic Models for Time Series Classification

Voice2Series-Reprogramming Voice2Series: Reprogramming Acoustic Models for Time Series Classification International Conference on Machine Learning (IC

49 Jan 03, 2023
Explainer for black box models that predict molecule properties

Explaining why that molecule exmol is a package to explain black-box predictions of molecules. The package uses model agnostic explanations to help us

White Laboratory 172 Dec 19, 2022
A tensorflow/keras implementation of StyleGAN to generate images of new Pokemon.

PokeGAN A tensorflow/keras implementation of StyleGAN to generate images of new Pokemon. Dataset The model has been trained on dataset that includes 8

19 Jul 26, 2022
ML-PersonalWork - Big assignment PersonalWork in Machine Learning, 2021 autumn BUAA.

ML-PersonalWork - Big assignment PersonalWork in Machine Learning, 2021 autumn BUAA.

Snapdragon Lee 2 Dec 16, 2022
PRTR: Pose Recognition with Cascade Transformers

PRTR: Pose Recognition with Cascade Transformers Introduction This repository is the official implementation for Pose Recognition with Cascade Transfo

mlpc-ucsd 133 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
An improvement of FasterGICP: Acceptance-rejection Sampling based 3D Lidar Odometry

fasterGICP This package is an improvement of fast_gicp Please cite our paper if possible. W. Jikai, M. Xu, F. Farzin, D. Dai and Z. Chen, "FasterGICP:

79 Dec 31, 2022
Deep Learning Models for Causal Inference

Extensive tutorials for learning how to build deep learning models for causal inference using selection on observables in Tensorflow 2.

Bernard J Koch 151 Dec 31, 2022
Official PyTorch Implementation of GAN-Supervised Dense Visual Alignment

GAN-Supervised Dense Visual Alignment — Official PyTorch Implementation Paper | Project Page | Video This repo contains training, evaluation and visua

944 Jan 07, 2023
Tensorforce: a TensorFlow library for applied reinforcement learning

Tensorforce: a TensorFlow library for applied reinforcement learning Introduction Tensorforce is an open-source deep reinforcement learning framework,

Tensorforce 3.2k Jan 02, 2023
This is the official code of our paper "Diversity-based Trajectory and Goal Selection with Hindsight Experience Relay" (PRICAI 2021)

Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay This is the official implementation of our paper "Diversity-based Traje

Tianhong Dai 6 Jul 18, 2022
Measure WWjj polarization fraction

WlWl Polarization Measure WWjj polarization fraction Paper: arXiv:2109.09924 Notice: This code can only be used for the inference process, if you want

4 Apr 10, 2022
An Implementation of Transformer in Transformer in TensorFlow for image classification, attention inside local patches

Transformer-in-Transformer An Implementation of the Transformer in Transformer paper by Han et al. for image classification, attention inside local pa

Rishit Dagli 40 Jul 25, 2022
Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, and finding their unique parameters (e.g. death rate).

DINN We introduce Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, a

19 Dec 10, 2022
Moment-DETR code and QVHighlights dataset

Moment-DETR QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries Jie Lei, Tamara L. Berg, Mohit Bansal For dataset de

Jie Lei 雷杰 133 Dec 22, 2022
ppo_pytorch_cpp - an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch

PPO Pytorch C++ This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch. It uses a simple TestEnvironment t

Martin Huber 59 Dec 09, 2022
GANsformer: Generative Adversarial Transformers Drew A

GANformer: Generative Adversarial Transformers Drew A. Hudson* & C. Lawrence Zitnick Update: We released the new GANformer2 paper! *I wish to thank Ch

Drew Arad Hudson 1.2k Jan 02, 2023