labert's Introduction

Length-Controllable Image Captioning (ECCV2020)

This repo provides the implemetation of the paper Length-Controllable Image Captioning.

Install

conda create --name labert python=3.7
conda activate labert

conda install pytorch=1.3.1 torchvision cudatoolkit=10.1 -c pytorch
pip install h5py tqdm transformers==2.1.1
pip install git+https://github.com/salaniz/pycocoevalcap

Data & Pre-trained Models

Prepare MSCOCO data follow link.
Download pretrained Bert and Faster-RCNN from Baidu Cloud Disk [code: 0j9f] or Google Drive.
- It's an unified checkpoint file, containing a pretrained Bert-base and the fc6 layer of the Faster-RCNN.
Download our pretrained LaBERT model from Baidu Cloud Disk [code: fpke] or Google Drive.

Scripts

Train

python -m torch.distributed.launch \
  --nproc_per_node=$NUM_GPUS \
  --master_port=4396 train.py \
  save_dir $PATH_TO_TRAIN_OUTPUT \
  samples_per_gpu $NUM_SAMPLES_PER_GPU

Continue train

python -m torch.distributed.launch \
  --nproc_per_node=$NUM_GPUS \
  --master_port=4396 train.py \
  save_dir $PATH_TO_TRAIN_OUTPUT \
  samples_per_gpu $NUM_SAMPLES_PER_GPU \
  model_path $PATH_TO_MODEL

Inference

python inference.py \
  model_path $PATH_TO_MODEL \
  save_dir $PATH_TO_TEST_OUTPUT \
  samples_per_gpu $NUM_SAMPLES_PER_GPU

Evaluate

python evaluate.py \
  --gt_caption data/id2captions_test.json \
  --pd_caption $PATH_TO_TEST_OUTPUT/caption_results.json \
  --save_dir $PATH_TO_TEST_OUTPUT

Cite

Please consider citing our paper in your publications if the project helps your research.

@article{deng2020length,
  title={Length-Controllable Image Captioning},
  author={Deng, Chaorui and Ding, Ning and Tan, Mingkui and Wu, Qi},
  journal={arXiv preprint arXiv:2007.09580},
  year={2020}
}

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