This repo provides the implemetation of the paper Length-Controllable Image Captioning.
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- 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-baseand thefc6layer of the Faster-RCNN.
- It's an unified checkpoint file, containing a pretrained
- Download our pretrained LaBERT model from Baidu Cloud Disk [code: fpke] or Google Drive.
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 $NUM_SAMPLES_PER_GPUContinue train
python -m torch.distributed.launch \
--nproc_per_node=$NUM_GPUS \
--master_port=4396 train.py \
save_dir $PATH_TO_TRAIN_OUTPUT \
samples_per_gpu $NUM_SAMPLES_PER_GPU \
model_path $PATH_TO_MODELInference
python inference.py \
model_path $PATH_TO_MODEL \
save_dir $PATH_TO_TEST_OUTPUT \
samples_per_gpu $NUM_SAMPLES_PER_GPUEvaluate
python evaluate.py \
--gt_caption data/id2captions_test.json \
--pd_caption $PATH_TO_TEST_OUTPUT/caption_results.json \
--save_dir $PATH_TO_TEST_OUTPUTPlease 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}
}