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Baseline for Large Scale Movie Description Challenge

A baseline code for LSMDC19 Task 1: "Multi Sentence Video Description".

This code is written in pytorch and is based on adv-inf and self-critical.pytorch repositories.

Requirements

Python 2.7 (because there is no coco-caption version for python 3)
PyTorch 0.4
coco-caption (located in movie_eval; run ./get_stanford_models.sh to set up SPICE evaluation)

Clone the repository recursively to get the submodules.

git clone --recursive https://github.com/jamespark3922/lsmdc

Download LSMDC captions and preprocess them

Download caption csv files from the website. Then, run the following command to preprocess the captions.

python prepro_vocab.py --input_path $CSV_PATH

This will result in two files, LSMDC16_info_someone.json and LSMDC16_labels_someone.h5, which will be used as inputs for training and testing.

Then, move the csv files to movie_eval/data to be used as reference for evaluation.

mkdir movie_eval/data
mv *.csv movie_eval/data

Precomputed Features

The website shares precomputed visual features in the download page.

  • i3d (3.3GB)
  • resnet152 (6.8GB)

After downloading them all, unzip them to your preferred feature directory.

Training

python train.py --input_json LSMDC16_info_someone.json --input_fc_dir feats/i3d/ --input_img_dir feats/resnet152/ --input_label_h5 LSMDC16_labels_someone.h5 --learning_rate 5e-4 --learning_rate_decay_start 0 --scheduled_sampling_start 0 --val_videos_use -1 --losses_print_every 10 --batch_size 16

The training scheme follows the format in self-critical.pytorch repository.

The train script will dump checkpoints into the folder specified by --checkpoint_path (default = save/).
We save the model every epoch, along with the best-performing checkpoint on validation and the latest checkpoint.
To resume training, you can specify --g_start_from with corresponding --g_start_epoch (default = 'latest') option to be the path saving infos.pkl and model_$G_START_EPOCH.pth (usually you could just set --g_start_from and --checkpoint_path to be the same).

If you'd like to evaluate BLEU/METEOR/CIDEr scores during training in addition to validation cross entropy loss, use --language_eval 1 option, but don't forget to download the coco-caption code.

You can see more details in opts.py

Context: The generator model uses the hidden state of previous sentence as "context", starting at epoch --g_context_epoch (default = after 10 epochs); before then, the context feature is zero-padded.

Pretrained Model

We share pretrained models here.

Result for the public test set is:

CIDER METEOR BLEU-4 SPICE
gen_60.pth (ep 60) 0.119 0.0825 0.013 0.151

(Note that METEOR score is a bit higher than previous 2017 challenge submissions, becaue captions have been updated for the current version).

Evaluation

After training is done, one can evaluate the captions in the public test set.

The normal inference using greedy max or beamsearch can be run with the following command:

python eval.py --g_model_path save/gen_best.pth --infos_path save/infos.pkl  --sample_max 1 --id $id --beam_size $beam_size

You can also do random sampling by setting --sample_max 0 and specifying --top_num n option. (n > 1 performs top-k sampling, 0 < n < 1 performs nucleus sampling)

If you are using the pretrained model, then you will need to update the path to your input files.

python eval.py --g_model_path save/gen_60.pth --infos_path save/infos.pkl  --sample_max 1 --id $id --beam_size $beam_size --input_json LSMDC16_info_someone.json --input_fc_dir feats/i3d/ --input_img_dir feats/resnet152/ --input_label_h5 LSMDC16_labels_someone.h5

To run on the blind test split, simply add --split blind_test option.

The captions will be saved in movie_eval/captions/caption_$id.json and results in movie_eval/results/result_$id.json

Reference

@article{lsmdc,
author = {Rohrbach, Anna and Torabi, Atousa and Rohrbach, Marcus and Tandon, Niket and  Pal, Chris and Larochelle, Hugo and Courville, Aaron and Schiele, Bernt},
title = {Movie Description},
journal={International Journal of Computer Vision},
year = {2017},
url = {http://link.springer.com/article/10.1007/s11263-016-0987-1?wt_mc=Internal.Event.1.SEM.ArticleAuthorOnlineFirst}}

@article{park2019advinf,
  title= Adversarial Inference for Multi-Sentence Video Descriptions,
  author={Park, Jae Sung and Rohrbach, Marcus and Darrell, Trevor and Rohrbach, Anna},
  jorunal={CVPR 2019},
  year={2019}
}