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Improving Generation and Evaluation of Visual Stories via Semantic Consistency

PyTorch code for the NAACL 2021 paper "Improving Generation and Evaluation of Visual Stories via Semantic Consistency". Link to arXiv paper: https://arxiv.org/abs/2105.10026

Update (1/24/2022)

The model weights from best checkpoint previously available in this repository were incorrect. We have updated the link to the best checkpoint, and also made corrections in the inference and evaluation scripts for using the correct mode (test vs. val). The inference and evaluation for FID, classification scores has been verified with this codebase now, to reproduce the numbers in Table 1. Please see the VLCStoryGAN repo for FID results. - Authors.

Requirements:

This code has been tested on torch==1.7.1 and torchvision==0.8.2

Prepare Repository:

Download the PororoSV dataset and associated files from here and save it as ./data. Download GloVe embeddings (glove.840B.300D) from here. The default location of the embeddings is ./data/ (see ./dcsgan/miscc/config.py).

Training DuCo-StoryGAN:

To train DuCo-StoryGAN, first train the VideoCaptioning model on the PororoSV dataset:
python train_mart.py --data_dir <path-to-data-directory>
Default parameters were used to train the model used in our paper.

Next, train the generative model:
python train_gan.py --cfg ./cfg/pororo_s1_duco.yml --data_dir <path-to-data-directory>
If training DuCo-StoryGAN on a new dataset, make sure to train the Video Captioning model (see below) before training the GAN. The vocabulary file prepared for the video-captioning model is re-used for generating common input_ids for both models. Change location of video captioning checkpoint in config file.

Unless specified, the default output root directory for all model checkpoints is ./out/

Training Evaluation Models:

• Video Captioning Model
  The video captioning model trained for DuCo-StoryGAN (see above) is used for evaluation. python train_mart.py --data_dir <path-to-data-directory>

• Hierarchical Deep Multimodal Similarity (H-DAMSM)
  python train_damsm.py --cfg ./cfg/pororo_damsm.yml --data_dir <path-to-data-directory>

• Character Classifier
  python train_classifier.py --data_dir <path-to-data-directory> --model_name inception --save_path ./models/inception --batch_size 8 --learning_rate 1e-05

Inference from DuCo-StoryGAN:

Use the following command to infer from trained weights for DuCo-StoryGAN:
python train_gan.py --cfg ./cfg/pororo_s1_duco_eval.yml --data_dir <path-to-data-directory> --checkpoint <path-to-weights> --infer_dir <path-to-output-directory>

Download our pretrained checkpoint from here. The predictions on PororoSV test set with this checkpoint are available here

Evaluation:

Download the pretrained models for evaluations:
Character Classifier, Video Captioning

Use the following command to evaluate classification accuracy of generated images:
python eval_scripts/eval_classifier.py --image_path <path to generated images directory or numpy file> --data_dir <path to ground truth image directory> --model_path <path-to-classifier model> --model_name inception --mode <val or test>

Use the following command to evaluate BLEU Score of generated images:
python eval_scripts/translate.py --batch_size 50 --pred_dir <path to generated images directory; same as classifier> --data_dir <path to ground truth image directory> --checkpoint_file <path-to-captioning-model> --eval_mode <val or test>

Acknowledgements

The code in this repository has been adapted from the MART, StoryGAN and MirrorGAN codebases.