CVPR-code-release

Contents

1. Environment Setup
2. Training

Environment Setup

All the code has been run and tested on:

• Python 2.7.15 (coco-caption requires 2.7)
• Pytorch 1.0.0
• CUDA 9.0
• TITAN X/Xp and GTX 1080Ti GPUs

First clone the repository:

git clone https://github.com/shenkev/Caption-Images-through-a-Lifetime-by-Asking-Questions.git

• Go into the downloaded code directory
• Add the project to PYTHONPATH

cd <path_to_downloaded_directory>
export PYTHONPATH=$PWD

1. Python dependencies and Stanford NLP

chmod +x setup.sh
./setup.sh

This will:

• Install python dependencies
• Download Stanford NLP package for parsing part-of-speech
• Download coco-caption
• Download pyciderevalcap

2. Download images and preprocess them

• Download the images from this link. We need the 2014 training images and 2014 val images.

• You should put the train2014/ and val2014/ in a directory of your choice, denoted as $IMAGE_ROOT.

• Download pretrained resnet model from here and place in Utils/preprocess/checkpoint

• Preprocess images the images by running

python Utils/preprocess/preprocess_imgs.py --input_json Data/annotation/dataset_coco.json --output_dir $IMAGE_ROOT/features --images_root $IMAGE_ROOT

Warning: the prepro script will fail with the default MSCOCO data because one of their images is corrupted. See this issue for the fix, it involves manually replacing one image in the dataset.

3. Download training data and preprocessing

• Download training data here
• Unzip it into Data/annotation
• Precompute indexes for CIDEr

python Utils/preprocess/preprocess_cider.py --data_file Data/annotation/cap_train.p --output_file Data/annotation/coco-words

• Prepare lifelong learning data splits

python Utils/preprocess/preprocess_llsplits.py --data_file Data/annotation/cap_train.p --output_file Data/annotation/train3_split --warmup 3 --num_splits 4 --num_caps 2

• You can play with the chunk sizes and # chunks using warmup and num_splits parameters

Training

• You can either download trained caption, question generator, VQA modules or train them yourself

1. Download pretrained modules

• You can download trained Caption, Question generator, VQA modules
• Download model checkpoints here
• Place in Data/model_checkpoints
• The captioning module was trained using 10% warmup data

1. Training modules

• Train caption module
• In Experiments/caption.json change exp_dir to the working directory, img_dir to $IMAGE_ROOT

python Scripts/train_caption3.py --experiment Experiments/caption3.json

• Train VQA module
• In Experiments/vqa.json change exp_dir to the working directory, img_dir to $IMAGE_ROOT

python Scripts/train_vqa.py --experiment Experiments/vqa.json

• Train question generator module
• In Experiments/question3.json change exp_dir to the working directory, img_dir to $IMAGE_ROOT, vqa_path to vqa model checkpoint and cap_path to caption model checkpoint

python Scripts/train_quegen.py --experiment Experiments/question3.json

2. Lifelong training

• In Experiments/lifelong3.json change exp_dir to the working directory, img_dir to $IMAGE_ROOT, vqa_path to vqa model checkpoint and cap_path to caption model checkpoint, quegen_path to question generator model checkpoint

• You can play with parameters H, lamda, k

python Scripts/train_lifelong.py --experiment Experiments/lifelong3.json

• Track training

cd Results/lifelong
tensorboard --logdir tensorboard/

• Visualize qualitative results

cd Results/lifelong/lifelong3