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Learning

To reproduce results in the paper, follow instruction in the below after dataset preparation. If you did not prepare dataset, please refer to this document.

Pretraining with Visual Genome

The proposed model can be pretrained with the following script.

python pretrain/trainer.py --model_type proposed --prefix expand_depth --max_train_iter 4810 --expand_depth False

Note that we use multiple seed to pretrain a model to count variance of transfer result introduced by different pretrained models. Specifically, we use three different seeds for each models. For convenience, following script can be used to run experiments with three different seeds.

./scripts/pretrain_proposed.sh

In the main paper, we compare with three baselines that should be pretrained. Following are pretraining scripts for the baselines.

Downloading pretrained parameters

If you want to skip pretraining step, you can simply download pretrained files from this [link].

  • Download link for pretrained parameters [link]

Download the file and extract it to train_dir.

Transfer to VQA

Export word weight

Transferring task conditional visual classifier to VQA requires rearranging the answers because the answer indices for pretraining and VQA are different. For rearrangement, use the following script to export word weight.

python pretrain/export_word_weights.py --checkpoint {$CHECKPOINT_PATH}

This script will export word weight from pretrained task conditional visual classifier, which is then rearranged by VQA training script. The CHECKPOINT_PATH should be path to model checkpointed at some iteration. e.g. CHECKPOINT_PATH=train_dir/proposed_expand_depth_bs512_lr0.001_dpFalse_seed234/model-4801 Following script will export word weight of proposed model with seed=234 after 4801 iterations.

python pretrain/export_word_weights.py --checkpoint train_dir/proposed_expand_depth_bs512_lr0.001_dpFalse_seed234/model-4801

Note that experiments in the main paper is conducted by transferring task conditional visual classifier preatrained for 4801 iterations. In case of separable classifier model, different script should be used because parameters for this model is defined differently.

python pretrain/export_word_weights_model_separable.py --checkpoint {$CHECKPOINT_PATH}

Training for VQA

Transfer for VQA uses the following script. This script can be used with various arguments to reproduce results in the main paper.

python vqa/trainer.py --model_type ${MODEL_TYPE} --pretrained_param_path ${MODEL_PATH} --pretrain_word_weight_dir ${WORD_WEIGHT_DIR} --prefix ${TRAIN_DIR_NAME_PREFIX} --seed ${VQA_SEED}

Here is an example script:

python vqa/trainer.py --model_type proposed --pretrained_param_path train_dir/proposed_expand_depth_bs512_lr0.001_dpFalse_seed234/model-4801 --pretrain_word_weight_dir train_dir/proposed_expand_depth_bs512_lr0.001_dpFalse_seed234/word_weights_model-4801 --prefix pretrainseed234 --seed 234

The VQA training scripts we used for experiment in the main paper can be found in ./scripts directory. Following is the list of scripts and the corresponding experiments.

# Model comparison for VQA with out-of-vocabulary answers
vqa_proposed.sh
vqa_separable_classifier.sh
vqa_answer_embedding.sh
vqa_standard_vqa.sh
# Data comparison for VQA with out-of-vocabulary answers
vqa_proposed.sh
vqa_description.sh
vqa_wordnet.sh
# Combining knowledge learned by VQA
vqa_proposed_seen_in_test.sh
vqa_answer_embedding_seen_in_test.sh
vqa_standard_vqa_seen_in_test.sh

Evaluation for VQA

After training VQA models with scripts above, we should run the following evaluation scripts in sequence.

python vqa/eval_multiple_model.py --train_dirs ${LIST_OF_TRAIN_DIR}
python vqa/eval_collection.py --train_dirs ${LIST_OF_TRAIN_DIR}

vqa/eval_multiple_model.py script will evaluate every checkpoints in the directories listed in --train_dirs and evaluate results on the target VQA data. vqa/eval_collection.py script will collect all evaluation results in a single directory and generate two summary files

collect_eval_test_result.pkl
collect_eval_test_result.txt

The collect_eval_test_result.pkl file contains all numbers required to draw a result plot and collect_eval_test_result.txt is a text version file that can be easily checked.

See ./plot_for_paper.ipynb for how the evaluation results in collect_eval_test_result.pkl are plotted.

Note that for Combining knowledge learned by VQA experiments use different scripts for collection evaluation results. The evaluation script is

python vqa/eval_collection_seen_in_test.py --train_dirs ${LIST_OF_TRAIN_DIR}

Also note that Combining knowledge learned by VQA experiments require correctly setting --qa_split_dir arguments for both vqa/eval_multiple_model.py script and vqa/eval_collection_seen_in_test.py. It should be data/preprocessed/vqa_v2/qa_split_objattr_answer_3div4_genome_memft_check_all_answer_thres1_50000_thres2_-1_with_seen_answer_in_test in default dataset configuration.