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self_critical_vqa

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Code for NeurIPS 2019 paper ``Self-Critical Reasoning for Robust Visual Question Answering''

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# Code for Self-Critical Reasoning  for Robust Visual Question Answering (NeurIPS 2019 Spotlight)
This repo contains codes for ''Self-Critical Reasoning  for Robust Visual Question Answering'' with VQA-X human textual explanations
This repo contains code modified from [here](https://github.com/SinghJasdeep/Attention-on-Attention-for-VQA), many thanks!

## Prerequisites
Python 3.7.1 
PyTorch 1.1.0 
spaCy (we use en_core_web_lg spaCy model) 
h5py, pickle, json, cv2

## Preprocessing
Please download the detection features from this [google drive](https://drive.google.com/drive/folders/1IXTsTudZtYLqmKzsXxIZbXfCnys_Izxr?usp=sharing) and put it to 'data' folder 
Please run ``bash tools/download.sh`` to download other useful data files including VQA QA pairs and Glove embeddings 
Please run ``bash tools/preprocess.sh`` to preprocess the data 
``mkdir saved_models``

## Training
The training propocess is split to three stage or two stage:

#### Three stage version (pretrain on CP, fine-tune using influential strengthening loss and fine-tune with both.)
(1) Pretrain on VQA-CP train dataset by runnning 
``CUDA_VISIBLE_DEVICES=0 python main.py --load_hint -1 --use_all 1 --learning_rate 0.001 --split v2cp_train --split_test v2cp_test --max_epochs 40`` 
After the pretraining you will have a saved model in ``saved_models`` named by the start training time. 
Alternatively, you can directly download a model from [here](http://www.cs.utexas.edu/~jialinwu/dataset/pretrain_vqacp_model.pth).

(2) Pretrain using the influential strengthening loss 
Here, please replace the 86-th line in the ``train.py`` with your VQA-CP pretrained models. 
Then, please run the following line to strengthen the most influential object. 
``CUDA_VISIBLE_DEVICES=0 python main.py --load_hint 0 --use_all 0 --learning_rate 0.00001 --split v2cp_train_vqx --split_test v2cp_test --max_epochs 12 --hint_loss_weight 20`` 
After the pretraining you will have anthor saved model in ``saved_models`` named by the start training time. 
Alternatively, you can directly download a model from [here](http://www.cs.utexas.edu/~jialinwu/dataset/pretrain_vqacp_vqx_model.pth).

(3) Training with the self-critical objectives. 
Here, please replace the 82-th line in the ``train.py`` with your influence strengthened pretrained models. 
Then, please run the following line for training. 
``CUDA_VISIBLE_DEVICES=0 python main.py --load_hint 1 --use_all 0 --learning_rate 0.00001 --split v2cp_train_vqx --split_test v2cp_test --max_epochs 5 --hint_loss_weight 20 --compare_loss_weight 1500`` 
Alternatively, you can directly download a model from [here](http://www.cs.utexas.edu/~jialinwu/dataset/pretrain_vqacp_vqx_final_model.pth).

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