komal-sharan / original_thesis_copy

thesis code with model

Tensorflow Implementation of Stacked Attention Networks for Image Question Answering

Provide tensorflow edition for SAN, stacked attention network for image question answering model. The LSTM and CNN based question models are provided, and they both using two attention layers. This code is modified from a tensorflow edition for deeper LSTM and normalized CNN VQA (VQA-tensorflow).

Requirements

The code is written in Python and requires Tensorflow(>r1.0). The preprocssinng code is in Python.
(I also provide an old version(r0.10) for tensorflow model in branch r0.10)

Prepare Data (modified from VQA-tensorflow)

(Some texts are copied from the original readme.md) The first thing you need to do is to download the data and do some preprocessing. Head over to the data/ folder and run

Download and Preprocess Dataset

for VQA 1.0:

$ python vqa_preprocessing.py --download True --split 1

We modify a version for VQA 2.0:

$ python vqa_preprocessing_v2.py --download True --split 1

--download Ture means you choose to download the VQA data from the VQA website and --split 1 means you use COCO train set to train and validation set to evaluation. --split 2 means you use COCO train+val set to train and test set to evaluate. After this step, it will generate two files under the data folder. vqa_raw_train.json and vqa_raw_test.json

Preprocess Texts

Once you have these, we are ready to get the question and image features. Back to the main folder, run

$ python prepro.py --input_train_json data/vqa_raw_train.json --input_test_json data/vqa_raw_test.json --num_ans 1000

If you want to use model_VQA_w2v.py (VQA-tensorflow + word2vec) , run

$ python prepro_w2v.py --input_train_json data/vqa_raw_train.json --input_test_json data/vqa_raw_test.json --num_ans 1000

to get the question features. --num_ans specifiy how many top answers you want to use during training. You will also see some question and answer statistics in the terminal output. This will generate two files in your main folder, data_prepro.h5 and data_prepro.json.

Extract image features

To get the image features, run

$ python prepro_img.py

Here we use caffe to extract the pool5 feature map instead of fc7 from VGG_ILSVRC_19_layers model. The path of the caffe model and the output file is designated in the script. After this step, you can get the image feature data_img.h5. We have prepared everything and ready to launch training.

Training and Testing

The san_lstm_att.py is for the LSTM based question model, and san_cnn_att.py is for the CNN based question model.
To train on the prepared dataset, comment out test(). Take LSTM basaed question model for example, we simply run the program with python.

$ python san_lstm_att.py

with the default parameter, this will take several hours and will generate the model under model/san_lstm_att.
To test, comment out train() and run the same program, this will generate san_lstm_att.json.
Modify the json file name in s2i.py, then run the program to correct the generated json files.

$ python s2i.py

This will generate the result OpenEnded_mscoco_lstm_results.json. To evaluate the accuracy of generate result, you need to download the VQA evaluation tools.

Demo Website

We also provide a demo website project for this code, please see demo/. Here are some results: