Jevin754/Optimization_of_image_description_metrics_using_policy_gradient_methods

Tensorflow implement of paper: Optimization of image description metrics using policy gradient methods

Optimization of image description metrics using policy gradient methods

This is Tensorflow implement of paper: Optimization of image description metrics using policy gradient methods.

Attention

This codes are a little coarse. When I worked on this paper, I also have some questions. But the authors didn't reply me after I sent e-mails, and I don't know why.

So, Please contact me anytime if you have any doubt.

My e-mail: xinpeng_chen@whu.edu.cn

I will appreciate that if you have any advice.

How to run the code

Step 1

Go into the ./inception directory, the python script which used to extract features is: extract_inception_bottleneck_feature.py.

In this python script, there are few parameters you should modified:

• image_path: the MSCOCO image path, e.g. /path/to/msococo/train2014, /path/to/msococo/val2014, /path/to/msococo/test2014
• feats_save_path: the feature directory which you want to saved.
• model_path: the pre-trained inception-V3 tensorflow model. And I uploaded this model on the Google Drive: tensorflow_inception_graph.pb

After you modified the parameters, we can extract image features, in the terminal:

$ CUDA_VISIBLE_DEVICES=3 python extract_inception_bottleneck_feature.py

Also, you can run the code without GPU:

$ CUDA_VISIBLE_DEVICES="" python extract_inception_bottleneck_feature.py

In my experiment, I save the train2014 image feature in the folder: ./inception/train_feats, val2014 image feature are saved in the folder: ./inception/val_feats, and the test2014 image features are saved in the folder: test_feats And at the same time, I saved the train2014+val2014 image features in the folder: ./inception/train_val_feats

Step 2

Run the scripts:

$ python pre_train_json.py
$ python pre_val_json.py
$ python split_train_val_data.py

The python script pre_train_json.py, it is used to process the ./data/captions_train2014.json, it generated a file: ./data/train_images_captions.pkl, it is a dict which save the captions of each image, like this:


The script pre_val_json.py, it is used to process the ./data/captions_val2014.json. it generated a file: ./data/val_images_captions.pkl.

The script split_train_val_data.py, because according to the paper, it only use 1665 validation images, the other validation images are used to training. So, I split the validation images into two parts, the 0~1665 images are used to validation, the left are used to training.

Step 3

Run the scripts:

$ python create_train_val_all_reference.py

and

$ create_train_val_each_reference.py

Let me explain the two scripts, the first script create_train_val_all_reference.py, it will generate a JSON file named train_val_all_reference.json(about 70M), it saves the ground-truth captions of training and validation images.

The second script create_train_val_each_reference.py, it will generate JSON files of every training and validation images. And it saves every JSON file in the folder: ./train_val_reference_json/

Step 4

Run the script:

$ python build_vocab.py

This script will build the vocabulary dict. In the data folder, it will generate three files:

• word_to_idx.pkl
• idx_to_word.pkl
• bias_init_vector.npy

By the way, I filter the words more than 5 times, you can change this parameter in the script.

Step 5

In this step, we follow the algorithm in the paper:


First, we train the the basic model with MLE(Maximum Likehood Estimation):

$ CUDA_VISIBLE_DEVICES=0 ipython
>>> import image_caption
>>> image_caption.Train_with_MLE()

After training the basic model, you can test and validate the model on test data and validation data:

>>> image_caption.Test_with_MLE()
>>> image_caption.Val_with_MLE()

Second, we train B_phi using MC estimates of Q_theta on a small dataset D(1665 images):

>>> image_caption.Sample_Q_with_MC()
>>> image_caption.Train_Bphi_Model()

After we get the B_phi model, we use RG to optimize the generation:

>>> image_caption.Train_SGD_update()

I have runned several epochs, here I compared the RL results with the no RL results:

This shows that the policy gradient method is beneficial for image caption.

COCO evalution

In the ./coco_caption/ folder, we can evaluate the generation results and our each trained model. Please see the python scripts.