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vtranse/STA Tensorflow

visual translation embedding network for visual relation detection, CVPR 2017, tensorflow

Shuffle-Then-Assemble: Learning Object-Agnostic Visual Relationship Features, ECCV, tensorflow

Installation

1. Install ipython, if you do not have ipython, you can install this tool (strongly recommended: https://ipython.org/install.html)

pip install ipython

2. Install TensorFlow v1.3.0 or newer type.

pip install tensorflow-gpu==1.3.0

3.Download this repository or clone with Git

git clone https://github.com/yangxuntu/vtranse.git

3. Install easydict

pip install easydict

Training and Testing Vtranse

1. Download dataset (VRD dataset is used as example)

a). Download the dataset form https://share.weiyun.com/55KK78Y, and the file is named as 'sg_dataset.zip'.

b). Use the following commend to unzip the downloaded data:

unzip sg_dataset.zip -d sg_dataset

c).In the path where you put vtranse folder, use the following commend to make a new folder 'dataset/VRD':

mkdir -p ~/dataset/VRD/json_dataset
mkdir -p ~/dataset/VRD/sg_dataset

d). Move the files in sg_dataset into the created dataset, by using the following commends:

mv sg_dataset/annotations_test.json dataset/VRD/json_dataset
mv sg_dataset/annotations_train.json dataset/VRD/json_dataset
mv sg_dataset/sg_test_images dataset/VRD/sg_dataset
mv sg_dataset/sg_train_images dataset/VRD/sg_dataset

e). Change the root path in file 'vtranse/model/config.py': open this file and find the term '__C.DIR' which is named as '/home/yangxu/rd' to suitable path where you put this vtrase folder.

f). Pre-process the VRD dataset to the vrd_roidb.npz which can be used to train the network. Open ipython using following commend:

ipython

And then use following commend to pre-process data in vrd folder:

run process/vrd_pred_process.py

After runing this file, you will find that there is one 'vrd_roidb.npz' file in the foloder 'vtranse/input'

2. Training

a). Download pre-trained model of faster-rcnn on VRD dataset from https://share.weiyun.com/5skGi9N, and the file names are 'vrd_vgg_pretrained.ckpt.data-00000-of-00001', 'vrd_vgg_pretrained.ckpt.index', 'vrd_vgg_pretrained.ckpt.meta' and 'vrd_vgg_pretrained.ckpt.pkl'. After downloading them, using the following commend to move them into the 'vtranse/pre_trained' file:

mv vrd_vgg_pretrained.ckpt.data-00000-of-00001 vtranse/pretrained_para
mv vrd_vgg_pretrained.ckpt.index vtranse/pretrained_para
mv vrd_vgg_pretrained.ckpt.meta vtranse/pretrained_para
mv vrd_vgg_pretrained.ckpt.pkl vtranse/pretrained_para

b). Create a folder which is used to save the trained results

mkdir -p ~vtranse/pred_para/vrd_vgg

c). After downloading and moving files to suitable folder, using 'vtranse/train_file/train_vrd_vgg.py' to train vtranse network on VRD dataset.

ipython
run train_file/train_vrd_vgg.py

d). When training, you can see the results like that:

t: 100.0, rd_loss: 4.83309404731, acc: 0.0980000074953
t: 200.0, rd_loss: 3.81237616211, acc: 0.263000019006
t: 300.0, rd_loss: 3.51845422685, acc: 0.290333356783
t: 400.0, rd_loss: 3.31810754955, acc: 0.292666691653
t: 500.0, rd_loss: 3.48527273357, acc: 0.277666689083
t: 600.0, rd_loss: 3.06100189149, acc: 0.340666691475
t: 700.0, rd_loss: 3.02625158072, acc: 0.334666692317
t: 800.0, rd_loss: 3.06034492403, acc: 0.330333357863
t: 900.0, rd_loss: 3.16739703059, acc: 0.322666690871
...

3. Testing

a). After training vtranse, you will find files like 'vrd_vgg0001.ckpt' in the 'vtranse/pred_para/vrd_vgg' folder. And then you can test your trained model

b). Open the file 'vtranse/test_file/test_vrd_vgg_pred.py' and change the variable 'model_path' to the suitable pretrained model's name.

c). Create a folder to save the result of detected relationships using the following commend:

mkdir -p ~vtranse/pred_res

d). After changing the name of your model, using following commend to get the relationship detection results:

ipython
run test_file/test_vrd_vgg_pred.py

e). After testing, you can run the file 'vtranse/test_file/eva_vrd_vgg_pred.py' to evaluate your detected result:

ipython
run test_file/eva_vrd_vgg_pred.py

VG dataset

1). Download VG dataset. This dataset can be downloaded from their offical website: https://visualgenome.org/. After downloading these files, you should using the following commend to put these images into the folder 'dataset/VG/images/VG_100K'

mkdir -p ~dataset/VG/images/VG_100K
mv images/VG100K dataset/VG/images/VG_100K
mv images/VG100K dataset/VG/images/VG_100K

2). Download training/testing split Since this dataset is so noisy, and I use one filtered type which is provided by https://drive.google.com/file/d/1C6MDiqWQupMrPOgk4T12zWiAJAZmY1aa/view?usp=drive_web, you can download the split form this link. After downloading this file, you can use the following commend to pre-process the vg dataset

mkdir -p ~dataset/VG/imdb
mv vg1_2_meta.h5 dataset/VG/imdb
ipython
run process/vg_pred_process.py

3). Training, Testing and Evaluation After pre-processing Vg dataset, you can using similar process like VRD dataset to train, test and evaluate your model by using following commends:

ipython
run train_file/train_vg_vgg.py

ipython
run test_file/test_vg_vgg_pred.py

ipython
run test_file/eva_vg_vgg_pred.py

Citation:

@inproceedings{Zhang_2017_CVPR,
  author    = {Hanwang Zhang, Zawlin Kyaw, Shih-Fu Chang, Tat-Seng Chua},
  title     = {Visual Translation Embedding Network for Visual Relation Detection},
  booktitle = {CVPR},
  year      = {2017},
}

Results of VRD (R100)

Results of VG (R100)

References:

1. VRD project: https://cs.stanford.edu/people/ranjaykrishna/vrd/

2. Visual Genome https://visualgenome.org

3. Vtranse Caffe Type: https://github.com/zawlin/cvpr17_vtranse

4. The faster rcnn code which I used to train the detection part in this file: https://github.com/endernewton/tf-faster-rcnn

Contact

1. If you have any problems of this programming, you can eamil to s170018@e.ntu.edu.sg.