Attention-Translation-Relation Network for Scalable Scene Graph Generation

Code for our ICCV Scene Graph Representation and Learning Workshop paper, 2019.

Requirements

Tested with Python 3.5 and 3.6. Tested versions of important requirements:

• numpy==1.16.2
• torch==1.0.1
• opencv-python==4.0.0.21

Other packages you may need:

• colorlog==4.0.2
• h5py==2.9.0
• matplotlib==3.0.3
• xmltodict==0.12.0

Setup

1. Clone the repository

git clone https://github.com/deeplab-ai/atr-net.git
cd atr-net

2. Setup Faster-RCNN

./scripts/setup_faster_rcnn.sh

3. Download images (edit ./scripts/download_images.sh to select datasets to download)

./scripts/download_images.sh

4. Download annotations (edit ./scripts/download_data.sh to select datasets)

./scripts/download_data.sh

5. Download GloVe

./scripts/download_glove_vectors.sh

6. Transform annotations and create project folders (edit prepare_data.py to select datasets)

python3 prepare_data.py

Train/test a model

python3 main.py --dataset=DATASET --task=TASK --model=MODEL

See main.py for other input arguments. Also see config.py for choices on dataset and task arguments. Example:

python3 main.py --dataset=VG200 --task=predcls --model=atr_net

For further questions

Open an issue!