Skip to content

Latest commit

 

History

History
78 lines (55 loc) · 4.37 KB

readme.md

File metadata and controls

78 lines (55 loc) · 4.37 KB
Raw

Pose-based Modular Network for Human-Object Interaction Detection

Official Pytorch implementation for Pose-based Modular Network for Human-Object Interaction Detection.

overview

Code Overview

In this project, we implement our method based on VS-GATs. The structure of the code in this project is similar to VS-GATs. You can check it for the description of each file.

Getting Started

Prerequisites

This codebase was tested with Python 3.6, Pytorch 1.1.0, torchvision 0.3, CUDA 10.0, Ubuntu 16.04.

Installation

  1. Clone this repository.

    git clone https://github.com/birlrobotics/PMN.git

  2. Install Python dependencies:

    pip install -r requirements.txt

Prepare Data

Download Original Data (Optional)

  1. Download the original HICO-DET dataset and put it into datasets/hico.
  2. Follow here to prepare the original data of V-COCO dataset in datasets/vcoco folder.
  3. (For VS-GATs) Download the pretrain word2vec model on GoogleNews and put it into ./datasets/word2vec

Download the Processed Data

  • Download our processed data for HICO-DET and V-COCO and put them into datasets/processed with the original file name.

Download the Pretrained Model of VS-GATs

  • In our method, we build our module based on the VS-GATs which is fixed when training. Download the pretrained model of VS-GATs for HICO-DET and V-COCO and put them into ./checkpoints with the original file name.

Training

  • On HICO-DET dataset:

    python hico_train.py --exp_ver='hico_pmn' --b_s=32  --d_p=0.2 --bn='true' --n_layers=1 --b_l 0 3  --lr=3e-5

  • Similarly, for V-COCO datset:

    python vcoco_train.py --exp_ver='vcoco_pmn' --b_s=32  --d_p=0.2 --bn='true' --n_layers=1 --b_l 0 3 --o_c_l 64 64 64 64 --lr=3e-5

  • You can visualized the training process through tensorboard: tensorboard --logdir='log/'.

  • Checkpoints will be saved in checkpoints/ folder.

Testing

  • Run the following script: option 'final_ver' means the name of which experiment and 'path_to_the_checkpoint_file' means where the checkpoint file is. (You can use the checkpoint of HICO-DET and V-COCO to reproduce the detection results in our paper.).

    bash hico_eval.sh 'final_ver' 'path_to_the_checkpoint_file'

  • For V-COCO dataset, you first need to cover the original ./datasets/vcoco/vsrl_eval.py with the new one in ./result/vsrl_eval.py because we add some codes to save the detection results. Then run:

    python vcoco_eval.py -p='path_to_the_checkpoint_file'

  • Results will be saved in result/ folder.

Results

  • Please check the paper for the quantitative results and several qualitative detection results are as follow: detection_results

Acknowledgemen

In this project, some codes which process the data and eval the model are built upon VS-GATs: Visual-Semantic Graph Attention Networks for Human-Object Interaction Detecion, ECCV2018-Learning Human-Object Interactions by Graph Parsing Neural Networks and ICCV2019-No-Frills Human-Object Interaction Detection: Factorization, Layout Encodings, and Training Techniques. Thanks them for their great works.