This repository contains the application of Cross-Path Consistency Learning at QPIC, based on the official implementation of QPIC in here.
- Requirements
Install torch 1.8.0 and other requirements.txt.
pip install -r requirements.txt
HICO-DET dataset can be downloaded here. After finishing downloading, unpack the tarball (hico_20160224_det.tar.gz) to the data directory.
Instead of using the original annotations files, we use the annotation files provided by the PPDM authors. The annotation files can be downloaded from here. The downloaded annotation files have to be placed as follows.
qpic
|─ data
│ └─ hico_20160224_det
| |─ annotations
| | |─ trainval_hico.json
| | |─ test_hico.json
| | └─ corre_hico.npy
: :
First clone the repository of V-COCO from here, and then follow the instruction to generate the file instances_vcoco_all_2014.json. Next, download the prior file prior.pickle from here. Place the files and make directories as follows.
qpic
|─ data
│ └─ v-coco
| |─ data
| | |─ instances_vcoco_all_2014.json
| | :
| |─ prior.pickle
| |─ images
| | |─ train2014
| | | |─ COCO_train2014_000000000009.jpg
| | | :
| | └─ val2014
| | |─ COCO_val2014_000000000042.jpg
| | :
| |─ annotations
: :
For our implementation, the annotation file have to be converted to the HOIA format. The conversion can be conducted as follows.
PYTHONPATH=data/v-coco \
python convert_vcoco_annotations.py \
--load_path data/v-coco/data \
--prior_path data/v-coco/prior.pickle \
--save_path data/v-coco/annotations
Note that only Python2 can be used for this conversion because vsrl_utils.py in the v-coco repository shows a error with Python3.
V-COCO annotations with the HOIA format, corre_vcoco.npy, test_vcoco.json, and trainval_vcoco.json will be generated to annotations directory.
Our QPIC have to be pre-trained with the COCO object detection dataset. For the HICO-DET training, this pre-training can be omitted by using the parameters of DETR. The parameters can be downloaded from here for the ResNet50 backbone, and here for the ResNet101 backbone. For the V-COCO training, this pre-training has to be carried out because some images of the V-COCO evaluation set are contained in the training set of DETR. You have to pre-train QPIC without those overlapping images by yourself for the V-COCO evaluation.
For HICO-DET, move the downloaded parameters to the params directory and convert the parameters with the following command.
python convert_parameters.py \
--load_path params/detr-r50-e632da11.pth \
--save_path params/detr-r50-pre-hico-cpc.pth
For V-COCO, convert the pre-trained parameters with the following command.
python convert_parameters.py \
--load_path params/detr-r50-e632da11.pth \
--save_path params/detr-r50-pre-vcoco-cpc.pth \
--dataset vcoco
After the preparation, you can start the training with the following command.
For the HICO-DET training.
GPUS_PER_NODE=8 ./tools/run_dist_launch.sh 8 ./configs/hico_train.sh
For the V-COCO training.
GPUS_PER_NODE=8 ./tools/run_dist_launch.sh 8 ./configs/vcoco_train.sh
For evaluation of main inference path P1 (x->HOI), --path_id should be set to 0.
Indexes of Augmented paths are range to 1~3. (1: x->HO->I, 2: x->HI->O, 3: x->OI->H)
HICODET
python main.py \
--pretrained checkpoints/hicodet/logs/qpic_cpc_resnet50_hico.pth \
--hoi \
--dataset_file hico \
--hoi_path data/hico_20160224_det \
--num_obj_classes 80 \
--num_verb_classes 117 \
--share_dec_param \
--backbone resnet50 \
--path_id 0 \
--eval
VCOCO
python generate_vcoco_official.py \
--param_path checkpoints/vcoco/logs/qpic_cpc_resnet50_vcoco.pth \
--save_path checkpoints/vcoco/logs/vcoco.pickle \
--share_dec_param \
--path_id 0 \
--hoi_path data/v-coco
@inproceedings{park2022consistency,
title={Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection},
author={Park, Jihwan and Lee, SeungJun and Heo, Hwan and Choi, Hyeong Kyu and Kim, Hyunwoo J},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2022}
}