[CVPR 2023]Prototype-based Embedding Network for Scene Graph Generation

This repository contains the official code implementation for the paper Prototype-based Embedding Network for Scene Graph Generation.

Installation

Check INSTALL.md for installation instructions.

Dataset

Check DATASET.md for instructions of dataset preprocessing.

Train

We provide scripts for training the models

export CUDA_VISIBLE_DEVICES=1
export NUM_GUP=1
echo "TRAINING Predcls"

MODEL_NAME='PE-NET_PredCls'
mkdir ./checkpoints/${MODEL_NAME}/
cp ./tools/relation_train_net.py ./checkpoints/${MODEL_NAME}/
cp ./maskrcnn_benchmark/modeling/roi_heads/relation_head/roi_relation_predictors.py ./checkpoints/${MODEL_NAME}/
cp ./maskrcnn_benchmark/modeling/roi_heads/relation_head/model_transformer.py ./checkpoints/${MODEL_NAME}/
cp ./maskrcnn_benchmark/modeling/roi_heads/relation_head/loss.py ./checkpoints/${MODEL_NAME}/
cp ./scripts/train.sh ./checkpoints/${MODEL_NAME}/
cp ./maskrcnn_benchmark/modeling/roi_heads/relation_head/relation_head.py ./checkpoints/${MODEL_NAME}

python3 \
  tools/relation_train_net.py \
  --config-file "configs/e2e_relation_X_101_32_8_FPN_1x.yaml" \
  MODEL.ROI_RELATION_HEAD.USE_GT_BOX True \
  MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL True \
  MODEL.ROI_RELATION_HEAD.PREDICT_USE_BIAS True \
  MODEL.ROI_RELATION_HEAD.PREDICTOR PrototypeEmbeddingNetwork \
  DTYPE "float32" \
  SOLVER.IMS_PER_BATCH 8 TEST.IMS_PER_BATCH $NUM_GUP \
  SOLVER.MAX_ITER 60000 SOLVER.BASE_LR 1e-3 \
  SOLVER.SCHEDULE.TYPE WarmupMultiStepLR \
  MODEL.ROI_RELATION_HEAD.BATCH_SIZE_PER_IMAGE 512 \
  SOLVER.STEPS "(28000, 48000)" SOLVER.VAL_PERIOD 30000 \
  SOLVER.CHECKPOINT_PERIOD 30000 GLOVE_DIR ./datasets/vg/ \
  MODEL.PRETRAINED_DETECTOR_CKPT ./checkpoints/pretrained_faster_rcnn/model_final.pth \
  OUTPUT_DIR ./checkpoints/${MODEL_NAME} \
  SOLVER.PRE_VAL False \
  SOLVER.GRAD_NORM_CLIP 5.0;

Test

We provide scripts for testing the models

export CUDA_VISIBLE_DEVICES=3
export NUM_GUP=1
echo "Testing!!!!"
MODEL_NAME="PE-NET_PredCls"
python3 \
    tools/relation_test_net.py \
    --config-file "configs/e2e_relation_X_101_32_8_FPN_1x.yaml" \
    MODEL.ROI_RELATION_HEAD.USE_GT_BOX True \
    MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL True \
    MODEL.ROI_RELATION_HEAD.PREDICTOR PrototypeEmbeddingNetwork \
    TEST.IMS_PER_BATCH 1 DTYPE "float32" \
    GLOVE_DIR ./datasets/vg/ \
    MODEL.PRETRAINED_DETECTOR_CKPT ./checkpoints/pretrained_faster_rcnn/model_final.pth \
    MODEL.WEIGHT ./checkpoints/${MODEL_NAME}/model_final.pth \
    OUTPUT_DIR ./checkpoints/${MODEL_NAME} \
    TEST.ALLOW_LOAD_FROM_CACHE False \

Device

All our experiments are conducted on one NVIDIA GeForce RTX 3090, if you wanna run it on your own device, make sure to follow distributed training instructions in Scene-Graph-Benchmark.pytorch.

The Trained Model Weights

We provide the weights for the model. Due to random seeds and machines, they are not completely consistent with those reported in the paper, but they are within the allowable error range.

Model	R@20	R@50	R@100	mR@20	mR@50	mR@100	Google Drive
PE-Net (PredCls)	58.21	65.23	67.34	25.83	31.42	33.48	Model Link \| Log Link
PE-Net (SGCls)	35.28	39.13	40.19	15.23	18.22	19.34	Model Link \| Log Link
PE-Net (SGDet)	23.36	30.41	34.84	9.16	12.25	14.34	Model Link \| Log Link

Tips

We use the rel_nms operation provided by RU-Net and HL-Net in PredCls and SGCls to filter the predicted relation predicates, which encourages diverse prediction results.

Help

Be free to contact me (zheng_chaofan@foxmail.com) if you have any questions!

Acknowledgement

The code is implemented based on Scene-Graph-Benchmark.pytorch.

Citation

@inproceedings{zheng2023prototype,
  title={Prototype-based Embedding Network for Scene Graph Generation},
  author={Zheng, Chaofan and Lyu, Xinyu and Gao, Lianli and Dai, Bo and Song, Jingkuan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={22783--22792},
  year={2023}
}

VL-Group/PENET