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Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation in Pytorch

LICENSE Python PyTorch

This repository contains the code for our paper Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation, which has been accepted by CVPR 2022.

Installation

Check INSTALL.md for installation instructions, the recommended configuration is cuda-10.1 & pytorch-1.6.

Dataset

Check DATASET.md for instructions of dataset preprocessing (VG & GQA).

Pretrained Models

For VG dataset, the pretrained object detector we used is provided by Scene-Graph-Benchmark, you can download it from this link. For GQA dataset, we pretrained a new object detector, you can get it from this link. However, we recommend you to pretrain a new one on GQA since we do not pretrain it for multiple times to choose the best pre-trained model for extracting offline region-level features.

Perform training on Scene Graph Generation

Set the dataset path

First, please refer to the SHA_GCL_extra/dataset_path.py and set the datasets_path to be your dataset path, and organize all the files like this:

datasets
  |-- vg
    |--detector_model
      |--pretrained_faster_rcnn
        |--model_final.pth
      |--GQA
        |--model_final_from_vg.pth       
    |--glove
      |--.... (glove files, will autoly download)
    |--VG_100K
      |--.... (images)
    |--VG-SGG-with-attri.h5 
    |--VG-SGG-dicts-with-attri.json
    |--image_data.json    
  |--gqa
    |--images
      |--.... (images)
    |--GQA_200_ID_Info.json
    |--GQA_200_Train.json
    |--GQA_200_Test.json

Choose a dataset

You can choose the training/testing dataset by setting the following parameter:

GLOBAL_SETTING.DATASET_CHOICE 'VG'  #['VG', 'GQA']

Choose a task

To comprehensively evaluate the performance, we follow three conventional tasks: 1) Predicate Classification (PredCls) predicts the relationships of all the pairwise objects by employing the given ground-truth bounding boxes and classes; 2) Scene Graph Classification (SGCls) predicts the objects classes and their pairwise relationships by employing the given ground-truth object bounding boxes; and 3) Scene Graph Detection (SGDet) detects all the objects in an image, and predicts their bounding boxes, classes, and pairwise relationships.

For Predicate Classification (PredCls), you need to set:

MODEL.ROI_RELATION_HEAD.USE_GT_BOX True MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL True

For Scene Graph Classification (SGCls):

MODEL.ROI_RELATION_HEAD.USE_GT_BOX True MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False

For Scene Graph Detection (SGDet):

MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False

Choose your model

We abstract various SGG models to be different relation-head predictors in the file roi_heads/relation_head/roi_relation_predictors.py, which are independent of the Faster R-CNN backbone and relation-head feature extractor. You can use GLOBAL_SETTING.RELATION_PREDICTOR to select one of them:

GLOBAL_SETTING.RELATION_PREDICTOR 'TransLike_GCL'

Notice the candidate choice is "MotifsLikePredictor", "VCTreePredictor", "TransLikePredictor", "MotifsLike_GCL", "VCTree_GCL", "TransLike_GCL". The last three are with our GCL decoder.

The default settings are under configs/SHA_GCL_e2e_relation_X_101_32_8_FPN_1x.yaml and maskrcnn_benchmark/config/defaults.py. The priority is command > yaml > defaults.py.

Choose your Encoder (For "MotifsLike" and "TransLike")

You need to further choose an object/relation encoder for "MotifsLike" or "TransLike" predictor, by setting the following parameter:

GLOBAL_SETTING.BASIC_ENCODER 'Hybrid-Attention'

Notice the candidate choice is 'Self-Attention', 'Cross-Attention', 'Hybrid-Attention' for TransLike Model, and 'Motifs', 'VTransE' for MotifsLike Model.

Choose one group split (for GCL only)

You can change the number of groups when using our GCL decoder, by setting the following parameter:

GLOBAL_SETTING.GCL_SETTING.GROUP_SPLIT_MODE 'divide4' # ['divide4', ''divide3', 'divide5', 'average']

For VG dataset, 'divide4' (5 groups), 'divide3' (6 groups), 'divide5' (4 groups) and average (5 groups). You can refer SHA_GCL_extra/get_your_own_group/get_group_splits.py to get your own group divisions.

Choose the knowledge transfer method (for GCL only)

You can choose the knowledge transfer method by setting the following parameter:

GLOBAL_SETTING.GCL_SETTING.KNOWLEDGE_TRANSFER_MODE 'KL_logit_TopDown' # ['None', 'KL_logit_Neighbor', 'KL_logit_TopDown', 'KL_logit_BottomUp', 'KL_logit_BiDirection']

Examples of the Training Command

Training Example 1 : (VG, TransLike, Hybrid-Attention, divide4, Topdown, PredCls)

CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port 10025 --nproc_per_node=1 tools/relation_train_net.py --config-file "configs/SHA_GCL_e2e_relation_X_101_32_8_FPN_1x.yaml" GLOBAL_SETTING.DATASET_CHOICE 'VG' GLOBAL_SETTING.RELATION_PREDICTOR 'TransLike_GCL' GLOBAL_SETTING.BASIC_ENCODER 'Hybrid-Attention' GLOBAL_SETTING.GCL_SETTING.GROUP_SPLIT_MODE 'divide4' GLOBAL_SETTING.GCL_SETTING.KNOWLEDGE_TRANSFER_MODE 'KL_logit_TopDown' MODEL.ROI_RELATION_HEAD.USE_GT_BOX True MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL True SOLVER.IMS_PER_BATCH 8 TEST.IMS_PER_BATCH 8 DTYPE "float16" SOLVER.MAX_ITER 60000 SOLVER.VAL_PERIOD 5000 SOLVER.CHECKPOINT_PERIOD 5000 GLOVE_DIR /home/share/datasets/vg/glove OUTPUT_DIR /home/share/datasets/output/SHA_GCL_VG_PredCls_test

Training Example 2 : (GQA_200, MotifsLike, Motifs, divide4, Topdown, SGCls)

CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port 10025 --nproc_per_node=1 tools/relation_train_net.py --config-file "configs/SHA_GCL_e2e_relation_X_101_32_8_FPN_1x.yaml" GLOBAL_SETTING.DATASET_CHOICE 'GQA_200' GLOBAL_SETTING.RELATION_PREDICTOR 'MotifsLike_GCL' GLOBAL_SETTING.BASIC_ENCODER 'Motifs' GLOBAL_SETTING.GCL_SETTING.GROUP_SPLIT_MODE 'divide4' GLOBAL_SETTING.GCL_SETTING.KNOWLEDGE_TRANSFER_MODE 'KL_logit_TopDown' MODEL.ROI_RELATION_HEAD.USE_GT_BOX True MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False SOLVER.IMS_PER_BATCH 8 TEST.IMS_PER_BATCH 1 DTYPE "float16" SOLVER.MAX_ITER 60000 SOLVER.VAL_PERIOD 5000 SOLVER.CHECKPOINT_PERIOD 5000 GLOVE_DIR /home/share/datasets/vg/glove OUTPUT_DIR /home/share/datasets/output/Motifs_GCL_GQA_SGCls_test

Evaluation

You can download our training model (SHA_GCL_VG_PredCls) from this link. You can evaluate it by running the following command.

CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port 10025 --nproc_per_node=1 tools/relation_test_net.py --config-file "configs/SHA_GCL_e2e_relation_X_101_32_8_FPN_1x.yaml" GLOBAL_SETTING.DATASET_CHOICE 'VG' GLOBAL_SETTING.RELATION_PREDICTOR 'TransLike_GCL' GLOBAL_SETTING.BASIC_ENCODER 'Hybrid-Attention' GLOBAL_SETTING.GCL_SETTING.GROUP_SPLIT_MODE 'divide4' GLOBAL_SETTING.GCL_SETTING.KNOWLEDGE_TRANSFER_MODE 'KL_logit_TopDown' MODEL.ROI_RELATION_HEAD.USE_GT_BOX True MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL True TEST.IMS_PER_BATCH 8 DTYPE "float16" GLOVE_DIR /home/share/datasets/vg/glove OUTPUT_DIR /home/share/datasets/output/SHA_GCL_VG_PredCls_test

If you want to get more training models in our paper, please email me at dongxingning1998@gmail.com.

Citation

@inproceedings{dong2022stacked,
  title={Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation},
  author={Dong, Xingning and Gan, Tian and Song, Xuemeng and Wu, Jianlong and Cheng, Yuan and Nie, Liqiang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={19427--19436},
  year={2022}
}

We welcome you to commit issue or contact us (E-mail: dongxingning1998@gmail.com) if you have any problem when reading the paper or reproducing the code.

Acknowledgment

Our code is on top of Scene-Graph-Benchmark, we sincerely thank them for their well-designed codebase.