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Environment-Invariant Curriculum Relation Learning for Fine-Grained Scene Graph Generation in Pytorch

Installation

Check INSTALL.md for installation instructions, the recommended configuration is cuda-10.1 & pytorch-1.7.1.

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

Perform training on Scene Graph Generation

Set the dataset path

First, 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 'EICR_model'

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

You need to further choose an object/relation encoder for "Motifs" or "VCTree" or "Self-Attention" predictor, by setting the following parameter:

GLOBAL_SETTING.BASIC_ENCODER 'Motifs'

Examples of the Training Command

Training Example 1 : (VG, Motifs, PredCls)

CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port 10050 --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 'EICR_model' 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 True SOLVER.IMS_PER_BATCH 4 TEST.IMS_PER_BATCH 1 DTYPE "float16" SOLVER.MAX_ITER 120000 SOLVER.VAL_PERIOD 10000 SOLVER.CHECKPOINT_PERIOD 10000 GLOVE_DIR /data/myk/newreason/SHA/datasets/vg OUTPUT_DIR /data/myk/newreason/ICCV23/SHA/output/VG_predcls_EICR SOLVER.SCHEDULE.TYPE WarmupMultiStepLR    SOLVER.STEPS "(56000, 96000)"

Training Example 2 : (GQA_200, Motifs, SGCls)

CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port 10050 --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 'EICR_model' 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 4 TEST.IMS_PER_BATCH 1 DTYPE "float16" SOLVER.MAX_ITER 120000 SOLVER.VAL_PERIOD 10000 SOLVER.CHECKPOINT_PERIOD 10000 GLOVE_DIR /data/myk/newreason/SHA/datasets/vg OUTPUT_DIR /data/myk/newreason/ICCV23/SHA/output/VG_predcls_EICR SOLVER.SCHEDULE.TYPE WarmupMultiStepLR    SOLVER.STEPS "(56000, 96000)"

Evaluation

You can evaluate it by running the following command.

CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.launch --master_port 10083 --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 'GQA_200' GLOBAL_SETTING.RELATION_PREDICTOR 'EICR_model' 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 TEST.IMS_PER_BATCH 1 DTYPE "float16" GLOVE_DIR /home/myk/home/reason/newreason/SHA/datasets/vg/glove OUTPUT_DIR /home/myk/home/reason/newreason/SHA/output/GQA_precl_motif3samples_09aplha_start30000end60000/

Citation

@inproceedings{min2023environment,
  title={Environment-Invariant Curriculum Relation Learning for Fine-Grained Scene Graph Generation},
  author={Min, Yukuan and Wu, Aming and Deng, Cheng},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={13296--13307},
  year={2023}
}

Acknowledgment

Our code is on top of SHA-GCL-for-SGG, we sincerely thank them for their well-designed codebase.