scottblack1998 / energy-based-scene-graph

Code release for Energy-Based Learning for Scene Graph Genertaion

Energy-Based Learning for Scene Graph Generation

This repository contains the code for our paper Energy-Based Learning for Scene Graph Generation accepted at CVPR 2021.

Envirioment setup

To setup the environment with all the required dependancies follow the steps in Install.md.
Note: By default the cudatoolkit version is set to 10.0. When creating an environment on your machine check you cuda compiler version by running nvcc --version and adjust the cudatoolkit version appopriately. Version mismatches can lead to the build failing or segmentaion fault error when running the code.

DATASET

Check Dataset.md for details on downloading the datasets.

Pre-Trained Models

We realsed the weights for the pretained VCTree model on the Visual Genome dataset trained using both cross-entropy based and energy-based training.

EBM	CE
VCTree-Predcls	VCTree-PredCLS
VCTree-SGCLS	VCTree-SGCLS
VCTree-SGDET	VCTree-SGDET

To train you own models you can obtain the weights for the pretrained detectron from this repository.

Training for Energy Based Scene Graph Generation

python -m torch.distributed.launch --master_port 10001 --nproc_per_node=4 \
    tools/energy_joint_train_cd.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 VCTreePredictor \
    SOLVER.IMS_PER_BATCH 16  TEST.IMS_PER_BATCH 4 \
    DTYPE float16 SOLVER.MAX_ITER 50000 SOLVER.VAL_PERIOD 2000 \
    SOLVER.CHECKPOINT_PERIOD 2000 \
    GLOVE_DIR $GLOVE_DIR \
    MODEL.PRETRAINED_DETECTOR_CKPT $PRETRAINED_DETECTOR_PATH \
    OUTPUT_DIR $OUTPUT_DIR \
    SOLVER.BASE_LR 0.001 SAMPLER.LR 1.0 SAMPLER.ITERS 20 SAMPLER.VAR 0.001 SAMPLER.GRAD_CLIP 0.01 MODEL.DEV_RUN False

The above scripts trains a model using 4 GPUs. Here how to change the training behavior for various requirements.

1. Scene Graph Genration Tasks
   1. For PredCLS set
      MODEL.ROI_RELATION_HEAD.USE_GT_BOX True MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL True
   2. For SGCLS set
      MODEL.ROI_RELATION_HEAD.USE_GT_BOX True MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False
   3. For SGDet set
      MODEL.ROI_RELATION_HEAD.USE_GT_BOX False MODEL.ROI_RELATION_HEAD.USE_GT_OBJECT_LABEL False
2. Changing scene graph prediction model
   Change MODEL.ROI_RELATION_HEAD.PREDICTOR to one of the available models
   • VCTreePredictor
   • MotifPredictor
   • IMPPredictor
   • TransformerPredictor (change SOLVER.BASE_LR to 0.001, SOLVER.SCHEDULE.TYPE to WarmupMultiStepLR, SOLVER.MAX_ITER to 16000, SOLVER.IMS_PER_BATCH to 16, SOLVER.STEPS to (10000, 16000). From this repo)
   • If you would like to implement your own scene graph prection model just add the implementaion to maskrcnn_benchmark/modeling/roi_heads/relation_head/roi_relation_predictors.py
3. Modifying Sampler
   Current implementation only has a single sampler (SGLD). You can implement samplers of your choice in maskrcnn_benchmark/modeling/energy_head/sampler.py. To change the parametes of the sampler use the fields under SAMPLER in the config.

Acknowledgment

This repository is developed on top of the scene graph benchmarking framwork develped by KaihuaTang