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Segmentation-Grounded Scene Graph Generation

This repository contains the code for the CVPR 2021 paper titled "Segmentation-Grounded Scene Graph Generation".

Bibtext

@inproceedings{khandelwal2021segmentation,
  title={Segmentation-grounded Scene Graph Generation},
  author={Khandelwal, Siddhesh and Suhail, Mohammed and Sigal, Leonid},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2021}
}

Requirements

To setup the environment with all the required dependencies, follow the steps detailed in INSTALL.md. Additionally, please rename the cloned repository from segmentation-sg to segmentationsg.

Prepare Dataset

The approach requires access to Visual Genome and MS-COCO datasets.

MS-COCO is publicly available here. We use the 2017 Train/Val splits in our experiments.
We use the Visual Genome filtered data widely used in the Scene Graph community. Please see the Unbiased Scene Graph Generation repo on instructions to download this dataset.

Pretrain Object Detector

Before the scene graph models can be trained, the first step involves jointly pre-training the object detector to accurately predict bounding boxes on Visual Genome and segmentation masks on MS-COCO.

If using the ResNeXt-101 backbone, the pre-training can be achieved by running the following command

python pretrain_object_detector_withcoco.py --config-file ../configs/pretrain_object_detector_coco.yaml --num-gpus 4 --resume DATASETS.VISUAL_GENOME.IMAGES <PATH TO VG_100K IMAGES> DATASETS.VISUAL_GENOME.MAPPING_DICTIONARY <PATH TO VG-SGG-dicts-with-attri.json> DATASETS.VISUAL_GENOME.IMAGE_DATA <PATH TO image_data.json> DATASETS.VISUAL_GENOME.VG_ATTRIBUTE_H5 <PATH TO VG-SGG-with-attri.h5> DATASETS.MSCOCO.ANNOTATIONS <PATH TO MSCOCO ANNOTATIONS> DATASETS.MSCOCO.DATAROOT <PATH TO MSCOCO IMAGES> OUTPUT_DIR <PATH TO CHECKPOINT DIR>

If using the VGG-16 backbone, the pre-training can be achieved by running the following command

python pretrain_object_detector_withcoco.py  --config-file ../configs/pretrain_object_detector_vgg_coco.yaml --num-gpus 4 --resume DATASETS.VISUAL_GENOME.IMAGES <PATH TO VG_100K IMAGES> DATASETS.VISUAL_GENOME.MAPPING_DICTIONARY <PATH TO VG-SGG-dicts-with-attri.json> DATASETS.VISUAL_GENOME.IMAGE_DATA <PATH TO image_data.json> DATASETS.VISUAL_GENOME.VG_ATTRIBUTE_H5 <PATH TO VG-SGG-with-attri.h5> DATASETS.MSCOCO.ANNOTATIONS <PATH TO MSCOCO ANNOTATIONS> DATASETS.MSCOCO.DATAROOT <PATH TO MSCOCO IMAGES> OUTPUT_DIR <PATH TO CHECKPOINT DIR>

The jointly trained pre-trained weights can be found here.

Train Scene Graph Model

Once the object detector pre-training is complete, prepare the pre-training weights to be used with scene graph training. Run the following script to achieve this

import torch
pretrain_model = torch.load('<Path to Pretrained Model Weights (example: model_final.pth)>')
pretrain_weight = {}
pretrain_weight['model'] = pretrain_model['model']
with open('<Weight Save Path (example: model_weights.pth)>', 'wb') as f:
    torch.save(pretrain_weight, f)

Depending on the task, the scene graph training can then be run as follows. The training scripts are available in the scripts folder.

Predicate Classification (PredCls)

python train_SG_segmentation_head.py --config-file ../configs/sg_dev_masktransfer.yaml --num-gpus 4 --resume DATALOADER.NUM_WORKERS 2 \
MODEL.WEIGHTS <PATH TO PRETRAINED WEIGHTS> \
OUTPUT_DIR <PATH TO CHECKPOINT DIR> \
DATASETS.VISUAL_GENOME.IMAGES <PATH TO VG_100K IMAGES> DATASETS.VISUAL_GENOME.MAPPING_DICTIONARY <PATH TO VG-SGG-dicts-with-attri.json> DATASETS.VISUAL_GENOME.IMAGE_DATA <PATH TO image_data.json> DATASETS.VISUAL_GENOME.VG_ATTRIBUTE_H5 <PATH TO VG-SGG-with-attri.h5> \
DATASETS.MSCOCO.ANNOTATIONS <PATH TO MSCOCO ANNOTATIONS> DATASETS.MSCOCO.DATAROOT <PATH TO MSCOCO IMAGES> \
MODEL.MASK_ON True  \
MODEL.ROI_SCENEGRAPH_HEAD.USE_GT_BOX True MODEL.ROI_SCENEGRAPH_HEAD.USE_GT_OBJECT_LABEL True \
MODEL.ROI_SCENEGRAPH_HEAD.USE_MASK_ATTENTION True MODEL.ROI_SCENEGRAPH_HEAD.MASK_ATTENTION_TYPE 'Weighted' \
MODEL.ROI_SCENEGRAPH_HEAD.SIGMOID_ATTENTION True TEST.EVAL_PERIOD 100000 \
MODEL.ROI_RELATION_FEATURE_EXTRACTORS.MULTIPLY_LOGITS_WITH_MASKS False \
MODEL.ROI_BOX_FEATURE_EXTRACTORS.BOX_FEATURE_MASK True \
MODEL.ROI_BOX_FEATURE_EXTRACTORS.CLASS_LOGITS_WITH_MASK False SOLVER.IMS_PER_BATCH 16 DATASETS.SEG_DATA_DIVISOR 2 \
MODEL.ROI_SCENEGRAPH_HEAD.PREDICTOR 'MotifSegmentationPredictorC' MODEL.ROI_HEADS.REFINE_SEG_MASKS False

SceneGraph Classification (SGCls)

python train_SG_segmentation_head.py --config-file ../configs/sg_dev_masktransfer.yaml --num-gpus 4 --resume DATALOADER.NUM_WORKERS 2 \
MODEL.WEIGHTS <PATH TO PRETRAINED WEIGHTS> \    
OUTPUT_DIR <PATH TO CHECKPOINT DIR> \
DATASETS.VISUAL_GENOME.IMAGES <PATH TO VG_100K IMAGES> DATASETS.VISUAL_GENOME.MAPPING_DICTIONARY <PATH TO VG-SGG-dicts-with-attri.json> DATASETS.VISUAL_GENOME.IMAGE_DATA <PATH TO image_data.json> DATASETS.VISUAL_GENOME.VG_ATTRIBUTE_H5 <PATH TO VG-SGG-with-attri.h5> \
DATASETS.MSCOCO.ANNOTATIONS <PATH TO MSCOCO ANNOTATIONS> DATASETS.MSCOCO.DATAROOT <PATH TO MSCOCO IMAGES> \
MODEL.MASK_ON True  \
MODEL.ROI_SCENEGRAPH_HEAD.USE_GT_BOX True MODEL.ROI_SCENEGRAPH_HEAD.USE_GT_OBJECT_LABEL False \
MODEL.ROI_SCENEGRAPH_HEAD.USE_MASK_ATTENTION True MODEL.ROI_SCENEGRAPH_HEAD.MASK_ATTENTION_TYPE 'Weighted' \
MODEL.ROI_SCENEGRAPH_HEAD.SIGMOID_ATTENTION True TEST.EVAL_PERIOD 100000 \
MODEL.ROI_RELATION_FEATURE_EXTRACTORS.MULTIPLY_LOGITS_WITH_MASKS False \
MODEL.ROI_BOX_FEATURE_EXTRACTORS.BOX_FEATURE_MASK True \
MODEL.ROI_BOX_FEATURE_EXTRACTORS.CLASS_LOGITS_WITH_MASK False SOLVER.IMS_PER_BATCH 16 DATASETS.SEG_DATA_DIVISOR 2 \
MODEL.ROI_SCENEGRAPH_HEAD.PREDICTOR 'MotifSegmentationPredictorC' MODEL.ROI_HEADS.REFINE_SEG_MASKS False

SceneGraph Prediction (SGPred)

python train_SG_segmentation_head.py --config-file ../configs/sg_dev_masktransfer.yaml --num-gpus 4 --resume DATALOADER.NUM_WORKERS 2 \
MODEL.WEIGHTS <PATH TO PRETRAINED WEIGHTS> \
OUTPUT_DIR <PATH TO CHECKPOINT DIR> \
DATASETS.VISUAL_GENOME.IMAGES <PATH TO VG_100K IMAGES> DATASETS.VISUAL_GENOME.MAPPING_DICTIONARY <PATH TO VG-SGG-dicts-with-attri.json> DATASETS.VISUAL_GENOME.IMAGE_DATA <PATH TO image_data.json> DATASETS.VISUAL_GENOME.VG_ATTRIBUTE_H5 <PATH TO VG-SGG-with-attri.h5> \
DATASETS.MSCOCO.ANNOTATIONS <PATH TO MSCOCO ANNOTATIONS> DATASETS.MSCOCO.DATAROOT <PATH TO MSCOCO IMAGES> \
MODEL.MASK_ON True  \
MODEL.ROI_SCENEGRAPH_HEAD.USE_GT_BOX False MODEL.ROI_SCENEGRAPH_HEAD.USE_GT_OBJECT_LABEL False \
MODEL.ROI_SCENEGRAPH_HEAD.USE_MASK_ATTENTION True MODEL.ROI_SCENEGRAPH_HEAD.MASK_ATTENTION_TYPE 'Weighted' \
MODEL.ROI_SCENEGRAPH_HEAD.SIGMOID_ATTENTION True TEST.EVAL_PERIOD 100000 \
MODEL.ROI_RELATION_FEATURE_EXTRACTORS.MULTIPLY_LOGITS_WITH_MASKS False \
MODEL.ROI_BOX_FEATURE_EXTRACTORS.BOX_FEATURE_MASK True \
MODEL.ROI_BOX_FEATURE_EXTRACTORS.CLASS_LOGITS_WITH_MASK False SOLVER.IMS_PER_BATCH 16 DATASETS.SEG_DATA_DIVISOR 2 \
MODEL.ROI_SCENEGRAPH_HEAD.PREDICTOR 'MotifSegmentationPredictorC' MODEL.ROI_HEADS.REFINE_SEG_MASKS False TEST.DETECTIONS_PER_IMAGE 40

Note that these commands augment our approach to Neural Motifs with ResNeXt 101 backbone. To use VCTree, use

MODEL.ROI_SCENEGRAPH_HEAD.PREDICTOR 'VCTreeSegmentationPredictorC'

To use VGG-16 backbone, use

--config-file ../configs/sg_dev_masktransfer_vgg.yaml

Evaluation

Evaluation can be done using the --eval-only flag. For example, evaluation can be run on the PredCLS model as follows,

python train_SG_segmentation_head.py --eval-only --config-file ../configs/sg_dev_masktransfer.yaml --num-gpus 4 --resume DATALOADER.NUM_WORKERS 2 \
MODEL.WEIGHTS <PATH TO PRETRAINED WEIGHTS> \
OUTPUT_DIR <PATH TO CHECKPOINT DIR> \
DATASETS.VISUAL_GENOME.IMAGES <PATH TO VG_100K IMAGES> DATASETS.VISUAL_GENOME.MAPPING_DICTIONARY <PATH TO VG-SGG-dicts-with-attri.json> DATASETS.VISUAL_GENOME.IMAGE_DATA <PATH TO image_data.json> DATASETS.VISUAL_GENOME.VG_ATTRIBUTE_H5 <PATH TO VG-SGG-with-attri.h5> \
DATASETS.MSCOCO.ANNOTATIONS <PATH TO MSCOCO ANNOTATIONS> DATASETS.MSCOCO.DATAROOT <PATH TO MSCOCO IMAGES> \
MODEL.MASK_ON True  \
MODEL.ROI_SCENEGRAPH_HEAD.USE_GT_BOX True MODEL.ROI_SCENEGRAPH_HEAD.USE_GT_OBJECT_LABEL True \
MODEL.ROI_SCENEGRAPH_HEAD.USE_MASK_ATTENTION True MODEL.ROI_SCENEGRAPH_HEAD.MASK_ATTENTION_TYPE 'Weighted' \
MODEL.ROI_SCENEGRAPH_HEAD.SIGMOID_ATTENTION True TEST.EVAL_PERIOD 100000 \
MODEL.ROI_RELATION_FEATURE_EXTRACTORS.MULTIPLY_LOGITS_WITH_MASKS False \
MODEL.ROI_BOX_FEATURE_EXTRACTORS.BOX_FEATURE_MASK True \
MODEL.ROI_BOX_FEATURE_EXTRACTORS.CLASS_LOGITS_WITH_MASK False SOLVER.IMS_PER_BATCH 16 DATASETS.SEG_DATA_DIVISOR 2 \
MODEL.ROI_SCENEGRAPH_HEAD.PREDICTOR 'MotifSegmentationPredictorC' MODEL.ROI_HEADS.REFINE_SEG_MASKS False

Note: The default training/testing assumes 4 GPUs. It can be modified to suit other GPU configurations, but would require changing the learning rate and batch sizes accordingly. Please look at SOLVER.REFERENCE_WORLD_SIZE parameter in the detectron2 configurations for details on how this can be done automatically.