Update 🔥

Now, the repo supports Pytorch2.0+Cuda11.8

Create your virtual environment an install the following dependencies according to your system specs.

• PyTorch 2.0 + cuda 11.8

• batch images inference

• /data/tsv_generation/tsv_create.py to create images tsv files

• scripts/inference-gpus.sh to run inference on the tsv files

Scene Graph Benchmark in PyTorch 1.7

This project is based on maskrcnn-benchmark

Highlights

• Upgrad to pytorch 1.7
• Multi-GPU training and inference
• Batched inference: can perform inference using multiple images per batch per GPU.
• Fast and flexible tsv dataset format
• Remove FasterRCNN detector dependency: during relation head training, can plugin bounding boxes from any detector.
• Provides pre-trained models for different scene graph detection algorithms (IMP, MSDN, GRCNN, Neural Motif, RelDN).
• Provides bounding box level and relation level feature extraction functionalities
• Provides large detector backbones (ResNxt152)

Installation

Check INSTALL.md for installation instructions.

Model Zoo and Baselines

Pre-trained models can be found in SCENE_GRAPH_MODEL_ZOO.md

Visualization and Demo

We provide a helper class to simplify writing inference pipelines using pre-trained models (Currently only support objects and attributes). Here is how we would do it. Run the following commands:

# visualize VinVL object detection
# pretrained models at https://penzhanwu2.blob.core.windows.net/sgg/sgg_benchmark/vinvl_model_zoo/vinvl_vg_x152c4.pth
# the associated labelmap at https://penzhanwu2.blob.core.windows.net/sgg/sgg_benchmark/vinvl_model_zoo/VG-SGG-dicts-vgoi6-clipped.json
python tools/demo/demo_image.py --config_file sgg_configs/vgattr/vinvl_x152c4.yaml --img_file demo/woman_fish.jpg --save_file output/woman_fish_x152c4.obj.jpg MODEL.WEIGHT pretrained_model/vinvl_vg_x152c4.pth MODEL.ROI_HEADS.NMS_FILTER 1 MODEL.ROI_HEADS.SCORE_THRESH 0.2 TEST.IGNORE_BOX_REGRESSION False

# visualize VinVL object-attribute detection
# pretrained models at https://penzhanwu2.blob.core.windows.net/sgg/sgg_benchmark/vinvl_model_zoo/vinvl_vg_x152c4.pth
# the associated labelmap at https://penzhanwu2.blob.core.windows.net/sgg/sgg_benchmark/vinvl_model_zoo/VG-SGG-dicts-vgoi6-clipped.json
python tools/demo/demo_image.py --config_file sgg_configs/vgattr/vinvl_x152c4.yaml --img_file demo/woman_fish.jpg --save_file output/woman_fish_x152c4.attr.jpg --visualize_attr MODEL.WEIGHT pretrained_model/vinvl_vg_x152c4.pth MODEL.ROI_HEADS.NMS_FILTER 1 MODEL.ROI_HEADS.SCORE_THRESH 0.2 TEST.IGNORE_BOX_REGRESSION False

# visualize OpenImage scene graph generation by RelDN
# pretrained models at https://penzhanwu2.blob.core.windows.net/sgg/sgg_benchmark/sgg_model_zoo/sgg_oi_vrd_model_zoo/RX152FPN_reldn_oi_best.pth
python tools/demo/demo_image.py --config_file sgg_configs/vrd/R152FPN_vrd_reldn.yaml --img_file demo/1024px-Gen_Robert_E_Lee_on_Traveler_at_Gettysburg_Pa.jpg --save_file output/1024px-Gen_Robert_E_Lee_on_Traveler_at_Gettysburg_Pa.reldn_relation.jpg --visualize_relation MODEL.ROI_RELATION_HEAD.DETECTOR_PRE_CALCULATED False

# visualize Visual Genome scene graph generation by neural motif
python tools/demo/demo_image.py --config_file sgg_configs/vg_vrd/rel_danfeiX_FPN50_nm.yaml --img_file demo/1024px-Gen_Robert_E_Lee_on_Traveler_at_Gettysburg_Pa.jpg --save_file demo/1024px-Gen_Robert_E_Lee_on_Traveler_at_Gettysburg_Pa_vgnm.jpg --visualize_relation MODEL.ROI_RELATION_HEAD.DETECTOR_PRE_CALCULATED False DATASETS.LABELMAP_FILE "visualgenome/VG-SGG-dicts-danfeiX-clipped.json" DATA_DIR /home/penzhan/GitHub/maskrcnn-benchmark-1/datasets1 MODEL.ROI_RELATION_HEAD.USE_BIAS True MODEL.ROI_RELATION_HEAD.FILTER_NON_OVERLAP True MODEL.ROI_HEADS.DETECTIONS_PER_IMG 64 MODEL.ROI_RELATION_HEAD.SHARE_BOX_FEATURE_EXTRACTOR False MODEL.ROI_RELATION_HEAD.NEURAL_MOTIF.OBJ_LSTM_NUM_LAYERS 0 MODEL.ROI_RELATION_HEAD.NEURAL_MOTIF.EDGE_LSTM_NUM_LAYERS 2 TEST.IMS_PER_BATCH 2

Perform training

For the following examples to work, you need to first install this repo.

You will also need to download the dataset. Datasets can be downloaded by azcopy with following command:

path/to/azcopy copy 'https://penzhanwu2.blob.core.windows.net/sgg/sgg_benchmark/datasets/TASK_NAME' <target folder> --recursive

TASK_NAME could be visualgenome, openimages_v5c.

We recommend to symlink the path to the dataset to datasets/ as follows

# symlink the dataset
cd ~/github/maskrcnn-benchmark
mkdir -p datasets/openimages_v5c/
ln -s /vrd datasets/openimages_v5c/vrd

You can also prepare your own datasets.

Follow tsv dataset creation instructions tools/mini_tsv/README.md

Single GPU training

python tools/train_sg_net.py --config-file "/path/to/config/file.yaml"

This should work out of the box and is very similar to what we should do for multi-GPU training. But the drawback is that it will use much more GPU memory. The reason is that we set in the configuration files a global batch size that is divided over the number of GPUs. So if we only have a single GPU, this means that the batch size for that GPU will be 4x larger, which might lead to out-of-memory errors.

Multi-GPU training

We use internally torch.distributed.launch in order to launch multi-gpu training. This utility function from PyTorch spawns as many Python processes as the number of GPUs we want to use, and each Python process will only use a single GPU.

export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/train_sg_net.py --config-file "path/to/config/file.yaml"

Evaluation

You can test your model directly on single or multiple gpus. To evaluate relations, one needs to output "relation_scores_all" in the TSV_SAVE_SUBSET. Here are a few example command line for evaluating on 4 GPUS:

export NGPUS=4

python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_sg_net.py --config-file CONFIG_FILE_PATH

# vg IMP evaluation
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_sg_net.py --config-file sgg_configs/vg_vrd/rel_danfeiX_FPN50_imp.yaml

# vg MSDN evaluation
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_sg_net.py --config-file sgg_configs/vg_vrd/rel_danfeiX_FPN50_msdn.yaml

# vg neural motif evaluation
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_sg_net.py --config-file sgg_configs/vg_vrd/rel_danfeiX_FPN50_nm.yaml

# vg GRCNN evaluation
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_sg_net.py --config-file sgg_configs/vg_vrd/rel_danfeiX_FPN50_grcnn.yaml

# vg RelDN evaluation
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_sg_net.py --config-file sgg_configs/vg_vrd/rel_danfeiX_FPN50_reldn.yaml

# oi IMP evaluation
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_sg_net.py --config-file sgg_configs/oi_vrd/R152FPN_imp_bias_oi.yaml

# oi MSDN evaluation
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_sg_net.py --config-file sgg_configs/oi_vrd/R152FPN_msdn_bias_oi.yaml

# oi neural motif evaluation
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_sg_net.py --config-file sgg_configs/oi_vrd/R152FPN_motif_oi.yaml

# oi GRCNN evaluation
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_sg_net.py --config-file sgg_configs/oi_vrd/R152FPN_grcnn_oi.yaml

# oi RelDN evaluation
python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_sg_net.py --config-file sgg_configs/vrd/R152FPN_vrd_reldn.yaml

To evaluate in sgcls mode:

export NGPUS=4

python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_sg_net.py --config-file CONFIG_FILE_PATH MODEL.ROI_BOX_HEAD.FORCE_BOXES True MODEL.ROI_RELATION_HEAD.MODE "sgcls"

To evaluate in predcls mode:

export NGPUS=4

python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_sg_net.py --config-file CONFIG_FILE_PATH MODEL.ROI_RELATION_HEAD.MODE "predcls"

To evaluate with ground truth bbox and ground truth pairs:

export NGPUS=4

python -m torch.distributed.launch --nproc_per_node=$NGPUS tools/test_sg_net.py --config-file CONFIG_FILE_PATH MODEL.ROI_RELATION_HEAD.FORCE_RELATIONS True

Abstractions

For more information on some of the main abstractions in our implementation, see ABSTRACTIONS.md.

Adding your own dataset

This implementation adds support for TSV style datasets. But adding support for training on a new dataset can be done as follows:

from maskrcnn_benchmark.data.datasets.relation_tsv import RelationTSVDataset

class MyDataset(RelationTSVDataset):
    def __init__(self, yaml_file, extra_fields=(), transforms=None,
            is_load_label=True, **kwargs):

        super(MyDataset, self).__init__(yaml_file, extra_fields, transforms, is_load_label, **kwargs)

    def your_own_function(self, idx, call=False):
        # you can overwrite function or add your own functions this way
        pass

That's it. You can also add extra fields to the boxlist, such as segmentation masks (using structures.segmentation_mask.SegmentationMask), or even your own instance type.

For a full example of how the VGTSVDataset is implemented, check maskrcnn_benchmark/data/datasets/vg_tsv.py.

Once you have created your dataset, it needs to be added in a couple of places:

• maskrcnn_benchmark/data/datasets/__init__.py: add it to __all__
• maskrcnn_benchmark/data/datasets/utils/config_args.py: add it's name as an option to tsv_dataset_name

Adding your own evaluation

To enable your dataset for testing, add a corresponding if statement in maskrcnn_benchmark/data/datasets/evaluation/__init__.py:

if isinstance(dataset, datasets.MyDataset):
        return your_evaluation(**args)

VinVL Feature extraction

The output feature will be encoded as base64

# extract vision features with VinVL object-attribute detection model
# pretrained models at https://penzhanwu2.blob.core.windows.net/sgg/sgg_benchmark/vinvl_model_zoo/vinvl_vg_x152c4.pth
# the associated labelmap at https://penzhanwu2.blob.core.windows.net/sgg/sgg_benchmark/vinvl_model_zoo/VG-SGG-dicts-vgoi6-clipped.json
python tools/test_sg_net.py --config-file sgg_configs/vgattr/vinvl_x152c4.yaml TEST.IMS_PER_BATCH 2 MODEL.WEIGHT models/vinvl/vinvl_vg_x152c4.pth MODEL.ROI_HEADS.NMS_FILTER 1 MODEL.ROI_HEADS.SCORE_THRESH 0.2 DATA_DIR "../maskrcnn-benchmark-1/datasets1" TEST.IGNORE_BOX_REGRESSION True MODEL.ATTRIBUTE_ON True

To extract relation features (union bounding box's feature), in yaml file, set TEST.OUTPUT_RELATION_FEATURE to True, add relation_feature in TEST.TSV_SAVE_SUBSET.

To extract bounding box features, in yaml file, set TEST.OUTPUT_FEATURE to True, add feature in TEST.TSV_SAVE_SUBSET.

Troubleshooting

If you have issues running or compiling this code, we have compiled a list of common issues in TROUBLESHOOTING.md. If your issue is not present there, please feel free to open a new issue.

Citations

Please consider citing this project in your publications if it helps your research. The following is a BibTeX reference. The BibTeX entry requires the url LaTeX package.

@misc{han2021image,
      title={Image Scene Graph Generation (SGG) Benchmark},
      author={Xiaotian Han and Jianwei Yang and Houdong Hu and Lei Zhang and Jianfeng Gao and Pengchuan Zhang},
      year={2021},
      eprint={2107.12604},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

License

maskrcnn-benchmark is released under the MIT license. See LICENSE for additional details.

Acknowledgement