update citation & fix broken url
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Xiaotian Han
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README.md
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README.md
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@ -215,12 +215,13 @@ free to open a new issue.
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## Citations
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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.
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```
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@misc{han2021sgbenchmark,
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author = {Xiaotian Han and Jianwei Yang and Houdong Hu and Lei Zhang and Pengchuan Zhang},
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title = {{Scene Graph Benchmark}},
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year = {2021},
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howpublished = {\url{https://github.com/microsoft/scene_graph_benchmark}},
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note = {Accessed: [Insert date here]}
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@misc{han2021image,
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title={Image Scene Graph Generation (SGG) Benchmark},
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author={Xiaotian Han and Jianwei Yang and Houdong Hu and Lei Zhang and Jianfeng Gao and Pengchuan Zhang},
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year={2021},
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eprint={2107.12604},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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@ -10,13 +10,13 @@ All the following models are inferenced using unconstraint method, the detection
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model | recall@50 | wmAP(Triplet) | mAP(Triplet) | wmAP(Phrase) | mAP(Phrase) | Triplet proposal recall | Phrase proposal recall | model | config
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-----------|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:
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IMP, no bias | 71.64 | 30.56 | 36.47 | 32.90 | 40.61 | 72.57 | 75.87 | [link](https://penzhanwu2.blob.core.windows.net/phillytools/data/maskrcnn/pretrained_model/sgg_model_zoo/oi_R152_imp_nobias.pth) | [link](sgg_configs/oi_vrd/R152FPN_imp_nobias_oi.yaml)
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IMP, bias | 71.81 | 30.88 | 45.97 | 33.25 | 50.42 | 72.81 | 76.04 | [link](https://penzhanwu2.blob.core.windows.net/phillytools/data/maskrcnn/pretrained_model/sgg_model_zoo/oi_R152_imp_bias.pth) | [link](sgg_configs/oi_vrd/R152FPN_imp_bias_oi.yaml)
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MSDN, no bias | 71.76 | 30.40 | 36.76 | 32.81 | 40.89 | 72.54 | 75.85 | [link](https://penzhanwu2.blob.core.windows.net/phillytools/data/maskrcnn/pretrained_model/sgg_model_zoo/oi_R152_msdn_nobias.pth) | [link](sgg_configs/oi_vrd/R152FPN_msdn_nobias_oi.yaml)
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MSDN, bias | 71.48 | 30.22 | 34.49 | 32.58 | 38.71 | 72.45 | 75.62 | [link](https://penzhanwu2.blob.core.windows.net/phillytools/data/maskrcnn/pretrained_model/sgg_model_zoo/oi_R152_msdn_bias.pth) | [link](sgg_configs/oi_vrd/R152FPN_msdn_bias_oi.yaml)
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Neural Motif, bias | 72.54 | 29.35 | 29.26 | 33.10 | 35.02 | 73.64 | 78.70 | [link](https://penzhanwu2.blob.core.windows.net/phillytools/data/maskrcnn/pretrained_model/sgg_model_zoo/oi_R152_nm.pth) | [link](sgg_configs/oi_vrd/R152FPN_motif_oi.yaml)
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GRCNN, bias | 74.17 | 34.73 | 39.56 | 37.04 | 43.63 | 74.11 | 77.32 | [link](https://penzhanwu2.blob.core.windows.net/phillytools/data/maskrcnn/pretrained_model/sgg_model_zoo/oi_R152_grcnn.pth) | [link](sgg_configs/oi_vrd/R152FPN_grcnn_oi.yaml)
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RelDN | 75.40 | 40.85 | 44.24 | 49.16 | 50.60 | 78.74 | 90.39 | [link](https://penzhanwu2.blob.core.windows.net/phillytools/data/maskrcnn/pretrained_model/sgg_model_zoo/oi_R152_reldn.pth) | [link](sgg_configs/oi_vrd/R152FPN_reldn_oi.yaml)
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IMP, no bias | 71.64 | 30.56 | 36.47 | 32.90 | 40.61 | 72.57 | 75.87 | [link](https://penzhanwu2.blob.core.windows.net/sgg/sgg_benchmark/sgg_model_zoo/oi_R152_imp_nobias.pth) | [link](sgg_configs/oi_vrd/R152FPN_imp_nobias_oi.yaml)
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IMP, bias | 71.81 | 30.88 | 45.97 | 33.25 | 50.42 | 72.81 | 76.04 | [link](https://penzhanwu2.blob.core.windows.net/sgg/sgg_benchmark/sgg_model_zoo/oi_R152_imp_bias.pth) | [link](sgg_configs/oi_vrd/R152FPN_imp_bias_oi.yaml)
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MSDN, no bias | 71.76 | 30.40 | 36.76 | 32.81 | 40.89 | 72.54 | 75.85 | [link](https://penzhanwu2.blob.core.windows.net/sgg/sgg_benchmark/sgg_model_zoo/oi_R152_msdn_nobias.pth) | [link](sgg_configs/oi_vrd/R152FPN_msdn_nobias_oi.yaml)
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MSDN, bias | 71.48 | 30.22 | 34.49 | 32.58 | 38.71 | 72.45 | 75.62 | [link](https://penzhanwu2.blob.core.windows.net/sgg/sgg_benchmark/sgg_model_zoo/oi_R152_msdn_bias.pth) | [link](sgg_configs/oi_vrd/R152FPN_msdn_bias_oi.yaml)
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Neural Motif, bias | 72.54 | 29.35 | 29.26 | 33.10 | 35.02 | 73.64 | 78.70 | [link](https://penzhanwu2.blob.core.windows.net/sgg/sgg_benchmark/sgg_model_zoo/oi_R152_nm.pth) | [link](sgg_configs/oi_vrd/R152FPN_motif_oi.yaml)
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GRCNN, bias | 74.17 | 34.73 | 39.56 | 37.04 | 43.63 | 74.11 | 77.32 | [link](https://penzhanwu2.blob.core.windows.net/sgg/sgg_benchmark/sgg_model_zoo/oi_R152_grcnn.pth) | [link](sgg_configs/oi_vrd/R152FPN_grcnn_oi.yaml)
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RelDN | 75.40 | 40.85 | 44.24 | 49.16 | 50.60 | 78.74 | 90.39 | [link](https://penzhanwu2.blob.core.windows.net/sgg/sgg_benchmark/sgg_model_zoo/oi_R152_reldn.pth) | [link](sgg_configs/oi_vrd/R152FPN_reldn_oi.yaml)
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### Visual Genome
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@ -113,6 +113,44 @@ def train(cfg, local_rank, distributed):
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return model
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def run_test(cfg, model, distributed):
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if distributed:
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model = model.module
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torch.cuda.empty_cache() # TODO check if it helps
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iou_types = ("bbox",)
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if cfg.MODEL.MASK_ON:
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iou_types = iou_types + ("segm",)
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if cfg.MODEL.KEYPOINT_ON:
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iou_types = iou_types + ("keypoints",)
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output_folders = [None] * len(cfg.DATASETS.TEST)
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dataset_names = cfg.DATASETS.TEST
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if cfg.OUTPUT_DIR:
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for idx, dataset_name in enumerate(dataset_names):
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output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
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mkdir(output_folder)
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output_folders[idx] = output_folder
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data_loaders_val = make_data_loader(cfg, is_train=False, is_distributed=distributed)
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labelmap_file = config_dataset_file(cfg.DATA_DIR, cfg.DATASETS.LABELMAP_FILE)
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for output_folder, dataset_name, data_loader_val in zip(output_folders, dataset_names, data_loaders_val):
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inference(
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model,
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cfg,
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data_loader_val,
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dataset_name=dataset_name,
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iou_types=iou_types,
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box_only=False if cfg.MODEL.RETINANET_ON else cfg.MODEL.RPN_ONLY,
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bbox_aug=cfg.TEST.BBOX_AUG.ENABLED,
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device=cfg.MODEL.DEVICE,
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expected_results=cfg.TEST.EXPECTED_RESULTS,
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expected_results_sigma_tol=cfg.TEST.EXPECTED_RESULTS_SIGMA_TOL,
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output_folder=output_folder,
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skip_performance_eval=cfg.TEST.SKIP_PERFORMANCE_EVAL,
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labelmap_file=labelmap_file,
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save_predictions=cfg.TEST.SAVE_PREDICTIONS,
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)
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synchronize()
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def main():
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parser = argparse.ArgumentParser(description="PyTorch Object Detection Training")
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parser.add_argument(
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