# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. """Centralized catalog of paths.""" import os def try_to_find(file, return_dir=False, search_path=["./DATASET", "./OUTPUT", "./data", "./MODEL"]): if not file: return file if file.startswith("catalog://"): return file DATASET_PATH = ["./"] if "DATASET" in os.environ: DATASET_PATH.append(os.environ["DATASET"]) DATASET_PATH += search_path for path in DATASET_PATH: if os.path.exists(os.path.join(path, file)): if return_dir: return path else: return os.path.join(path, file) print("Cannot find {} in {}".format(file, DATASET_PATH)) exit(1) class DatasetCatalog(object): DATASETS = { # pretrained grounding dataset # mixed vg and coco "mixed_train": { "coco_img_dir": "coco/train2014", "vg_img_dir": "gqa/images", "ann_file": "mdetr_annotations/final_mixed_train.json", }, "mixed_train_no_coco": { "coco_img_dir": "coco/train2014", "vg_img_dir": "gqa/images", "ann_file": "mdetr_annotations/final_mixed_train_no_coco.json", }, # flickr30k "flickr30k_train": { "img_folder": "flickr30k/flickr30k_images/train", "ann_file": "mdetr_annotations/final_flickr_separateGT_train.json", "is_train": True, }, "flickr30k_val": { "img_folder": "flickr30k/flickr30k_images/val", "ann_file": "mdetr_annotations/final_flickr_separateGT_val.json", "is_train": False, }, "flickr30k_test": { "img_folder": "flickr30k/flickr30k_images/test", "ann_file": "mdetr_annotations/final_flickr_separateGT_test.json", "is_train": False, }, # refcoco "refexp_all_val": { "img_dir": "coco/train2014", "ann_file": "mdetr_annotations/final_refexp_val.json", "is_train": False, }, "refcoco_train": { "img_dir": "coco/train2014", "ann_file": "mdetr_annotations/finetune_refcoco_train.json", "is_train": True, }, "refcoco_val": { "img_dir": "coco/train2014", "ann_file": "mdetr_annotations/finetune_refcoco_val.json", "is_train": False, }, "refcoco_real_val": { "img_dir": "coco/train2014", "ann_file": "mdetr_annotations/finetune_refcoco_val.json", "is_train": False, }, "refcoco_testA": { "img_dir": "coco/train2014", "ann_file": "mdetr_annotations/finetune_refcoco_testA.json", "is_train": False, }, "refcoco_testB": { "img_dir": "coco/train2014", "ann_file": "mdetr_annotations/finetune_refcoco_testB.json", "is_train": False, }, "refcoco+_train": { "img_dir": "coco/train2014", "ann_file": "mdetr_annotations/finetune_refcoco+_train.json", "is_train": True, }, "refcoco+_val": { "img_dir": "coco/train2014", "ann_file": "mdetr_annotations/finetune_refcoco+_val.json", "is_train": False, }, "refcoco+_testA": { "img_dir": "coco/train2014", "ann_file": "mdetr_annotations/finetune_refcoco+_testA.json", "is_train": False, }, "refcoco+_testB": { "img_dir": "coco/train2014", "ann_file": "mdetr_annotations/finetune_refcoco+_testB.json", "is_train": False, }, "refcocog_train": { "img_dir": "coco/train2014", "ann_file": "mdetr_annotations/finetune_refcocog_train.json", "is_train": True, }, "refcocog_val": { "img_dir": "coco/train2014", "ann_file": "mdetr_annotations/finetune_refcocog_val.json", "is_train": False, }, "refcocog_test": { "img_dir": "coco/train2014", "ann_file": "mdetr_annotations/finetune_refcocog_test_corrected.json", "is_train": False, }, # gqa "gqa_val": {"img_dir": "gqa/images", "ann_file": "mdetr_annotations/final_gqa_val.json", "is_train": False}, # phrasecut "phrasecut_train": { "img_dir": "gqa/images", "ann_file": "mdetr_annotations/finetune_phrasecut_train.json", "is_train": True, }, # caption "bing_caption_train": { "yaml_path": "BingData/predict_yaml", "yaml_name": "dreamstime_com_dyhead_objvg_e39", "yaml_name_no_coco": "dreamstime_com_Detection_Pretrain_NoCOCO_Packed125", "is_train": True, }, # od to grounding # coco tsv "coco_dt_train": { "dataset_file": "coco_dt", "yaml_path": "coco_tsv/coco_obj.yaml", "is_train": True, }, "COCO_odinw_train_8copy_dt_train": { "dataset_file": "coco_odinw_dt", "yaml_path": "coco_tsv/COCO_odinw_train_8copy.yaml", "is_train": True, }, "COCO_odinw_val_dt_train": { "dataset_file": "coco_odinw_dt", "yaml_path": "coco_tsv/COCO_odinw_val.yaml", "is_train": False, }, # lvis tsv "lvisv1_dt_train": { "dataset_file": "lvisv1_dt", "yaml_path": "coco_tsv/LVIS_v1_train.yaml", "is_train": True, }, "LVIS_odinw_train_8copy_dt_train": { "dataset_file": "coco_odinw_dt", "yaml_path": "coco_tsv/LVIS_odinw_train_8copy.yaml", "is_train": True, }, # object365 tsv "object365_dt_train": { "dataset_file": "object365_dt", "yaml_path": "Objects365/objects365_train_vgoiv6.cas2000.yaml", "is_train": True, }, "object365_odinw_2copy_dt_train": { "dataset_file": "object365_odinw_dt", "yaml_path": "Objects365/objects365_train_odinw.cas2000_2copy.yaml", "is_train": True, }, "objects365_odtsv_train": { "dataset_file": "objects365_odtsv", "yaml_path": "Objects365/train.cas2000.yaml", "is_train": True, }, "objects365_odtsv_val": { "dataset_file": "objects365_odtsv", "yaml_path": "Objects365/val.yaml", "is_train": False, }, # ImagetNet OD "imagenetod_train_odinw_2copy_dt": { "dataset_file": "imagenetod_odinw_dt", "yaml_path": "imagenet_od/imagenetod_train_odinw_2copy.yaml", "is_train": True, }, # OpenImage OD "oi_train_odinw_dt": { "dataset_file": "oi_odinw_dt", "yaml_path": "openimages_v5c/oi_train_odinw.cas.2000.yaml", "is_train": True, }, # vg tsv "vg_dt_train": { "dataset_file": "vg_dt", "yaml_path": "visualgenome/train_vgoi6_clipped.yaml", "is_train": True, }, "vg_odinw_clipped_8copy_dt_train": { "dataset_file": "vg_odinw_clipped_8copy_dt", "yaml_path": "visualgenome/train_odinw_clipped_8copy.yaml", "is_train": True, }, "vg_vgoi6_clipped_8copy_dt_train": { "dataset_file": "vg_vgoi6_clipped_8copy_dt", "yaml_path": "visualgenome/train_vgoi6_clipped_8copy.yaml", "is_train": True, }, # coco json "coco_grounding_train": { "img_dir": "coco/train2017", "ann_file": "coco/annotations/instances_train2017.json", "is_train": True, }, "lvis_grounding_train": {"img_dir": "coco", "ann_file": "coco/annotations/lvis_od_train.json"}, "lvis_evaluation_val": { "img_dir": "lvis/coco2017", "ann_file": "lvis/lvis_v1_minival_inserted_image_name.json", "is_train": False, }, "lvis_val": { "img_dir": "coco", "ann_file": "coco/annotations/lvis_od_val.json"}, # legacy detection dataset "hsd_v001": {"img_dir": "hsd/20170901_Detection_HeadShoulder.V001/RawImages", "ann_file": "hsd/HSD_V001.json"}, "hsd_hddb": {"img_dir": "hddb/Images", "ann_file": "hddb/HDDB.json"}, "opencoco_train": {"img_dir": "openimages/train", "ann_file": "openimages/opencoco_train.json"}, "opencoco_val": {"img_dir": "openimages/val", "ann_file": "openimages/opencoco_val.json"}, "opencoco_test": {"img_dir": "openimages/test", "ann_file": "openimages/opencoco_test.json"}, "openhuman_train": {"img_dir": "openimages/train", "ann_file": "openimages/openhuman_train.json"}, "openhuman_val": {"img_dir": "openimages/val", "ann_file": "openimages/openhuman_val.json"}, "openhuman_test": {"img_dir": "openimages/test", "ann_file": "openimages/openhuman_test.json"}, "opencrowd_train": {"img_dir": "openimages/train", "ann_file": "openimages/opencrowd_train.json"}, "opencrowd_val": {"img_dir": "openimages/val", "ann_file": "openimages/opencrowd_val.json"}, "opencrowd_test": {"img_dir": "openimages/test", "ann_file": "openimages/opencrowd_test.json"}, "opencar_train": {"img_dir": "openimages/train", "ann_file": "openimages/opencar_train.json"}, "opencar_val": {"img_dir": "openimages/val", "ann_file": "openimages/opencar_val.json"}, "opencar_test": {"img_dir": "openimages/test", "ann_file": "openimages/opencar_test.json"}, "openhumancar_train": {"img_dir": "openimages/train", "ann_file": "openimages/openhumancar_train.json"}, "openhumancar_val": {"img_dir": "openimages/val", "ann_file": "openimages/openhumancar_val.json"}, "openhuamncar_test": {"img_dir": "openimages/test", "ann_file": "openimages/openhumancar_test.json"}, "open500_train": { "img_dir": "openimages/train", "ann_file": "openimages/openimages_challenge_2019_train_bbox.json", }, "open500_val": { "img_dir": "openimages/val", "ann_file": "openimages/openimages_challenge_2019_val_bbox.json", }, "openproposal_test": { "img_dir": "openimages/test2019", "ann_file": "openimages/proposals_test.json", }, "object365_train": {"img_dir": "object365/train", "ann_file": "object365/objects365_train.json"}, "object365_val": {"img_dir": "object365/val", "ann_file": "object365/objects365_val.json"}, "lvis_train": {"img_dir": "coco", "ann_file": "coco/annotations/lvis_od_train.json"}, "lvis_val": {"img_dir": "coco", "ann_file": "coco/annotations/lvis_od_val.json"}, "image200_train": {"img_dir": "imagenet-od/Data/DET/train", "ann_file": "imagenet-od/im200_train.json"}, "image200_val": {"img_dir": "imagenet-od/Data/DET/val", "ann_file": "imagenet-od/im200_val.json"}, "coco_2017_train": {"img_dir": "coco/train2017", "ann_file": "coco/annotations/instances_train2017.json"}, "coco_2017_val": {"img_dir": "coco/val2017", "ann_file": "coco/annotations/instances_val2017.json"}, "coco_2017_test": {"img_dir": "coco/test2017", "ann_file": "coco/annotations/image_info_test-dev2017.json"}, "coco10_train": {"img_dir": "coco/train2017", "ann_file": "coco/annotations/instances_minitrain2017.json"}, "coco_2014_train": {"img_dir": "coco/train2014", "ann_file": "coco/annotations/instances_train2014.json"}, "coco_2014_val": {"img_dir": "coco/val2014", "ann_file": "coco/annotations/instances_val2014.json"}, "coco_2014_minival": {"img_dir": "coco/val2014", "ann_file": "coco/annotations/instances_minival2014.json"}, "coco_2014_valminusminival": { "img_dir": "coco/val2014", "ann_file": "coco/annotations/instances_valminusminival2014.json", }, "coco_2014_train_partial": { "img_dir": "coco/train2014", "ann_file": "coco/annotations/partial0.2_train2014.json", }, "coco_2014_valminusminival_partial": { "img_dir": "coco/val2014", "ann_file": "coco/annotations/partial0.2_valminusminival2014.json", }, "coco_2014_train_few100": {"img_dir": "coco/train2014", "ann_file": "coco/annotations/few100_train2014.json"}, "coco_2014_train_few300": {"img_dir": "coco/train2014", "ann_file": "coco/annotations/few300_train2014.json"}, "coco_human_2014_train": {"img_dir": "coco/train2014", "ann_file": "coco/annotations/humans_train2014.json"}, "coco_human_2014_minival": {"img_dir": "coco/val2014", "ann_file": "coco/annotations/humans_minival2014.json"}, "coco_human_2014_valminusminival": { "img_dir": "coco/val2014", "ann_file": "coco/annotations/humans_valminusminival2014.json", }, "coco_car_2014_train": {"img_dir": "coco/train2014", "ann_file": "coco/annotations/car_train2014.json"}, "coco_car_2014_minival": {"img_dir": "coco/val2014", "ann_file": "coco/annotations/car_minival2014.json"}, "coco_car_2014_valminusminival": { "img_dir": "coco/val2014", "ann_file": "coco/annotations/car_valminusminival2014.json", }, "coco_humancar_2014_train": { "img_dir": "coco/train2014", "ann_file": "coco/annotations/humancar_train2014.json", }, "coco_humancar_2014_minival": { "img_dir": "coco/val2014", "ann_file": "coco/annotations/humancar_minival2014.json", }, "coco_humancar_2014_valminusminival": { "img_dir": "coco/val2014", "ann_file": "coco/annotations/humancar_valminusminival2014.json", }, "coco_keypoint_2017_train": { "img_dir": "coco/train2017", "ann_file": "coco/annotations/person_keypoints_train2017.json", }, "coco_keypoint_2017_val": { "img_dir": "coco/val2017", "ann_file": "coco/annotations/person_keypoints_val2017.json", }, "coco_headshoulder_2017_train": { "img_dir": "coco/train2017", "ann_file": "coco/annotations/headshoulder_train2017.json", }, "coco_headshoulder_2017_val": { "img_dir": "coco/val2017", "ann_file": "coco/annotations/headshoulder_val2017.json", }, "coco_hskeypoint_2017_train": { "img_dir": "coco/train2017", "ann_file": "coco/annotations/person_hskeypoints_train2017.json", }, "coco_hskeypoint_2017_val": { "img_dir": "coco/val2017", "ann_file": "coco/annotations/person_hskeypoints_val2017.json", }, "voc_2007_train": {"data_dir": "voc/VOC2007", "split": "train"}, "voc_2007_train_cocostyle": { "img_dir": "voc/VOC2007/JPEGImages", "ann_file": "voc/VOC2007/Annotations/pascal_train2007.json", }, "voc_2007_val": {"data_dir": "voc/VOC2007", "split": "val"}, "voc_2007_val_cocostyle": { "img_dir": "voc/VOC2007/JPEGImages", "ann_file": "voc/VOC2007/Annotations/pascal_val2007.json", }, "voc_2007_test": {"data_dir": "voc/VOC2007", "split": "test"}, "voc_2007_test_cocostyle": { "img_dir": "voc/VOC2007/JPEGImages", "ann_file": "voc/VOC2007/Annotations/pascal_test2007.json", }, "voc_2012_train": {"data_dir": "voc/VOC2012", "split": "train"}, "voc_2012_train_cocostyle": { "img_dir": "voc/VOC2012/JPEGImages", "ann_file": "voc/VOC2012/Annotations/pascal_train2012.json", }, "voc_2012_val": {"data_dir": "voc/VOC2012", "split": "val"}, "voc_2012_val_cocostyle": { "img_dir": "voc/VOC2012/JPEGImages", "ann_file": "voc/VOC2012/Annotations/pascal_val2012.json", }, "voc_2012_test": { "data_dir": "voc/VOC2012", "split": "test" # PASCAL VOC2012 doesn't made the test annotations available, so there's no json annotation }, "cityscapes_fine_instanceonly_seg_train_cocostyle": { "img_dir": "cityscapes/images", "ann_file": "cityscapes/annotations/instancesonly_filtered_gtFine_train.json", }, "cityscapes_fine_instanceonly_seg_val_cocostyle": { "img_dir": "cityscapes/images", "ann_file": "cityscapes/annotations/instancesonly_filtered_gtFine_val.json", }, "cityscapes_fine_instanceonly_seg_test_cocostyle": { "img_dir": "cityscapes/images", "ann_file": "cityscapes/annotations/instancesonly_filtered_gtFine_test.json", }, "crowdhuman_train": {"img_dir": "CrowdHuman/Images", "ann_file": "CrowdHuman/crowdhuman_train.json"}, "crowdhuman_val": {"img_dir": "CrowdHuman/Images", "ann_file": "CrowdHuman/crowdhuman_val.json"}, "crowdhead_train": {"img_dir": "CrowdHuman/Images", "ann_file": "CrowdHuman/crowdhead_train.json"}, "crowdhead_val": {"img_dir": "CrowdHuman/Images", "ann_file": "CrowdHuman/crowdhead_val.json"}, "crowdfull_train": {"img_dir": "CrowdHuman/Images", "ann_file": "CrowdHuman/crowdfull_train.json"}, "crowdfull_val": {"img_dir": "CrowdHuman/Images", "ann_file": "CrowdHuman/crowdfull_val.json"}, "ternium_train": {"img_dir": "ternium/images", "ann_file": "ternium/train_annotation.json"}, "ternium_val": {"img_dir": "ternium/images", "ann_file": "ternium/val_annotation.json"}, "ternium_test": {"img_dir": "ternium/images", "ann_file": "ternium/test_annotation.json"}, "ternium_test_crop": {"img_dir": "ternium/test_motion_crop", "ann_file": "ternium/test_motion_crop.json"}, "ternium_train_aug": {"img_dir": "ternium/train_crop_aug", "ann_file": "ternium/train_crop_aug.json"}, "ternium_test_aug": {"img_dir": "ternium/test_crop_aug", "ann_file": "ternium/test_motion_crop_aug.json"}, "ternium_vh_train": { "img_dir": "ternium-vehicle/train_dataset_coco/images", "ann_file": "ternium-vehicle/train_dataset_coco/coco_annotation.json", }, "ternium_vh_val": { "img_dir": "ternium-vehicle/validation_dataset_coco/images", "ann_file": "ternium-vehicle/validation_dataset_coco/coco_annotation.json", }, "msra_traffic": {"img_dir": "msra-traffic/Images", "ann_file": "msra-traffic/annotation.json"}, "msra_traffic_car": {"img_dir": "msra-traffic/Images", "ann_file": "msra-traffic/car_annotation.json"}, "msra_traffic_humancar": { "img_dir": "msra-traffic/Images", "ann_file": "msra-traffic/humancar_annotation.json", }, "jigsaw_car_train": {"img_dir": "jigsaw", "ann_file": "jigsaw/train.json"}, "jigsaw_car_val": {"img_dir": "jigsaw", "ann_file": "jigsaw/val.json"}, "miotcd_train": {"img_dir": "MIO-TCD/MIO-TCD-Localization", "ann_file": "MIO-TCD/train.json"}, "miotcd_val": {"img_dir": "MIO-TCD/MIO-TCD-Localization", "ann_file": "MIO-TCD/val.json"}, "detrac_train": {"img_dir": "detrac/Insight-MVT_Annotation_Train", "ann_file": "detrac/train.json"}, "detrac_val": {"img_dir": "detrac/Insight-MVT_Annotation_Train", "ann_file": "detrac/val.json"}, "mrw": {"img_dir": "mrw/clips", "ann_file": "mrw/annotations.json"}, "mrw_bg": {"img_dir": "mrw/bg", "ann_file": "mrw/bg_annotations.json"}, "webmarket_bg": {"img_dir": "webmarket", "ann_file": "webmarket/bg_annotations.json"}, "mot17_train": {"img_dir": "mot/MOT17Det", "ann_file": "mot/MOT17Det/train.json"}, "egohands": {"img_dir": "egohands/images", "ann_file": "egohands/egohands.json"}, "hof": {"img_dir": "hof/images_original_size", "ann_file": "hof/train.json"}, "vlmhof": {"img_dir": "vlmhof/RGB", "ann_file": "vlmhof/train.json"}, "vgghands_train": {"img_dir": "vgghands/training_dataset", "ann_file": "vgghands/training.json"}, "vgghands_val": {"img_dir": "vgghands/validation_dataset", "ann_file": "vgghands/validation.json"}, "vgghands_test": {"img_dir": "vgghands/test_dataset", "ann_file": "vgghands/test.json"}, "od:coco_train": {"img_dir": "coco/train2017", "ann_file": "coco/annotations/od_train2017.json"}, "od:coco_val": {"img_dir": "coco/val2017", "ann_file": "coco/annotations/od_val2017.json"}, "od:lvis_train": {"img_dir": "coco", "ann_file": "coco/annotations/od_train-lvis.json"}, "od:lvis_val": {"img_dir": "coco", "ann_file": "coco/annotations/od_val-lvis.json"}, "od:o365_train": {"img_dir": "object365/train", "ann_file": "object365/od_train.json"}, "od:o365_val": {"img_dir": "object365/val", "ann_file": "object365/od_val.json"}, "od:oi500_train": { "img_dir": "openimages/train", "ann_file": "openimages/od_train2019.json", "paste_dir": "openimages/panoptic_train_challenge_2019", "paste_file": "openimages/panoptic_train2019.json", }, "od:oi500_val": { "img_dir": "openimages/val", "ann_file": "openimages/od_val2019.json", "paste_dir": "openimages/panoptic_val_challenge_2019", "paste_file": "openimages/panoptic_val2019.json", }, "od:im200_train": {"img_dir": "imagenet-od/Data/DET/train", "ann_file": "imagenet-od/train.json"}, "od:im200_val": {"img_dir": "imagenet-od/Data/DET/val", "ann_file": "imagenet-od/val.json"}, "cv:animal661_train": {"img_dir": "cvtasks/animal-661/images", "ann_file": "cvtasks/animal-661/train.json"}, "cv:animal661_test": {"img_dir": "cvtasks/animal-661/images", "ann_file": "cvtasks/animal-661/test.json"}, "cv:seeingai_train": {"img_dir": "cvtasks/SeeingAI/train.tsv", "ann_file": "cvtasks/SeeingAI/train.json"}, "cv:seeingai_test": {"img_dir": "cvtasks/SeeingAI/test.tsv", "ann_file": "cvtasks/SeeingAI/test.json"}, "cv:office_train": { "img_dir": "cvtasks/Ping-Office-Env/train.tsv", "ann_file": "cvtasks/Ping-Office-Env/train.json", }, "cv:office_test": { "img_dir": "cvtasks/Ping-Office-Env/test.tsv", "ann_file": "cvtasks/Ping-Office-Env/test.json", }, "cv:logo_train": {"img_dir": "cvtasks/Ping-Logo", "ann_file": "cvtasks/Ping-Logo/train.json"}, "cv:logo_test": {"img_dir": "cvtasks/Ping-Logo", "ann_file": "cvtasks/Ping-Logo/test.json"}, "cv:nba_train": {"img_dir": "cvtasks/Ping-NBA", "ann_file": "cvtasks/Ping-NBA/train.json"}, "cv:nba_test": {"img_dir": "cvtasks/Ping-NBA", "ann_file": "cvtasks/Ping-NBA/test.json"}, "cv:traffic_train": {"img_dir": "cvtasks/TrafficData/train.tsv", "ann_file": "cvtasks/TrafficData/train.json"}, "cv:traffic_test": {"img_dir": "cvtasks/TrafficData/test.tsv", "ann_file": "cvtasks/TrafficData/test.json"}, "cv:fashion5k_train": {"img_dir": "cvtasks/fashion5k", "ann_file": "cvtasks/fashion5k/train.json"}, "cv:fashion5k_test": {"img_dir": "cvtasks/fashion5k", "ann_file": "cvtasks/fashion5k/test.json"}, "cv:malaria_train": {"img_dir": "cvtasks/malaria", "ann_file": "cvtasks/malaria/train.json"}, "cv:malaria_test": {"img_dir": "cvtasks/malaria", "ann_file": "cvtasks/malaria/test.json"}, "cv:product_train": { "img_dir": "cvtasks/product_detection", "ann_file": "cvtasks/product_detection/train.json", }, "cv:product_test": {"img_dir": "cvtasks/product_detection", "ann_file": "cvtasks/product_detection/test.json"}, "vl:vg_train": {"yaml_file": "vlp/visualgenome/train_vgoi6_clipped.yaml"}, "vl:vg_test": {"yaml_file": "vlp/visualgenome/test_vgoi6_clipped.yaml"}, "imagenet_train": {"img_dir": "imagenet-tsv/train.tsv", "ann_file": None}, "imagenet_val": {"img_dir": "imagenet-tsv/val.tsv", "ann_file": None}, "paco_lvis_v1_train_grounding":{ "img_dir": "coco", "ann_file": "paco/paco_lvis_v1_train.json" }, "paco_lvis_v1_val":{ "img_dir": "coco", "ann_file": "paco/paco_lvis_v1_val.json" }, "paco_lvis_v1_test": { "img_dir": "coco", "ann_file": "paco/paco_lvis_v1_test.json" }, "omnilabel_val": {"img_dir": "omnilabel/", "ann_file": "omnilabel/dataset_all_val_v0.1.3.json"}, "omnilabel_val_coco": {"img_dir": "omnilabel/", "ann_file": "omnilabel/dataset_all_val_v0.1.3_coco.json"}, "omnilabel_val_o365": {"img_dir": "omnilabel/", "ann_file": "omnilabel/dataset_all_val_v0.1.3_object365.json"}, "omnilabel_val_oi_v5": {"img_dir": "omnilabel/", "ann_file": "omnilabel/dataset_all_val_v0.1.3_openimagesv5.json"}, "omnilabel_test": {"img_dir": "omnilabel/", "ann_file": "omnilabel/dataset_all_test_v0.1.3.json"}, } @staticmethod def set(name, info): DatasetCatalog.DATASETS.update({name: info}) @staticmethod def get(name): if name.endswith("_bg"): attrs = DatasetCatalog.DATASETS[name] data_dir = try_to_find(attrs["ann_file"], return_dir=True) args = dict( root=os.path.join(data_dir, attrs["img_dir"]), ann_file=os.path.join(data_dir, attrs["ann_file"]), ) return dict( factory="Background", args=args, ) else: if "bing" in name.split("_"): attrs = DatasetCatalog.DATASETS["bing_caption_train"] else: attrs = DatasetCatalog.DATASETS[name] # if "yaml_file" in attrs: # yaml_file = try_to_find(attrs["yaml_file"], return_dir=False) # args = dict(yaml_file=yaml_file) # return dict( # factory="VGTSVDataset", # args=args, # ) # elif attrs["img_dir"].endswith('tsv'): # try: # data_dir = try_to_find(attrs["img_dir"], return_dir=True) # if attrs["ann_file"] is None: # map_file = None # elif attrs["ann_file"].startswith("./"): # map_file = attrs["ann_file"] # else: # map_file = os.path.join(data_dir, attrs["ann_file"]) # except: # return None # args = dict( # tsv_file=os.path.join(data_dir, attrs["img_dir"]), # anno_file=map_file, # ) # return dict( # factory="TSVDataset", # args=args, # ) if "voc" in name and "split" in attrs: data_dir = try_to_find(attrs["data_dir"], return_dir=True) args = dict( data_dir=os.path.join(data_dir, attrs["data_dir"]), split=attrs["split"], ) return dict( factory="PascalVOCDataset", args=args, ) elif "omnilabel" in name: img_dir = try_to_find(attrs["img_dir"], return_dir=True) ann_dir = try_to_find(attrs["ann_file"], return_dir=True) args = dict( img_folder=os.path.join(img_dir, attrs["img_dir"]), ann_file=os.path.join(ann_dir, attrs["ann_file"]), ) return dict( factory="OmniLabelDataset", args=args, ) elif "mixed" in name: vg_img_dir = try_to_find(attrs["vg_img_dir"], return_dir=True) coco_img_dir = try_to_find(attrs["coco_img_dir"], return_dir=True) ann_file = try_to_find(attrs["ann_file"], return_dir=True) args = dict( img_folder_coco=os.path.join(coco_img_dir, attrs["coco_img_dir"]), img_folder_vg=os.path.join(vg_img_dir, attrs["vg_img_dir"]), ann_file=os.path.join(ann_file, attrs["ann_file"]), ) return dict( factory="MixedDataset", args=args, ) elif "flickr" in name: img_dir = try_to_find(attrs["img_folder"], return_dir=True) ann_dir = try_to_find(attrs["ann_file"], return_dir=True) args = dict( img_folder=os.path.join(img_dir, attrs["img_folder"]), ann_file=os.path.join(ann_dir, attrs["ann_file"]), is_train=attrs["is_train"], ) return dict( factory="FlickrDataset", args=args, ) elif "refexp" in name or "refcoco" in name: img_dir = try_to_find(attrs["img_dir"], return_dir=True) ann_dir = try_to_find(attrs["ann_file"], return_dir=True) args = dict( img_folder=os.path.join(img_dir, attrs["img_dir"]), ann_file=os.path.join(ann_dir, attrs["ann_file"]), ) return dict( factory="RefExpDataset", args=args, ) elif "gqa" in name: img_dir = try_to_find(attrs["img_dir"], return_dir=True) ann_dir = try_to_find(attrs["ann_file"], return_dir=True) args = dict( img_folder=os.path.join(img_dir, attrs["img_dir"]), ann_file=os.path.join(ann_dir, attrs["ann_file"]), ) return dict( factory="GQADataset", args=args, ) elif "phrasecut" in name: img_dir = try_to_find(attrs["img_dir"], return_dir=True) ann_dir = try_to_find(attrs["ann_file"], return_dir=True) args = dict( img_folder=os.path.join(img_dir, attrs["img_dir"]), ann_file=os.path.join(ann_dir, attrs["ann_file"]), ) return dict( factory="PhrasecutDetection", args=args, ) elif "_caption" in name: yaml_path = try_to_find(attrs["yaml_path"], return_dir=True) if "no_coco" in name: yaml_name = attrs["yaml_name_no_coco"] else: yaml_name = attrs["yaml_name"] yaml_file_name = "{}.{}.yaml".format(yaml_name, name.split("_")[2]) args = dict(yaml_file=os.path.join(yaml_path, attrs["yaml_path"], yaml_file_name)) return dict( factory="CaptionTSV", args=args, ) elif "inferencecap" in name: yaml_file_name = try_to_find(attrs["yaml_path"]) args = dict(yaml_file=yaml_file_name) return dict( factory="CaptionTSV", args=args, ) elif "pseudo_data" in name: args = dict(yaml_file=try_to_find(attrs["yaml_path"])) return dict( factory="PseudoData", args=args, ) elif "_dt" in name: dataset_file = attrs["dataset_file"] yaml_path = try_to_find(attrs["yaml_path"], return_dir=True) args = dict( name=dataset_file, yaml_file=os.path.join(yaml_path, attrs["yaml_path"]), ) return dict( factory="CocoDetectionTSV", args=args, ) elif "_odtsv" in name: dataset_file = attrs["dataset_file"] yaml_path = try_to_find(attrs["yaml_path"], return_dir=True) args = dict( name=dataset_file, yaml_file=os.path.join(yaml_path, attrs["yaml_path"]), ) return dict( factory="ODTSVDataset", args=args, ) elif "_grounding" in name: img_dir = try_to_find(attrs["img_dir"], return_dir=True) ann_dir = try_to_find(attrs["ann_file"], return_dir=True) args = dict( img_folder=os.path.join(img_dir, attrs["img_dir"]), ann_file=os.path.join(ann_dir, attrs["ann_file"]), ) return dict( factory="CocoGrounding", args=args, ) elif "lvis_evaluation" in name: img_dir = try_to_find(attrs["img_dir"], return_dir=True) ann_dir = try_to_find(attrs["ann_file"], return_dir=True) args = dict( img_folder=os.path.join(img_dir, attrs["img_dir"]), ann_file=os.path.join(ann_dir, attrs["ann_file"]), ) return dict( factory="LvisDetection", args=args, ) elif "paco" in name: img_dir = try_to_find(attrs["img_dir"], return_dir=True) ann_dir = try_to_find(attrs["ann_file"], return_dir=True) args = dict( img_folder=os.path.join(img_dir, attrs["img_dir"]), ann_file=os.path.join(ann_dir, attrs["ann_file"]), ) return dict( factory="PacoDetection", args=args, ) else: ann_dir = try_to_find(attrs["ann_file"], return_dir=True) img_dir = try_to_find(attrs["img_dir"], return_dir=True) args = dict( root=os.path.join(img_dir, attrs["img_dir"]), ann_file=os.path.join(ann_dir, attrs["ann_file"]), ) for k, v in attrs.items(): args.update({k: os.path.join(ann_dir, v)}) return dict( factory="COCODataset", args=args, ) raise RuntimeError("Dataset not available: {}".format(name)) class ModelCatalog(object): S3_C2_DETECTRON_URL = "https://dl.fbaipublicfiles.com/detectron" C2_IMAGENET_MODELS = { "MSRA/R-50": "ImageNetPretrained/MSRA/R-50.pkl", "MSRA/R-50-GN": "ImageNetPretrained/47261647/R-50-GN.pkl", "MSRA/R-101": "ImageNetPretrained/MSRA/R-101.pkl", "MSRA/R-101-GN": "ImageNetPretrained/47592356/R-101-GN.pkl", "FAIR/20171220/X-101-32x8d": "ImageNetPretrained/20171220/X-101-32x8d.pkl", "FAIR/20171220/X-101-64x4d": "ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl", } C2_DETECTRON_SUFFIX = "output/train/coco_2014_train%3Acoco_2014_valminusminival/generalized_rcnn/model_final.pkl" C2_DETECTRON_MODELS = { "35857197/e2e_faster_rcnn_R-50-C4_1x": "01_33_49.iAX0mXvW", "35857345/e2e_faster_rcnn_R-50-FPN_1x": "01_36_30.cUF7QR7I", "35857890/e2e_faster_rcnn_R-101-FPN_1x": "01_38_50.sNxI7sX7", "36761737/e2e_faster_rcnn_X-101-32x8d-FPN_1x": "06_31_39.5MIHi1fZ", "35858791/e2e_mask_rcnn_R-50-C4_1x": "01_45_57.ZgkA7hPB", "35858933/e2e_mask_rcnn_R-50-FPN_1x": "01_48_14.DzEQe4wC", "35861795/e2e_mask_rcnn_R-101-FPN_1x": "02_31_37.KqyEK4tT", "36761843/e2e_mask_rcnn_X-101-32x8d-FPN_1x": "06_35_59.RZotkLKI", } @staticmethod def get(name): if name.startswith("Caffe2Detectron/COCO"): return ModelCatalog.get_c2_detectron_12_2017_baselines(name) if name.startswith("ImageNetPretrained"): return ModelCatalog.get_c2_imagenet_pretrained(name) raise RuntimeError("model not present in the catalog {}".format(name)) @staticmethod def get_c2_imagenet_pretrained(name): prefix = ModelCatalog.S3_C2_DETECTRON_URL name = name[len("ImageNetPretrained/") :] name = ModelCatalog.C2_IMAGENET_MODELS[name] url = "/".join([prefix, name]) return url @staticmethod def get_c2_detectron_12_2017_baselines(name): # Detectron C2 models are stored following the structure # prefix//2012_2017_baselines/.yaml./suffix # we use as identifiers in the catalog Caffe2Detectron/COCO// prefix = ModelCatalog.S3_C2_DETECTRON_URL suffix = ModelCatalog.C2_DETECTRON_SUFFIX # remove identification prefix name = name[len("Caffe2Detectron/COCO/") :] # split in and model_id, model_name = name.split("/") # parsing to make it match the url address from the Caffe2 models model_name = "{}.yaml".format(model_name) signature = ModelCatalog.C2_DETECTRON_MODELS[name] unique_name = ".".join([model_name, signature]) url = "/".join([prefix, model_id, "12_2017_baselines", unique_name, suffix]) return url