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| # -*- coding: utf-8 -*- | |
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| """ | |
| This file registers pre-defined datasets at hard-coded paths, and their metadata. | |
| We hard-code metadata for common datasets. This will enable: | |
| 1. Consistency check when loading the datasets | |
| 2. Use models on these standard datasets directly and run demos, | |
| without having to download the dataset annotations | |
| We hard-code some paths to the dataset that's assumed to | |
| exist in "./datasets/". | |
| Users SHOULD NOT use this file to create new dataset / metadata for new dataset. | |
| To add new dataset, refer to the tutorial "docs/DATASETS.md". | |
| """ | |
| import os | |
| from detectron2.data import DatasetCatalog, MetadataCatalog | |
| from .builtin_meta import ADE20K_SEM_SEG_CATEGORIES, _get_builtin_metadata | |
| from .cityscapes import load_cityscapes_instances, load_cityscapes_semantic | |
| from .cityscapes_panoptic import register_all_cityscapes_panoptic | |
| from .coco import load_sem_seg, register_coco_instances | |
| from .coco_panoptic import register_coco_panoptic, register_coco_panoptic_separated | |
| from .lvis import get_lvis_instances_meta, register_lvis_instances | |
| from .pascal_voc import register_pascal_voc | |
| # ==== Predefined datasets and splits for COCO ========== | |
| _PREDEFINED_SPLITS_COCO = {} | |
| _PREDEFINED_SPLITS_COCO["coco"] = { | |
| "coco_2014_train": ("coco/train2014", "coco/annotations/instances_train2014.json"), | |
| "coco_2014_val": ("coco/val2014", "coco/annotations/instances_val2014.json"), | |
| "coco_2014_minival": ("coco/val2014", "coco/annotations/instances_minival2014.json"), | |
| "coco_2014_valminusminival": ( | |
| "coco/val2014", | |
| "coco/annotations/instances_valminusminival2014.json", | |
| ), | |
| "coco_2017_train": ("coco/train2017", "coco/annotations/instances_train2017.json"), | |
| "coco_2017_val": ("coco/val2017", "coco/annotations/instances_val2017.json"), | |
| "coco_2017_test": ("coco/test2017", "coco/annotations/image_info_test2017.json"), | |
| "coco_2017_test-dev": ("coco/test2017", "coco/annotations/image_info_test-dev2017.json"), | |
| "coco_2017_val_100": ("coco/val2017", "coco/annotations/instances_val2017_100.json"), | |
| } | |
| _PREDEFINED_SPLITS_COCO["coco_person"] = { | |
| "keypoints_coco_2014_train": ( | |
| "coco/train2014", | |
| "coco/annotations/person_keypoints_train2014.json", | |
| ), | |
| "keypoints_coco_2014_val": ("coco/val2014", "coco/annotations/person_keypoints_val2014.json"), | |
| "keypoints_coco_2014_minival": ( | |
| "coco/val2014", | |
| "coco/annotations/person_keypoints_minival2014.json", | |
| ), | |
| "keypoints_coco_2014_valminusminival": ( | |
| "coco/val2014", | |
| "coco/annotations/person_keypoints_valminusminival2014.json", | |
| ), | |
| "keypoints_coco_2017_train": ( | |
| "coco/train2017", | |
| "coco/annotations/person_keypoints_train2017.json", | |
| ), | |
| "keypoints_coco_2017_val": ("coco/val2017", "coco/annotations/person_keypoints_val2017.json"), | |
| "keypoints_coco_2017_val_100": ( | |
| "coco/val2017", | |
| "coco/annotations/person_keypoints_val2017_100.json", | |
| ), | |
| } | |
| _PREDEFINED_SPLITS_COCO_PANOPTIC = { | |
| "coco_2017_train_panoptic": ( | |
| # This is the original panoptic annotation directory | |
| "coco/panoptic_train2017", | |
| "coco/annotations/panoptic_train2017.json", | |
| # This directory contains semantic annotations that are | |
| # converted from panoptic annotations. | |
| # It is used by PanopticFPN. | |
| # You can use the script at detectron2/datasets/prepare_panoptic_fpn.py | |
| # to create these directories. | |
| "coco/panoptic_stuff_train2017", | |
| ), | |
| "coco_2017_val_panoptic": ( | |
| "coco/panoptic_val2017", | |
| "coco/annotations/panoptic_val2017.json", | |
| "coco/panoptic_stuff_val2017", | |
| ), | |
| "coco_2017_val_100_panoptic": ( | |
| "coco/panoptic_val2017_100", | |
| "coco/annotations/panoptic_val2017_100.json", | |
| "coco/panoptic_stuff_val2017_100", | |
| ), | |
| } | |
| def register_all_coco(root): | |
| for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_COCO.items(): | |
| for key, (image_root, json_file) in splits_per_dataset.items(): | |
| # Assume pre-defined datasets live in `./datasets`. | |
| register_coco_instances( | |
| key, | |
| _get_builtin_metadata(dataset_name), | |
| os.path.join(root, json_file) if "://" not in json_file else json_file, | |
| os.path.join(root, image_root), | |
| ) | |
| for ( | |
| prefix, | |
| (panoptic_root, panoptic_json, semantic_root), | |
| ) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items(): | |
| prefix_instances = prefix[: -len("_panoptic")] | |
| instances_meta = MetadataCatalog.get(prefix_instances) | |
| image_root, instances_json = instances_meta.image_root, instances_meta.json_file | |
| # The "separated" version of COCO panoptic segmentation dataset, | |
| # e.g. used by Panoptic FPN | |
| register_coco_panoptic_separated( | |
| prefix, | |
| _get_builtin_metadata("coco_panoptic_separated"), | |
| image_root, | |
| os.path.join(root, panoptic_root), | |
| os.path.join(root, panoptic_json), | |
| os.path.join(root, semantic_root), | |
| instances_json, | |
| ) | |
| # The "standard" version of COCO panoptic segmentation dataset, | |
| # e.g. used by Panoptic-DeepLab | |
| register_coco_panoptic( | |
| prefix, | |
| _get_builtin_metadata("coco_panoptic_standard"), | |
| image_root, | |
| os.path.join(root, panoptic_root), | |
| os.path.join(root, panoptic_json), | |
| instances_json, | |
| ) | |
| # ==== Predefined datasets and splits for LVIS ========== | |
| _PREDEFINED_SPLITS_LVIS = { | |
| "lvis_v1": { | |
| "lvis_v1_train": ("coco/", "lvis/lvis_v1_train.json"), | |
| "lvis_v1_val": ("coco/", "lvis/lvis_v1_val.json"), | |
| "lvis_v1_test_dev": ("coco/", "lvis/lvis_v1_image_info_test_dev.json"), | |
| "lvis_v1_test_challenge": ("coco/", "lvis/lvis_v1_image_info_test_challenge.json"), | |
| }, | |
| "lvis_v0.5": { | |
| "lvis_v0.5_train": ("coco/", "lvis/lvis_v0.5_train.json"), | |
| "lvis_v0.5_val": ("coco/", "lvis/lvis_v0.5_val.json"), | |
| "lvis_v0.5_val_rand_100": ("coco/", "lvis/lvis_v0.5_val_rand_100.json"), | |
| "lvis_v0.5_test": ("coco/", "lvis/lvis_v0.5_image_info_test.json"), | |
| }, | |
| "lvis_v0.5_cocofied": { | |
| "lvis_v0.5_train_cocofied": ("coco/", "lvis/lvis_v0.5_train_cocofied.json"), | |
| "lvis_v0.5_val_cocofied": ("coco/", "lvis/lvis_v0.5_val_cocofied.json"), | |
| }, | |
| } | |
| def register_all_lvis(root): | |
| for dataset_name, splits_per_dataset in _PREDEFINED_SPLITS_LVIS.items(): | |
| for key, (image_root, json_file) in splits_per_dataset.items(): | |
| register_lvis_instances( | |
| key, | |
| get_lvis_instances_meta(dataset_name), | |
| os.path.join(root, json_file) if "://" not in json_file else json_file, | |
| os.path.join(root, image_root), | |
| ) | |
| # ==== Predefined splits for raw cityscapes images =========== | |
| _RAW_CITYSCAPES_SPLITS = { | |
| "cityscapes_fine_{task}_train": ("cityscapes/leftImg8bit/train/", "cityscapes/gtFine/train/"), | |
| "cityscapes_fine_{task}_val": ("cityscapes/leftImg8bit/val/", "cityscapes/gtFine/val/"), | |
| "cityscapes_fine_{task}_test": ("cityscapes/leftImg8bit/test/", "cityscapes/gtFine/test/"), | |
| } | |
| def register_all_cityscapes(root): | |
| for key, (image_dir, gt_dir) in _RAW_CITYSCAPES_SPLITS.items(): | |
| meta = _get_builtin_metadata("cityscapes") | |
| image_dir = os.path.join(root, image_dir) | |
| gt_dir = os.path.join(root, gt_dir) | |
| inst_key = key.format(task="instance_seg") | |
| DatasetCatalog.register( | |
| inst_key, | |
| lambda x=image_dir, y=gt_dir: load_cityscapes_instances( | |
| x, y, from_json=True, to_polygons=True | |
| ), | |
| ) | |
| MetadataCatalog.get(inst_key).set( | |
| image_dir=image_dir, gt_dir=gt_dir, evaluator_type="cityscapes_instance", **meta | |
| ) | |
| sem_key = key.format(task="sem_seg") | |
| DatasetCatalog.register( | |
| sem_key, lambda x=image_dir, y=gt_dir: load_cityscapes_semantic(x, y) | |
| ) | |
| MetadataCatalog.get(sem_key).set( | |
| image_dir=image_dir, | |
| gt_dir=gt_dir, | |
| evaluator_type="cityscapes_sem_seg", | |
| ignore_label=255, | |
| **meta, | |
| ) | |
| # ==== Predefined splits for PASCAL VOC =========== | |
| def register_all_pascal_voc(root): | |
| SPLITS = [ | |
| ("voc_2007_trainval", "VOC2007", "trainval"), | |
| ("voc_2007_train", "VOC2007", "train"), | |
| ("voc_2007_val", "VOC2007", "val"), | |
| ("voc_2007_test", "VOC2007", "test"), | |
| ("voc_2012_trainval", "VOC2012", "trainval"), | |
| ("voc_2012_train", "VOC2012", "train"), | |
| ("voc_2012_val", "VOC2012", "val"), | |
| ] | |
| for name, dirname, split in SPLITS: | |
| year = 2007 if "2007" in name else 2012 | |
| register_pascal_voc(name, os.path.join(root, dirname), split, year) | |
| MetadataCatalog.get(name).evaluator_type = "pascal_voc" | |
| def register_all_ade20k(root): | |
| root = os.path.join(root, "ADEChallengeData2016") | |
| for name, dirname in [("train", "training"), ("val", "validation")]: | |
| image_dir = os.path.join(root, "images", dirname) | |
| gt_dir = os.path.join(root, "annotations_detectron2", dirname) | |
| name = f"ade20k_sem_seg_{name}" | |
| DatasetCatalog.register( | |
| name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="png", image_ext="jpg") | |
| ) | |
| MetadataCatalog.get(name).set( | |
| stuff_classes=ADE20K_SEM_SEG_CATEGORIES[:], | |
| image_root=image_dir, | |
| sem_seg_root=gt_dir, | |
| evaluator_type="sem_seg", | |
| ignore_label=255, | |
| ) | |
| # True for open source; | |
| # Internally at fb, we register them elsewhere | |
| if __name__.endswith(".builtin"): | |
| # Assume pre-defined datasets live in `./datasets`. | |
| _root = os.path.expanduser(os.getenv("DETECTRON2_DATASETS", "datasets")) | |
| register_all_coco(_root) | |
| register_all_lvis(_root) | |
| register_all_cityscapes(_root) | |
| register_all_cityscapes_panoptic(_root) | |
| register_all_pascal_voc(_root) | |
| register_all_ade20k(_root) | |