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# Copyright (c) Facebook, Inc. and its affiliates. | |
import logging | |
import os | |
from fvcore.common.timer import Timer | |
from detectron2.structures import BoxMode | |
from fvcore.common.file_io import PathManager | |
from detectron2.data import DatasetCatalog, MetadataCatalog | |
from detectron2.data.datasets.lvis import get_lvis_instances_meta | |
logger = logging.getLogger(__name__) | |
__all__ = ["custom_load_lvis_json", "custom_register_lvis_instances"] | |
def custom_register_lvis_instances(name, metadata, json_file, image_root): | |
""" | |
""" | |
DatasetCatalog.register(name, lambda: custom_load_lvis_json( | |
json_file, image_root, name)) | |
MetadataCatalog.get(name).set( | |
json_file=json_file, image_root=image_root, | |
evaluator_type="lvis", **metadata | |
) | |
def custom_load_lvis_json(json_file, image_root, dataset_name=None): | |
''' | |
Modifications: | |
use `file_name` | |
convert neg_category_ids | |
add pos_category_ids | |
''' | |
from lvis import LVIS | |
json_file = PathManager.get_local_path(json_file) | |
timer = Timer() | |
lvis_api = LVIS(json_file) | |
if timer.seconds() > 1: | |
logger.info("Loading {} takes {:.2f} seconds.".format( | |
json_file, timer.seconds())) | |
catid2contid = {x['id']: i for i, x in enumerate( | |
sorted(lvis_api.dataset['categories'], key=lambda x: x['id']))} | |
if len(lvis_api.dataset['categories']) == 1203: | |
for x in lvis_api.dataset['categories']: | |
assert catid2contid[x['id']] == x['id'] - 1 | |
img_ids = sorted(lvis_api.imgs.keys()) | |
imgs = lvis_api.load_imgs(img_ids) | |
anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids] | |
ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] | |
assert len(set(ann_ids)) == len(ann_ids), \ | |
"Annotation ids in '{}' are not unique".format(json_file) | |
imgs_anns = list(zip(imgs, anns)) | |
logger.info("Loaded {} images in the LVIS v1 format from {}".format( | |
len(imgs_anns), json_file)) | |
dataset_dicts = [] | |
for (img_dict, anno_dict_list) in imgs_anns: | |
record = {} | |
if "file_name" in img_dict: | |
file_name = img_dict["file_name"] | |
if img_dict["file_name"].startswith("COCO"): | |
file_name = file_name[-16:] | |
record["file_name"] = os.path.join(image_root, file_name) | |
elif 'coco_url' in img_dict: | |
# e.g., http://images.cocodataset.org/train2017/000000391895.jpg | |
file_name = img_dict["coco_url"][30:] | |
record["file_name"] = os.path.join(image_root, file_name) | |
elif 'tar_index' in img_dict: | |
record['tar_index'] = img_dict['tar_index'] | |
record["height"] = img_dict["height"] | |
record["width"] = img_dict["width"] | |
record["not_exhaustive_category_ids"] = img_dict.get( | |
"not_exhaustive_category_ids", []) | |
record["neg_category_ids"] = img_dict.get("neg_category_ids", []) | |
# NOTE: modified by Xingyi: convert to 0-based | |
record["neg_category_ids"] = [ | |
catid2contid[x] for x in record["neg_category_ids"]] | |
if 'pos_category_ids' in img_dict: | |
record['pos_category_ids'] = [ | |
catid2contid[x] for x in img_dict.get("pos_category_ids", [])] | |
if 'captions' in img_dict: | |
record['captions'] = img_dict['captions'] | |
if 'caption_features' in img_dict: | |
record['caption_features'] = img_dict['caption_features'] | |
image_id = record["image_id"] = img_dict["id"] | |
objs = [] | |
for anno in anno_dict_list: | |
assert anno["image_id"] == image_id | |
if anno.get('iscrowd', 0) > 0: | |
continue | |
obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS} | |
obj["category_id"] = catid2contid[anno['category_id']] | |
if 'segmentation' in anno: | |
segm = anno["segmentation"] | |
valid_segm = [poly for poly in segm \ | |
if len(poly) % 2 == 0 and len(poly) >= 6] | |
# assert len(segm) == len( | |
# valid_segm | |
# ), "Annotation contains an invalid polygon with < 3 points" | |
if not len(segm) == len(valid_segm): | |
print('Annotation contains an invalid polygon with < 3 points') | |
assert len(segm) > 0 | |
obj["segmentation"] = segm | |
objs.append(obj) | |
record["annotations"] = objs | |
dataset_dicts.append(record) | |
return dataset_dicts | |
_CUSTOM_SPLITS_LVIS = { | |
"lvis_v1_train+coco": ("coco/", "lvis/lvis_v1_train+coco_mask.json"), | |
"lvis_v1_train_norare": ("coco/", "lvis/lvis_v1_train_norare.json"), | |
} | |
for key, (image_root, json_file) in _CUSTOM_SPLITS_LVIS.items(): | |
custom_register_lvis_instances( | |
key, | |
get_lvis_instances_meta(key), | |
os.path.join("datasets", json_file) if "://" not in json_file else json_file, | |
os.path.join("datasets", image_root), | |
) | |
def get_lvis_22k_meta(): | |
from .lvis_22k_categories import CATEGORIES | |
cat_ids = [k["id"] for k in CATEGORIES] | |
assert min(cat_ids) == 1 and max(cat_ids) == len( | |
cat_ids | |
), "Category ids are not in [1, #categories], as expected" | |
# Ensure that the category list is sorted by id | |
lvis_categories = sorted(CATEGORIES, key=lambda x: x["id"]) | |
thing_classes = [k["name"] for k in lvis_categories] | |
meta = {"thing_classes": thing_classes} | |
return meta | |
_CUSTOM_SPLITS_LVIS_22K = { | |
"lvis_v1_train_22k": ("coco/", "lvis/lvis_v1_train_lvis-22k.json"), | |
} | |
for key, (image_root, json_file) in _CUSTOM_SPLITS_LVIS_22K.items(): | |
custom_register_lvis_instances( | |
key, | |
get_lvis_22k_meta(), | |
os.path.join("datasets", json_file) if "://" not in json_file else json_file, | |
os.path.join("datasets", image_root), | |
) |