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import json
import os
import pickle
import logging
import datasets
import pycocotools.mask as mask
import dotenv
logger = logging.getLogger(__name__)
# Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{DBLP:journals/corr/LinMBHPRDZ14,
author = {Tsung{-}Yi Lin and
Michael Maire and
Serge J. Belongie and
Lubomir D. Bourdev and
Ross B. Girshick and
James Hays and
Pietro Perona and
Deva Ramanan and
Piotr Doll{'{a} }r and
C. Lawrence Zitnick},
title = {Microsoft {COCO:} Common Objects in Context},
journal = {CoRR},
volume = {abs/1405.0312},
year = {2014},
url = {http://arxiv.org/abs/1405.0312},
archivePrefix = {arXiv},
eprint = {1405.0312},
timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
# Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
COCO is a large-scale object detection, segmentation, and captioning dataset.
"""
# Add a link to an official homepage for the dataset here
_HOMEPAGE = "http://cocodataset.org/#home"
# Add the licence for the dataset here if you can find it
_LICENSE = ""
# Add link to the official dataset URLs here
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
# This script is supposed to work with local (downloaded) COCO dataset.
_URLs = {}
_BASE_REGION_FEATURES = {
"region_id": datasets.Value("int64"),
"image_id": datasets.Value("int32"),
"phrases": [datasets.Value("string")],
"x": datasets.Value("int32"),
"y": datasets.Value("int32"),
"width": datasets.Value("int32"),
"height": datasets.Value("int32"),
}
_BASE_MASK_FEATURES = {
"size": [datasets.Value("int32")],
"counts": datasets.Value("string"),
}
_BASE_MASK_REGION_FEATURES = {
"region_id": datasets.Value("int64"),
"image_id": datasets.Value("int32"),
"phrases": [datasets.Value("string")],
"x": datasets.Value("int32"),
"y": datasets.Value("int32"),
"width": datasets.Value("int32"),
"height": datasets.Value("int32"),
"mask": _BASE_MASK_FEATURES,
}
_ANNOTATION_FEATURES = {
"region_descriptions": {"regions": [_BASE_REGION_FEATURES]},
"mask_region_descriptions": {"regions": [_BASE_MASK_REGION_FEATURES]},
}
_BASE_IMAGE_METADATA_FEATURES = {
"image_id": datasets.Value("int32"),
# "caption_id": datasets.Value("int64"),
# "caption": datasets.Value("string"),
"height": datasets.Value("int32"),
"width": datasets.Value("int32"),
"file_name": datasets.Value("string"),
"coco_url": datasets.Value("string"),
# "image_path": datasets.Value("string"),
"task_type": datasets.Value("string"),
}
_SPLIT_BYS = {
"refclef": ["unc", "berkeley"],
# NOTE: use refer2 by UNC authors
# "refcoco": ["unc", "google"],
"refcoco": ["unc"],
"refcoco+": ["unc"],
"refcocog": ["umd", "google"],
}
_SPLITS = {
"refclef-unc": ["train", "val", "testA", "testB", "testC"],
"refclef-berkeley": ["train", "val", "test"],
# **{f"refcoco-{_split_by}": ["train", "val", "test"] for _split_by in _SPLIT_BYS["refcoco"]},
# **{f"refcoco+-{_split_by}": ["train", "val", "test"] for _split_by in _SPLIT_BYS["refcoco+"]},
**{f"refcoco-{_split_by}": ["train", "val", "testA", "testB"] for _split_by in _SPLIT_BYS["refcoco"]},
**{f"refcoco+-{_split_by}": ["train", "val", "testA", "testB"] for _split_by in _SPLIT_BYS["refcoco+"]},
**{f"refcocog-{_split_by}": ["train", "val"] for _split_by in _SPLIT_BYS["refcocog"]},
}
datasets.Split("testA")
datasets.Split("testB")
class RefCOCOBuilderConfig(datasets.BuilderConfig):
def __init__(
self,
name,
splits,
with_image=True,
with_mask=True,
base_url=None,
sas_key=None,
task_type="caption",
**kwargs,
):
super().__init__(name, **kwargs)
self.splits = splits
self.dataset_name = name.split("-")[0]
self.split_by = name.split("-")[-1]
self.with_image = with_image
self.with_mask = with_mask
self.base_url = base_url
self.sas_key = sas_key
self.task_type = task_type
@property
def features(self):
annoation_type = "mask_region_descriptions" if self.with_mask else "region_descriptions"
logger.info(f"Using annotation type: {annoation_type} due to with_mask={self.with_mask}")
return datasets.Features(
{
**({"image": datasets.Image()} if self.with_image else {}),
**_BASE_IMAGE_METADATA_FEATURES,
**_ANNOTATION_FEATURES[annoation_type],
}
)
# Name of the dataset usually match the script name with CamelCase instead of snake_case
class RefCOCODataset(datasets.GeneratorBasedBuilder):
"""An example dataset script to work with the local (downloaded) COCO dataset"""
VERSION = datasets.Version("0.0.0")
BUILDER_CONFIG_CLASS = RefCOCOBuilderConfig
BUILDER_CONFIGS = [RefCOCOBuilderConfig(name=name, splits=splits) for name, splits in _SPLITS.items()]
DEFAULT_CONFIG_NAME = "refcoco-unc"
config: RefCOCOBuilderConfig
def _info(self):
# This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
features = self.config.features
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# NOTE: we use base_url instead of data_dir
# When we use data_dir, all the paths are relative to the data_dir.
base_url = self.config.base_url
if base_url is None:
raise ValueError(
"This script is supposed to work with local or remote RefCOCO dataset. It is either a local path or remote url. The argument `base_url` in `load_dataset()` is required."
)
logger.info(f"Using base_url: {base_url}")
# _DL_URLS = {
# "train": os.path.join(data_dir, "train2017.zip"),
# "val": os.path.join(data_dir, "val2017.zip"),
# "test": os.path.join(data_dir, "test2017.zip"),
# "annotations_trainval": os.path.join(data_dir, "annotations_trainval2017.zip"),
# "image_info_test": os.path.join(data_dir, "image_info_test2017.zip"),
# }
_DL_URLS = {}
if self.config.dataset_name in ["refcoco", "refcoco+", "refcocog"]:
_DL_URLS["image_dir"] = os.path.join(base_url, "train2014.zip")
elif self.config.dataset_name == "refclef":
_DL_URLS["image_dir"] = os.path.join(base_url, "saiapr_tc-12.zip")
else:
raise ValueError(f"Unknown dataset name: {self.config.dataset_name}")
_DL_URLS["annotation_dir"] = os.path.join(base_url, f"{self.config.dataset_name}.zip")
sas_key = self.config.sas_key
if sas_key is None:
# NOTE(xiaoke): load sas_key from .env
logger.info(f"Try to load sas_key from .env file: {dotenv.load_dotenv('.env')}.")
sas_key = os.getenv("REFCOCO_SAS_KEY")
if sas_key is not None and not os.path.exists(base_url):
logger.info(f"Using sas_key: {sas_key}")
_DL_URLS = {k: f"{v}{sas_key}" for k, v in _DL_URLS.items()}
if dl_manager.is_streaming is True:
raise ValueError(
"dl_manager.is_streaming is True, which is very slow due to the random access inside zip files with streaming loading."
)
archive_path = dl_manager.download_and_extract(_DL_URLS)
# NOTE(xiaoke): prepare data for index generation
with open(
os.path.join(archive_path["annotation_dir"], self.config.dataset_name, f"refs({self.config.split_by}).p"),
"rb",
) as fp:
refs = pickle.load(fp)
with open(
os.path.join(archive_path["annotation_dir"], self.config.dataset_name, f"instances.json"),
"r",
encoding="UTF-8",
) as fp:
instances = json.load(fp)
self.data = {}
self.data["dataset"] = self.config.dataset_name
self.data["refs"] = refs
self.data["images"] = instances["images"]
self.data["annotations"] = instances["annotations"]
self.data["categories"] = instances["categories"]
self.createIndex()
print(f"num refs: {len(self.Refs)}")
splits = []
for split in self.config.splits:
if split == "train":
dataset = datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
# gen_kwargs={
# "json_path": os.path.join(
# archive_path["annotations_trainval"], "annotations", "captions_train2017.json"
# ),
# "image_dir": os.path.join(archive_path["train"], "train2017"),
# "split": "train",
# },
gen_kwargs={
"image_dir": archive_path["image_dir"],
"split": split,
},
)
elif split in ["val"]:
dataset = datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
# gen_kwargs={
# "json_path": os.path.join(
# archive_path["annotations_trainval"], "annotations", "captions_val2017.json"
# ),
# "image_dir": os.path.join(archive_path["val"], "val2017"),
# "split": "valid",
# },
gen_kwargs={
"image_dir": archive_path["image_dir"],
"split": split,
},
)
elif split == "test":
dataset = datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
# gen_kwargs={
# "json_path": os.path.join(
# archive_path["image_info_test"], "annotations", "image_info_test2017.json"
# ),
# "image_dir": os.path.join(archive_path["test"], "test2017"),
# "split": "test",
# },
gen_kwargs={
"image_dir": archive_path["image_dir"],
"split": split,
},
)
elif split in ["testA", "testB", "testC"]:
dataset = datasets.SplitGenerator(
name=datasets.Split(split),
# These kwargs will be passed to _generate_examples
# gen_kwargs={
# "json_path": os.path.join(
# archive_path["image_info_test"], "annotations", "image_info_test2017.json"
# ),
# "image_dir": os.path.join(archive_path["test"], "test2017"),
# "split": "test",
# },
gen_kwargs={
"image_dir": archive_path["image_dir"],
"split": split,
},
)
else:
raise ValueError(f"Unknown split name: {split}")
splits.append(dataset)
return splits
def _generate_examples(
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
self,
image_dir,
split,
):
"""Yields examples as (key, example) tuples."""
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is here for legacy reason (tfds) and is not important in itself.
ref_ids = self.getRefIds(split=split)
img_ids = self.getImgIds(ref_ids=ref_ids)
logger.info(f"Generating examples from {len(ref_ids)} refs and {len(img_ids)} images in split {split}...")
if self.config.dataset_name in ["refcoco", "refcoco+", "refcocog"]:
image_dir_name = "train2014"
elif self.config.dataset_name == "refclef":
image_dir_name = "saiapr_tc-12"
else:
raise ValueError(f"Unknown dataset name: {self.config.dataset_name}")
for idx, img_id in enumerate(img_ids):
img = self.Imgs[img_id]
image_metadata = {
"coco_url": img.get("coco_url", None),
"file_name": img["file_name"],
"height": img["height"],
"width": img["width"],
"image_id": img["id"],
}
image_dict = (
{"image": os.path.join(image_dir, image_dir_name, img["file_name"])} if self.config.with_image else {}
)
annotation = []
img_to_refs = self.imgToRefs[img_id]
for img_to_ref in img_to_refs:
ref_to_ann = self.refToAnn[img_to_ref["ref_id"]]
x, y, width, height = ref_to_ann["bbox"]
# NOTE: we need to convert float to int
annotation_dict = {
"image_id": img_to_ref["image_id"],
"region_id": img_to_ref["ref_id"],
"x": int(x),
"y": int(y),
"width": int(width),
"height": int(height),
}
annotation_dict["phrases"] = [sent["sent"] for sent in img_to_ref["sentences"]]
if self.config.with_mask:
if type(ref_to_ann["segmentation"][0]) == list:
rle = mask.frPyObjects(ref_to_ann["segmentation"], img["height"], img["width"])
else:
rle = ref_to_ann["segmentation"]
mask_dict = rle[0] # should be a dict, rather a list
annotation_dict["mask"] = {
"size": mask_dict["size"],
"counts": mask_dict["counts"].decode("utf-8"), # NOTE: otherwise, it leads to core dump error.
}
annotation.append(annotation_dict)
annotation = {"regions": annotation}
yield idx, {**image_dict, **image_metadata, **annotation, "task_type": self.config.task_type}
"""
{
'coco_url': Value(dtype='string', id=None),
'file_name': Value(dtype='string', id=None),
'height': Value(dtype='int32', id=None),
'image': Image(decode=True, id=None),
'image_id': Value(dtype='int32', id=None),
'regions': [{
'height': Value(dtype='int32', id=None),
'image_id': Value(dtype='int32', id=None),
'mask': {
'counts': Value(dtype='string', id=None),
'size': [Value(dtype='int32', id=None)]
},
'phrases': [Value(dtype='string', id=None)],
'region_id': Value(dtype='int32', id=None),
'width': Value(dtype='int32', id=None),
'x': Value(dtype='int32', id=None),
'y': Value(dtype='int32', id=None)
}],
'width': Value(dtype='int32', id=None)
}
"""
# _features = [
# "image_id",
# "caption_id",
# "caption",
# "height",
# "width",
# "file_name",
# "coco_url",
# "image_path",
# "id",
# ]
# features = list(_features)
# if split in "valid":
# split = "val"
# with open(json_path, "r", encoding="UTF-8") as fp:
# data = json.load(fp)
# # list of dict
# images = data["images"]
# entries = images
# # build a dict of image_id -> image info dict
# d = {image["id"]: image for image in images}
# # list of dict
# if split in ["train", "val"]:
# annotations = data["annotations"]
# # build a dict of image_id ->
# for annotation in annotations:
# _id = annotation["id"]
# image_info = d[annotation["image_id"]]
# annotation.update(image_info)
# annotation["id"] = _id
# entries = annotations
# for id_, entry in enumerate(entries):
# entry = {k: v for k, v in entry.items() if k in features}
# if split == "test":
# entry["image_id"] = entry["id"]
# entry["id"] = -1
# entry["caption"] = -1
# entry["caption_id"] = entry.pop("id")
# entry["image_path"] = os.path.join(image_dir, entry["file_name"])
# entry = {k: entry[k] for k in _features if k in entry}
# yield str((entry["image_id"], entry["caption_id"])), entry
def createIndex(self):
# create sets of mapping
# 1) Refs: {ref_id: ref}
# 2) Anns: {ann_id: ann}
# 3) Imgs: {image_id: image}
# 4) Cats: {category_id: category_name}
# 5) Sents: {sent_id: sent}
# 6) imgToRefs: {image_id: refs}
# 7) imgToAnns: {image_id: anns}
# 8) refToAnn: {ref_id: ann}
# 9) annToRef: {ann_id: ref}
# 10) catToRefs: {category_id: refs}
# 11) sentToRef: {sent_id: ref}
# 12) sentToTokens: {sent_id: tokens}
logger.info(f"creating index for {self.config.name}...")
# fetch info from instances
Anns, Imgs, Cats, imgToAnns = {}, {}, {}, {}
for ann in self.data["annotations"]:
Anns[ann["id"]] = ann
imgToAnns[ann["image_id"]] = imgToAnns.get(ann["image_id"], []) + [ann]
for img in self.data["images"]:
Imgs[img["id"]] = img
for cat in self.data["categories"]:
Cats[cat["id"]] = cat["name"]
# fetch info from refs
Refs, imgToRefs, refToAnn, annToRef, catToRefs = {}, {}, {}, {}, {}
Sents, sentToRef, sentToTokens = {}, {}, {}
for ref in self.data["refs"]:
# ids
ref_id = ref["ref_id"]
ann_id = ref["ann_id"]
category_id = ref["category_id"]
image_id = ref["image_id"]
# add mapping related to ref
Refs[ref_id] = ref
imgToRefs[image_id] = imgToRefs.get(image_id, []) + [ref]
catToRefs[category_id] = catToRefs.get(category_id, []) + [ref]
refToAnn[ref_id] = Anns[ann_id]
annToRef[ann_id] = ref
# add mapping of sent
for sent in ref["sentences"]:
Sents[sent["sent_id"]] = sent
sentToRef[sent["sent_id"]] = ref
sentToTokens[sent["sent_id"]] = sent["tokens"]
# create class members
self.Refs = Refs
self.Anns = Anns
self.Imgs = Imgs
self.Cats = Cats
self.Sents = Sents
self.imgToRefs = imgToRefs
self.imgToAnns = imgToAnns
self.refToAnn = refToAnn
self.annToRef = annToRef
self.catToRefs = catToRefs
self.sentToRef = sentToRef
self.sentToTokens = sentToTokens
logger.info("index created.")
"""
Dataset Statistic:
refcoco-unc
Refs 50000
Anns 196771
Imgs 19994
Cats 80
Sents 142210
imgToRefs 19994
imgToAnns 19994
refToAnn 50000
annToRef 50000
catToRefs 78
sentToRef 142210
sentToTokens 142210
"""
def getRefIds(self, image_ids=[], cat_ids=[], ref_ids=[], split=""):
image_ids = image_ids if type(image_ids) == list else [image_ids]
cat_ids = cat_ids if type(cat_ids) == list else [cat_ids]
ref_ids = ref_ids if type(ref_ids) == list else [ref_ids]
if len(image_ids) == len(cat_ids) == len(ref_ids) == len(split) == 0:
refs = self.data["refs"]
else:
if not len(image_ids) == 0:
refs = [self.imgToRefs[image_id] for image_id in image_ids]
else:
refs = self.data["refs"]
if not len(cat_ids) == 0:
refs = [ref for ref in refs if ref["category_id"] in cat_ids]
if not len(ref_ids) == 0:
refs = [ref for ref in refs if ref["ref_id"] in ref_ids]
if not len(split) == 0:
if split in ["testA", "testB", "testC"]:
# we also consider testAB, testBC, ...
refs = [ref for ref in refs if split[-1] in ref["split"]]
elif split in ["testAB", "testBC", "testAC"]:
# rarely used I guess...
refs = [ref for ref in refs if ref["split"] == split]
elif split == "test":
refs = [ref for ref in refs if "test" in ref["split"]]
elif split == "train" or split == "val":
refs = [ref for ref in refs if ref["split"] == split]
else:
raise ValueError("No such split [%s]" % split)
ref_ids = [ref["ref_id"] for ref in refs]
return ref_ids
def getImgIds(self, ref_ids=[]):
ref_ids = ref_ids if type(ref_ids) == list else [ref_ids]
if not len(ref_ids) == 0:
image_ids = list(set([self.Refs[ref_id]["image_id"] for ref_id in ref_ids]))
else:
image_ids = list(self.Imgs.keys())
return image_ids
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