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import json
import os
import datasets
import dotenv
from pycocotools.coco import COCO
logger = datasets.logging.get_logger(__name__)
_BASE_IMAGE_METADATA_FEATURES = {
"image_id": datasets.Value("int32"),
"width": datasets.Value("int32"),
"height": datasets.Value("int32"),
"file_name": datasets.Value("string"),
"coco_url": datasets.Value("string"),
"task_type": datasets.Value("string"),
}
_BASE_REGION_FEATURES = {
# NOTE: one of them is 900100184613, which is out of the range of int32
"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,
# "area": datasets.Value("int32"),
# "phrase_conf": datasets.Value("float32"),
}
_ANNOTATION_FEATURES = {
"region_descriptions": {"regions": [_BASE_REGION_FEATURES]},
"mask_region_descriptions": {"regions": [_BASE_MASK_REGION_FEATURES]},
}
class COCOBuilderConfig(datasets.BuilderConfig):
def __init__(
self,
name,
splits,
with_image: bool = True,
with_mask: bool = False,
coco_zip_url: str = None,
coco_annotations_zip_url: str = None,
task_type: str = "caption",
**kwargs,
):
super().__init__(name, **kwargs)
self.splits = splits
self.with_image = with_image
self.with_mask = with_mask
self.coco_zip_url = coco_zip_url
self.coco_annotations_zip_url = coco_annotations_zip_url
self.task_type = task_type
@property
def features(self):
if self.with_mask is True:
raise ValueError("with_mask=True is not supported yet in COCO caption.")
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 COCOCaptionPseudoRegionDataset(datasets.GeneratorBasedBuilder):
"""An example dataset script to work with the local (downloaded) COCO dataset"""
VERSION = datasets.Version("0.0.0")
BUILDER_CONFIG_CLASS = COCOBuilderConfig
BUILDER_CONFIGS = [
# NOTE: we do not need test as it lacks visual promptsc
# COCOBuilderConfig(name="2017", splits=["train", "valid", "test"]),
COCOBuilderConfig(name="2017", splits=["train", "valid"]),
]
DEFAULT_CONFIG_NAME = "2017"
config: COCOBuilderConfig
def _info(self):
return datasets.DatasetInfo(features=self.config.features)
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
# data_dir = self.config.data_dir
coco_zip_url = self.config.coco_zip_url
coco_annotations_zip_url = self.config.coco_annotations_zip_url
if coco_zip_url is None:
raise ValueError(
"This script is supposed to work with local (downloaded) COCO dataset. The argument `coco_zip_url` in `load_dataset()` is required."
)
if coco_annotations_zip_url is None:
raise ValueError(
"This script is supposed to work with local (downloaded) COCO dataset. The argument `coco_annotations_zip_url` in `load_dataset()` is required."
)
# NOTE(xiaoke): load sas_key from .env
logger.info(f"Try to load sas_key from .env file: {dotenv.load_dotenv('.env')}.")
coco_zip_url_sas_key = os.getenv("COCO_ZIP_URL_SAS_KEY", "")
coco_annotations_zip_url_sas_key = os.getenv("COCO_ANNOTATIONS_ZIP_URL_SAS_KEY", "")
_DL_URLS = {
"train": os.path.join(coco_zip_url, "train2017.zip") + coco_zip_url_sas_key,
"val": os.path.join(coco_zip_url, "val2017.zip") + coco_zip_url_sas_key,
"test": os.path.join(coco_zip_url, "test2017.zip") + coco_zip_url_sas_key,
"annotations_trainval": os.path.join(coco_annotations_zip_url, "annotations_trainval2017.zip")
+ coco_annotations_zip_url_sas_key,
"image_info_test": os.path.join(coco_annotations_zip_url, "image_info_test2017.zip")
+ coco_annotations_zip_url_sas_key,
}
archive_path = dl_manager.download_and_extract(_DL_URLS)
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"),
"image_dir": archive_path["train"],
},
)
elif split in ["val", "valid", "validation", "dev"]:
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"),
"image_dir": archive_path["val"],
},
)
# NOTE: we do not need test as it lacks visual prompts
# 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",
# },
# )
else:
continue
splits.append(dataset)
return splits
def _generate_examples(
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
self,
json_path,
image_dir,
):
"""Yields examples as (key, example) tuples."""
coco = COCO(json_path)
img_ids = coco.getImgIds()
for idx, img_id in enumerate(img_ids):
img = coco.imgs[img_id]
image_metadata = {
"coco_url": img["coco_url"],
"file_name": img["file_name"],
"height": img["height"],
"width": img["width"],
"image_id": img["id"],
}
image_dict = {"image": os.path.join(image_dir, img["file_name"])} if self.config.with_image else {}
if img_id not in coco.imgToAnns:
continue
annotation = []
width, height = img["width"], img["height"]
for ann in coco.imgToAnns[img_id]:
x, y, width, height = 0, 0, width, height
annotation_dict = {
# NOTE: one of them is 900100184613, which is out of the range of int32
"region_id": ann["id"],
"image_id": ann["image_id"],
"x": x,
"y": y,
"width": width,
"height": height,
}
phrases = []
phrases.append(ann["caption"])
# TODO: add supercategory
# phrases.append(category["supercategory"])
annotation_dict["phrases"] = phrases
if self.config.with_mask:
mask_dict = coco.annToRLE(ann)
mask_dict = {
"size": mask_dict["size"],
"counts": mask_dict["counts"].decode("utf-8"), # NOTE: otherwise, it leads to core dump error.
}
annotation_dict["mask"] = mask_dict
annotation.append(annotation_dict)
annotation = {"regions": annotation}
yield idx, {**image_dict, **image_metadata, **annotation, "task_type": self.config.task_type}
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