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# coding=utf-8
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""COCO"""
import json
import os
from pathlib import Path
import datasets
_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},
eprinttype = {arXiv},
eprint = {1405.0312},
timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
biburl = {https://dblp.org/rec/journals/corr/LinMBHPRDZ14.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_DESCRIPTION = """
MS COCO is a large-scale object detection, segmentation, and captioning dataset.
COCO has several features: Object segmentation, Recognition in context, Superpixel stuff segmentation, 330K images (>200K labeled), 1.5 million object instances, 80 object categories, 91 stuff categories, 5 captions per image, 250,000 people with keypoints.
"""
_HOMEPAGE = "https://cocodataset.org/#home"
_LICENSE = "CC BY 4.0"
_IMAGES_URLS = {
"train": "http://images.cocodataset.org/zips/train2014.zip",
"validation": "http://images.cocodataset.org/zips/val2014.zip",
}
_KARPATHY_FILES_URL = "https://cs.stanford.edu/people/karpathy/deepimagesent/caption_datasets.zip"
_SPLIT_MAP = {"train": "train2014", "validation": "val2014"}
_FEATURES = datasets.Features(
{
"image": datasets.Image(),
"filepath": datasets.Value("string"),
"sentids": [datasets.Value("int32")],
"filename": datasets.Value("string"),
"imgid": datasets.Value("int32"),
"split": datasets.Value("string"),
"sentences": {
"tokens": [datasets.Value("string")],
"raw": datasets.Value("string"),
"imgid": datasets.Value("int32"),
"sentid": datasets.Value("int32"),
},
"cocoid": datasets.Value("int32"),
}
)
_FEATURES_CAPTIONS = datasets.Features(
{
"image": datasets.Image(),
"filepath": datasets.Value("string"),
"sentids": [datasets.Value("int32")],
"filename": datasets.Value("string"),
"imgid": datasets.Value("int32"),
"split": datasets.Value("string"),
"sentences_tokens": [[datasets.Value("string")]],
"sentences_raw": [datasets.Value("string")],
"sentences_sentid": [datasets.Value("int32")],
"cocoid": datasets.Value("int32"),
}
)
class COCO(datasets.GeneratorBasedBuilder):
"""COCO"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="2014", version=VERSION, description="2014 version of COCO with Karpathy annotations and splits"
),
datasets.BuilderConfig(
name="2014_captions",
version=VERSION,
description="Same as 2014 but with all captions of one image gathered in a single example",
),
]
DEFAULT_CONFIG_NAME = "2014"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=_FEATURES if self.config.name == "2014" else _FEATURES_CAPTIONS,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
annotation_file = os.path.join(dl_manager.download_and_extract(_KARPATHY_FILES_URL), "dataset_coco.json")
image_folders = {k: Path(v) for k, v in dl_manager.download_and_extract(_IMAGES_URLS).items()}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"annotation_file": annotation_file,
"image_folders": image_folders,
"split_key": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"annotation_file": annotation_file,
"image_folders": image_folders,
"split_key": "validation",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"annotation_file": annotation_file,
"image_folders": image_folders,
"split_key": "test",
},
),
]
def _generate_examples(self, annotation_file, image_folders, split_key):
if self.config.name == "2014_captions":
return self._generate_examples_2014_captions(annotation_file, image_folders, split_key)
elif self.config.name == "2014":
return self._generate_examples_2014(annotation_file, image_folders, split_key)
def _generate_examples_2014_captions(self, annotation_file, image_folders, split_key):
with open(annotation_file, "r", encoding="utf-8") as fi:
annotations = json.load(fi)
for image_metadata in annotations["images"]:
if split_key == "train":
if image_metadata["split"] != "train" and image_metadata["split"] != "restval":
continue
elif split_key == "validation":
if image_metadata["split"] != "val":
continue
elif split_key == "test":
if image_metadata["split"] != "test":
continue
if "val2014" in image_metadata["filename"]:
image_path = image_folders["validation"] / _SPLIT_MAP["validation"]
else:
image_path = image_folders["train"] / _SPLIT_MAP["train"]
image_path = image_path / image_metadata["filename"]
record = {
"image": str(image_path.absolute()),
"filepath": image_metadata["filename"],
"sentids": image_metadata["sentids"],
"filename": image_metadata["filename"],
"imgid": image_metadata["imgid"],
"split": image_metadata["split"],
"cocoid": image_metadata["cocoid"],
"sentences_tokens": [caption["tokens"] for caption in image_metadata["sentences"]],
"sentences_raw": [caption["raw"] for caption in image_metadata["sentences"]],
"sentences_sentid": [caption["sentid"] for caption in image_metadata["sentences"]],
}
yield record["imgid"], record
def _generate_examples_2014(self, annotation_file, image_folders, split_key):
counter = 0
with open(annotation_file, "r", encoding="utf-8") as fi:
annotations = json.load(fi)
for image_metadata in annotations["images"]:
if split_key == "train":
if image_metadata["split"] != "train" and image_metadata["split"] != "restval":
continue
elif split_key == "validation":
if image_metadata["split"] != "val":
continue
elif split_key == "test":
if image_metadata["split"] != "test":
continue
if "val2014" in image_metadata["filename"]:
image_path = image_folders["validation"] / _SPLIT_MAP["validation"]
else:
image_path = image_folders["train"] / _SPLIT_MAP["train"]
image_path = image_path / image_metadata["filename"]
for caption in image_metadata["sentences"]:
yield counter, {
"image": str(image_path.absolute()),
"filepath": image_metadata["filename"],
"sentids": image_metadata["sentids"],
"filename": image_metadata["filename"],
"imgid": image_metadata["imgid"],
"split": image_metadata["split"],
"sentences": {
"tokens": caption["tokens"],
"raw": caption["raw"],
"imgid": caption["imgid"],
"sentid": caption["sentid"],
},
"cocoid": image_metadata["cocoid"],
}
counter += 1
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