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coco_dataset.py ADDED
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+ import json
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+ import os
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+ import datasets
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+
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+
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+ class COCOBuilderConfig(datasets.BuilderConfig):
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+
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+ def __init__(self, name, splits, **kwargs):
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+ super().__init__(name, **kwargs)
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+ self.splits = splits
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+
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+
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+ # Add BibTeX citation
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+ # Find for instance the citation on arxiv or on the dataset repo/website
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+ _CITATION = """\
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+ @article{DBLP:journals/corr/LinMBHPRDZ14,
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+ author = {Tsung{-}Yi Lin and
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+ Michael Maire and
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+ Serge J. Belongie and
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+ Lubomir D. Bourdev and
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+ Ross B. Girshick and
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+ James Hays and
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+ Pietro Perona and
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+ Deva Ramanan and
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+ Piotr Doll{'{a} }r and
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+ C. Lawrence Zitnick},
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+ title = {Microsoft {COCO:} Common Objects in Context},
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+ journal = {CoRR},
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+ volume = {abs/1405.0312},
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+ year = {2014},
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+ url = {http://arxiv.org/abs/1405.0312},
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+ archivePrefix = {arXiv},
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+ eprint = {1405.0312},
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+ timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
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+ biburl = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14},
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+ bibsource = {dblp computer science bibliography, https://dblp.org}
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+ }
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+ """
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+
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+ # Add description of the dataset here
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+ # You can copy an official description
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+ _DESCRIPTION = """\
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+ COCO is a large-scale object detection, segmentation, and captioning dataset.
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+ """
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+
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+ # Add a link to an official homepage for the dataset here
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+ _HOMEPAGE = "http://cocodataset.org/#home"
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+
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+ # Add the licence for the dataset here if you can find it
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+ _LICENSE = ""
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+
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+ # Add link to the official dataset URLs here
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+ # The HuggingFace dataset library don't host the datasets but only point to the original files
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+ # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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+
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+ # This script is supposed to work with local (downloaded) COCO dataset.
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+ _URLs = {}
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+
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+
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+ # Name of the dataset usually match the script name with CamelCase instead of snake_case
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+ class COCODataset(datasets.GeneratorBasedBuilder):
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+ """An example dataset script to work with the local (downloaded) COCO dataset"""
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+
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+ VERSION = datasets.Version("0.0.0")
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+
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+ BUILDER_CONFIG_CLASS = COCOBuilderConfig
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+ BUILDER_CONFIGS = [
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+ COCOBuilderConfig(name='2017', splits=['train', 'valid', 'test']),
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+ ]
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+ DEFAULT_CONFIG_NAME = "2017"
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+
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+ def _info(self):
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+ # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
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+
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+ feature_dict = {
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+ "image_id": datasets.Value("int64"),
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+ "caption_id": datasets.Value("int64"),
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+ "caption": datasets.Value("string"),
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+ "height": datasets.Value("int64"),
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+ "width": datasets.Value("int64"),
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+ "file_name": datasets.Value("string"),
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+ "coco_url": datasets.Value("string"),
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+ "image_path": datasets.Value("string"),
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+ }
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+
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+ features = datasets.Features(feature_dict)
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+
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+ return datasets.DatasetInfo(
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+ # This is the description that will appear on the datasets page.
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+ description=_DESCRIPTION,
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+ # This defines the different columns of the dataset and their types
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+ features=features, # Here we define them above because they are different between the two configurations
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+ # If there's a common (input, target) tuple from the features,
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+ # specify them here. They'll be used if as_supervised=True in
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+ # builder.as_dataset.
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+ supervised_keys=None,
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+ # Homepage of the dataset for documentation
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+ homepage=_HOMEPAGE,
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+ # License for the dataset if available
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+ license=_LICENSE,
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+ # Citation for the dataset
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+ citation=_CITATION,
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ """Returns SplitGenerators."""
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+ # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
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+ # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
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+
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+ data_dir = self.config.data_dir
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+ if not data_dir:
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+ raise ValueError(
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+ "This script is supposed to work with local (downloaded) COCO dataset. The argument `data_dir` in `load_dataset()` is required."
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+ )
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+
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+ splits = []
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+ for split in self.config.splits:
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+ if split == 'train':
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+ dataset = datasets.SplitGenerator(
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+ name=datasets.Split.TRAIN,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={
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+ "json_path": os.path.join(data_dir, f"captions_train{self.config.name}.json"),
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+ "image_dir": os.path.join(data_dir, f'train{self.config.name}'),
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+ "split": "train",
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+ }
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+ )
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+ elif split in ['val', 'valid', 'validation', 'dev']:
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+ dataset = datasets.SplitGenerator(
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+ name=datasets.Split.VALIDATION,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={
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+ "json_path": os.path.join(data_dir, f"captions_val{self.config.name}.json"),
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+ "image_dir": os.path.join(data_dir, f'val{self.config.name}'),
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+ "split": "valid",
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+ },
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+ )
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+ elif split == 'test':
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+ dataset = datasets.SplitGenerator(
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+ name=datasets.Split.TEST,
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+ # These kwargs will be passed to _generate_examples
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+ gen_kwargs={
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+ "json_path": os.path.join(data_dir, f'image_info_test{self.config.name}.json'),
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+ "image_dir": os.path.join(data_dir, f'test{self.config.name}'),
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+ "split": "test",
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+ },
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+ )
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+ else:
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+ continue
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+
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+ splits.append(dataset)
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+
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+ return splits
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+
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+ def _generate_examples(
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+ # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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+ self, json_path, image_dir, split
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+ ):
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+ """ Yields examples as (key, example) tuples. """
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+ # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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+ # The `key` is here for legacy reason (tfds) and is not important in itself.
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+
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+ _features = ["image_id", "caption_id", "caption", "height", "width", "file_name", "coco_url", "image_path", "id"]
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+ features = list(_features)
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+
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+ if split in "valid":
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+ split = "val"
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+
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+ with open(json_path, 'r', encoding='UTF-8') as fp:
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+ data = json.load(fp)
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+
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+ # list of dict
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+ images = data["images"]
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+ entries = images
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+
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+ # build a dict of image_id -> image info dict
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+ d = {image["id"]: image for image in images}
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+
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+ # list of dict
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+ if split in ["train", "val"]:
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+ annotations = data["annotations"]
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+
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+ # build a dict of image_id ->
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+ for annotation in annotations:
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+ _id = annotation["id"]
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+ image_info = d[annotation["image_id"]]
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+ annotation.update(image_info)
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+ annotation["id"] = _id
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+
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+ entries = annotations
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+
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+ for id_, entry in enumerate(entries):
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+
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+ entry = {k: v for k, v in entry.items() if k in features}
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+
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+ if split == "test":
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+ entry["image_id"] = entry["id"]
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+ entry["id"] = -1
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+ entry["caption"] = -1
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+
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+ entry["caption_id"] = entry.pop("id")
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+ entry["image_path"] = os.path.join(image_dir, entry["file_name"])
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+
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+ entry = {k: entry[k] for k in _features if k in entry}
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+
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+ yield str((entry["image_id"], entry["caption_id"])), entry
download_coco.txt ADDED
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+ mkdir data
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+ cd data
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+ wget http://images.cocodataset.org/zips/train2017.zip
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+ wget http://images.cocodataset.org/zips/val2017.zip
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+ wget http://images.cocodataset.org/zips/test2017.zip
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+ wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
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+ wget http://images.cocodataset.org/annotations/image_info_test2017.zip
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+ unzip train2017.zip
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+ unzip val2017.zip
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+ unzip test2017.zip
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+ unzip annotations_trainval2017.zip
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+ unzip image_info_test2017.zip
dummy_data/annotations_trainval2017.zip ADDED
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+ oid sha256:973b53d5b4748ff251816b6d741dcf19c9a94bfa5b38edcf6164592ebd610476
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+ size 7615
dummy_data/image_info_test2017.zip ADDED
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+ size 3682
dummy_data/test2017.zip ADDED
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+ size 3023535
dummy_data/train2017.zip ADDED
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dummy_data/val2017.zip ADDED
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