allow to extract files

coco_dataset.py

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+import json
+import os
+import datasets
+
+
+class COCOBuilderConfig(datasets.BuilderConfig):
+
+    def __init__(self, name, splits, **kwargs):
+        super().__init__(name, **kwargs)
+        self.splits = splits
+
+
+# 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 = {}
+
+
+# Name of the dataset usually match the script name with CamelCase instead of snake_case
+class COCODataset(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 = [
+        COCOBuilderConfig(name='2017', splits=['train', 'valid', 'test']),
+    ]
+    DEFAULT_CONFIG_NAME = "2017"
+
+    def _info(self):
+        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+
+        feature_dict = {
+            "image_id": datasets.Value("int64"),
+            "caption_id": datasets.Value("int64"),
+            "caption": datasets.Value("string"),
+            "height": datasets.Value("int64"),
+            "width": datasets.Value("int64"),
+            "file_name": datasets.Value("string"),
+            "coco_url": datasets.Value("string"),
+            "image_path": datasets.Value("string"),
+        }
+
+        features = datasets.Features(feature_dict)
+
+        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
+
+        data_dir = self.config.data_dir
+        if not data_dir:
+            raise ValueError(
+                "This script is supposed to work with local (downloaded) COCO dataset. The argument `data_dir` in `load_dataset()` is required."
+            )
+
+        _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"),
+        }
+        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"),
+                        "split": "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"),
+                        "split": "valid",
+                    },
+                )
+            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, 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.
+
+        _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