LocalizedNarratives.py

# 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""Localized Narratives"""
import json
import datasets


_CITATION = """
@inproceedings{PontTuset_eccv2020,
  author    = {Jordi Pont-Tuset and Jasper Uijlings and Soravit Changpinyo and Radu Soricut and Vittorio Ferrari},
  title     = {Connecting Vision and Language with Localized Narratives},
  booktitle = {ECCV},
  year      = {2020}
}
"""

_DESCRIPTION = """
Localized Narratives, a new form of multimodal image annotations connecting vision and language.
We ask annotators to describe an image with their voice while simultaneously hovering their mouse over the region they are describing.
Since the voice and the mouse pointer are synchronized, we can localize every single word in the description.
This dense visual grounding takes the form of a mouse trace segment per word and is unique to our data.
We annotated 849k images with Localized Narratives: the whole COCO, Flickr30k, and ADE20K datasets, and 671k images of Open Images, all of which we make publicly available.
"""

_HOMEPAGE = "https://google.github.io/localized-narratives/"

_LICENSE = "CC BY 4.0"

_ANNOTATION_URLs = {
    "train": [
        "https://storage.googleapis.com/localized-narratives/annotations/open_images_train_v6_localized_narratives-00000-of-00010.jsonl",
        "https://storage.googleapis.com/localized-narratives/annotations/open_images_train_v6_localized_narratives-00001-of-00010.jsonl",
        "https://storage.googleapis.com/localized-narratives/annotations/open_images_train_v6_localized_narratives-00002-of-00010.jsonl",
        "https://storage.googleapis.com/localized-narratives/annotations/open_images_train_v6_localized_narratives-00003-of-00010.jsonl",
        "https://storage.googleapis.com/localized-narratives/annotations/open_images_train_v6_localized_narratives-00004-of-00010.jsonl",
        "https://storage.googleapis.com/localized-narratives/annotations/open_images_train_v6_localized_narratives-00005-of-00010.jsonl",
        "https://storage.googleapis.com/localized-narratives/annotations/open_images_train_v6_localized_narratives-00006-of-00010.jsonl",
        "https://storage.googleapis.com/localized-narratives/annotations/open_images_train_v6_localized_narratives-00007-of-00010.jsonl",
        "https://storage.googleapis.com/localized-narratives/annotations/open_images_train_v6_localized_narratives-00008-of-00010.jsonl",
        "https://storage.googleapis.com/localized-narratives/annotations/open_images_train_v6_localized_narratives-00009-of-00010.jsonl",
    ],
    "validation": [
        "https://storage.googleapis.com/localized-narratives/annotations/open_images_validation_localized_narratives.jsonl"
    ],
    "test": [
        "https://storage.googleapis.com/localized-narratives/annotations/open_images_test_localized_narratives.jsonl"
    ],
}


_FEATURES = {
    "OpenImages": datasets.Features(
        {
            "image": datasets.Image(),
            "image_url": datasets.Value("string"),
            "dataset_id": datasets.Value("string"),
            "image_id": datasets.Value("string"),
            "annotator_id": datasets.Value("int32"),
            "caption": datasets.Value("string"),
            "timed_caption": datasets.Sequence(
                {
                    "utterance": datasets.Value("string"),
                    "start_time": datasets.Value("float32"),
                    "end_time": datasets.Value("float32"),
                }
            ),
            "traces": datasets.Sequence(
                datasets.Sequence(
                    {
                        "x": datasets.Value("float32"),
                        "y": datasets.Value("float32"),
                        "t": datasets.Value("float32"),
                    }
                )
            ),
            "voice_recording": datasets.Value("string"),
        }
    ),
    "OpenImages_captions": datasets.Features(
        {
            "image": datasets.Image(),
            "image_url": datasets.Value("string"),
            "dataset_id": datasets.Value("string"),
            "image_id": datasets.Value("string"),
            "annotator_ids": [datasets.Value("int32")],
            "captions": [datasets.Value("string")],
        }
    ),
}


class LocalizedNarrativesOpenImages(datasets.GeneratorBasedBuilder):
    """Builder for Localized Narratives."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="OpenImages",
            version=VERSION,
            description="OpenImages subset of Localized Narratives"
        ),
        datasets.BuilderConfig(
            name="OpenImages_captions",
            version=VERSION,
            description="OpenImages subset of Localized Narratives where captions are groupped per image (images can have multiple captions). For this subset, `timed_caption`, `traces` and `voice_recording` are not available."
        ),
    ]

    DEFAULT_CONFIG_NAME = "OpenImages"

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=_FEATURES[self.config.name],
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        annotation_files = dl_manager.download(_ANNOTATION_URLs)
        return [
            datasets.SplitGenerator(
                name=split_name,
                gen_kwargs={"annotation_list": annotation_list, "split": split_name},
            )
            for split_name, annotation_list in annotation_files.items()
        ]

    def _generate_examples(self, annotation_list: str, split: str):
        if self.config.name == "OpenImages":
            return self._generate_examples_original_format(annotation_list, split)
        elif self.config.name == "OpenImages_captions":
            return self._generate_examples_aggregated_captions(annotation_list, split)

    def _generate_examples_original_format(self, annotation_list: str, split: str):
        counter = 0
        for annotation_file in annotation_list:
            with open(annotation_file, "r", encoding="utf-8") as fi:
                for line in fi:
                    annotation = json.loads(line)
                    image_url = f"https://s3.amazonaws.com/open-images-dataset/{split}/{annotation['image_id']}.jpg"
                    yield counter, {
                        "image": image_url,
                        "image_url": image_url,
                        "dataset_id": annotation["dataset_id"],
                        "image_id": annotation["image_id"],
                        "annotator_id": annotation["annotator_id"],
                        "caption": annotation["caption"],
                        "timed_caption": annotation["timed_caption"],
                        "traces": annotation["traces"],
                        "voice_recording": annotation["voice_recording"],
                    }
                    counter += 1

    def _generate_examples_aggregated_captions(self, annotation_list: str, split: str):
        result = {}
        for annotation_file in annotation_list:
            with open(annotation_file, "r", encoding="utf-8") as fi:
                for line in fi:
                    annotation = json.loads(line)
                    image_url = f"https://s3.amazonaws.com/open-images-dataset/{split}/{annotation['image_id']}.jpg"
                    image_id = annotation["image_id"]
                    if image_id in result:
                        assert result[image_id]["dataset_id"] == annotation["dataset_id"]
                        assert result[image_id]["image_id"] == annotation["image_id"]
                        result[image_id]["annotator_ids"].append(annotation["annotator_id"])
                        result[image_id]["captions"].append(annotation["caption"])
                    else:
                        result[image_id] = {
                            "image": image_url,
                            "image_url": image_url,
                            "dataset_id": annotation["dataset_id"],
                            "image_id": image_id,
                            "annotator_ids": [annotation["annotator_id"]],
                            "captions": [annotation["caption"]],
                        }
        counter = 0
        for r in result.values():
            yield counter, r
            counter += 1