referit.py

# Copyright 2020 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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import os.path as osp

import datasets
from .refer import REFER


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This RefCOCO dataset is designed to load refcoco, refcoco+, and refcocog.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points
# to the original files.
# This can be an arbitrary nested dict/list of URLs
# (see below in `_split_generators` method)
_URLS = {}


VALID_SPLIT_NAMES = ("train", "val", "testA", "testB")


class ReferitBuilderConfig(datasets.BuilderConfig):

    def __init__(self, name: str, split_by: str, **kwargs):
        super().__init__(name, **kwargs)
        self.split_by = split_by


# TODO: Name of the dataset usually matches the script name with CamelCase
# instead of snake_case
class ReferitDataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("0.0.1")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable
    # options
    # You can create your own builder configuration class to store attribute,
    # inheriting from datasets.BuilderConfig
    BUILDER_CONFIG_CLASS = ReferitBuilderConfig

    # You will be able to load one or the other configurations
    # in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        # refcoco
        ReferitBuilderConfig(
            name="refcoco", split_by="unc",
            version=VERSION, description="refcoco."),
        # refcoco+
        ReferitBuilderConfig(
            name="refcoco+", split_by="unc",
            version=VERSION, description="refcoco+"),
        # refcocog
        ReferitBuilderConfig(
            name="refcocog", split_by="umd",
            version=VERSION, description="refcocog"),
    ]

    # It's not mandatory to have a default configuration.
    # Just use one if it make sense.
    DEFAULT_CONFIG_NAME = "refcoco"

    def _info(self):
        self.config: ReferitBuilderConfig
        features = datasets.Features(
            {
                "ref_id": datasets.Value("int32"),
                "img_id": datasets.Value("int32"),
                "ann_id": datasets.Value("int32"),
                "file_name": datasets.Value("string"),
                "image_path": datasets.Value("string"),
                "height": datasets.Value("int32"),
                "width": datasets.Value("int32"),
                "coco_url": datasets.Value("string"),
                "sentences": [datasets.Value("string")],
                "segmentation": [[[datasets.Value("float")]]],
                "bbox": [[datasets.Value("float")]],
                "area": datasets.Value("float"),
                "iscrowd": datasets.Value("int32"),
                "category_id": datasets.Value("int32"),
            }
        )
        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,
            # If there's a common (input, target) tuple from the features,
            # uncomment supervised_keys line below and specify them.
            # They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # 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):
        # TODO: 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

        # dl_manager is a datasets.download.DownloadManager that can be used
        # to download and extract URLS. It can accept any type
        # or nested list/dict and will give back the same structure with
        # the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached
        # folder where they are extracted is returned instead of the archive
        # urls = _URLS[self.config.name]
        # data_dir = dl_manager.download_and_extract(urls)
        splits = []
        split_names = ("train", "val", "test")
        if self.config.name in ("refcoco", "refcoco+"):
            split_names += ("testA", "testB")
        for split in split_names:
            splits.append(datasets.SplitGenerator(
                name=datasets.NamedSplit(split),
                gen_kwargs={
                    "split": split,
                },
            ))
        return splits

    # method parameters are unpacked from `gen_kwargs` as given in
    # `_split_generators`
    def _generate_examples(self, split: str):
        # TODO: This method handles input defined in _split_generators to
        # yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important
        # in itself, but must be unique for each example.
        refer = REFER(data_root=self.config.data_dir,
                      dataset=self.config.name,
                      splitBy=self.config.split_by)
        ref_ids = refer.getRefIds(split=split)
        for ref_id in ref_ids:
            ref = refer.loadRefs(ref_id)[0]
            ann_id = ref['ann_id']
            ann = refer.loadAnns(ann_id)[0]
            img_id = ann['image_id']
            img = refer.loadImgs(img_id)[0]
            file_name = img['file_name']
            image_path = osp.join(
                self.config.data_dir, "images", "train2014", file_name)
            descriptions = [r['raw'] for r in ref['sentences']]
            yield ref_id, {
                "ref_id": ref_id,
                "img_id": img_id,
                "ann_id": ann_id,
                "file_name": file_name,
                "image_path": image_path,
                "height": img['height'],
                "width": img['width'],
                "coco_url": img['coco_url'],
                "sentences": descriptions,
                "segmentation": [ann['segmentation']],
                "bbox": [ann['bbox']],
                "area": ann['area'],
                "iscrowd": ann['iscrowd'],
                "category_id": ann['category_id'],
            }