LILA.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 csv
import json
import os
import math
import requests
from io import BytesIO
from zipfile import ZipFile
from urllib.request import urlopen
import pandas as pd

import datasets

# 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 new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# 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 = ""

_LILA_SAS_URLS = pd.read_csv("https://lila.science/wp-content/uploads/2020/03/lila_sas_urls.txt")
_LILA_SAS_URLS.rename(columns={"# name": "name"}, inplace=True)

# How do I make these point to the particular commit ID?
_LILA_URLS = {
    "Caltech Camera Traps": "https://huggingface.co/datasets/NimaBoscarino/LILA/resolve/main/data/Caltech_Camera_Traps.jsonl",
    "ENA24": "https://huggingface.co/datasets/NimaBoscarino/LILA/resolve/main/data/ENA24.jsonl",
    "Missouri Camera Traps": "",
    "NACTI": "",
    "WCS Camera Traps": "",
    "Wellington Camera Traps": "",
    "Island Conservation Camera Traps": "",
    "Channel Islands Camera Traps": "",
    "Idaho Camera Traps": "",
    "Snapshot Serengeti": "",
    "Snapshot Karoo": "",
    "Snapshot Kgalagadi": "",
    "Snapshot Enonkishu": "",
    "Snapshot Camdeboo": "",
    "Snapshot Mountain Zebra": "",
    "Snapshot Kruger": "",
    "SWG Camera Traps": "",
    "Orinoquia Camera Traps": "",
}

# TODO: Just to make the Dataset viewer on the Hub work
DEFAULT_CONFIG_NAME = "Caltech Camera Traps"

class LILAConfig(datasets.BuilderConfig):
    """Builder Config for LILA"""

    def __init__(self, image_base_url, metadata_url, **kwargs):
        """BuilderConfig for LILA.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(LILAConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
        self.image_base_url = image_base_url
        self.metadata_url = metadata_url


class LILA(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.1.0")

    BUILDER_CONFIGS = [
        LILAConfig(
            name=row.name,
            # description="TODO: Description",
            image_base_url=row.image_base_url,
            metadata_url=_LILA_URLS[row.name]
        ) for row in _LILA_SAS_URLS.itertuples()
    ]

    def _get_features(self) -> datasets.Features:
        # TODO: Use ClassLabel for categories...
        # TODO: Deal with 404s -> In my manual preprocessing, or in the datasets library?

        if self.config.name == 'Caltech Camera Traps':
            return datasets.Features({
                "id": datasets.Value("string"), "file_name": datasets.Value("string"),
                "width": datasets.Value("int32"), "height": datasets.Value("int32"),
                "seq_num_frames": datasets.Value("int32"),
                "date_captured": datasets.Value("date32"),
                "seq_id": datasets.Value("string"),
                "location": datasets.Value("string"),
                "rights_holder": datasets.Value("string"),
                "frame_num": datasets.Value("int32"),


                "annotations": datasets.Sequence({
                    "id": datasets.Value("string"),
                    "category_id": datasets.Value("int32"),
                }),

                "bboxes": datasets.Sequence({
                    "id": datasets.Value("string"),
                    "category_id": datasets.Value("int32"),
                    "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
                }),

                "image": datasets.Image(decode=False),
            })
        elif self.config.name == 'ENA24':
            return datasets.Features({
                "id": datasets.Value("string"), "file_name": datasets.Value("string"),
                "width": datasets.Value("int32"), "height": datasets.Value("int32"),
                "annotations": datasets.Sequence({
                    "id": datasets.Value("string"),
                    "category_id": datasets.Value("int32"),
                    "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
                }),
                "image": datasets.Image(decode=False),
            })

    def _info(self):
        features = self._get_features()

        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, 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):
        archive_path = dl_manager.download_and_extract(self.config.metadata_url)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": archive_path,
                    "split": "train",
                },
            ),
        ]

    def _generate_examples(self, filepath, split):
        with open(filepath) as f:
            for line in f:
                example = json.loads(line)
                image_url = f"{self.config.image_base_url}/{example['file_name']}"
                yield example["id"], {
                    **example,
                    "image": image_url
                }