| # USAGE: this script is used to create an image dataset that is NOT hosted on HuggingFace but points to the original files | |
| # to download and generate the dataset. | |
| import os | |
| import datasets | |
| from datasets.tasks import ImageClassification | |
| _DESCRIPTION = """\ | |
| Images collected using Wild Sage Nodes to detect wild fires. | |
| """ | |
| _HOMEPAGE = "https://sagecontinuum.org/" | |
| _LICENSE = "MIT" | |
| # 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 = "https://web.lcrc.anl.gov/public/waggle/datasets/smoke-example.tar" | |
| _NAMES = [ | |
| "cloud", | |
| "other", | |
| "smoke" | |
| ] | |
| _PROMPT = "What is shown in the image?" | |
| _CHOICES = _NAMES | |
| class smokedataset_QA(datasets.GeneratorBasedBuilder): | |
| def _info(self): | |
| 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= datasets.Features( | |
| { | |
| "image": datasets.Image(), | |
| "label": datasets.ClassLabel(names=_NAMES), | |
| "prompt": datasets.Value(dtype='string'), | |
| "choices": datasets.Sequence(datasets.Value("string")) | |
| } | |
| ), | |
| # 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=None, | |
| # Homepage of the dataset for documentation | |
| homepage=_HOMEPAGE, | |
| # License for the dataset if available | |
| license=_LICENSE | |
| # Citation for the dataset | |
| # citation=_CITATION, | |
| ) | |
| # 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 | |
| def _split_generators(self, dl_manager): | |
| # 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 | |
| data_dir = dl_manager.download(_URLS) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "images": dl_manager.iter_archive(data_dir), | |
| "split": "test", | |
| "prompt": _PROMPT, | |
| "choices": _CHOICES | |
| }, | |
| ) | |
| ] | |
| # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| def _generate_examples(self, images, prompt, choices, split): | |
| for file_path, file_obj in images: | |
| label = file_path.split("/")[1] | |
| yield file_path,{ | |
| "image": {"path": file_path, "bytes": file_obj.read()}, | |
| "label": label, | |
| "prompt": prompt, | |
| "choices": choices | |
| } |