| import csv | |
| import datasets | |
| from datasets import DatasetDict | |
| LABELS = {"aerial", "interior", "exterior", "upshot", "skyline", "night"} | |
| _DATA_URL = { | |
| "train": [f"data/train_images.tar.gz" for i in range(5)], | |
| "validation": ["data/validation_images.tar.gz"], | |
| "test": ["data/test_images.tar.gz"], | |
| } | |
| class Sample(datasets.GeneratorBasedBuilder): | |
| DEFAULT_WRITER_BATCH_SIZE = 1000 | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description="A sample dataset to illustrate how to use HF APIs", | |
| features=datasets.Features( | |
| { | |
| "image": datasets.Image(), | |
| "label": datasets.ClassLabel(names=list(LABELS)), | |
| } | |
| ), | |
| homepage="github.com/SOM-Enterprise/hf-dataset-sample-representation", | |
| citation="None", | |
| task_templates=[ | |
| datasets.ImageClassification(image_column="image", label_column="label")], | |
| ) | |
| def _split_generators(self, dl_manager): | |
| archives = dl_manager.download(_DATA_URL) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "archives": [dl_manager.iter_archive(archive) for archive in archives["train"]], | |
| "split": "train", | |
| } | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "archives": [dl_manager.iter_archive(archive) for archive in archives["validation"]], | |
| "split": "validation", | |
| } | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "archives": [dl_manager.iter_archive(archive) for archive in archives["test"]], | |
| "split": "test", | |
| } | |
| ) | |
| ] | |
| def _generate_examples(self, archives, split): | |
| labels_dict = {} | |
| with open('metadata.csv', newline='') as csvfile: | |
| reader = csv.DictReader(csvfile) | |
| for row in reader: | |
| labels_dict[row['id']] = set(row['label'].split('|')) | |
| idx = 0 | |
| for archive in archives: | |
| for path, file in archive: | |
| if path.endswith(".jpeg"): | |
| if split != "test": | |
| labels = labels_dict.get(path.split('/')[-1]) | |
| label = labels if labels else [''] | |
| else: | |
| label = -1 | |
| ex = {"image": {"path": path, "bytes": file.read()}, "label": label} | |
| yield idx, ex | |
| idx += 1 |