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import os |
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import json |
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import collections |
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import datasets |
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from datasets import NamedSplit |
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from datasets.download.download_manager import DownloadManager |
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_DESCRIPTION = """\ |
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This dataset contains all THIENVIET products images and annotations split in training |
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and validation. |
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""" |
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_URL = "https://huggingface.co./datasets/chanelcolgate/yenthienviet/resolve/main/data/yenthienviet_coco_hf.zip" |
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_CATEGORIES = [ |
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"hop_dln", |
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"hop_jn", |
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"hop_vtg", |
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"hop_ytv", |
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"lo_kids", |
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"lo_ytv", |
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"loc_dln", |
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"loc_jn", |
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"loc_kids", |
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"loc_ytv", |
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] |
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class Yenthienviet(datasets.GeneratorBasedBuilder): |
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"""Yenthienviet dataset.""" |
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VERSION = datasets.Version("1.0.0") |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"image_id": datasets.Value("int64"), |
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"image": datasets.Image(), |
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"width": datasets.Value("int32"), |
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"height": datasets.Value("int32"), |
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"objects": datasets.Sequence( |
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{ |
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"id": datasets.Value("int64"), |
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"area": datasets.Value("int64"), |
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"bbox": datasets.Sequence( |
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datasets.Value("float32"), length=4 |
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), |
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"category": datasets.ClassLabel(names=_CATEGORIES), |
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} |
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), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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) |
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def _split_generators(self, dl_manager: DownloadManager): |
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archive = dl_manager.download(_URL) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"annotation_file_path": "annotations/train.json", |
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"files": dl_manager.iter_archive(archive), |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"annotation_file_path": "annotations/test.json", |
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"files": dl_manager.iter_archive(archive), |
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}, |
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), |
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datasets.SplitGenerator( |
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name=NamedSplit("val"), |
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gen_kwargs={ |
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"annotation_file_path": "annotations/val.json", |
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"files": dl_manager.iter_archive(archive), |
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}, |
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), |
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] |
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def _generate_examples(self, annotation_file_path, files): |
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def process_annot(annot, category_id_to_category): |
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return { |
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"id": annot["id"], |
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"area": annot["area"], |
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"bbox": annot["bbox"], |
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"category": category_id_to_category[annot["category_id"]], |
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} |
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image_id_to_image = [] |
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idx = 0 |
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for path, f in files: |
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file_name = os.path.basename(path) |
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if path == annotation_file_path: |
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annotations = json.load(f) |
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category_id_to_category = { |
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category["id"]: category["name"] |
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for category in annotations["categories"] |
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} |
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image_id_to_annotations = collections.defaultdict(list) |
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for annot in annotations["annotations"]: |
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image_id_to_annotations[annot["image_id"]].append(annot) |
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image_id_to_image = { |
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annot["file_name"]: annot for annot in annotations["images"] |
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} |
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elif file_name in image_id_to_image: |
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image = image_id_to_image[file_name] |
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objects = [ |
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process_annot(annot, category_id_to_category) |
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for annot in image_id_to_annotations[image["id"]] |
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] |
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yield idx, { |
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"image_id": image["id"], |
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"image": {"path": path, "bytes": f.read()}, |
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"width": image["width"], |
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"height": image["height"], |
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"objects": objects, |
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} |
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idx += 1 |
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