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import os |
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import datasets |
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_DESCRIPTION = """\ |
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A segmentation dataset for [TODO: complete...] |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/alkzar90/cell_benchmark" |
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_EXTENSION = [".jpg", ".png"] |
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_URL_BASE = "https://huggingface.co/datasets/alkzar90/cell_benchmark/resolve/main/data/" |
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_SPLIT_URLS = { |
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"train": _URL_BASE + "train.zip", |
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"val": _URL_BASE + "val.zip", |
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"test": _URL_BASE + "test.zip", |
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"masks_train": _URL_BASE + "masks/train.zip", |
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"masks_val": _URL_BASE + "masks/val.zip", |
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"masks_test": _URL_BASE + "masks/test.zip", |
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} |
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class Cellsegmentation(datasets.GeneratorBasedBuilder): |
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def _info(self): |
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features = datasets.Features({ |
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"image": datasets.Image(), |
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"masks": datasets.Image(), |
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}) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features(features), |
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supervised_keys=("image", "masks"), |
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homepage=_HOMEPAGE, |
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citation="", |
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) |
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def _split_generators(self, dl_manager): |
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data_files = dl_manager.download_and_extract(_SPLIT_URLS) |
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splits = [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"files" : dl_manager.iter_files([data_files["train"]]), |
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"masks": dl_manager.iter_files([data_files["masks_train"]]), |
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"split": "training", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"files" : dl_manager.iter_files([data_files["val"]]), |
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"masks": dl_manager.iter_files([data_files["masks_val"]]), |
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"split": "validation", |
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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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"files" : dl_manager.iter_files([data_files["test"]]), |
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"masks": dl_manager.iter_files([data_files["masks_test"]]), |
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"split": "test", |
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} |
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) |
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] |
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return splits |
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def _generate_examples(self, files, masks, split): |
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for i, path in enumerate(zip(files, masks)): |
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yield i, { |
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"image": path[0], |
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"masks": path[1], |
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} |
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