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""" |
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BIOSCAN-5M Dataset Loader |
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Author: Zahra Gharaee (https://github.com/zahrag) |
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License: MIT License |
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Description: |
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This custom dataset loader provides structured access to the BIOSCAN-5M dataset, |
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which includes millions of annotated insect images and associated metadata |
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for machine learning and biodiversity research. It supports multiple image resolutions |
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(e.g., cropped and original), and predefined splits for training, evaluation, |
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and pretraining. The loader integrates with the Hugging Face `datasets` library |
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to simplify data access and preparation. |
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Usage |
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To load the dataset from dataset.py: |
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from datasets import load_dataset |
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ds = load_dataset("dataset.py", name="cropped_256_eval", split="validation", trust_remote_code=True) |
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""" |
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import os |
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import csv |
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import datasets |
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import json |
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_CITATION = """\n----Citation:\n@inproceedings{gharaee2024bioscan5m, |
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title={{BIOSCAN-5M}: A Multimodal Dataset for Insect Biodiversity}, |
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booktitle={Advances in Neural Information Processing Systems}, |
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author={Zahra Gharaee and Scott C. Lowe and ZeMing Gong and Pablo Millan Arias |
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and Nicholas Pellegrino and Austin T. Wang and Joakim Bruslund Haurum |
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and Iuliia Zarubiieva and Lila Kari and Dirk Steinke and Graham W. Taylor |
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and Paul Fieguth and Angel X. Chang}, |
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editor={A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang}, |
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pages={36285--36313}, |
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publisher={Curran Associates, Inc.}, |
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year={2024}, |
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volume={37}, |
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url={https://proceedings.neurips.cc/paper_files/paper/2024/file/3fdbb472813041c9ecef04c20c2b1e5a-Paper-Datasets_and_Benchmarks_Track.pdf} |
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}\n""" |
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_DESCRIPTION = ( |
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"\n----Description:\n'BIOSCAN-5M' is a comprehensive multimodal dataset containing data for over 5 million insect specimens.\n" |
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"Released in 2024, this dataset substantially enhances existing image-based biological resources by incorporating:\n" |
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"- Taxonomic labels\n- Raw nucleotide barcode sequences \n- Assigned barcode index numbers\n- Geographical information\n" |
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"- Specimen size information\n\n" |
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"-------------- Dataset Feature Descriptions --------------\n" |
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"1- processid: A unique number assigned by BOLD (International Barcode of Life Consortium).\n" |
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"2- sampleid: A unique identifier given by the collector.\n" |
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"3- taxon: Bio.info: Most specific taxonomy rank.\n" |
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"4- phylum: Bio.info: Taxonomic classification label at phylum rank.\n" |
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"5- class: Bio.info: Taxonomic classification label at class rank.\n" |
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"6- order: Bio.info: Taxonomic classification label at order rank.\n" |
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"7- family: Bio.info: Taxonomic classification label at family rank.\n" |
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"8- subfamily: Bio.info: Taxonomic classification label at subfamily rank.\n" |
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"9- genus: Bio.info: Taxonomic classification label at genus rank.\n" |
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"10- species: Bio.info: Taxonomic classification label at species rank.\n" |
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"11- dna_bin: Bio.info: Barcode Index Number (BIN).\n" |
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"12- dna_barcode: Bio.info: Nucleotide barcode sequence.\n" |
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"13- country: Geo.info: Country associated with the site of collection.\n" |
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"14- province_state: Geo.info: Province/state associated with the site of collection.\n" |
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"15- coord-lat: Geo.info: Latitude (WGS 84; decimal degrees) of the collection site.\n" |
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"16- coord-lon: Geo.info: Longitude (WGS 84; decimal degrees) of the collection site.\n" |
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"17- image_measurement_value: Size.info: Number of pixels occupied by the organism.\n" |
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"18- area_fraction: Size.info: Fraction of the original image the cropped image comprises.\n" |
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"19- scale_factor: Size.info: Ratio of the cropped image to the cropped_256 image.\n" |
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"20- inferred_ranks: An integer indicating at which taxonomic ranks the label is inferred.\n" |
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"21- split: Split set (partition) the sample belongs to.\n" |
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"22- index_bioscan_1M_insect: An index to locate organism in BIOSCAN-1M Insect metadata.\n" |
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"23- chunk: The packaging subdirectory name (or empty string) for this image.\n" |
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) |
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license = "\n----License:\nCC BY 3.0: Creative Commons Attribution 3.0 Unported (https://creativecommons.org/licenses/by/3.0/)\n" |
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SUPPORTED_FORMATS = {"csv": "csv", "jsonld": "jsonld"} |
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SUPPORTED_PACKAGES = { |
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"original_256": "BIOSCAN_5M_original_256.zip", |
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"original_256_pretrain": "BIOSCAN_5M_original_256_pretrain.zip", |
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"original_256_train": "BIOSCAN_5M_original_256_train.zip", |
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"original_256_eval": "BIOSCAN_5M_original_256_eval.zip", |
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"cropped_256": "BIOSCAN_5M_cropped_256.zip", |
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"cropped_256_pretrain": "BIOSCAN_5M_cropped_256_pretrain.zip", |
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"cropped_256_train": "BIOSCAN_5M_cropped_256_train.zip", |
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"cropped_256_eval": "BIOSCAN_5M_cropped_256_eval.zip", |
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} |
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def safe_cast(value, cast_type): |
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try: |
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return cast_type(value) if value else None |
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except ValueError: |
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return None |
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def extract_info_from_filename(package_name): |
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""" |
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Extract imgtype and split_name using string ops. |
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Assumes package_name format: BIOSCAN_5M_<imgtype>[_<split_name>].zip |
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""" |
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if package_name not in SUPPORTED_PACKAGES.values(): |
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raise ValueError( |
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f"Unsupported package: {package_name}\n" |
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f"Supported packages are:\n - " + "\n - ".join(sorted(SUPPORTED_PACKAGES.values())) |
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) |
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core = package_name.replace("BIOSCAN_5M_", "").replace(".zip", "") |
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parts = core.split("_") |
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if len(parts) == 2: |
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imgtype = "_".join(parts) |
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data_split = "full" |
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elif len(parts) == 3: |
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imgtype = "_".join(parts[:2]) |
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data_split = parts[2] |
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else: |
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imgtype, data_split = None, None |
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return imgtype, data_split |
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class BIOSCAN5MConfig(datasets.BuilderConfig): |
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def __init__(self, metadata_format="csv", package_name="BIOSCAN_5M_cropped_256.zip", **kwargs): |
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super().__init__(**kwargs) |
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self.metadata_format = metadata_format |
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self.package_name = package_name |
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class BIOSCAN5M(datasets.GeneratorBasedBuilder): |
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"""Custom dataset loader for BIOSCAN-5M (images + metadata).""" |
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BUILDER_CONFIGS = [ |
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BIOSCAN5MConfig( |
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name="cropped_256_eval", |
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version=datasets.Version("0.0.0"), |
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description="Cropped_256 images for evaluation splits.", |
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metadata_format=SUPPORTED_FORMATS["csv"], |
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package_name=SUPPORTED_PACKAGES["cropped_256_eval"], |
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), |
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BIOSCAN5MConfig( |
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name="cropped_256_train", |
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version=datasets.Version("0.0.0"), |
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description="Cropped_256 images for training split.", |
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metadata_format=SUPPORTED_FORMATS["csv"], |
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package_name=SUPPORTED_PACKAGES["cropped_256_train"], |
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), |
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BIOSCAN5MConfig( |
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name="cropped_256_pretrain", |
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version=datasets.Version("0.0.0"), |
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description="Cropped images for pretraining split.", |
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metadata_format=SUPPORTED_FORMATS["csv"], |
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package_name=SUPPORTED_PACKAGES["cropped_256_pretrain"], |
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), |
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BIOSCAN5MConfig( |
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name="cropped_256", |
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version=datasets.Version("0.0.0"), |
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description="Cropped_256 images for full splits.", |
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metadata_format=SUPPORTED_FORMATS["csv"], |
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package_name=SUPPORTED_PACKAGES["cropped_256"], |
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), |
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BIOSCAN5MConfig( |
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name="original_256_eval", |
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version=datasets.Version("0.0.0"), |
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description="Original_256 images for evaluation splits.", |
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metadata_format=SUPPORTED_FORMATS["csv"], |
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package_name=SUPPORTED_PACKAGES["original_256_eval"], |
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), |
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BIOSCAN5MConfig( |
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name="original_256_train", |
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version=datasets.Version("0.0.0"), |
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description="Original_256 images for training split.", |
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metadata_format=SUPPORTED_FORMATS["csv"], |
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package_name=SUPPORTED_PACKAGES["original_256_train"], |
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), |
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BIOSCAN5MConfig( |
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name="original_256_pretrain", |
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version=datasets.Version("0.0.0"), |
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description="Original images for pretraining split.", |
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metadata_format=SUPPORTED_FORMATS["csv"], |
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package_name=SUPPORTED_PACKAGES["original_256_pretrain"], |
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), |
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BIOSCAN5MConfig( |
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name="original_256", |
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version=datasets.Version("0.0.0"), |
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description="Original_256 images for full splits.", |
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metadata_format=SUPPORTED_FORMATS["csv"], |
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package_name=SUPPORTED_PACKAGES["original_256"], |
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), |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features({ |
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"image": datasets.Image(), |
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"processid": datasets.Value("string"), |
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"sampleid": datasets.Value("string"), |
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"taxon": datasets.Value("string"), |
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"phylum": datasets.Value("string"), |
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"class": datasets.Value("string"), |
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"order": datasets.Value("string"), |
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"family": datasets.Value("string"), |
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"subfamily": datasets.Value("string"), |
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"genus": datasets.Value("string"), |
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"species": datasets.Value("string"), |
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"dna_bin": datasets.Value("string"), |
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"dna_barcode": datasets.Value("string"), |
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"country": datasets.Value("string"), |
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"province_state": datasets.Value("string"), |
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"coord-lat": datasets.Value("float"), |
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"coord-lon": datasets.Value("float"), |
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"image_measurement_value": datasets.Value("int64"), |
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"area_fraction": datasets.Value("float"), |
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"scale_factor": datasets.Value("float"), |
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"inferred_ranks": datasets.Value("int32"), |
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"split": datasets.Value("string"), |
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"index_bioscan_1M_insect": datasets.Value("int32"), |
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"chunk": datasets.Value("string"), |
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}), |
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supervised_keys=None, |
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homepage="https://huggingface.co/datasets/bioscan-ml/BIOSCAN-5M", |
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citation=_CITATION, |
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license=license, |
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) |
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def _split_generators(self, dl_manager, **kwargs ): |
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"""Custom dataset split generator""" |
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metadata_format = self.config.metadata_format |
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package_name = self.config.package_name |
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imgtype, data_split = extract_info_from_filename(package_name) |
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metadata_url = "https://huggingface.co/datasets/bioscan-ml/BIOSCAN-5M/resolve/main/BIOSCAN_5M_Insect_Dataset_metadata_MultiTypes.zip" |
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metadata_archive = dl_manager.download_and_extract(metadata_url) |
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metadata_file = os.path.join( |
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metadata_archive, |
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f"bioscan5m/metadata/{metadata_format}/BIOSCAN_5M_Insect_Dataset_metadata.{metadata_format}" |
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) |
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image_url = f"https://huggingface.co/datasets/bioscan-ml/BIOSCAN-5M/resolve/main/{package_name}" |
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image_archives = dl_manager.download_and_extract([image_url]) |
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image_dirs = [archive for archive in image_archives] |
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eval_splits = [ |
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"val", "test", "val_unseen", "test_unseen", "key_unseen", "other_heldout" |
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] |
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splits = ["pretrain", "train"] + eval_splits |
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hf_splits = { |
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"train": datasets.Split.TRAIN, |
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"val": datasets.Split.VALIDATION, |
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"test": datasets.Split.TEST, |
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} |
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if data_split == "full": |
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return [ |
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datasets.SplitGenerator( |
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name=hf_splits.get(split, split), |
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gen_kwargs={ |
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"metadata_path": metadata_file, |
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"image_dirs": image_dirs, |
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"split": split, |
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"imgtype": imgtype, |
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}, |
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) |
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for split in splits |
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] |
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elif data_split == "eval": |
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return [ |
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datasets.SplitGenerator( |
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name=hf_splits.get(split, split), |
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gen_kwargs={ |
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"metadata_path": metadata_file, |
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"image_dirs": image_dirs, |
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"split": split, |
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"imgtype": imgtype, |
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}, |
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) |
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for split in eval_splits |
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] |
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else: |
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return [ |
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datasets.SplitGenerator( |
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name=hf_splits.get(data_split, data_split), |
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gen_kwargs={ |
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"metadata_path": metadata_file, |
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"image_dirs": image_dirs, |
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"split": data_split, |
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"imgtype": imgtype, |
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}, |
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) |
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] |
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def _generate_examples(self, metadata_path, image_dirs, split, imgtype): |
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if metadata_path.endswith(".csv"): |
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with open(metadata_path, encoding="utf-8") as f: |
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reader = csv.DictReader(f) |
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for idx, row in enumerate(reader): |
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if row["split"] != split: |
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continue |
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processid = row["processid"] |
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chunk = row.get("chunk", "").strip() if row.get("chunk") else "" |
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if chunk == "": |
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rel_path = f"bioscan5m/images/{imgtype}/{split}/{processid}.jpg" |
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else: |
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rel_path = f"bioscan5m/images/{imgtype}/{split}/{chunk}/{processid}.jpg" |
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image_path = None |
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for image_dir in image_dirs: |
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potential_path = os.path.join(image_dir, rel_path) |
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if os.path.exists(potential_path): |
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image_path = potential_path |
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break |
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if image_path is None: |
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print(f" ---- Image NOT Found! ---- \n{potential_path}") |
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continue |
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yield idx, { |
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"image": image_path, |
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"processid": row["processid"], |
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"sampleid": row["sampleid"], |
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"taxon": row["taxon"], |
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"phylum": row["phylum"] or None, |
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"class": row["class"] or None, |
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"order": row["order"] or None, |
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"family": row["family"] or None, |
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"subfamily": row["subfamily"] or None, |
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"genus": row["genus"] or None, |
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"species": row["species"] or None, |
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"dna_bin": row["dna_bin"] or None, |
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"dna_barcode": row["dna_barcode"], |
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"country": row["country"] or None, |
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"province_state": row["province_state"] or None, |
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"coord-lat": safe_cast(row["coord-lat"], float), |
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"coord-lon": safe_cast(row["coord-lon"], float), |
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"image_measurement_value": safe_cast(row["image_measurement_value"], float), |
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"area_fraction": safe_cast(row["area_fraction"], float), |
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"scale_factor": safe_cast(row["scale_factor"], float), |
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"inferred_ranks": safe_cast(row["inferred_ranks"], int), |
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"split": row["split"], |
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"index_bioscan_1M_insect": safe_cast(row["index_bioscan_1M_insect"], float), |
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"chunk": row["chunk"] or None, |
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} |
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elif metadata_path.endswith(".jsonld"): |
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with open(metadata_path, encoding="utf-8") as f: |
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metadata = json.load(f) |
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for idx, row in enumerate(metadata): |
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if row["split"] != split: |
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continue |
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processid = row["processid"] |
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chunk = row.get("chunk", "").strip() if row.get("chunk") else "" |
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if chunk == "": |
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rel_path = f"bioscan5m/images/{imgtype}/{split}/{processid}.jpg" |
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else: |
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rel_path = f"bioscan5m/images/{imgtype}/{split}/{chunk}/{processid}.jpg" |
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image_path = None |
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for image_dir in image_dirs: |
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potential_path = os.path.join(image_dir, rel_path) |
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if os.path.exists(potential_path): |
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image_path = potential_path |
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break |
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if image_path is None: |
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print(f" ---- Image NOT Found! ---- \n{potential_path}") |
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continue |
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yield idx, { |
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"image": image_path, |
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"processid": row["processid"], |
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"sampleid": row["sampleid"], |
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"taxon": row["taxon"], |
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"phylum": row["phylum"] or None, |
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"class": row["class"] or None, |
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"order": row["order"] or None, |
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"family": row["family"] or None, |
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"subfamily": row["subfamily"] or None, |
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"genus": row["genus"] or None, |
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"species": row["species"] or None, |
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"dna_bin": row["dna_bin"] or None, |
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"dna_barcode": row["dna_barcode"], |
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"country": row["country"] or None, |
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"province_state": row["province_state"] or None, |
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"coord-lat": safe_cast(row["coord-lat"], float), |
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"coord-lon": safe_cast(row["coord-lon"], float), |
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"image_measurement_value": safe_cast(row["image_measurement_value"], float), |
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"area_fraction": safe_cast(row["area_fraction"], float), |
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"scale_factor": safe_cast(row["scale_factor"], float), |
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"inferred_ranks": safe_cast(row["inferred_ranks"], int), |
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"split": row["split"], |
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"index_bioscan_1M_insect": safe_cast(row["index_bioscan_1M_insect"], float), |
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"chunk": row["chunk"] or None, |
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} |
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else: |
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raise ValueError( |
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f"Unsupported format: {os.path.splitext(metadata_path.lower())[1]}\n" |
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f"Supported formats are:\n - " + "\n - ".join(sorted(SUPPORTED_FORMATS.values())) |
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) |
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