Upload bioscan5m.py

BIOSCAN5M Dataloader

bioscan5m.py

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+"""
+BIOSCAN-5M Dataset Loader
+
+Author: Zahra Gharaee (https://github.com/zahrag)
+License: MIT License
+Description:
+    This custom dataset loader provides structured access to the BIOSCAN-5M dataset,
+    which includes millions of annotated insect images and associated metadata
+    for machine learning and biodiversity research. It supports multiple image resolutions
+    (e.g., cropped and original), and predefined splits for training, evaluation,
+    and pretraining. The loader integrates with the Hugging Face `datasets` library
+    to simplify data access and preparation.
+
+Usage
+
+To load the dataset locally from bioscan5m.py:
+
+    from datasets import load_dataset
+    ds = load_dataset("bioscan5m.py", name="cropped_256_eval", split="validation", trust_remote_code=True)
+
+To load the dataset from the Hugging Face Hub:
+
+    from datasets import load_dataset
+    ds = load_dataset("bioscan-ml/BIOSCAN-5M", name="cropped_256_eval", split="validation", trust_remote_code=True)
+"""
+
+
+import os
+import csv
+import datasets
+import json
+
+
+_CITATION = """\n----Citation:\n@inproceedings{gharaee2024bioscan5m,
+  title={{BIOSCAN-5M}: A Multimodal Dataset for Insect Biodiversity},
+  booktitle={Advances in Neural Information Processing Systems},
+  author={Zahra Gharaee and Scott C. Lowe and ZeMing Gong and Pablo Millan Arias
+          and Nicholas Pellegrino and Austin T. Wang and Joakim Bruslund Haurum
+          and Iuliia Zarubiieva and Lila Kari and Dirk Steinke and Graham W. Taylor
+          and Paul Fieguth and Angel X. Chang},
+  editor={A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
+  pages={36285--36313},
+  publisher={Curran Associates, Inc.},
+  year={2024},
+  volume={37},
+  url={https://proceedings.neurips.cc/paper_files/paper/2024/file/3fdbb472813041c9ecef04c20c2b1e5a-Paper-Datasets_and_Benchmarks_Track.pdf}
+}\n"""
+
+_DESCRIPTION = (
+    "\n----Description:\n'BIOSCAN-5M' is a comprehensive multimodal dataset containing data for over 5 million insect specimens.\n"
+    "Released in 2024, this dataset substantially enhances existing image-based biological resources by incorporating:\n"
+    "- Taxonomic labels\n- Raw nucleotide barcode sequences \n- Assigned barcode index numbers\n- Geographical information\n"
+    "- Specimen size information\n\n"
+    "-------------- Dataset Feature Descriptions --------------\n"
+    "1- processid: A unique number assigned by BOLD (International Barcode of Life Consortium).\n"
+    "2- sampleid: A unique identifier given by the collector.\n"
+    "3- taxon: Bio.info: Most specific taxonomy rank.\n"
+    "4- phylum: Bio.info: Taxonomic classification label at phylum rank.\n"
+    "5- class: Bio.info: Taxonomic classification label at class rank.\n"
+    "6- order: Bio.info: Taxonomic classification label at order rank.\n"
+    "7- family: Bio.info: Taxonomic classification label at family rank.\n"
+    "8- subfamily: Bio.info: Taxonomic classification label at subfamily rank.\n"
+    "9- genus: Bio.info: Taxonomic classification label at genus rank.\n"
+    "10- species: Bio.info: Taxonomic classification label at species rank.\n"
+    "11- dna_bin: Bio.info: Barcode Index Number (BIN).\n"
+    "12- dna_barcode: Bio.info: Nucleotide barcode sequence.\n"
+    "13- country: Geo.info: Country associated with the site of collection.\n"
+    "14- province_state: Geo.info: Province/state associated with the site of collection.\n"
+    "15- coord-lat: Geo.info: Latitude (WGS 84; decimal degrees) of the collection site.\n"
+    "16- coord-lon: Geo.info: Longitude (WGS 84; decimal degrees) of the collection site.\n"
+    "17- image_measurement_value: Size.info: Number of pixels occupied by the organism.\n"
+    "18- area_fraction: Size.info: Fraction of the original image the cropped image comprises.\n"
+    "19- scale_factor: Size.info: Ratio of the cropped image to the cropped_256 image.\n"
+    "20- inferred_ranks: An integer indicating at which taxonomic ranks the label is inferred.\n"
+    "21- split: Split set (partition) the sample belongs to.\n"
+    "22- index_bioscan_1M_insect: An index to locate organism in BIOSCAN-1M Insect metadata.\n"
+    "23- chunk: The packaging subdirectory name (or empty string) for this image.\n"
+)
+
+license = "\n----License:\nCC BY 3.0: Creative Commons Attribution 3.0 Unported (https://creativecommons.org/licenses/by/3.0/)\n"
+
+SUPPORTED_FORMATS = {"csv": "csv", "jsonld": "jsonld"}
+
+SUPPORTED_PACKAGES = {
+        "original_256": "BIOSCAN_5M_original_256.zip",
+        "original_256_pretrain": "BIOSCAN_5M_original_256_pretrain.zip",
+        "original_256_train": "BIOSCAN_5M_original_256_train.zip",
+        "original_256_eval": "BIOSCAN_5M_original_256_eval.zip",
+        "cropped_256": "BIOSCAN_5M_cropped_256.zip",
+        "cropped_256_pretrain": "BIOSCAN_5M_cropped_256_pretrain.zip",
+        "cropped_256_train": "BIOSCAN_5M_cropped_256_train.zip",
+        "cropped_256_eval": "BIOSCAN_5M_cropped_256_eval.zip",
+    }
+
+
+def safe_cast(value, cast_type):
+    try:
+        return cast_type(value) if value else None
+    except ValueError:
+        return None
+
+def extract_info_from_filename(package_name):
+    """
+    Extract imgtype and split_name using string ops.
+    Assumes package_name format: BIOSCAN_5M_<imgtype>[_<split_name>].zip
+
+    """
+
+    if package_name not in SUPPORTED_PACKAGES.values():
+        raise ValueError(
+            f"Unsupported package: {package_name}\n"
+            f"Supported packages are:\n  - " + "\n  - ".join(sorted(SUPPORTED_PACKAGES.values()))
+        )
+
+    # Remove prefix and suffix
+    core = package_name.replace("BIOSCAN_5M_", "").replace(".zip", "")
+
+    parts = core.split("_")
+
+    if len(parts) == 2:
+        imgtype = "_".join(parts)
+        data_split = "full"
+    elif len(parts) == 3:
+        imgtype = "_".join(parts[:2])
+        data_split = parts[2]
+    else:
+        imgtype, data_split = None, None  # Unexpected format
+
+    return imgtype, data_split
+
+
+class BIOSCAN5MConfig(datasets.BuilderConfig):
+    def __init__(self, metadata_format="csv", package_name="BIOSCAN_5M_cropped_256.zip", **kwargs):
+        super().__init__(**kwargs)
+        self.metadata_format = metadata_format
+        self.package_name = package_name
+
+
+class BIOSCAN5M_Dataset(datasets.GeneratorBasedBuilder):
+    """Custom dataset loader for BIOSCAN-5M (images + metadata)."""
+
+    BUILDER_CONFIGS = [
+        BIOSCAN5MConfig(
+            name="cropped_256_eval",
+            version=datasets.Version("0.0.0"),
+            description="Cropped_256 images for evaluation splits.",
+            metadata_format=SUPPORTED_FORMATS["csv"],
+            package_name=SUPPORTED_PACKAGES["cropped_256_eval"],
+        ),
+        BIOSCAN5MConfig(
+            name="cropped_256_train",
+            version=datasets.Version("0.0.0"),
+            description="Cropped_256 images for training split.",
+            metadata_format=SUPPORTED_FORMATS["csv"],
+            package_name=SUPPORTED_PACKAGES["cropped_256_train"],
+        ),
+        BIOSCAN5MConfig(
+            name="cropped_256_pretrain",
+            version=datasets.Version("0.0.0"),
+            description="Cropped images for pretraining split.",
+            metadata_format=SUPPORTED_FORMATS["csv"],
+            package_name=SUPPORTED_PACKAGES["cropped_256_pretrain"],
+        ),
+        BIOSCAN5MConfig(
+            name="cropped_256",
+            version=datasets.Version("0.0.0"),
+            description="Cropped_256 images for full splits.",
+            metadata_format=SUPPORTED_FORMATS["csv"],
+            package_name=SUPPORTED_PACKAGES["cropped_256"],
+        ),
+        BIOSCAN5MConfig(
+            name="original_256_eval",
+            version=datasets.Version("0.0.0"),
+            description="Original_256 images for evaluation splits.",
+            metadata_format=SUPPORTED_FORMATS["csv"],
+            package_name=SUPPORTED_PACKAGES["original_256_eval"],
+        ),
+        BIOSCAN5MConfig(
+            name="original_256_train",
+            version=datasets.Version("0.0.0"),
+            description="Original_256 images for training split.",
+            metadata_format=SUPPORTED_FORMATS["csv"],
+            package_name=SUPPORTED_PACKAGES["original_256_train"],
+        ),
+        BIOSCAN5MConfig(
+            name="original_256_pretrain",
+            version=datasets.Version("0.0.0"),
+            description="Original images for pretraining split.",
+            metadata_format=SUPPORTED_FORMATS["csv"],
+            package_name=SUPPORTED_PACKAGES["original_256_pretrain"],
+        ),
+        BIOSCAN5MConfig(
+            name="original_256",
+            version=datasets.Version("0.0.0"),
+            description="Original_256 images for full splits.",
+            metadata_format=SUPPORTED_FORMATS["csv"],
+            package_name=SUPPORTED_PACKAGES["original_256"],
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "image": datasets.Image(),
+                "processid": datasets.Value("string"),
+                "sampleid": datasets.Value("string"),
+                "taxon": datasets.Value("string"),
+                "phylum": datasets.Value("string"),
+                "class": datasets.Value("string"),
+                "order": datasets.Value("string"),
+                "family": datasets.Value("string"),
+                "subfamily": datasets.Value("string"),
+                "genus": datasets.Value("string"),
+                "species": datasets.Value("string"),
+                "dna_bin": datasets.Value("string"),
+                "dna_barcode": datasets.Value("string"),
+                "country": datasets.Value("string"),
+                "province_state": datasets.Value("string"),
+                "coord-lat": datasets.Value("float"),
+                "coord-lon": datasets.Value("float"),
+                "image_measurement_value": datasets.Value("int64"),
+                "area_fraction": datasets.Value("float"),
+                "scale_factor": datasets.Value("float"),
+                "inferred_ranks": datasets.Value("int32"),
+                "split": datasets.Value("string"),
+                "index_bioscan_1M_insect": datasets.Value("int32"),
+                "chunk": datasets.Value("string"),
+            }),
+            supervised_keys=None,
+            homepage="https://huggingface.co/datasets/bioscan-ml/BIOSCAN-5M",
+            citation=_CITATION,
+            license=license,
+        )
+
+    def _split_generators(self, dl_manager, **kwargs ):
+        """Custom dataset split generator"""
+
+        metadata_format = self.config.metadata_format
+        package_name = self.config.package_name
+
+        imgtype, data_split = extract_info_from_filename(package_name)
+
+        # Download metadata
+        metadata_url = "https://huggingface.co/datasets/bioscan-ml/BIOSCAN-5M/resolve/main/BIOSCAN_5M_Insect_Dataset_metadata_MultiTypes.zip"
+        metadata_archive = dl_manager.download_and_extract(metadata_url)
+        metadata_file = os.path.join(
+            metadata_archive,
+            f"bioscan5m/metadata/{metadata_format}/BIOSCAN_5M_Insect_Dataset_metadata.{metadata_format}"
+        )
+
+        # Download image archives
+        image_url = f"https://huggingface.co/datasets/bioscan-ml/BIOSCAN-5M/resolve/main/{package_name}"
+        image_archives = dl_manager.download_and_extract([image_url])
+        image_dirs = [archive for archive in image_archives]
+
+        # Define all available splits
+        eval_splits = [
+            "val", "test", "val_unseen", "test_unseen", "key_unseen", "other_heldout"
+        ]
+        splits = ["pretrain", "train"] + eval_splits
+
+        hf_splits = {
+            "train": datasets.Split.TRAIN,
+            "val": datasets.Split.VALIDATION,
+            "test": datasets.Split.TEST,
+        }
+
+        if data_split == "full":  # All partitions
+            return [
+                datasets.SplitGenerator(
+                    name=hf_splits.get(split, split),
+                    gen_kwargs={
+                        "metadata_path": metadata_file,
+                        "image_dirs": image_dirs,
+                        "split": split,
+                        "imgtype": imgtype,
+                    },
+                )
+                for split in splits
+            ]
+
+        elif data_split == "eval":  # Evaluation partitions
+            return [
+                datasets.SplitGenerator(
+                    name=hf_splits.get(split, split),
+                    gen_kwargs={
+                        "metadata_path": metadata_file,
+                        "image_dirs": image_dirs,
+                        "split": split,
+                        "imgtype": imgtype,
+                    },
+                )
+                for split in eval_splits
+            ]
+
+        else:   # train and pretrain partitions
+            return [
+                datasets.SplitGenerator(
+                    name=hf_splits.get(data_split, data_split),
+                    gen_kwargs={
+                        "metadata_path": metadata_file,
+                        "image_dirs": image_dirs,
+                        "split": data_split,
+                        "imgtype": imgtype,
+                    },
+                )
+            ]
+
+    def _generate_examples(self, metadata_path, image_dirs, split, imgtype):
+
+        if metadata_path.endswith(".csv"):
+            with open(metadata_path, encoding="utf-8") as f:
+                reader = csv.DictReader(f)
+                for idx, row in enumerate(reader):
+                    if row["split"] != split:
+                        continue  # Skip others and keep the chosen split samples
+
+                    processid = row["processid"]
+                    chunk = row.get("chunk", "").strip() if row.get("chunk") else ""
+
+                    # Construct expected relative path
+                    if chunk == "":
+                        rel_path = f"bioscan5m/images/{imgtype}/{split}/{processid}.jpg"
+                    else:
+                        rel_path = f"bioscan5m/images/{imgtype}/{split}/{chunk}/{processid}.jpg"
+
+                    # Search for the image file inside extracted image_dirs
+                    image_path = None
+                    for image_dir in image_dirs:
+                        potential_path = os.path.join(image_dir, rel_path)
+                        if os.path.exists(potential_path):
+                            image_path = potential_path
+                            break  # Image found; end search
+
+                    if image_path is None:
+                        print(f" ---- Image NOT Found! ---- \n{potential_path}")
+                        continue
+
+                    yield idx, {
+                        "image": image_path,
+                        "processid": row["processid"],
+                        "sampleid": row["sampleid"],
+                        "taxon": row["taxon"],
+                        "phylum": row["phylum"] or None,
+                        "class": row["class"] or None,
+                        "order": row["order"] or None,
+                        "family": row["family"] or None,
+                        "subfamily": row["subfamily"] or None,
+                        "genus": row["genus"] or None,
+                        "species": row["species"] or None,
+                        "dna_bin": row["dna_bin"] or None,
+                        "dna_barcode": row["dna_barcode"],
+                        "country": row["country"] or None,
+                        "province_state": row["province_state"] or None,
+                        "coord-lat": safe_cast(row["coord-lat"], float),
+                        "coord-lon": safe_cast(row["coord-lon"], float),
+                        "image_measurement_value": safe_cast(row["image_measurement_value"], float),
+                        "area_fraction": safe_cast(row["area_fraction"], float),
+                        "scale_factor": safe_cast(row["scale_factor"], float),
+                        "inferred_ranks": safe_cast(row["inferred_ranks"], int),
+                        "split": row["split"],
+                        "index_bioscan_1M_insect": safe_cast(row["index_bioscan_1M_insect"], float),
+                        "chunk": row["chunk"] or None,
+                    }
+        elif metadata_path.endswith(".jsonld"):
+            with open(metadata_path, encoding="utf-8") as f:
+                metadata = json.load(f)
+                for idx, row in enumerate(metadata):
+                    if row["split"] != split:
+                        continue  # Skip others and keep the chosen split samples
+
+                    processid = row["processid"]
+                    chunk = row.get("chunk", "").strip() if row.get("chunk") else ""
+
+                    # Construct expected relative path
+                    if chunk == "":
+                        rel_path = f"bioscan5m/images/{imgtype}/{split}/{processid}.jpg"
+                    else:
+                        rel_path = f"bioscan5m/images/{imgtype}/{split}/{chunk}/{processid}.jpg"
+
+                    # Search for the image file inside extracted image_dirs
+                    image_path = None
+                    for image_dir in image_dirs:
+                        potential_path = os.path.join(image_dir, rel_path)
+                        if os.path.exists(potential_path):
+                            image_path = potential_path
+                            break  # Image found; end search
+
+                    if image_path is None:
+                        print(f" ---- Image NOT Found! ---- \n{potential_path}")
+                        continue
+
+                    yield idx, {
+                        "image": image_path,
+                        "processid": row["processid"],
+                        "sampleid": row["sampleid"],
+                        "taxon": row["taxon"],
+                        "phylum": row["phylum"] or None,
+                        "class": row["class"] or None,
+                        "order": row["order"] or None,
+                        "family": row["family"] or None,
+                        "subfamily": row["subfamily"] or None,
+                        "genus": row["genus"] or None,
+                        "species": row["species"] or None,
+                        "dna_bin": row["dna_bin"] or None,
+                        "dna_barcode": row["dna_barcode"],
+                        "country": row["country"] or None,
+                        "province_state": row["province_state"] or None,
+                        "coord-lat": safe_cast(row["coord-lat"], float),
+                        "coord-lon": safe_cast(row["coord-lon"], float),
+                        "image_measurement_value": safe_cast(row["image_measurement_value"], float),
+                        "area_fraction": safe_cast(row["area_fraction"], float),
+                        "scale_factor": safe_cast(row["scale_factor"], float),
+                        "inferred_ranks": safe_cast(row["inferred_ranks"], int),
+                        "split": row["split"],
+                        "index_bioscan_1M_insect": safe_cast(row["index_bioscan_1M_insect"], float),
+                        "chunk": row["chunk"] or None,
+                    }
+        else:
+            raise ValueError(
+                f"Unsupported format: {os.path.splitext(metadata_path.lower())[1]}\n"
+                f"Supported formats are:\n  - " + "\n  - ".join(sorted(SUPPORTED_FORMATS.values()))
+            )
+
+
+
+