Add script to load dataset
Browse files
unsplash25k_image_embeddings.py
ADDED
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"""TODO: Add a description here."""
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import csv
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import json
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import os
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import datasets
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from safetensors import safe_open
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import pandas as pd
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# TODO: Add BibTeX citation
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# Find for instance the citation on arxiv or on the dataset repo/website
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_CITATION = """\
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@InProceedings{huggingface:dataset,
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title = {A great new dataset},
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author={huggingface, Inc.
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},
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year={2022}
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}
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"""
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_DESCRIPTION = """\
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This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
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"""
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# TODO: Add a link to an official homepage for the dataset here
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_HOMEPAGE = ""
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# TODO: Add the licence for the dataset here if you can find it
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_LICENSE = ""
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# TODO: Add link to the official dataset URLs here
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# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
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# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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_URLS = {
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"image_ids": "https://huggingface.co/datasets/JLD/unsplash25k-image-embeddings/blob/main/data/image_ids.feather.zstd",
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"embeddings": "https://huggingface.co/datasets/JLD/unsplash25k-image-embeddings/blob/main/data/unsplash_embeddings.safetensors"
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}
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class Unsplash25kImageEmbeddingsDataset(datasets.GeneratorBasedBuilder):
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"""_summary_
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Args:
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datasets (_type_): _description_
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"""
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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("string"),
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"image_embedding": datasets.Features({'x': datasets.Array2D(shape=(1, 512), dtype='float16')})
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}
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)
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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# This defines the different columns of the dataset and their types
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features=features, # Here we define them above because they are different between the two configurations
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# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
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# specify them. They'll be used if as_supervised=True in builder.as_dataset.
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# supervised_keys=("sentence", "label"),
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# Homepage of the dataset for documentation
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homepage=_HOMEPAGE,
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# License for the dataset if available
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license=_LICENSE,
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# Citation for the dataset
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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embeddings_path = dl_manager.download(_URLS["embeddings"])
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image_ids_path = dl_manager.download(_URLS["image_ids"])
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return [
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datasets.SplitGenerator(
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name=datasets.Split.ALL,
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gen_kwargs={
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"embeddings_path": embeddings_path,
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"image_ids": image_ids_path
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}
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)
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]
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# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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def _generate_examples(self, embeddings_path, image_ids_path):
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tensors = {}
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image_ids = pd.read_feather(image_ids_path, compression="zstd")
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with safe_open(embeddings_path, framework="pt", device="cpu") as f:
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for key in f.keys():
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tensors[key] = f.get_tensor(key)
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for num_id, image_id in enumerate(image_ids):
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yield {"image_id": image_id, "image_embedding": tensors["embeddings"][num_id]}
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