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JLD
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unsplash25k-image-embeddings

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unsplash25k-image-embeddings / unsplash25k-image-embeddings.py

JLD

Correct bug with image ID loaded

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	"""TODO: Add a description here."""


	import csv
	import json
	import os

	import datasets
	from safetensors import safe_open
	import pandas as pd

	# TODO: Add BibTeX citation
	# Find for instance the citation on arxiv or on the dataset repo/website
	_CITATION = """\
	@InProceedings{huggingface:dataset,
	title = {A great new dataset},
	author={huggingface, Inc.
	},
	year={2022}
	}
	"""

	_DESCRIPTION = """\
	This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = ""

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = ""

	# TODO: Add link to the official dataset URLs here
	# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
	# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
	_URLS = {
	"image_ids": "https://huggingface.co/datasets/JLD/unsplash25k-image-embeddings/resolve/main/data/image_ids.feather.zstd",
	"embeddings": "https://huggingface.co/datasets/JLD/unsplash25k-image-embeddings/resolve/main/data/unsplash_embeddings.safetensors"
	}

	class Unsplash25kImageEmbeddingsDataset(datasets.GeneratorBasedBuilder):
	"""_summary_

	Args:
	datasets (_type_): _description_
	"""
	def _info(self):
	features = datasets.Features(
	{
	"image_id": datasets.Value("string"),
	# "image_embedding": datasets.Features({'x': datasets.Array2D(shape=(1, 512), dtype='float16')})
	"image_embedding": datasets.Array2D(shape=(1, 512), dtype='float32')
	}
	)
	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features, # Here we define them above because they are different between the two configurations
	# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
	# specify them. They'll be used if as_supervised=True in builder.as_dataset.
	# supervised_keys=("sentence", "label"),
	# Homepage of the dataset for documentation
	homepage=_HOMEPAGE,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	embeddings_path = dl_manager.download(_URLS["embeddings"])
	image_ids_path = dl_manager.download(_URLS["image_ids"])
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"embeddings_path": embeddings_path,
	"image_ids_path": image_ids_path
	}
	)
	]
	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, embeddings_path, image_ids_path):
	tensors = {}
	image_ids = pd.read_feather(image_ids_path)
	with safe_open(embeddings_path, framework="pt", device="cpu") as f:
	for key in f.keys():
	tensors[key] = f.get_tensor(key)
	for num_id in range(len(image_ids)):
	yield num_id, {"image_id": image_ids.iloc[num_id].image_id, "image_embedding": tensors["embeddings"][num_id].unsqueeze(0)}