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""" VASR Loading Script """ |
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import json |
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import os |
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import pandas as pd |
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import datasets |
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from huggingface_hub import hf_hub_url |
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_CITATION = """ |
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""" |
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_DESCRIPTION = """\ |
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VASR is a challenging dataset for evaluating computer vision commonsense reasoning abilities. Given a triplet of images, the task is to select an image candidate B' that completes the analogy (A to A' is like B to what?). Unlike previous work on visual analogy that focused on simple image transformations, we tackle complex analogies requiring understanding of scenes. Our experiments demonstrate that state-of-the-art models struggle with carefully chosen distractors (±53%, compared to 90% human accuracy). |
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""" |
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_HOMEPAGE = "https://vasr-dataset.github.io/" |
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_LICENSE = "https://creativecommons.org/licenses/by/4.0/" |
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_URL = "https://huggingface.co/datasets/nlphuji/vasr/blob/main" |
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_URLS = { |
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"train": os.path.join(_URL, "train_gold.csv"), |
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"dev": os.path.join(_URL, "dev_gold.csv"), |
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"test": os.path.join(_URL, "test_gold.csv"), |
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} |
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class Vasr(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.1.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="TEST", version=VERSION, description="vasr dataset gold test"), |
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datasets.BuilderConfig(name="VALIDATION", version=VERSION, description="vasr dataset gold validation"), |
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datasets.BuilderConfig(name="TRAIN", version=VERSION, description="vasr dataset gold train") |
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] |
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IMAGE_EXTENSION = "jpg" |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"A_img": [datasets.Value("string")], |
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"B_img": [datasets.Value("string")], |
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"C_img": [datasets.Value("string")], |
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"candidates": [datasets.Value("string")], |
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"label": datasets.Value("int64"), |
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"D_img": [datasets.Value("string")], |
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"A_verb": [datasets.Value("string")], |
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"B_verb": [datasets.Value("string")], |
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"C_verb": [datasets.Value("string")], |
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"D_verb": [datasets.Value("string")], |
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"diff_item_A": [datasets.Value("string")], |
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"diff_item_A_str_first": [datasets.Value("string")], |
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"diff_item_B": [datasets.Value("string")], |
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"diff_item_B_str_first": [datasets.Value("string")], |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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downloaded_files = dl_manager.download_and_extract(_URLS) |
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data_dir = dl_manager.download_and_extract({ |
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"images_dir": hf_hub_url("datasets/nlphuji/vasr", filename="vasr_images.zip") |
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}) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TEST, |
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gen_kwargs={**data_dir, **{'filepath': downloaded_files["test"]}}), |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, |
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gen_kwargs={**data_dir, **{'filepath': downloaded_files["train"]}}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, |
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gen_kwargs={**data_dir, **{'filepath': downloaded_files["dev"]}}), |
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] |
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def _generate_examples(self, examples_csv, images_dir): |
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df = pd.read_csv(examples_csv) |
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for r_idx, r in df.iterrows(): |
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r_dict = r.to_dict() |
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r_dict['candidates'] = json.loads(r_dict['candidates']) |
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candidates_images = [os.path.join(images_dir, "vasr_images", f"{x}.{self.IMAGE_EXTENSION}") for x in |
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r_dict['candidates']] |
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r_dict['candidates_images'] = candidates_images |
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yield r_idx, r_dict |