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"""NVLR2 loading script."""
import json
import os
import datasets
_CITATION = """\
@article{DBLP:journals/corr/abs-2202-01994,
author = {Yamini Bansal and
Behrooz Ghorbani and
Ankush Garg and
Biao Zhang and
Maxim Krikun and
Colin Cherry and
Behnam Neyshabur and
Orhan Firat},
title = {Data Scaling Laws in {NMT:} The Effect of Noise and Architecture},
journal = {CoRR},
volume = {abs/2202.01994},
year = {2022},
url = {https://arxiv.org/abs/2202.01994},
eprinttype = {arXiv},
eprint = {2202.01994},
timestamp = {Mon, 24 Oct 2022 10:21:23 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2202-01994.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_DESCRIPTION = """\
The Natural Language for Visual Reasoning corpora are two language grounding datasets containing natural language sentences grounded in images. The task is to determine whether a sentence is true about a visual input. The data was collected through crowdsourcings, and solving the task requires reasoning about sets of objects, comparisons, and spatial relations. This includes two corpora: NLVR, with synthetically generated images, and NLVR2, which includes natural photographs.
"""
_HOMEPAGE = "https://lil.nlp.cornell.edu/nlvr/"
_LICENSE = "CC BY 4.0"
_URL_JSON = "https://raw.githubusercontent.com/lil-lab/nlvr/master/nlvr2/data/"
_URL_IMG = f"https://lil.nlp.cornell.edu/resources/NLVR2/"
_SPLITS = {
"train": "train",
"validation": "dev",
"test": "test",
}
class NLVR2Dataset(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
DEFAULT_CONFIG_NAME = "default"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"identifier": datasets.Value("string"),
"sentence": datasets.Value("string"),
"left_image": datasets.Image(),
"right_image": datasets.Image(),
"label": datasets.features.ClassLabel(names=["True", "False"]),
}
),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls = {
"default": {
"train": os.path.join(_URL_JSON, f'{_SPLITS["train"]}.json'),
"validation": os.path.join(_URL_JSON, f'{_SPLITS["validation"]}.json'),
"test1": os.path.join(_URL_JSON, f'{_SPLITS["test"]}1.json'),
"test2": os.path.join(_URL_JSON, f'{_SPLITS["test"]}2.json'),
},
}
files_path = dl_manager.download_and_extract(urls)
images_files = {
"train": os.path.join(_URL_IMG, f'{_SPLITS["train"]}_img.zip'),
"validation": os.path.join(_URL_IMG, f'{_SPLITS["validation"]}_img.zip'),
"test1": os.path.join(_URL_IMG, f'{_SPLITS["test"]}1_img.zip'),
"test2": os.path.join(_URL_IMG, f'{_SPLITS["test"]}2.zip'),
}
train_img_path = os.path.join(dl_manager.extract(images_files["train"]), "images", "train")
validation_img_path = os.path.join(dl_manager.download_and_extract(images_files["validation"]), "dev")
test1_img_path = os.path.join(dl_manager.download_and_extract(images_files["test1"]), "test1")
test2_img_path = os.path.join(dl_manager.download_and_extract(images_files["test2"]), "test2")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"files_paths": [files_path[self.config.name]["train"]], "images_paths": [train_img_path]},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"files_paths": [files_path[self.config.name]["validation"]], "images_paths": [validation_img_path]},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"files_paths": [files_path[self.config.name]["test1"], files_path[self.config.name]["test2"]], "images_paths": [test1_img_path, test2_img_path]},
),
]
def _generate_examples(self, files_paths, images_paths):
idx = 0
for i, files_path in enumerate(files_paths):
for line in open(files_path).readlines():
ex = json.loads(line)
common_img_identifier = ex["identifier"].split("-")
left_img_identifier = f"{common_img_identifier[0]}-{common_img_identifier[1]}-{common_img_identifier[2]}-img0.png"
right_img_identifier = f"{common_img_identifier[0]}-{common_img_identifier[1]}-{common_img_identifier[2]}-img1.png"
if common_img_identifier[0] == "train":
directory = str(ex["directory"])
left_image_path = str(os.path.join(images_paths[i], directory, left_img_identifier))
right_image_path = str(os.path.join(images_paths[i], directory, right_img_identifier))
else:
left_image_path = str(os.path.join(images_paths[i], left_img_identifier))
right_image_path = str(os.path.join(images_paths[i], right_img_identifier))
assert (os.path.exists(left_image_path))
assert (os.path.exists(right_image_path))
record = {
"identifier": ex["identifier"],
"sentence": ex["sentence"],
"left_image": left_image_path,
"right_image": right_image_path,
"label": ex["label"],
}
idx += 1
yield idx, record
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