NLVR2.py

"""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_BASE = "https://raw.githubusercontent.com/lil-lab/nlvr/master/nlvr2/data/"
_SPLITS = {
    # "train": "train.json",
    "validation": "dev.json",
    # "test1": "test1.json",
    # "test2": "test2.json",
}
_JZ_IMG_FOLDER_PATH = f"{os.environ['cnw_ALL_CCFRSCRATCH']}/local_datasets/nlvr2/"
_IMG_SPLITS = {
    # "train": "train_nlvr2.tar.gz",
    "validation": "dev_nlvr2.tar.gz",
    # "test1": "test1_nlvr2.tar.gz",
    # "test2": "test2_nlvr2.tar.gz",
}


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.Value("string"),
                }
            ),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        urls = {
            "Default": {
                # "train": os.path.join(_URL_BASE, _SPLITS["train"]),
                "validation": os.path.join(_URL_BASE, _SPLITS["validation"]),
                # "test1": os.path.join(_URL_BASE, _SPLITS["test1"]),
                # "test2": os.path.join(_URL_BASE, _SPLITS["test2"]),
            },
        }
        files_path = dl_manager.download_and_extract(urls)

        images_files = {
            # "train": os.path.join(_JZ_IMG_FOLDER_PATH, _IMG_SPLITS["train"]),
            "validation": os.path.join(_JZ_IMG_FOLDER_PATH, _IMG_SPLITS["validation"]),
            # "test1": os.path.join(_JZ_IMG_FOLDER_PATH, _IMG_SPLITS["test1"]),
            # "test2": os.path.join(_JZ_IMG_FOLDER_PATH, _IMG_SPLITS["test2"]),
        }
        print(f"img_path:{images_files['validation']}")
        # train_img_path = os.path.join(dl_manager.extract(images_files["train"]),  "train_nlvr2")
        validation_img_path = os.path.join(dl_manager.extract(images_files["validation"]),  "dev_nlvr2")
        print(f"img_path2:{validation_img_path}")
        print(f"{os.path.exists(os.path.join(validation_img_path,'dev-946-1-img0.png'))}")
        # test1_img_path = os.path.join(dl_manager.extract(images_files["test1"]),  "test1_nlvr2")
        return [
            # datasets.SplitGenerator(
            #     name=datasets.Split.TRAIN,
            #     gen_kwargs={"files_paths": [files_path[self.config.name]["train"]], "images_path": train_img_path, "split_name": "train"},
            # ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"files_paths": [files_path[self.config.name]["validation"]], "images_path": validation_img_path, "split_name": "dev"},
            ),
            # datasets.SplitGenerator(
            #     name=datasets.Split.TEST,
            #     gen_kwargs={"files_paths": [files_path[self.config.name]["test1"], files_path[self.config.name]["test2"]], "images_path": test1_img_path, "split_name": "test1"},
            # ),
        ]

    def _generate_examples(self, files_paths, images_path, split_name):
        idx = 0
        for path in files_paths:
            for line in open(path).readlines():
                ex = json.loads(line)
                common_img_identifier = ex["identifier"].split("-")
                left_img_identifier = f"{split_name}-{common_img_identifier[1]}-{common_img_identifier[2]}-img0.png"
                right_img_identifier = f"{split_name}-{common_img_identifier[1]}-{common_img_identifier[2]}-img1.png"
                left_image_path = str(os.path.join(images_path, left_img_identifier))
                right_image_path = str(os.path.join(images_path, right_img_identifier))
                if os.path.exists(left_image_path) and 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