create loading script for NVLR2

NLVR2.py

@@ -0,0 +1,113 @@
+"""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"  # TODO need to credit both ms coco and vqa authors!
+
+_URL_BASE = "https://github.com/lil-lab/nlvr/tree/master/nlvr2/data"
+_SPLITS = {
+    "train": "train.json",
+    "validation": "dev.json",
+    "test1": "test1.json",
+    "test2": "test2.json",
+}
+
+
+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)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"files_path": files_path[self.config.name]["train"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"files_path": files_path[self.config.name]["validation"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST1,
+                gen_kwargs={"files_path": files_path[self.config.name]["test1"]},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST2,
+                gen_kwargs={"files_path": files_path[self.config.name]["test2"]},
+            ),
+        ]
+
+    def _generate_examples(self, files_path):
+        json_file = json.load(open(files_path, "r"))
+
+        for i, ex in enumerate(json_file):
+            record = {
+                "identifier": ex["identifier"],
+                "sentence": ex["sentence"],
+                "left_image": str(ex["left_url"]),
+                "right_image": str(ex["right_url"]),
+                "label": ex["label"],
+            }
+            yield i, record