init commit

.gitignore

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+.ipynb_checkpoints
+.idea
+images/
+text/

ScienceQA.py

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+import json
+from pathlib import Path
+
+import datasets
+
+_DESCRIPTION = """Science Question Answering (ScienceQA), a new benchmark that consists of 21,208 multimodal 
+multiple choice questions with a diverse set of science topics and annotations of their answers 
+with corresponding lectures and explanations. 
+The lecture and explanation provide general external knowledge and specific reasons,
+respectively, for arriving at the correct answer."""
+
+# Lets use the project page instead of the github repo
+_HOMEPAGE = "https://scienceqa.github.io"
+
+_CITATION = """\
+@inproceedings{lu2022learn,
+    title={Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering},
+    author={Lu, Pan and Mishra, Swaroop and Xia, Tony and Qiu, Liang and Chang, Kai-Wei and Zhu, Song-Chun and Tafjord, Oyvind and Clark, Peter and Ashwin Kalyan},
+    booktitle={The 36th Conference on Neural Information Processing Systems (NeurIPS)},
+    year={2022}
+}
+"""
+
+_LICENSE = "Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)"
+
+
+class ScienceQA(datasets.GeneratorBasedBuilder):
+    """Science Question Answering (ScienceQA), a new benchmark that consists of 21,208 multimodal
+multiple choice questions with a diverse set of science topics and annotations of their answers
+with corresponding lectures and explanations.
+The lecture and explanation provide general external knowledge and specific reasons,
+respectively, for arriving at the correct answer."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+                description=_DESCRIPTION,
+                features=datasets.Features(
+                        {
+                            "image": datasets.Image(),
+                            "question": datasets.Value("string"),
+                            "choices": datasets.features.Sequence(datasets.Value("string")),
+                            "answer": datasets.Value("int8"),
+                            "hint": datasets.Value("string"),
+                            "task": datasets.Value("string"),
+                            "grade": datasets.Value("string"),
+                            "subject": datasets.Value("string"),
+                            "topic": datasets.Value("string"),
+                            "category": datasets.Value("string"),
+                            "skill": datasets.Value("string"),
+                            "lecture": datasets.Value("string"),
+                            "solution": datasets.Value("string")
+                            }
+                        ),
+                homepage=_HOMEPAGE,
+                citation=_CITATION,
+                license=_LICENSE,
+                )
+
+    def _split_generators(self, dl_manager):
+        text_path = Path.cwd() / 'text' / 'problems.json'
+        image_dir = Path.cwd() / 'images'
+        return [
+            datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "text_path": text_path,
+                        "image_dir": image_dir,
+                        "split": "train",
+                        },
+                    ),
+            datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "text_path": text_path,
+                        "image_dir": image_dir,
+                        "split": "val",
+                        },
+                    ),
+            datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "text_path": text_path,
+                        "image_dir": image_dir,
+                        "split": "test"
+                        },
+                    ),
+            ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, text_path, image_dir, split):
+        with open(text_path, encoding="utf-8") as f:
+            # Load all the text. Note that if this was HUGE, we would need to find a better way to load the json
+            data = json.load(f)
+            ignore_keys = ['image', 'split']
+
+            # Get image_id from its annoying location
+            for image_id, row in data.items():
+                # Only look for the rows in our split
+                if row['split'] == split:
+
+                    # Note, not all rows have images.
+                    # Get all the image data we need
+                    if row['image']:
+                        image_path = image_dir / split / image_id / 'image.png'
+                        image_bytes = image_path.read_bytes()
+                        image_dict = {'path': str(image_path), 'bytes': image_bytes}
+                    else:
+                        image_dict = None
+
+                    # Keep only the keys we need
+                    relevant_row = {k: v for k, v in row.items() if k not in ignore_keys}
+
+                    return_dict = {
+                        'image': image_dict,
+                        **relevant_row
+                        }
+                    yield image_id, return_dict

download.sh

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+#!/bin/bash
+# Modified from the original here: https://github.com/lupantech/ScienceQA/blob/main/tools/download.sh
+
+cd images
+
+if [ -d "train" ];
+then
+  echo "Already downloaded train"
+else
+  ls -alF
+  wget https://scienceqa.s3.us-west-1.amazonaws.com/images/train.zip
+  unzip -q train.zip
+  rm train.zip
+fi
+
+if [ -d "val" ];
+then
+  echo "Already downloaded val"
+else
+  ls -alF
+  wget https://scienceqa.s3.us-west-1.amazonaws.com/images/val.zip
+  unzip -q val.zip
+  rm val.zip
+fi
+
+if [ -d "test" ];
+then
+  echo "Already downloaded test"
+else
+  ls -alF
+  wget https://scienceqa.s3.us-west-1.amazonaws.com/images/test.zip
+  unzip -q test.zip
+  rm test.zip
+fi
+
+echo "Completed downloads!"