add vizwiz

VizWiz.py

@@ -0,0 +1,125 @@
+import json
+import os
+from pathlib import Path
+import datasets
+
+
+_CITATION = """
+@misc{gurari2018vizwiz,
+      title={VizWiz Grand Challenge: Answering Visual Questions from Blind People}, 
+      author={Danna Gurari and Qing Li and Abigale J. Stangl and Anhong Guo and Chi Lin and Kristen Grauman and Jiebo Luo and Jeffrey P. Bigham},
+      year={2018},
+      eprint={1802.08218},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+"""
+
+_HOMEPAGE = "https://vizwiz.org/tasks-and-datasets/vqa/"
+
+_DESCRIPTION = """
+The VizWiz-VQA dataset originates from a natural visual question answering setting where blind people 
+each took an image and recorded a spoken question about it, together with 10 crowdsourced answers per 
+visual question. The proposed challenge addresses the following two tasks for this dataset: predict the 
+answer to a visual question and (2) predict whether a visual question cannot be answered.
+"""
+
+_LICENSE = " Creative Commons Attribution 4.0 International License."
+
+_DATA_URL = {"train" : "https://vizwiz.cs.colorado.edu/VizWiz_final/images/train.zip",
+             "test"  : "https://vizwiz.cs.colorado.edu/VizWiz_final/images/test.zip",
+             "val"   : "https://vizwiz.cs.colorado.edu/VizWiz_final/images/val.zip" }
+
+_ANNOTATION_URL = "https://vizwiz.cs.colorado.edu/VizWiz_final/vqa_data/Annotations.zip"
+
+_FEATURES = datasets.Features(
+    {
+        "id"   : datasets.Value("int32"),
+        "image": datasets.Image(),
+        "filename": datasets.Value("string"),
+        "question": datasets.Value("string"),
+        "answers": datasets.Sequence(datasets.Value("string")),
+        "answers_original": [
+            {
+                "answer": datasets.Value("string"),
+                "answer_confidence": datasets.Value("string"),
+            }
+        ],
+        "answer_type": datasets.Value("string"),
+        "answerable": datasets.Value("int32")
+    }
+)
+
+
+class VizWiz(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+    def _info(self):
+        return datasets.DatasetInfo(
+            description = _DESCRIPTION,
+            features    = _FEATURES,
+            homepage    = _HOMEPAGE,
+            license     = _LICENSE,
+            citation    = _CITATION,
+        )
+    
+    def _split_generators(self, dl_manager):
+        ann_file_train = os.path.join(dl_manager.download_and_extract(_ANNOTATION_URL), "train.json")
+        ann_file_val   = os.path.join(dl_manager.download_and_extract(_ANNOTATION_URL), "val.json")
+        ann_file_test  = os.path.join(dl_manager.download_and_extract(_ANNOTATION_URL), "test.json")
+        image_folders = {k: Path(v) for k, v in dl_manager.download_and_extract(_DATA_URL).items()}
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "annotation_file": ann_file_train,
+                    "image_folders": image_folders,
+                    "split_key": 'train'
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "annotation_file": ann_file_val,
+                    "image_folders": image_folders,
+                    "split_key": "val"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "annotation_file": ann_file_test,
+                    "image_folders": image_folders,
+                    "split_key": "test"
+                },
+            ),
+        ]
+    
+    def _generate_examples(self, annotation_file,image_folders,split_key):
+        counter = 0
+        annotations = json.load(open(annotation_file))
+        for ann in annotations:
+            if split_key in ['train','val']:
+                answers = [answer["answer"] for answer in ann["answers"]]
+                answers_original = ann['answers']
+                answer_type = ann["answer_type"]
+                answerable  = ann["answerable"]
+                
+            else:
+                
+                answers = None
+                answers_original = None
+                answer_type = None
+                answerable  = None
+                
+            yield counter, {
+                        "id"   : counter,
+                        "image": str(image_folders[split_key]/split_key/ann['image']),
+                        "filename" : ann['image'],
+                        "question" : ann["question"],
+                        "answers"  : answers,
+                        "answers_original"  : answers_original,
+                        "answer_type"       : answer_type,
+                        "answerable"        : answerable
+                    }
+            counter += 1