loading script v0

NoCaps.py

@@ -0,0 +1,129 @@
+# Copyright 2022 The HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""NoCaps loading script."""
+
+
+import json
+
+from collections import defaultdict
+import datasets
+
+_CITATION = """\
+@inproceedings{agrawal2019nocaps,
+  title={nocaps: novel object captioning at scale},
+  author={Agrawal, Harsh and Desai, Karan and Wang, Yufei and Chen, Xinlei and Jain, Rishabh and Johnson, Mark and Batra, Dhruv and Parikh, Devi and Lee, Stefan and Anderson, Peter},
+  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
+  pages={8948--8957},
+  year={2019}
+}
+"""
+
+_DESCRIPTION = """\
+Dubbed NoCaps, for novel object captioning at scale, NoCaps consists of 166,100 human-generated captions describing 15,100 images from the Open Images validation and test sets.
+The associated training data consists of COCO image-caption pairs, plus Open Images image-level labels and object bounding boxes.
+Since Open Images contains many more classes than COCO, nearly 400 object classes seen in test images have no or very few associated training captions (hence, nocaps).
+"""
+
+_HOMEPAGE = "https://nocaps.org/"
+
+_LICENSE = "CC BY 2.0"
+
+_URLS = {
+    "validation": "https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json",
+    "test": "https://s3.amazonaws.com/nocaps/nocaps_test_image_info.json",
+}
+
+
+class NoCaps(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image": datasets.Image(),
+                "image_coco_url": datasets.Value("string"),
+                "image_date_captured": datasets.Value("string"),
+                "image_file_name": datasets.Value("string"),
+                "image_height": datasets.Value("int32"),
+                "image_width": datasets.Value("int32"),
+                "image_id": datasets.Value("int32"),
+                "image_license": datasets.Value("int8"),
+                "image_open_images_id": datasets.Value("string"),
+                "annotations_ids": datasets.Sequence(datasets.Value("int32")),
+                "annotations_captions": datasets.Sequence(datasets.Value("string")),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_file = dl_manager.download_and_extract(_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "data_file": data_file["validation"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_file": data_file["test"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_file):
+        with open(data_file, encoding="utf-8") as f:
+            data = json.load(f)
+
+        annotations = defaultdict(list)
+        if "annotations" in data:
+            # Only present for the validation split
+            for ann in data["annotations"]:
+                image_id = ann["image_id"]
+                caption_id = ann["id"]
+                caption = ann["caption"]
+                annotations[image_id].append((caption_id, caption))
+
+        counter = 0
+        for im in data["images"]:
+            image_coco_url = im["coco_url"]
+            image_date_captured = im["date_captured"]
+            image_file_name = im["file_name"]
+            image_height = im["height"]
+            image_width = im["width"]
+            image_id = im["id"]
+            image_license = im["license"]
+            image_open_images_id = im["open_images_id"]
+            yield counter, {
+                "image": image_coco_url,
+                "image_coco_url": image_coco_url,
+                "image_date_captured": image_date_captured,
+                "image_file_name": image_file_name,
+                "image_height": image_height,
+                "image_width": image_width,
+                "image_id": image_id,
+                "image_license": image_license,
+                "image_open_images_id": image_open_images_id,
+                "annotations_ids": [ann[0] for ann in annotations[image_id]],
+                "annotations_captions": [ann[1] for ann in annotations[image_id]],
+            }
+            counter += 1