Create coco.py

coco.py

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+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# 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.
+"""COCO"""
+import json
+import os
+from pathlib import Path
+
+import datasets
+
+
+_CITATION = """
+@article{DBLP:journals/corr/LinMBHPRDZ14,
+  author    = {Tsung{-}Yi Lin and
+               Michael Maire and
+               Serge J. Belongie and
+               Lubomir D. Bourdev and
+               Ross B. Girshick and
+               James Hays and
+               Pietro Perona and
+               Deva Ramanan and
+               Piotr Doll{\'{a}}r and
+               C. Lawrence Zitnick},
+  title     = {Microsoft {COCO:} Common Objects in Context},
+  journal   = {CoRR},
+  volume    = {abs/1405.0312},
+  year      = {2014},
+  url       = {http://arxiv.org/abs/1405.0312},
+  eprinttype = {arXiv},
+  eprint    = {1405.0312},
+  timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/LinMBHPRDZ14.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+_DESCRIPTION = """
+MS COCO is a large-scale object detection, segmentation, and captioning dataset.
+COCO has several features: Object segmentation, Recognition in context, Superpixel stuff segmentation, 330K images (>200K labeled), 1.5 million object instances, 80 object categories, 91 stuff categories, 5 captions per image, 250,000 people with keypoints.
+"""
+
+_HOMEPAGE = "https://cocodataset.org/#home"
+
+_LICENSE = "CC BY 4.0"
+
+
+_IMAGES_URLS = {
+    "train": "https://huggingface.co/datasets/nyanko7/coco-hosted/resolve/main/train2014.zip",
+    "validation": "hhttps://huggingface.co/datasets/nyanko7/coco-hosted/resolve/main/val2014.zip",
+}
+
+_KARPATHY_FILES_URL = "https://huggingface.co/datasets/nyanko7/coco-hosted/resolve/main/caption_datasets.zip"
+
+_SPLIT_MAP = {"train": "train2014", "validation": "val2014"}
+
+_FEATURES = datasets.Features(
+    {
+        "image": datasets.Image(),
+        "filepath": datasets.Value("string"),
+        "sentids": [datasets.Value("int32")],
+        "filename": datasets.Value("string"),
+        "imgid": datasets.Value("int32"),
+        "split": datasets.Value("string"),
+        "sentences": {
+            "tokens": [datasets.Value("string")],
+            "raw": datasets.Value("string"),
+            "imgid": datasets.Value("int32"),
+            "sentid": datasets.Value("int32"),
+        },
+        "cocoid": datasets.Value("int32"),
+    }
+)
+
+_FEATURES_CAPTIONS = datasets.Features(
+    {
+        "image": datasets.Image(),
+        "filepath": datasets.Value("string"),
+        "sentids": [datasets.Value("int32")],
+        "filename": datasets.Value("string"),
+        "imgid": datasets.Value("int32"),
+        "split": datasets.Value("string"),
+        "sentences_tokens": [[datasets.Value("string")]],
+        "sentences_raw": [datasets.Value("string")],
+        "sentences_sentid": [datasets.Value("int32")],
+        "cocoid": datasets.Value("int32"),
+    }
+)
+
+
+class COCO(datasets.GeneratorBasedBuilder):
+    """COCO"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="2014", version=VERSION, description="2014 version of COCO with Karpathy annotations and splits"
+        ),
+        datasets.BuilderConfig(
+            name="2014_captions",
+            version=VERSION,
+            description="Same as 2014 but with all captions of one image gathered in a single example",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "2014"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=_FEATURES if self.config.name == "2014" else _FEATURES_CAPTIONS,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        annotation_file = os.path.join(dl_manager.download_and_extract(_KARPATHY_FILES_URL), "dataset_coco.json")
+        image_folders = {k: Path(v) for k, v in dl_manager.download_and_extract(_IMAGES_URLS).items()}
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "annotation_file": annotation_file,
+                    "image_folders": image_folders,
+                    "split_key": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "annotation_file": annotation_file,
+                    "image_folders": image_folders,
+                    "split_key": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "annotation_file": annotation_file,
+                    "image_folders": image_folders,
+                    "split_key": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, annotation_file, image_folders, split_key):
+        if self.config.name == "2014_captions":
+            return self._generate_examples_2014_captions(annotation_file, image_folders, split_key)
+        elif self.config.name == "2014":
+            return self._generate_examples_2014(annotation_file, image_folders, split_key)
+
+    def _generate_examples_2014_captions(self, annotation_file, image_folders, split_key):
+        with open(annotation_file, "r", encoding="utf-8") as fi:
+            annotations = json.load(fi)
+
+            for image_metadata in annotations["images"]:
+                if split_key == "train":
+                    if image_metadata["split"] != "train" and image_metadata["split"] != "restval":
+                        continue
+                elif split_key == "validation":
+                    if image_metadata["split"] != "val":
+                        continue
+                elif split_key == "test":
+                    if image_metadata["split"] != "test":
+                        continue
+
+                if "val2014" in image_metadata["filename"]:
+                    image_path = image_folders["validation"] / _SPLIT_MAP["validation"]
+                else:
+                    image_path = image_folders["train"] / _SPLIT_MAP["train"]
+
+                image_path = image_path / image_metadata["filename"]
+
+                record = {
+                    "image": str(image_path.absolute()),
+                    "filepath": image_metadata["filename"],
+                    "sentids": image_metadata["sentids"],
+                    "filename": image_metadata["filename"],
+                    "imgid": image_metadata["imgid"],
+                    "split": image_metadata["split"],
+                    "cocoid": image_metadata["cocoid"],
+                    "sentences_tokens": [caption["tokens"] for caption in image_metadata["sentences"]],
+                    "sentences_raw": [caption["raw"] for caption in image_metadata["sentences"]],
+                    "sentences_sentid": [caption["sentid"] for caption in image_metadata["sentences"]],
+                }
+
+                yield record["imgid"], record
+
+    def _generate_examples_2014(self, annotation_file, image_folders, split_key):
+        counter = 0
+        with open(annotation_file, "r", encoding="utf-8") as fi:
+            annotations = json.load(fi)
+
+            for image_metadata in annotations["images"]:
+                if split_key == "train":
+                    if image_metadata["split"] != "train" and image_metadata["split"] != "restval":
+                        continue
+                elif split_key == "validation":
+                    if image_metadata["split"] != "val":
+                        continue
+                elif split_key == "test":
+                    if image_metadata["split"] != "test":
+                        continue
+
+                if "val2014" in image_metadata["filename"]:
+                    image_path = image_folders["validation"] / _SPLIT_MAP["validation"]
+                else:
+                    image_path = image_folders["train"] / _SPLIT_MAP["train"]
+
+                image_path = image_path / image_metadata["filename"]
+
+                for caption in image_metadata["sentences"]:
+                    yield counter, {
+                        "image": str(image_path.absolute()),
+                        "filepath": image_metadata["filename"],
+                        "sentids": image_metadata["sentids"],
+                        "filename": image_metadata["filename"],
+                        "imgid": image_metadata["imgid"],
+                        "split": image_metadata["split"],
+                        "sentences": {
+                            "tokens": caption["tokens"],
+                            "raw": caption["raw"],
+                            "imgid": caption["imgid"],
+                            "sentid": caption["sentid"],
+                        },
+                        "cocoid": image_metadata["cocoid"],
+                    }
+                    counter += 1