Upload omnilabel.py

omnilabel.py

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+# Copyright 2020 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.
+
+"""Omnilabel dataset."""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+_CITATION = """\
+@misc{schulter2023omnilabel,
+      title={OmniLabel: A Challenging Benchmark for Language-Based Object Detection},
+      author={Samuel Schulter and Vijay Kumar B G and Yumin Suh and Konstantinos M. Dafnis and Zhixing Zhang and Shiyu Zhao and Dimitris Metaxas},
+      year={2023},
+      eprint={2304.11463},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """OmniLabel Benchmark."""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://www.omnilabel.org"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "https://www.omnilabel.org/dataset/download#h.frhys3no2ecq"
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+# _URLS = {
+#     # "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
+#     # "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+# }
+
+
+_URLS = {
+    "val": "https://huggingface.co/datasets/xingyaoww/omnilabel/resolve/main/dataset_all_val_v0.1.3.json",
+}
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class OmniLabel(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    # BUILDER_CONFIGS = [
+    #     datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
+    #     datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
+    # ]
+
+    # DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "id": datasets.Value("int64"),  # int (Unique instance ID)
+                "image_id": datasets.Value(
+                    "int64"
+                ),  # the image id this annotation belongs to
+                "image_filename": datasets.Value(
+                    "string"
+                ),  # the image filename this annotation belongs to
+                "bbox": datasets.features.Sequence(
+                    datasets.Value("float64")
+                ),  # [x, y, width, height] (Bounding box co-ordinates for the instance)
+                "descriptions": datasets.features.Sequence(
+                    datasets.Value("string")
+                ),  # list of the object description text
+            }
+        )
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+
+        # urls = _URLS[self.config.name]
+        # data_dir = dl_manager.download_and_extract(urls)
+
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+        print("downloaded_files: ", downloaded_files)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": downloaded_files["val"],
+                    "split": "val",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        if split == "val":
+            with open(filepath) as f:
+                data = json.load(f)
+
+            IMAGE_ID_TO_FILENAME = {}
+            for image_info in data["images"]:
+                IMAGE_ID_TO_FILENAME[image_info["id"]] = image_info["file_name"]
+
+            DESCRIPTION_ID_TO_DESC = {}
+            for desc in data["descriptions"]:
+                DESCRIPTION_ID_TO_DESC[desc["id"]] = desc
+
+            for key, anno in enumerate(data["annotations"]):
+                image_id = anno["image_id"]
+                image_filename = IMAGE_ID_TO_FILENAME[image_id]
+                description_ids = anno["description_ids"]
+                descriptions = [
+                    DESCRIPTION_ID_TO_DESC[description_id]["text"]
+                    for description_id in description_ids
+                ]
+
+                yield key, {
+                    "id": anno["id"],
+                    "image_id": image_id,
+                    # TODO: make this a PIL.Image
+                    "image_filename": image_filename,
+                    "bbox": anno["bbox"],
+                    "descriptions": descriptions,
+                }