add dataset script

anakin.py

@@ -0,0 +1,154 @@
+import csv
+import json
+import os
+import random
+
+import datasets
+import pandas as pd
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@misc{black2023vader,
+      title={VADER: Video Alignment Differencing and Retrieval}, 
+      author={Alexander Black and Simon Jenni and Tu Bui and Md. Mehrab Tanjim and Stefano Petrangeli and Ritwik Sinha and Viswanathan Swaminathan and John Collomosse},
+      year={2023},
+      eprint={2303.13193},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+ANAKIN is a dataset of mANipulated videos and mAsK annotatIoNs.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://github.com/AlexBlck/vader"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "cc-by-4.0"
+
+# 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/datasets/AlexBlck/ANAKIN/raw/main/metadata.csv",
+}
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class Anakin(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="all",
+            version=VERSION,
+            description="Full video, trimmed video, edited video, masks (if exists), and edit description",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "all"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        if self.config.name == "all":
+            features = datasets.Features(
+                {
+                    "full": datasets.Value("string"),
+                    "trimmed": datasets.Value("string"),
+                    "edited": datasets.Value("string"),
+                    "masks": datasets.Value("string"),
+                    # "edit_description": datasets.Value("string"),
+                }
+            )
+        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
+            features = datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "option2": datasets.Value("string"),
+                    "second_domain_answer": datasets.Value("string")
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
+        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)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "metadata.csv"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "metadata.csv"),
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "metadata.csv"),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        random.seed(47)
+        root_url = "https://huggingface.co/datasets/AlexBlck/ANAKIN/resolve/main/"
+        df = pd.read_csv(filepath)
+        ids = df["video-id"].to_list()
+        random.shuffle(ids)
+        if split == "train":
+            for key, idx in enumerate(ids[:342]):
+                yield key, {
+                    "full": root_url + f"full/{idx}.mp4",
+                    "trimmed": root_url + f"trimmed/{idx}.mp4",
+                    "edited": root_url + f"edited/{idx}.mp4",
+                    "masks": root_url + f"masks/{idx}/",
+                }