fix filename

ActivityNet_Captions.py

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+# Lint as: python3
+"""TGIF: A New Dataset and Benchmark on Animated GIF Description"""
+
+import os
+import json
+import datasets
+
+_CITATION = """
+@inproceedings{krishna2017dense,
+    title={Dense-Captioning Events in Videos},
+    author={Krishna, Ranjay and Hata, Kenji and Ren, Frederic and Fei-Fei, Li and Niebles, Juan Carlos},
+    booktitle={International Conference on Computer Vision (ICCV)},
+    year={2017}
+}
+"""
+
+_DESCRIPTION = """\
+The ActivityNet Captions dataset connects videos to a series of temporally annotated sentence descriptions.
+Each sentence covers an unique segment of the video, describing multiple events that occur. These events
+may occur over very long or short periods of time and are not limited in any capacity, allowing them to 
+co-occur. On average, each of the 20k videos contains 3.65 temporally localized sentences, resulting in
+a total of 100k sentences. We find that the number of sentences per video follows a relatively normal
+distribution. Furthermore, as the video duration increases, the number of sentences also increases. 
+Each sentence has an average length of 13.48 words, which is also normally distributed. You can find more
+details of the dataset under the ActivityNet Captions Dataset section, and under supplementary materials 
+in the paper.
+"""
+
+_URL_BASE = "https://cs.stanford.edu/people/ranjaykrishna/densevid/"
+
+_DL_URL = "https://huggingface.co/datasets/Leyo/ActivityNet_Captions/resolve/main/captions.tar.gz"
+
+class ActivityNetConfig(datasets.BuilderConfig):
+    """BuilderConfig for ActivityNet Captions."""
+
+    def __init__(self, **kwargs):
+        super(ActivityNetConfig, self).__init__(
+            version=datasets.Version("2.1.0", ""), **kwargs)
+
+
+class ActivityNet(datasets.GeneratorBasedBuilder):
+
+    DEFAULT_CONFIG_NAME = "all"
+    BUILDER_CONFIGS = [
+        ActivityNetConfig(
+            name="all", description="All the ActivityNet Captions dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "video_id": datasets.Value("string"),
+                    "path": datasets.Value("string"),
+                    "duration": datasets.Value("float32"),
+                    "starts": datasets.features.Sequence(datasets.Value("float32")),
+                    "ends": datasets.features.Sequence(datasets.Value("float32")),
+                    "captions": datasets.features.Sequence(datasets.Value("string"))
+                }
+            ),
+            supervised_keys=None,
+            homepage=_URL_BASE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_path = dl_manager.download(_DL_URL)
+
+        train_splits = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "files": dl_manager.iterable(archive_path),
+                    "ids_file": os.path.join(archive_path, "train_ids.json"),
+                    "infos_file": os.path.join(archive_path, "train.json")
+                },
+            )
+        ]
+        dev_splits = [
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "files": dl_manager.iterable(archive_path),
+                    "ids_file": os.path.join(archive_path, "val_ids.json"),
+                    "infos_file": os.path.join(archive_path, "val_1.json")
+                },
+            )
+        ]
+        test_splits = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "files": dl_manager.iterable(archive_path),
+                    "ids_file": os.path.join(archive_path, "test_ids.json"),
+                    "infos_file": os.path.join(archive_path, "val_2.json")
+                },
+            )
+        ]
+        return train_splits + dev_splits + test_splits
+
+    def _generate_examples(self, files, ids_file, infos_file):
+        """This function returns the examples."""
+
+        for path, f in files:
+            if path == infos_file:
+                with open(f, encoding="utf-8") as json_file:
+                    infos = json.load(json_file)
+        for path, f in files:
+            if path == ids_file:
+                with open(f, encoding="utf-8") as json_file:
+                    ids = json.load(json_file)
+                    for idx, id in enumerate(ids):
+                        path = "https://www.youtube.com/watch?v=" + id[2:]
+                        starts = [timestamp[0]
+                                for timestamp in infos[id]["timestamps"]]
+                        ends = [timestamp[1] for timestamp in infos[id]["timestamps"]]
+                        yield idx, {
+                            "video_id": id,
+                            "path": path,
+                            "video_id": datasets.Value("string"),
+                            "path": datasets.Value("string"),
+                            "duration": infos[id]["duration"],
+                            "starts": starts,
+                            "ends": ends,
+                            "captions": infos[id]["sentences"],
+                        }