Delete tgif.py

tgif.py

@@ -1,101 +0,0 @@
-# Lint as: python3
-"""TGIF: A New Dataset and Benchmark on Animated GIF Description"""
-
-
-import os
-import csv
-import datasets
-
-
-_CITATION = """
-@InProceedings{tgif-cvpr2016,
-  author = {Li, Yuncheng and Song, Yale and Cao, Liangliang and Tetreault, Joel and Goldberg, Larry and Jaimes, Alejandro and Luo, Jiebo},
-  title = "{TGIF: A New Dataset and Benchmark on Animated GIF Description}",
-  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
-  month = {June},
-  year = {2016}
-}
-"""
-
-_DESCRIPTION = """\
-The Tumblr GIF (TGIF) dataset contains 100K animated GIFs and 120K sentences describing visual content of the animated GIFs. 
-The animated GIFs have been collected from Tumblr, from randomly selected posts published between May and June of 2015. 
-We provide the URLs of animated GIFs in this release. The sentences are collected via crowdsourcing, with a carefully designed
-annotationinterface that ensures high quality dataset. We provide one sentence per animated GIF for the training and validation splits,
-and three sentences per GIF for the test split. The dataset shall be used to evaluate animated GIF/video description techniques.
-"""
-
-_URL_BASE = "http://raingo.github.io/TGIF-Release/"
-
-_DL_URL = "https://github.com/raingo/TGIF-Release/archive/master.zip"
-
-class TGIFConfig(datasets.BuilderConfig):
-    """BuilderConfig for TGIF."""
-
-    def __init__(self, **kwargs):
-        super(TGIFConfig, self).__init__(version=datasets.Version("2.1.0", ""), **kwargs)
-
-class TGIF(datasets.GeneratorBasedBuilder):
-
-    DEFAULT_CONFIG_NAME = "all"
-    BUILDER_CONFIGS = [
-        TGIFConfig(name="all", description="All the TGIF dataset"),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "video_id": datasets.Value("string"),
-                    "caption": 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) + "data/"
-        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
-        local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {}
-        train_splits = [
-                datasets.SplitGenerator(
-                    name="train",
-                    gen_kwargs={
-                        "local_extracted_archive": local_extracted_archive.get("train"),
-                        "files": dl_manager.iter_archive(archive_path["train"]),
-                    },
-                )
-            ]
-        dev_splits = [
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    gen_kwargs={
-                        "local_extracted_archive": local_extracted_archive.get("dev"),
-                        "files": dl_manager.iter_archive(archive_path["dev"]),
-                    },
-                )
-            ]
-        test_splits = [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    gen_kwargs={
-                        "local_extracted_archive": local_extracted_archive.get("test"),
-                        "files": dl_manager.iter_archive(archive_path["test"]),
-                    },
-                )
-            ]
-        return train_splits + dev_splits + test_splits
-
-    def _generate_examples(self, files, local_extracted_archive):
-        """This function returns the examples."""
-
-        with open(os.path.join(files, "tgif-v1.0.tsv"), encoding="utf-8") as tsv_file:
-            tsv_reader = csv.reader(tsv_file, delimiter="\t", quotechar='"' )
-            for idx, (video_link, text) in enumerate(tsv_reader):
-                yield idx, {
-                    "video_id": video_link,
-                    "caption": text,
-                }