Create TGIF.py

TGIF.py

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+# 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,
+                }