create loading script

TGIF.py

@@ -0,0 +1,117 @@
+import csv
+import datasets
+import os
+import urllib.request
+
+_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_path": datasets.Value("string"),
+                    "video_bytes": datasets.Value("large_binary"),
+                    "en_global_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_and_extract(_DL_URL)
+        archive_data_path = os.path.join(
+            archive_path, "TGIF-Release-master/data/splits/")
+        infos_file = os.path.join(
+            archive_path, "TGIF-Release-master/data/tgif-v1.0.tsv")
+
+        train_splits = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "split_links_file": os.path.join(archive_data_path, "train.txt"),
+                    "infos_file": infos_file
+                },
+            )
+        ]
+        dev_splits = [
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "split_links_file": os.path.join(archive_data_path, "val.txt"),
+                    "infos_file": infos_file
+                },
+            )
+        ]
+        test_splits = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "split_links_file": os.path.join(archive_data_path, "test.txt"),
+                    "infos_file": infos_file
+                },
+            )
+        ]
+        return train_splits + dev_splits + test_splits
+
+    def _generate_examples(self, split_links_file, infos_file):
+        """This function returns the examples."""
+
+        dict = {}
+        with open(split_links_file, encoding="utf-8") as txt_file:
+            for line in txt_file:
+                line = line[0:-1]
+                dict[line] = []
+        with open(infos_file, encoding="utf-8") as tsv_file:
+            tsv_reader = csv.reader(tsv_file, delimiter="\t", quotechar='"')
+            for idx, (video_link, text) in enumerate(tsv_reader):
+                try:
+                    dict[video_link].append(text)
+                except Exception:
+                    pass
+        for idx, video_link in enumerate(dict):
+            video_data = urllib.request.urlopen(video_link).read()
+            video_bytes = bytearray(video_data)
+            yield idx, {
+                "video_path": video_link,
+                "video_bytes": video_bytes,
+                "en_global_captions": dict[video_link],
+            }