cleanup

ANAKIN.py

@@ -3,8 +3,6 @@ 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}, 
@@ -16,20 +14,14 @@ _CITATION = """\
 }
 """
 
-# 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)
 _METADATA_URL = "https://huggingface.co/datasets/AlexBlck/ANAKIN/raw/main/metadata.csv"
 
 _FOLDERS = {
@@ -40,9 +32,8 @@ _FOLDERS = {
 }
 
 
-# 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."""
+    """ANAKIN is a dataset of mANipulated videos and mAsK annotatIoNs."""
 
     VERSION = datasets.Version("1.0.0")
 
@@ -69,10 +60,9 @@ class Anakin(datasets.GeneratorBasedBuilder):
         ),
     ]
 
-    DEFAULT_CONFIG_NAME = "all"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    DEFAULT_CONFIG_NAME = "all"
 
     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(
                 {
@@ -124,28 +114,14 @@ class Anakin(datasets.GeneratorBasedBuilder):
                 }
             )
         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
+            features=features,
             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
         metadata_dir = dl_manager.download(_METADATA_URL)
         folders = _FOLDERS[self.config.name]
 
@@ -213,9 +189,7 @@ class Anakin(datasets.GeneratorBasedBuilder):
 
     def _generate_examples(self, files, masks, df, ids, return_time):
         for key, (idx, sample) in enumerate(zip(ids, files)):
-            print(idx)
             entry = df[df["video-id"] == idx]
-            print(entry)
             if idx in masks.keys():
                 sample["masks"] = [
                     {"path": p, "bytes": im.read()} for p, im in masks[idx]