Update script to hub

Boat_dataset.py

@@ -27,7 +27,7 @@ import datasets
 _CITATION = """\
 @InProceedings{huggingface:dataset,
 title = {Boat dataset},
-author={huggingface, Inc.
+author={Tzu-Chi Chen, Inc.
 },
 year={2024}
 }
@@ -36,11 +36,11 @@ year={2024}
 # Add description of the dataset here
 # You can copy an official description
 _DESCRIPTION = """\
-This new dataset is designed to solve this great object detection task.
+This dataset is designed to solve object detection task.
 """
 
 # Add a link to an official homepage for the dataset here
-_HOMEPAGE = ""
+_HOMEPAGE = "https://huggingface.co/datasets/zhuchi76/Boat_dataset"
 
 # Add the licence for the dataset here if you can find it
 _LICENSE = ""
@@ -49,112 +49,76 @@ _LICENSE = ""
 # 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)
 _URLS = {
-    "Boat_dataset": "ftp://arg.lab.nycu.edu.tw/arg-projectfile-download/detr/dataset/annotations.zip",
+    "images": f"{_HOMEPAGE}/data/images.tar.gz",
+    "anno": {
+        "train": f"{_HOMEPAGE}/data/instances_train2023.jsonl",
+        "val": f"{_HOMEPAGE}/data/instances_val2023.jsonl",
+        "test": f"{_HOMEPAGE}/data/instances_val2023r.jsonl"
+    },
 }
 
-
-
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
 class BoatDataset(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
 
     VERSION = datasets.Version("1.1.0")
 
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="Boat_dataset", version=VERSION, description="Images of real and virtual boats."),
-    ]
-
-    DEFAULT_CONFIG_NAME = "Boat_dataset"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
     def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-
-        objects = datasets.Features({
-            'id': datasets.Sequence(datasets.Value('int32')),
-            'area': datasets.Sequence(datasets.Value('float32')),
-            'bbox': datasets.Sequence(datasets.Sequence(datasets.Value('float32'), length=4)),  # [x, y, width, height]
-            'category': datasets.Sequence(datasets.Value('int32'))
-        })
-
         features=datasets.Features({
             'image_id': datasets.Value('int32'),
-            # 'image': datasets.Image(), # This is commented out because you can't directly store PIL images in the dataset.
-            'image_path': datasets.Value('string'),  # Store the path to the image file instead.
+            'image_path': datasets.Value('string'),
             'width': datasets.Value('int32'),
             'height': datasets.Value('int32'),
-            'objects': objects,
+            'objects': datasets.Features({
+                'id': datasets.Sequence(datasets.Value('int32')),
+                'area': datasets.Sequence(datasets.Value('float32')),
+                'bbox': datasets.Sequence(datasets.Sequence(datasets.Value('float32'), length=4)),  # [x, y, width, height]
+                'category': datasets.Sequence(datasets.Value('int32'))
+            }),
         })
         
         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
+        downloaded_files = dl_manager.download_and_extract(_URLS)
 
-        # 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
-        urls = _URLS[self.config.name]
-        data_dir = dl_manager.download_and_extract(urls)
+        image_dir = dl_manager.extract(downloaded_files['images'])
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "instances_train2023.jsonl"),
-                    "split": "train",
-                },
+                    'image_dir': image_dir,
+                    'annotations_file': downloaded_files['anno']['train'],
+                    'split': 'train'
+                }
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "instances_val2023.jsonl"),
-                    "split": "val",
-                },
+                    'image_dir': image_dir,
+                    'annotations_file': downloaded_files['anno']['val'],
+                    'split': 'val'
+                }
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "instances_val2023r.jsonl"),
-                    "split": "val_real",
-                },
-            ),
+                    'image_dir': image_dir,
+                    'annotations_file': downloaded_files['anno']['test'],
+                    'split': 'val_real'
+                }
+            )
         ]
 
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        with open(filepath, encoding="utf-8") as f:
+    def _generate_examples(self, image_dir, annotations_file, split):
+        with open(annotations_file, encoding="utf-8") as f:
             for key, row in enumerate(f):
                 try:
                     data = json.loads(row.strip())
-                    # Proceed to use 'data' for generating examples
                     yield key, {
                         "image_id": data["image_id"],
                         "image_path": data["image_path"],