Update script to hub
Browse files- Boat_dataset.py +34 -70
Boat_dataset.py
CHANGED
@@ -27,7 +27,7 @@ import datasets
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_CITATION = """\
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@InProceedings{huggingface:dataset,
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title = {Boat dataset},
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author={
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},
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year={2024}
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}
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@@ -36,11 +36,11 @@ year={2024}
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# Add description of the dataset here
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# You can copy an official description
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_DESCRIPTION = """\
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This
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"""
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# Add a link to an official homepage for the dataset here
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_HOMEPAGE = ""
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# Add the licence for the dataset here if you can find it
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_LICENSE = ""
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@@ -49,112 +49,76 @@ _LICENSE = ""
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# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
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# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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_URLS = {
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"
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}
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# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
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class BoatDataset(datasets.GeneratorBasedBuilder):
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"""TODO: Short description of my dataset."""
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VERSION = datasets.Version("1.1.0")
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# This is an example of a dataset with multiple configurations.
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# If you don't want/need to define several sub-sets in your dataset,
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# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
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# If you need to make complex sub-parts in the datasets with configurable options
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# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
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# BUILDER_CONFIG_CLASS = MyBuilderConfig
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# You will be able to load one or the other configurations in the following list with
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# data = datasets.load_dataset('my_dataset', 'first_domain')
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# data = datasets.load_dataset('my_dataset', 'second_domain')
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(name="Boat_dataset", version=VERSION, description="Images of real and virtual boats."),
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]
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DEFAULT_CONFIG_NAME = "Boat_dataset" # It's not mandatory to have a default configuration. Just use one if it make sense.
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def _info(self):
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# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
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objects = datasets.Features({
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'id': datasets.Sequence(datasets.Value('int32')),
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'area': datasets.Sequence(datasets.Value('float32')),
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'bbox': datasets.Sequence(datasets.Sequence(datasets.Value('float32'), length=4)), # [x, y, width, height]
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'category': datasets.Sequence(datasets.Value('int32'))
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})
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features=datasets.Features({
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'image_id': datasets.Value('int32'),
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'image_path': datasets.Value('string'), # Store the path to the image file instead.
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'width': datasets.Value('int32'),
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'height': datasets.Value('int32'),
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'objects':
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})
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return datasets.DatasetInfo(
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# This is the description that will appear on the datasets page.
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description=_DESCRIPTION,
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features=features, # Here we define them above because they are different between the two configurations
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# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
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# specify them. They'll be used if as_supervised=True in builder.as_dataset.
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# supervised_keys=("sentence", "label"),
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# Homepage of the dataset for documentation
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homepage=_HOMEPAGE,
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# License for the dataset if available
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license=_LICENSE,
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# Citation for the dataset
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
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# 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.
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# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
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urls = _URLS[self.config.name]
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data_dir = dl_manager.download_and_extract(urls)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={
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]
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with open(filepath, encoding="utf-8") as f:
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for key, row in enumerate(f):
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try:
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data = json.loads(row.strip())
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# Proceed to use 'data' for generating examples
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yield key, {
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"image_id": data["image_id"],
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"image_path": data["image_path"],
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_CITATION = """\
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@InProceedings{huggingface:dataset,
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title = {Boat dataset},
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author={Tzu-Chi Chen, Inc.
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},
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year={2024}
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}
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# Add description of the dataset here
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# You can copy an official description
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_DESCRIPTION = """\
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This dataset is designed to solve object detection task.
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"""
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# Add a link to an official homepage for the dataset here
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_HOMEPAGE = "https://huggingface.co/datasets/zhuchi76/Boat_dataset"
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# Add the licence for the dataset here if you can find it
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_LICENSE = ""
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# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
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# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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_URLS = {
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"images": f"{_HOMEPAGE}/data/images.tar.gz",
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"anno": {
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"train": f"{_HOMEPAGE}/data/instances_train2023.jsonl",
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"val": f"{_HOMEPAGE}/data/instances_val2023.jsonl",
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"test": f"{_HOMEPAGE}/data/instances_val2023r.jsonl"
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},
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}
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class BoatDataset(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.1.0")
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def _info(self):
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features=datasets.Features({
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'image_id': datasets.Value('int32'),
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'image_path': datasets.Value('string'),
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'width': datasets.Value('int32'),
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'height': datasets.Value('int32'),
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'objects': datasets.Features({
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'id': datasets.Sequence(datasets.Value('int32')),
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'area': datasets.Sequence(datasets.Value('float32')),
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'bbox': datasets.Sequence(datasets.Sequence(datasets.Value('float32'), length=4)), # [x, y, width, height]
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'category': datasets.Sequence(datasets.Value('int32'))
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}),
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})
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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downloaded_files = dl_manager.download_and_extract(_URLS)
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image_dir = dl_manager.extract(downloaded_files['images'])
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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'image_dir': image_dir,
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'annotations_file': downloaded_files['anno']['train'],
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'split': 'train'
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}
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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'image_dir': image_dir,
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'annotations_file': downloaded_files['anno']['val'],
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'split': 'val'
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}
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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'image_dir': image_dir,
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'annotations_file': downloaded_files['anno']['test'],
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'split': 'val_real'
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}
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)
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]
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def _generate_examples(self, image_dir, annotations_file, split):
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with open(annotations_file, encoding="utf-8") as f:
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for key, row in enumerate(f):
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try:
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data = json.loads(row.strip())
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yield key, {
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"image_id": data["image_id"],
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"image_path": data["image_path"],
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