CustomAnchorShape.py

# Hugging Face Loading script

from typing import List, Union
import numpy as np

import datasets
from .CustomAnchorShapeGenerator import CustomAnchorShape, filled_anchors


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# You can copy an official description
_DESCRIPTION = """\
This is Custom Anchor shape dataset.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""


class CustomAnchorShapeConfig(datasets.BuilderConfig):
    """Builder Config for CustomShape."""

    def __init__(self, size, custom_data, **kwargs):
        """BuilderConfig for CustomShape.
        
        Args:
            **kwargs: keyword arguments forwarded to super.
        """
        super(CustomAnchorShapeConfig, self).__init__(version=datasets.Version("1.0.0"),**kwargs)
        self.size = size
        self.custom_data = custom_data


class CustomAnchorShapeDataset(datasets.GeneratorBasedBuilder):
    """CustomShape dataset."""

    BUILDER_CONFIGS = [
        CustomAnchorShapeConfig(
            name='64size',
            description='64x64 size custom shape dataset.',
            size=64,
            custom_data={
                "train": ['s_curve', 
                          'swiss_roll', 
                          [0.5, 0.5], 
                          [[0.25, 0.25], [0.75, 0.75]],],
                "validation": ['s_curve', 
                               'swiss_roll', 
                               [0.5, 0.5], 
                               [0.25, 0.25],
                               [0.25, 0.75],
                               [0.75, 0.75],
                               [0.75, 0.25], 
                               [[0.25, 0.25], [0.75, 0.75]],
                               [[0.25, 0.75], [0.75, 0.25]],],
            }
        ),
        CustomAnchorShapeConfig(
            name='224size',
            description='224x224 size custom shape dataset.',
            size=224,
            custom_data={
                "train": ['s_curve', 
                          'swiss_roll', 
                          [0.5, 0.5], 
                          [[0.25, 0.25], [0.75, 0.75]],],
                "validation": ['s_curve', 
                               'swiss_roll', 
                               [0.5, 0.5], 
                               [0.25, 0.25],
                               [0.25, 0.75],
                               [0.75, 0.75],
                               [0.75, 0.25], 
                               [[0.25, 0.25], [0.75, 0.75]],
                               [[0.25, 0.75], [0.75, 0.25]],],
            }
        ),
    ]

    def _info(self):
        features = datasets.Features(
            {
                'image_id': datasets.Value('int64'),
                'image': datasets.Image(),
                'width': datasets.Value('int64'),
                'height': datasets.Value('int64'),
                'object': datasets.features.Sequence({
                    'bbox': datasets.features.Sequence(datasets.Value('float64')),
                })
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
        )

    def _split_generators(self, dl_manager):
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "size": self.config.size,
                    "custom_data": self.config.custom_data['train'],
                },
            ),
        ]

    def _generate_examples(
            self, 
            size: Union[int, tuple], 
            custom_data: List[Union[np.ndarray, str]],
        ):
        if isinstance(size, int):
            width, height = size, size
        else:
            width, height = size

        custom_shape = CustomAnchorShape(
            width, 
            height, 
            custom_data=custom_data,
        )
        
        for idx, data in enumerate(custom_shape.custom_data):
            yield idx, {
                'image_id': idx,
                'image': custom_shape.get_distribution(data, type='img'),
                'width': width,
                'height': height,
                'object': {
                    'bbox': custom_shape.get_distribution(data, type='1d'),
                }
            }