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JAWCF
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objects

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objects / objects.py

JAWCF

Create objects.py

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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.Wikipedia

# Lint as: python3
"""Dream!n character datasets"""

import datasets
import json

_CITATION = """
"""

_DESCRIPTION = "Dream!n object datasets"


_DATASET_URL = "https://huggingface.co/datasets/JAWCF/objects/resolve/main/images.tar.gz"

json_url = urlopen("https://huggingface.co/datasets/JAWCF/objects/resolve/main/objects.json")
DICT_DESC = json.loads(json_url.read())

class Characters(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features({
            'image': datasets.Image(),
            'text': datasets.Value('string')
        })
        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
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage="",
            # License for the dataset if available
            license="",
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        path = dl_manager.download(_DATASET_URL)
        image_iters = dl_manager.iter_archive(path)
        splits = [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "images": image_iters
                }
            ),
        ]
        return splits

    def _generate_examples(self, images):
        """Yields examples."""
        idx = 0
        for filepath, image in images:
            yield idx, {
                "image": {"path": filepath, "bytes": image.read()},
                "text" : DICT_DESC[filepath],
            }
            idx+=1

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.Wikipedia

	# Lint as: python3
	"""Dream!n character datasets"""

	import datasets
	import json

	_CITATION = """
	"""

	_DESCRIPTION = "Dream!n object datasets"


	_DATASET_URL = "https://huggingface.co/datasets/JAWCF/objects/resolve/main/images.tar.gz"

	json_url = urlopen("https://huggingface.co/datasets/JAWCF/objects/resolve/main/objects.json")
	DICT_DESC = json.loads(json_url.read())

	class Characters(datasets.GeneratorBasedBuilder):

	def _info(self):
	features = datasets.Features({
	'image': datasets.Image(),
	'text': datasets.Value('string')
	})
	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
	supervised_keys=None,
	# Homepage of the dataset for documentation
	homepage="",
	# License for the dataset if available
	license="",
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	path = dl_manager.download(_DATASET_URL)
	image_iters = dl_manager.iter_archive(path)
	splits = [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"images": image_iters
	}
	),
	]
	return splits

	def _generate_examples(self, images):
	"""Yields examples."""
	idx = 0
	for filepath, image in images:
	yield idx, {
	"image": {"path": filepath, "bytes": image.read()},
	"text" : DICT_DESC[filepath],
	}
	idx+=1