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# 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.
"""A dataset consisting of svgs, their png representations, and various masks"""
import csv
import json
import os
from math import floor
import datasets
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A dataset for understanding vector graphics},
author={eezy, Inc.
},
year={2023}
}
"""
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to provide a corpus for training machine vision tasks on the understanding of basic vector graphics
"""
_HOMEPAGE = "https://eezy.com"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "CC-BY"
# TODO: Add link to the official dataset URLs here
# 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 = {
"circles": "https://eezy-data-bucket.s3.amazonaws.com/public-datasets/basic_shapes_10k_v1/circles.tgz",
"squares": "https://eezy-data-bucket.s3.amazonaws.com/public-datasets/basic_shapes_10k_v1/squares.tgz",
"squares_and_circles": "https://eezy-data-bucket.s3.amazonaws.com/public-datasets/basic_shapes_10k_v1/squares_and_circles.tgz",
"scer": "https://eezy-data-bucket.s3.amazonaws.com/public-datasets/basic_shapes_10k_v1/scer.tgz"
}
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class BasicShapes10K(datasets.GeneratorBasedBuilder):
"""A dataset consisting of simple vector shapes and various kinds of masks"""
VERSION = datasets.Version("1.0.0")
SPLIT_COUNTS = {
'train': (0, 8000),
'dev': (8000, 9000),
'test': (9000, 10000)
}
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="mixed", version=VERSION, description="These images are a mixture of all the other datasets"),
datasets.BuilderConfig(name="circles", version=VERSION, description="These images only contain circles"),
datasets.BuilderConfig(name="squares", version=VERSION, description="These images only contain squares"),
datasets.BuilderConfig(name="squares_and_circles", version=VERSION, description="These images contain circles and squares"),
datasets.BuilderConfig(name="scer", version=VERSION, description="These images contain circles, squares, rectangles, and ellipses"),
]
DEFAULT_CONFIG_NAME = "mixed" # It's not mandatory to have a default configuration. Just use one if it make sense.
def _info(self):
features = datasets.Features(
{
"svg": datasets.Value("string"),
"png": datasets.Image(),
"layer_mask": datasets.Image(),
"object_mask": datasets.Image(),
}
)
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=("png", "layer_mask"),
# Homepage of the dataset for documentation
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
if self.config.name == 'mixed':
urls = _URLS
else:
urls = {self.config.name: _URLS[self.config.name]}
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"data_dir": data_dir,
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"data_dir": data_dir,
"split": "dev",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"data_dir": data_dir,
"split": "test"
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, data_dir, split):
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
start, stop = self.SPLIT_COUNTS[split]
domains = [self.config.name]
if self.config.name == 'mixed':
start = start * 4
stop = stop * 4
domains = [
'circles',
'squares',
'squares_and_circles',
'scer'
]
divisions = len(domains)
for key in range(start, stop):
domain = domains[key % divisions]
idx = floor(key / divisions)
yield f'{self.config.name}_{str(key).zfill(6)}', \
self._example_for_domain(data_dir, domain, idx, split)
def _example_for_domain(self, data_dir, domain, idx, split):
data = {}
svg_path = os.path.join(data_dir[domain], domain, 'svg', str(idx).zfill(6) + '.svg')
with open(svg_path, 'r') as file:
data['svg'] = file.read()
png_path = os.path.join(data_dir[domain], domain, 'png', str(idx).zfill(6) + '.png')
with open(png_path, 'rb') as file:
data['png'] = {"path": png_path, "bytes": file.read()}
if split != "test":
layer_mask_path = os.path.join(data_dir[domain], domain, 'layer_mask', str(idx).zfill(6) + '.png')
with open(layer_mask_path, 'rb') as file:
data['layer_mask'] = {"path": layer_mask_path, "bytes": file.read()}
object_mask_path = os.path.join(data_dir[domain], domain, 'obj_mask', str(idx).zfill(6) + '.png')
with open(object_mask_path, 'rb') as file:
data['object_mask'] = {"path": object_mask_path, "bytes": file.read()}
else:
data['layer_mask'] = ''
data['object_mask'] = ''
return data
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