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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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""
import zipfile
import os
import datasets
from PIL import Image
from io import BytesIO
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class sdbias(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="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
]
DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense.
def _info(self):
if self.config.name == "first_domain": # This is the name of the configuration selected in BUILDER_CONFIGS above
features = datasets.Features(
{
"adjective": datasets.Value("string"),
"profession": datasets.Value("string"),
"seed": datasets.Value("int32"),
"image": datasets.Image()
# These are the features of your dataset like images, labels ...
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description="bla",
# 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
homepage="bla",
# License for the dataset if available
license="bla",
# Citation for the dataset
citation="bli",
)
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
data_dir = "/mnt/1da05489-3812-4f15-a6e5-c8d3c57df39e/StableDiffusionBiasExplorer/zipped_images"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath":data_dir,
"split": "train",
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
zip_files = os.listdir(filepath)
key = 0
for zip_file in zip_files:
with zipfile.ZipFile(filepath + "/" + zip_file, "r") as zf:
for f in zf.filelist:
if ".jpg" in f.filename:
jpg_content = BytesIO(zf.read(f))
with Image.open(jpg_content) as image:
yield key, {
"adjective": zip_file.split("_", 1)[0],
"profession": zip_file.split("_", 1)[-1].replace(".zip",""),
"seed": int(f.filename.split("Seed_")[-1].split("/")[0]),
"image": image,
}
key+=1
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