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import datasets
from datasets import load_dataset
_CONSTITUENT_DATASETS = ['SAT-4', 'SAT-6', 'NASC-TG2', 'WHU-RS19', 'RSSCN7', 'RS_C11', 'SIRI-WHU', 'EuroSAT',
'NWPU-RESISC45', 'PatternNet', 'RSD46-WHU', 'GID', 'CLRS', 'Optimal-31',
'Airbus-Wind-Turbines-Patches', 'USTC_SmokeRS', 'Canadian_Cropland',
'Ships-In-Satellite-Imagery', 'Satellite-Images-of-Hurricane-Damage',
'Brazilian_Coffee_Scenes', 'Brazilian_Cerrado-Savanna_Scenes', 'Million-AID',
'UC_Merced_LandUse_MultiLabel', 'MLRSNet',
'MultiScene', 'RSI-CB256', 'AID_MultiLabel']
class SATINConfig(datasets.BuilderConfig):
"""BuilderConfig for SATIN"""
def __init__(self, name, **kwargs):
super(SATINConfig, self).__init__(version=datasets.Version("1.0.0"), **kwargs)
self.name = name
self.hf_dataset_name = 'jonathan-roberts1' + "/" + name
self.description = None
self.features = None
class SATIN(datasets.GeneratorBasedBuilder):
"""SATIN Images dataset"""
BUILDER_CONFIGS = [SATINConfig(name=dataset_name) for dataset_name in _CONSTITUENT_DATASETS]
def _info(self):
if self.config.description is None or self.config.features is None:
stream_dataset_info = load_dataset(self.config.hf_dataset_name, streaming=True, split='train').info
self.config.description = stream_dataset_info.description
self.config.features = stream_dataset_info.features
return datasets.DatasetInfo(
description=self.config.description,
features=self.config.features,
)
def _split_generators(self, dl_manager):
dataset = load_dataset(self.config.hf_dataset_name)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"data_path": dataset},
),
]
def _generate_examples(self, data_path):
# iterate over the Huggingface dataset and yield the idx, image and label
_DEFAULT_SPLIT = 'train'
huggingface_dataset = data_path['train']
features = huggingface_dataset.features
for idx, row in enumerate(huggingface_dataset):
features_dict = {feature: row[feature] for feature in features}
# Reorder features to make image the first feature
image = features_dict.pop('image')
features_dict = {'image': image, **features_dict}
yield idx, features_dict
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